From d37da96092f776c8cdc5bdacba932d47ceb7221f Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Fri, 20 Jan 2023 15:02:28 -0800 Subject: [PATCH 01/12] Fix issue #31 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 540b095..13d7350 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ [![R-CMD-check](https://github.com/USEPA/spsurvey/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/USEPA/spsurvey/actions/workflows/R-CMD-check.yaml) [![CRAN](http://www.r-pkg.org/badges/version/spsurvey)](https://cran.r-project.org/package=spsurvey) -[![cran checks](https://cranchecks.info/badges/worst/spsurvey)](https://cran.r-project.org/web/checks/check_results_spsurvey.html) +[![cran checks](https://badges.cranchecks.info/worst/spsurvey.svg)](https://cran.r-project.org/web/checks/check_results_spsurvey.html) [![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/spsurvey)](https://cran.r-project.org/package=spsurvey) From eb27746d9ff6a125dc1ef743cc56ef2318859c8c Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Fri, 20 Jan 2023 15:33:45 -0800 Subject: [PATCH 02/12] Fix issue #32 --- R/grts.R | 30 +++++++++++++++--------------- R/irs.R | 30 +++++++++++++++--------------- 2 files changed, 30 insertions(+), 30 deletions(-) diff --git a/R/grts.R b/R/grts.R index 3c3d715..cea09d6 100644 --- a/R/grts.R +++ b/R/grts.R @@ -798,11 +798,11 @@ grts <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = } else { legacy_sites_names_bad <- legacy_sites_names[legacy_sites_names %in% dsgn_names_extra] legacy_sites_temp <- legacy_sites[, legacy_sites_names_bad, drop = FALSE] - temp_geometry_col <- which(names(legacy_sites_temp) == attr(sites_legacy, "sf_column")) - legacy_sites_geometry_col <- which(names(legacy_sites) == attr(sites_legacy, "sf_column")) + temp_geometry_col <- which(names(legacy_sites_temp) == attr(legacy_sites_temp, "sf_column")) + legacy_sites_geometry_col <- which(names(legacy_sites) == attr(legacy_sites, "sf_column")) names(legacy_sites_temp)[-temp_geometry_col] <- paste("legacy_sites", names(legacy_sites_temp)[-temp_geometry_col], sep = "_") sites_legacy <- st_join(sites_legacy, legacy_sites_temp, join = st_nearest_feature) - sites_legacy <- subset(sites_legacy, select = c(add_names, legacy_sites_names_good[-legacy_sites_geometry_col], names(legacy_sites_temp))) + sites_legacy <- subset(sites_legacy, select = c(add_names, legacy_sites_names_good[-legacy_sites_geometry_col], names(legacy_sites_temp)[-temp_geometry_col])) for (i in names(sites_legacy)) { if (i %in% c("legacy_sites_xcoord", "legacy_sites_ycoord", "legacy_sites_idpts")) { names(sites_legacy)[which(names(sites_legacy) == i)] <- substring(i, first = 14) @@ -818,12 +818,12 @@ grts <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = } else { sframe_names_bad <- sframe_names[sframe_names %in% dsgn_names_extra] sframe_temp <- sframe[, sframe_names_bad, drop = FALSE] - temp_geometry_col <- which(names(sframe_temp) == attr(sites_legacy, "sf_column")) - sframe_geometry_col <- which(names(sframe) == attr(sites_legacy, "sf_column")) + temp_geometry_col <- which(names(sframe_temp) == attr(sframe_temp, "sf_column")) + sframe_geometry_col <- which(names(sframe) == attr(sframe, "sf_column")) names(sframe_temp)[-temp_geometry_col] <- paste("sframe", names(sframe_temp)[-temp_geometry_col], sep = "_") sites_legacy <- st_join(sites_legacy, sframe_temp, join = st_nearest_feature) sites_legacy <- subset(sites_legacy, - select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)) + select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)[-temp_geometry_col]) ) for (i in names(sites_legacy)) { if (i %in% c("sframe_xcoord", "sframe_ycoord", "sframe_idpts")) { @@ -844,11 +844,11 @@ grts <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = } else { sframe_names_bad <- sframe_names[sframe_names %in% dsgn_names_extra] sframe_temp <- sframe[, sframe_names_bad, drop = FALSE] - temp_geometry_col <- which(names(sframe_temp) == attr(sites_base, "sf_column")) - sframe_geometry_col <- which(names(sframe) == attr(sites_base, "sf_column")) + temp_geometry_col <- which(names(sframe_temp) == attr(sframe_temp, "sf_column")) + sframe_geometry_col <- which(names(sframe) == attr(sframe, "sf_column")) names(sframe_temp)[-temp_geometry_col] <- paste("sframe", names(sframe_temp)[-temp_geometry_col], sep = "_") sites_base <- st_join(sites_base, sframe_temp, join = st_nearest_feature) - sites_base <- subset(sites_base, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp))) + sites_base <- subset(sites_base, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)[-temp_geometry_col])) for (i in names(sites_base)) { if (i %in% c("sframe_xcoord", "sframe_ycoord", "sframe_idpts")) { names(sites_base)[which(names(sites_base) == i)] <- substring(i, first = 8) @@ -866,11 +866,11 @@ grts <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = } else { sframe_names_bad <- sframe_names[sframe_names %in% dsgn_names_extra] sframe_temp <- sframe[, sframe_names_bad, drop = FALSE] - temp_geometry_col <- which(names(sframe_temp) == attr(sites_over, "sf_column")) - sframe_geometry_col <- which(names(sframe) == attr(sites_over, "sf_column")) + temp_geometry_col <- which(names(sframe_temp) == attr(sframe_temp, "sf_column")) + sframe_geometry_col <- which(names(sframe) == attr(sframe, "sf_column")) names(sframe_temp)[-temp_geometry_col] <- paste("sframe", names(sframe_temp)[-temp_geometry_col], sep = "_") sites_over <- st_join(sites_over, sframe_temp, join = st_nearest_feature) - sites_over <- subset(sites_over, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp))) + sites_over <- subset(sites_over, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)[-temp_geometry_col])) for (i in names(sites_over)) { if (i %in% c("sframe_xcoord", "sframe_ycoord", "sframe_idpts")) { names(sites_over)[which(names(sites_over) == i)] <- substring(i, first = 8) @@ -888,11 +888,11 @@ grts <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = } else { sframe_names_bad <- sframe_names[sframe_names %in% dsgn_names_extra] sframe_temp <- sframe[, sframe_names_bad, drop = FALSE] - temp_geometry_col <- which(names(sframe_temp) == attr(sites_near, "sf_column")) - sframe_geometry_col <- which(names(sframe) == attr(sites_near, "sf_column")) + temp_geometry_col <- which(names(sframe_temp) == attr(sframe_temp, "sf_column")) + sframe_geometry_col <- which(names(sframe) == attr(sframe, "sf_column")) names(sframe_temp)[-temp_geometry_col] <- paste("sframe", names(sframe_temp)[-temp_geometry_col], sep = "_") sites_near <- st_join(sites_near, sframe_temp, join = st_nearest_feature) - sites_near <- subset(sites_near, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp))) + sites_near <- subset(sites_near, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)[-temp_geometry_col])) for (i in names(sites_near)) { if (i %in% c("sframe_xcoord", "sframe_ycoord", "sframe_idpts")) { names(sites_near)[which(names(sites_near) == i)] <- substring(i, first = 8) diff --git a/R/irs.R b/R/irs.R index 6904443..8bf442d 100644 --- a/R/irs.R +++ b/R/irs.R @@ -498,11 +498,11 @@ irs <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = N } else { legacy_sites_names_bad <- legacy_sites_names[legacy_sites_names %in% dsgn_names_extra] legacy_sites_temp <- legacy_sites[, legacy_sites_names_bad, drop = FALSE] - temp_geometry_col <- which(names(legacy_sites_temp) == attr(sites_legacy, "sf_column")) - legacy_sites_geometry_col <- which(names(legacy_sites) == attr(sites_legacy, "sf_column")) + temp_geometry_col <- which(names(legacy_sites_temp) == attr(legacy_sites_temp, "sf_column")) + legacy_sites_geometry_col <- which(names(legacy_sites) == attr(legacy_sites, "sf_column")) names(legacy_sites_temp)[-temp_geometry_col] <- paste("legacy_sites", names(legacy_sites_temp)[-temp_geometry_col], sep = "_") sites_legacy <- st_join(sites_legacy, legacy_sites_temp, join = st_nearest_feature) - sites_legacy <- subset(sites_legacy, select = c(add_names, legacy_sites_names_good[-legacy_sites_geometry_col], names(legacy_sites_temp))) + sites_legacy <- subset(sites_legacy, select = c(add_names, legacy_sites_names_good[-legacy_sites_geometry_col], names(legacy_sites_temp)[-temp_geometry_col])) for (i in names(sites_legacy)) { if (i %in% c("legacy_sites_xcoord", "legacy_sites_ycoord", "legacy_sites_idpts")) { names(sites_legacy)[which(names(sites_legacy) == i)] <- substring(i, first = 14) @@ -518,12 +518,12 @@ irs <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = N } else { sframe_names_bad <- sframe_names[sframe_names %in% dsgn_names_extra] sframe_temp <- sframe[, sframe_names_bad, drop = FALSE] - temp_geometry_col <- which(names(sframe_temp) == attr(sites_legacy, "sf_column")) - sframe_geometry_col <- which(names(sframe) == attr(sites_legacy, "sf_column")) + temp_geometry_col <- which(names(sframe_temp) == attr(sframe_temp, "sf_column")) + sframe_geometry_col <- which(names(sframe) == attr(sframe, "sf_column")) names(sframe_temp)[-temp_geometry_col] <- paste("sframe", names(sframe_temp)[-temp_geometry_col], sep = "_") sites_legacy <- st_join(sites_legacy, sframe_temp, join = st_nearest_feature) sites_legacy <- subset(sites_legacy, - select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)) + select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)[-temp_geometry_col]) ) for (i in names(sites_legacy)) { if (i %in% c("sframe_xcoord", "sframe_ycoord", "sframe_idpts")) { @@ -544,11 +544,11 @@ irs <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = N } else { sframe_names_bad <- sframe_names[sframe_names %in% dsgn_names_extra] sframe_temp <- sframe[, sframe_names_bad, drop = FALSE] - temp_geometry_col <- which(names(sframe_temp) == attr(sites_base, "sf_column")) - sframe_geometry_col <- which(names(sframe) == attr(sites_base, "sf_column")) + temp_geometry_col <- which(names(sframe_temp) == attr(sframe_temp, "sf_column")) + sframe_geometry_col <- which(names(sframe) == attr(sframe, "sf_column")) names(sframe_temp)[-temp_geometry_col] <- paste("sframe", names(sframe_temp)[-temp_geometry_col], sep = "_") sites_base <- st_join(sites_base, sframe_temp, join = st_nearest_feature) - sites_base <- subset(sites_base, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp))) + sites_base <- subset(sites_base, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)[-temp_geometry_col])) for (i in names(sites_base)) { if (i %in% c("sframe_xcoord", "sframe_ycoord", "sframe_idpts")) { names(sites_base)[which(names(sites_base) == i)] <- substring(i, first = 8) @@ -566,11 +566,11 @@ irs <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = N } else { sframe_names_bad <- sframe_names[sframe_names %in% dsgn_names_extra] sframe_temp <- sframe[, sframe_names_bad, drop = FALSE] - temp_geometry_col <- which(names(sframe_temp) == attr(sites_over, "sf_column")) - sframe_geometry_col <- which(names(sframe) == attr(sites_over, "sf_column")) + temp_geometry_col <- which(names(sframe_temp) == attr(sframe_temp, "sf_column")) + sframe_geometry_col <- which(names(sframe) == attr(sframe, "sf_column")) names(sframe_temp)[-temp_geometry_col] <- paste("sframe", names(sframe_temp)[-temp_geometry_col], sep = "_") sites_over <- st_join(sites_over, sframe_temp, join = st_nearest_feature) - sites_over <- subset(sites_over, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp))) + sites_over <- subset(sites_over, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)[-temp_geometry_col])) for (i in names(sites_over)) { if (i %in% c("sframe_xcoord", "sframe_ycoord", "sframe_idpts")) { names(sites_over)[which(names(sites_over) == i)] <- substring(i, first = 8) @@ -588,11 +588,11 @@ irs <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = N } else { sframe_names_bad <- sframe_names[sframe_names %in% dsgn_names_extra] sframe_temp <- sframe[, sframe_names_bad, drop = FALSE] - temp_geometry_col <- which(names(sframe_temp) == attr(sites_near, "sf_column")) - sframe_geometry_col <- which(names(sframe) == attr(sites_near, "sf_column")) + temp_geometry_col <- which(names(sframe_temp) == attr(sframe_temp, "sf_column")) + sframe_geometry_col <- which(names(sframe) == attr(sframe, "sf_column")) names(sframe_temp)[-temp_geometry_col] <- paste("sframe", names(sframe_temp)[-temp_geometry_col], sep = "_") sites_near <- st_join(sites_near, sframe_temp, join = st_nearest_feature) - sites_near <- subset(sites_near, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp))) + sites_near <- subset(sites_near, select = c(add_names, sframe_names_good[-sframe_geometry_col], names(sframe_temp)[-temp_geometry_col])) for (i in names(sites_near)) { if (i %in% c("sframe_xcoord", "sframe_ycoord", "sframe_idpts")) { names(sites_near)[which(names(sites_near) == i)] <- substring(i, first = 8) From bb1eca27c751aceeb5c499815d06692ca966e2e4 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Fri, 20 Jan 2023 16:08:03 -0800 Subject: [PATCH 03/12] Fix issue #33 --- R/attrisk_analysis.R | 14 ++++++++++-- R/diffrisk_analysis.R | 6 +++++ R/relrisk_analysis.R | 6 +++++ man/attrisk_analysis.Rd | 8 +++++-- man/diffrisk_analysis.Rd | 8 +++++-- man/relrisk_analysis.Rd | 8 +++++-- tests/testthat/test-attrisk_analysis.R | 30 +++++++++++++++++++++++++ tests/testthat/test-diffrisk_analysis.R | 30 +++++++++++++++++++++++++ tests/testthat/test-relrisk_analysis.R | 30 +++++++++++++++++++++++++ 9 files changed, 132 insertions(+), 8 deletions(-) diff --git a/R/attrisk_analysis.R b/R/attrisk_analysis.R index 648f8c6..45a4689 100644 --- a/R/attrisk_analysis.R +++ b/R/attrisk_analysis.R @@ -62,7 +62,9 @@ #' contains the values \code{"Poor"} and \code{"Good"} for the first and #' second levels, respectively, of each element in the \code{vars_response} #' argument and that uses values in the \code{vars_response} argument as names -#' for the list. The default value is NULL. +#' for the list. If \code{response_levels} is provided without names, +#' then the names of \code{response_levels} are set to \code{vars_response}. +#' The default value is NULL. #' #' @param stressor_levels List providing the category values (levels) for each #' element in the \code{vars_stressor} argument. Each element in the list @@ -74,7 +76,9 @@ #' contains the values \code{"Poor"} and \code{"Good"} for the first and #' second levels, respectively, of each element in the \code{vars_stressor} #' argument and that uses values in the \code{vars_stressor} argument as names -#' for the list. The default value is NULL. +#' for the list. If \code{stressor_levels} is provided without names, +#' then the names of \code{stressor_levels} are set to \code{vars_stressor}. +#' The default value is NULL. #' #' @param subpops Vector composed of character values that identify the #' names of subpopulation (domain) variables in \code{dframe}. @@ -572,6 +576,9 @@ attrisk_analysis <- function(dframe, vars_response, vars_stressor, response_leve msg <- "Argument response_levels must be the same length as argument vars_response.\n" error_vec <- c(error_vec, msg) } + if (is.null(names(response_levels))) { # set default names if none provided + names(response_levels) <- vars_response + } if (any(sapply(response_levels, function(x) length(x) != 2))) { error_ind <- TRUE msg <- "Each element of argument response_levels must contain only two values.\n" @@ -622,6 +629,9 @@ attrisk_analysis <- function(dframe, vars_response, vars_stressor, response_leve msg <- "Argument stressor_levels must be the same length as argument vars_stressor.\n" error_vec <- c(error_vec, msg) } + if (is.null(names(stressor_levels))) { # set default names if none provided + names(stressor_levels) <- vars_stressor + } if (any(sapply(stressor_levels, function(x) length(x) != 2))) { error_ind <- TRUE msg <- "Each element of argument stressor_levels must contain only two values.\n" diff --git a/R/diffrisk_analysis.R b/R/diffrisk_analysis.R index c23ef6f..a058261 100644 --- a/R/diffrisk_analysis.R +++ b/R/diffrisk_analysis.R @@ -308,6 +308,9 @@ diffrisk_analysis <- function(dframe, vars_response, vars_stressor, response_lev msg <- "Argument response_levels must be the same length as argument vars_response.\n" error_vec <- c(error_vec, msg) } + if (is.null(names(response_levels))) { # set default names if none provided + names(response_levels) <- vars_response + } if (any(sapply(response_levels, function(x) length(x) != 2))) { error_ind <- TRUE msg <- "Each element of argument response_levels must contain only two values.\n" @@ -358,6 +361,9 @@ diffrisk_analysis <- function(dframe, vars_response, vars_stressor, response_lev msg <- "Argument stressor_levels must be the same length as argument vars_stressor.\n" error_vec <- c(error_vec, msg) } + if (is.null(names(stressor_levels))) { # set default names if none provided + names(stressor_levels) <- vars_stressor + } if (any(sapply(stressor_levels, function(x) length(x) != 2))) { error_ind <- TRUE msg <- "Each element of argument stressor_levels must contain only two values.\n" diff --git a/R/relrisk_analysis.R b/R/relrisk_analysis.R index adf18c3..b365c7a 100644 --- a/R/relrisk_analysis.R +++ b/R/relrisk_analysis.R @@ -326,6 +326,9 @@ relrisk_analysis <- function(dframe, vars_response, vars_stressor, response_leve msg <- "Argument response_levels must be the same length as argument vars_response.\n" error_vec <- c(error_vec, msg) } + if (is.null(names(response_levels))) { # set default names if none provided + names(response_levels) <- vars_response + } if (any(sapply(response_levels, function(x) length(x) != 2))) { error_ind <- TRUE msg <- "Each element of argument response_levels must contain only two values.\n" @@ -376,6 +379,9 @@ relrisk_analysis <- function(dframe, vars_response, vars_stressor, response_leve msg <- "Argument stressor_levels must be the same length as argument vars_stressor.\n" error_vec <- c(error_vec, msg) } + if (is.null(names(stressor_levels))) { # set default names if none provided + names(stressor_levels) <- vars_stressor + } if (any(sapply(stressor_levels, function(x) length(x) != 2))) { error_ind <- TRUE msg <- "Each element of argument stressor_levels must contain only two values.\n" diff --git a/man/attrisk_analysis.Rd b/man/attrisk_analysis.Rd index 7cd6bf1..a1055a1 100644 --- a/man/attrisk_analysis.Rd +++ b/man/attrisk_analysis.Rd @@ -58,7 +58,9 @@ argument. If this argument equals NULL, then a named list is created that contains the values \code{"Poor"} and \code{"Good"} for the first and second levels, respectively, of each element in the \code{vars_response} argument and that uses values in the \code{vars_response} argument as names -for the list. The default value is NULL.} +for the list. If \code{response_levels} is provided without names, +then the names of \code{response_levels} are set to \code{vars_response}. +The default value is NULL.} \item{stressor_levels}{List providing the category values (levels) for each element in the \code{vars_stressor} argument. Each element in the list @@ -70,7 +72,9 @@ argument. If this argument equals NULL, then a named list is created that contains the values \code{"Poor"} and \code{"Good"} for the first and second levels, respectively, of each element in the \code{vars_stressor} argument and that uses values in the \code{vars_stressor} argument as names -for the list. The default value is NULL.} +for the list. If \code{stressor_levels} is provided without names, +then the names of \code{stressor_levels} are set to \code{vars_stressor}. +The default value is NULL.} \item{subpops}{Vector composed of character values that identify the names of subpopulation (domain) variables in \code{dframe}. diff --git a/man/diffrisk_analysis.Rd b/man/diffrisk_analysis.Rd index 0bb0219..a48d5b9 100644 --- a/man/diffrisk_analysis.Rd +++ b/man/diffrisk_analysis.Rd @@ -58,7 +58,9 @@ argument. If this argument equals NULL, then a named list is created that contains the values \code{"Poor"} and \code{"Good"} for the first and second levels, respectively, of each element in the \code{vars_response} argument and that uses values in the \code{vars_response} argument as names -for the list. The default value is NULL.} +for the list. If \code{response_levels} is provided without names, +then the names of \code{response_levels} are set to \code{vars_response}. +The default value is NULL.} \item{stressor_levels}{List providing the category values (levels) for each element in the \code{vars_stressor} argument. Each element in the list @@ -70,7 +72,9 @@ argument. If this argument equals NULL, then a named list is created that contains the values \code{"Poor"} and \code{"Good"} for the first and second levels, respectively, of each element in the \code{vars_stressor} argument and that uses values in the \code{vars_stressor} argument as names -for the list. The default value is NULL.} +for the list. If \code{stressor_levels} is provided without names, +then the names of \code{stressor_levels} are set to \code{vars_stressor}. +The default value is NULL.} \item{subpops}{Vector composed of character values that identify the names of subpopulation (domain) variables in \code{dframe}. diff --git a/man/relrisk_analysis.Rd b/man/relrisk_analysis.Rd index ae04f82..c841917 100644 --- a/man/relrisk_analysis.Rd +++ b/man/relrisk_analysis.Rd @@ -58,7 +58,9 @@ argument. If this argument equals NULL, then a named list is created that contains the values \code{"Poor"} and \code{"Good"} for the first and second levels, respectively, of each element in the \code{vars_response} argument and that uses values in the \code{vars_response} argument as names -for the list. The default value is NULL.} +for the list. If \code{response_levels} is provided without names, +then the names of \code{response_levels} are set to \code{vars_response}. +The default value is NULL.} \item{stressor_levels}{List providing the category values (levels) for each element in the \code{vars_stressor} argument. Each element in the list @@ -70,7 +72,9 @@ argument. If this argument equals NULL, then a named list is created that contains the values \code{"Poor"} and \code{"Good"} for the first and second levels, respectively, of each element in the \code{vars_stressor} argument and that uses values in the \code{vars_stressor} argument as names -for the list. The default value is NULL.} +for the list. If \code{stressor_levels} is provided without names, +then the names of \code{stressor_levels} are set to \code{vars_stressor}. +The default value is NULL.} \item{subpops}{Vector composed of character values that identify the names of subpopulation (domain) variables in \code{dframe}. diff --git a/tests/testthat/test-attrisk_analysis.R b/tests/testthat/test-attrisk_analysis.R index 0876c0a..a94335d 100644 --- a/tests/testthat/test-attrisk_analysis.R +++ b/tests/testthat/test-attrisk_analysis.R @@ -89,6 +89,36 @@ test_that("Attributable Risk: Unstratified single-stage analysis", { expect_equal(nrow(AttRisk_Estimates), 6) }) +AttRisk_Estimates <- attrisk_analysis( + dframe = NLA_IN, + vars_response = vars_response, vars_stressor = vars_stressor, + subpops = subpops, siteID = "SITE_ID", weight = "WGT_TP", xcoord = "XCOORD", + ycoord = "YCOORD", + response_levels = list("BENT_MMI_COND_2017" = c("Poor", "Good")), + stressor_levels = list("PTL_COND" = c("Poor", "Good"), "NTL_COND" = c("Poor", "Good")) +) + +test_that("Attributable Risk: Unstratified single-stage analysis (specify response/stressor levels)", { + expect_true(exists("AttRisk_Estimates")) + expect_equal(attributes(AttRisk_Estimates)$class, "data.frame") + expect_equal(nrow(AttRisk_Estimates), 6) +}) + +AttRisk_Estimates <- attrisk_analysis( + dframe = NLA_IN, + vars_response = vars_response, vars_stressor = vars_stressor, + subpops = subpops, siteID = "SITE_ID", weight = "WGT_TP", xcoord = "XCOORD", + ycoord = "YCOORD", + response_levels = list(c("Poor", "Good")), + stressor_levels = list(c("Poor", "Good"), c("Poor", "Good")) +) + +test_that("Attributable Risk: Unstratified single-stage analysis (unnamed response/stressor levels)", { + expect_true(exists("AttRisk_Estimates")) + expect_equal(attributes(AttRisk_Estimates)$class, "data.frame") + expect_equal(nrow(AttRisk_Estimates), 6) +}) + AttRisk_Estimates <- attrisk_analysis( dframe = NLA_IN, vars_response = vars_response, vars_stressor = vars_stressor, diff --git a/tests/testthat/test-diffrisk_analysis.R b/tests/testthat/test-diffrisk_analysis.R index a29d9d2..44a1c0f 100644 --- a/tests/testthat/test-diffrisk_analysis.R +++ b/tests/testthat/test-diffrisk_analysis.R @@ -89,6 +89,36 @@ test_that("Risk Difference: Unstratified single-stage analysis", { expect_equal(nrow(DiffRisk_Estimates), 6) }) +DiffRisk_Estimates <- diffrisk_analysis( + dframe = NLA_IN, + vars_response = vars_response, vars_stressor = vars_stressor, + subpops = subpops, siteID = "SITE_ID", weight = "WGT_TP", xcoord = "XCOORD", + ycoord = "YCOORD", + response_levels = list("BENT_MMI_COND_2017" = c("Poor", "Good")), + stressor_levels = list("PTL_COND" = c("Poor", "Good"), "NTL_COND" = c("Poor", "Good")) +) + +test_that("Risk Difference: Unstratified single-stage analysis (specify response/stressor levels)", { + expect_true(exists("DiffRisk_Estimates")) + expect_equal(attributes(DiffRisk_Estimates)$class, "data.frame") + expect_equal(nrow(DiffRisk_Estimates), 6) +}) + +DiffRisk_Estimates <- diffrisk_analysis( + dframe = NLA_IN, + vars_response = vars_response, vars_stressor = vars_stressor, + subpops = subpops, siteID = "SITE_ID", weight = "WGT_TP", xcoord = "XCOORD", + ycoord = "YCOORD", + response_levels = list(c("Poor", "Good")), + stressor_levels = list(c("Poor", "Good"), c("Poor", "Good")) +) + +test_that("Risk Difference: Unstratified single-stage analysis (unnamed response/stressor levels)", { + expect_true(exists("DiffRisk_Estimates")) + expect_equal(attributes(DiffRisk_Estimates)$class, "data.frame") + expect_equal(nrow(DiffRisk_Estimates), 6) +}) + DiffRisk_Estimates <- diffrisk_analysis( dframe = NLA_IN, vars_response = vars_response, vars_stressor = vars_stressor, diff --git a/tests/testthat/test-relrisk_analysis.R b/tests/testthat/test-relrisk_analysis.R index 696dccc..b0afc70 100644 --- a/tests/testthat/test-relrisk_analysis.R +++ b/tests/testthat/test-relrisk_analysis.R @@ -89,6 +89,36 @@ test_that("Relative Risk: Unstratified single-stage analysis", { expect_equal(nrow(RelRisk_Estimates), 6) }) +RelRisk_Estimates <- relrisk_analysis( + dframe = NLA_IN, + vars_response = vars_response, vars_stressor = vars_stressor, + subpops = subpops, siteID = "SITE_ID", weight = "WGT_TP", xcoord = "XCOORD", + ycoord = "YCOORD", + response_levels = list("BENT_MMI_COND_2017" = c("Poor", "Good")), + stressor_levels = list("PTL_COND" = c("Poor", "Good"), "NTL_COND" = c("Poor", "Good")) +) + +test_that("Relative Risk: Unstratified single-stage analysis (specify response/stressor levels)", { + expect_true(exists("RelRisk_Estimates")) + expect_equal(attributes(RelRisk_Estimates)$class, "data.frame") + expect_equal(nrow(RelRisk_Estimates), 6) +}) + +RelRisk_Estimates <- relrisk_analysis( + dframe = NLA_IN, + vars_response = vars_response, vars_stressor = vars_stressor, + subpops = subpops, siteID = "SITE_ID", weight = "WGT_TP", xcoord = "XCOORD", + ycoord = "YCOORD", + response_levels = list(c("Poor", "Good")), + stressor_levels = list(c("Poor", "Good"), c("Poor", "Good")) +) + +test_that("Relative Risk: Unstratified single-stage analysis (unnamed response/stressor levels)", { + expect_true(exists("RelRisk_Estimates")) + expect_equal(attributes(RelRisk_Estimates)$class, "data.frame") + expect_equal(nrow(RelRisk_Estimates), 6) +}) + RelRisk_Estimates <- relrisk_analysis( dframe = NLA_IN, vars_response = vars_response, vars_stressor = vars_stressor, From 94eebccf5ee558b9c71baa838f159beda2cb2ee3 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Fri, 20 Jan 2023 16:08:32 -0800 Subject: [PATCH 04/12] update version number in DESCRIPTION and NEWS --- DESCRIPTION | 2 +- NEWS.md | 19 +++++++++++++++++++ 2 files changed, 20 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 9ee25ba..461c195 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: spsurvey Title: Spatial Sampling Design and Analysis -Version: 5.4.1 +Version: 5.4.2 Authors@R: c( person("Michael", "Dumelle", role=c("aut","cre"), email = "Dumelle.Michael@epa.gov", comment = c(ORCID = "0000-0002-3393-5529")), diff --git a/NEWS.md b/NEWS.md index 65a614b..dd9c4e9 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,22 @@ +# spsurvey 5.4.2 + +## Minor Updates + +* Changed default behavior in `attrisk_analysis()`, `diffrisk_analysis()`, and + `relrisk_analysis()` regarding the handling of `response_levels` and `stressor_levels`. + Previously, if `response_levels` and `stressor_levels` were specified, + their elements required names. + Now, if `response_levels` is specified and its names are `NULL`, then its names are set to `vars_response`, + and if `stressor_levels` is specified and its names are `NULL`, then its names are set to `vars_stressor` (#33). + +## Bug Fixes + +* Fixed a bug that caused an erorr in `grts()` and `irs()` occurred when at least + one variable name in `sframe` was named `"siteID"`, `"siteuse"`, `"replsite"`, + `"lon_WGS84"`, `"lat_WGS84"`, `"stratum"`, `"wgt"`, `"ip"`, `"caty"`, `"aux"`, + `xcoord`, `ycoord`, or `idpts` and the name of the geometry column in `sframe` + was not named `"geometry"` (#32). + # spsurvey 5.4.1 ## Minor Updates From 1410a1f73c6257ba17df01b980ca06d6d2181266 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Tue, 7 Feb 2023 14:15:33 -0800 Subject: [PATCH 05/12] Fix issue #36 --- NEWS.md | 2 ++ R/attrisk_analysis.R | 12 ++++++------ R/cat_analysis.R | 12 ++++++------ R/change_analysis.R | 12 ++++++------ R/cont_analysis.R | 12 ++++++------ R/cont_cdftest.R | 12 ++++++------ R/diffrisk_analysis.R | 12 ++++++------ R/dsgn_check.R | 6 +++--- R/errorprnt.R | 4 ++-- R/grts.R | 4 ++-- R/grts_stratum.R | 4 ++-- R/irs.R | 4 ++-- R/irs_stratum.R | 4 ++-- R/relrisk_analysis.R | 12 ++++++------ R/stopprnt.R | 4 ++-- R/trend_analysis.R | 12 ++++++------ R/warnprnt.R | 22 +++++++++++----------- tests/testthat/test-attrisk_analysis.R | 11 +++++++++++ tests/testthat/test-cat_analysis.R | 8 ++++++++ tests/testthat/test-change_analysis.R | 11 +++++++++++ tests/testthat/test-cont_analysis.R | 9 +++++++++ tests/testthat/test-diffrisk_analysis.R | 11 +++++++++++ tests/testthat/test-grts.R | 5 +++++ tests/testthat/test-irs.R | 6 ++++++ tests/testthat/test-relrisk_analysis.R | 11 +++++++++++ tests/testthat/test-trend_analysis.R | 10 ++++++++++ 26 files changed, 158 insertions(+), 74 deletions(-) diff --git a/NEWS.md b/NEWS.md index dd9c4e9..0a82a49 100644 --- a/NEWS.md +++ b/NEWS.md @@ -9,6 +9,8 @@ Now, if `response_levels` is specified and its names are `NULL`, then its names are set to `vars_response`, and if `stressor_levels` is specified and its names are `NULL`, then its names are set to `vars_stressor` (#33). +* Warning and error messages from `grts()`, `irs()`, and `*_analysis()` functions now print using `message()` instead of `cat()`. This change makes the resulting output more consistent with standard practices and easier to suppress if desired (#36). + ## Bug Fixes * Fixed a bug that caused an erorr in `grts()` and `irs()` occurred when at least diff --git a/R/attrisk_analysis.R b/R/attrisk_analysis.R index 45a4689..a1cae70 100644 --- a/R/attrisk_analysis.R +++ b/R/attrisk_analysis.R @@ -674,17 +674,17 @@ attrisk_analysis <- function(dframe, vars_response, vars_stressor, response_leve if (error_ind) { error_vec <<- error_vec if (length(error_vec) == 1) { - cat("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") + message("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") } else { - cat(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) + message(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) } if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } stop("See the preceding message(s).") @@ -1228,9 +1228,9 @@ attrisk_analysis <- function(dframe, vars_response, vars_stressor, response_leve if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/cat_analysis.R b/R/cat_analysis.R index 60c0b6d..1360733 100644 --- a/R/cat_analysis.R +++ b/R/cat_analysis.R @@ -493,17 +493,17 @@ cat_analysis <- function(dframe, vars, subpops = NULL, siteID = NULL, weight = " if (error_ind) { error_vec <<- error_vec if (length(error_vec) == 1) { - cat("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") + message("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") } else { - cat(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) + message(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) } if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } stop("See the preceding message(s).") @@ -635,9 +635,9 @@ cat_analysis <- function(dframe, vars, subpops = NULL, siteID = NULL, weight = " if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/change_analysis.R b/R/change_analysis.R index 1ac3169..712d304 100644 --- a/R/change_analysis.R +++ b/R/change_analysis.R @@ -836,17 +836,17 @@ change_analysis <- function(dframe, vars_cat = NULL, vars_cont = NULL, test = "m if (error_ind) { error_vec <<- error_vec if (length(error_vec) == 1) { - cat("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") + message("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") } else { - cat(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) + message(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) } if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } stop("See the preceding message(s).") @@ -1061,9 +1061,9 @@ change_analysis <- function(dframe, vars_cat = NULL, vars_cont = NULL, test = "m if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/cont_analysis.R b/R/cont_analysis.R index 029bdcb..09c82ae 100644 --- a/R/cont_analysis.R +++ b/R/cont_analysis.R @@ -374,17 +374,17 @@ cont_analysis <- function(dframe, vars, subpops = NULL, siteID = NULL, if (error_ind) { error_vec <<- error_vec if (length(error_vec) == 1) { - cat("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") + message("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") } else { - cat(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) + message(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) } if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } stop("See the preceding message(s).") @@ -563,9 +563,9 @@ cont_analysis <- function(dframe, vars, subpops = NULL, siteID = NULL, if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/cont_cdftest.R b/R/cont_cdftest.R index 4282003..813fab9 100644 --- a/R/cont_cdftest.R +++ b/R/cont_cdftest.R @@ -565,17 +565,17 @@ cont_cdftest <- function(dframe, vars, subpops = NULL, surveyID = NULL, siteID = if (error_ind) { error_vec <<- error_vec if (length(error_vec) == 1) { - cat("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") + message("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") } else { - cat(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) + message(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) } if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } stop("See the preceding message(s).") @@ -865,9 +865,9 @@ cont_cdftest <- function(dframe, vars, subpops = NULL, surveyID = NULL, siteID = if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/diffrisk_analysis.R b/R/diffrisk_analysis.R index a058261..365c77e 100644 --- a/R/diffrisk_analysis.R +++ b/R/diffrisk_analysis.R @@ -406,17 +406,17 @@ diffrisk_analysis <- function(dframe, vars_response, vars_stressor, response_lev if (error_ind) { error_vec <<- error_vec if (length(error_vec) == 1) { - cat("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") + message("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") } else { - cat(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) + message(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) } if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } stop("See the preceding message(s).") @@ -684,9 +684,9 @@ diffrisk_analysis <- function(dframe, vars_response, vars_stressor, response_lev if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/dsgn_check.R b/R/dsgn_check.R index c09561a..813da71 100644 --- a/R/dsgn_check.R +++ b/R/dsgn_check.R @@ -424,9 +424,9 @@ dsgn_check <- function(sframe, sf_type, legacy_sites, legacy_option, stratum, se if (stop_ind) { names(stop_df) <- c("Design Input", "Error Message") stop_df <<- stop_df - cat("During the check of the input to grtspts, one or more errors were identified.\n") - cat("Enter the following command to view all input error messages: stopprnt()\n") - cat("To view a subset of the errors (e.g., errors 1 and 5) enter stopprnt(m=c(1,5))\n\n") + message("During the check of the input to grtspts, one or more errors were identified.\n") + message("Enter the following command to view all input error messages: stopprnt()\n") + message("To view a subset of the errors (e.g., errors 1 and 5) enter stopprnt(m=c(1,5))\n\n") opt <- options(show.error.messages = FALSE) on.exit(options(opt)) stop() diff --git a/R/errorprnt.R b/R/errorprnt.R index 6ea3d91..af8aacd 100644 --- a/R/errorprnt.R +++ b/R/errorprnt.R @@ -21,8 +21,8 @@ errorprnt <- function(error_vec = get("error_vec", envir = .GlobalEnv)) { m <- 1:length(error_vec) for (i in m) { - cat(paste0("Error Message ", i, ":\n")) - cat(paste(error_vec[i], "\n")) + message(paste0("Error Message ", i, ":\n")) + message(paste(error_vec[i], "\n")) } invisible(NULL) diff --git a/R/grts.R b/R/grts.R index cea09d6..2ab7ff4 100644 --- a/R/grts.R +++ b/R/grts.R @@ -930,9 +930,9 @@ grts <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/grts_stratum.R b/R/grts_stratum.R index 27e97cf..c873346 100644 --- a/R/grts_stratum.R +++ b/R/grts_stratum.R @@ -185,8 +185,8 @@ grts_stratum <- function(stratum, dsgn, sframe, sf_type, wgt_units = NULL, pt_de # check that number of legacy sites is less than or equal number of base sites # stop if not if (n_legacy > n_base) { - cat("Number of legacy sites is greater than number of base sites in at least one\n") - cat("stratum. Please check that all strata have fewer legacy sites than base sites.\n") + message("Number of legacy sites is greater than number of base sites in at least one\n") + message("stratum. Please check that all strata have fewer legacy sites than base sites.\n") opt <- options(show.error.messages = FALSE) on.exit(options(opt)) stop() diff --git a/R/irs.R b/R/irs.R index 8bf442d..e4d7474 100644 --- a/R/irs.R +++ b/R/irs.R @@ -630,9 +630,9 @@ irs <- function(sframe, n_base, stratum_var = NULL, seltype = NULL, caty_var = N if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/irs_stratum.R b/R/irs_stratum.R index 756c7a7..a48c920 100644 --- a/R/irs_stratum.R +++ b/R/irs_stratum.R @@ -156,8 +156,8 @@ irs_stratum <- function(stratum, dsgn, sframe, sf_type, wgt_units = NULL, pt_den # check that number of legacy sites is less than or equal number of base sites # stop if not if (n_legacy > n_base) { - cat("Number of legacy sites is greater than number of base sites in at least one\n") - cat("stratum. Please check that all strata have fewer legacy sites than base sites.\n") + message("Number of legacy sites is greater than number of base sites in at least one\n") + message("stratum. Please check that all strata have fewer legacy sites than base sites.\n") opt <- options(show.error.messages = FALSE) on.exit(options(opt)) stop() diff --git a/R/relrisk_analysis.R b/R/relrisk_analysis.R index b365c7a..1c21b1a 100644 --- a/R/relrisk_analysis.R +++ b/R/relrisk_analysis.R @@ -424,17 +424,17 @@ relrisk_analysis <- function(dframe, vars_response, vars_stressor, response_leve if (error_ind) { error_vec <<- error_vec if (length(error_vec) == 1) { - cat("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") + message("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") } else { - cat(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) + message(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) } if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } stop("See the preceding message(s).") @@ -980,9 +980,9 @@ relrisk_analysis <- function(dframe, vars_response, vars_stressor, response_leve if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/stopprnt.R b/R/stopprnt.R index 8ce04f9..d28a072 100644 --- a/R/stopprnt.R +++ b/R/stopprnt.R @@ -25,10 +25,10 @@ stopprnt <- function(stop_df = get("stop_df", envir = .GlobalEnv), m = 1:nrow(stop_df)) { - cat(paste("Input ", "Error Message\n")) + message(paste("Input ", "Error Message\n")) for (i in m) { - cat(paste(stop_df[i, 1], ": ", stop_df[i, 2], "\n")) + message(paste(stop_df[i, 1], ": ", stop_df[i, 2], "\n")) } invisible(NULL) diff --git a/R/trend_analysis.R b/R/trend_analysis.R index 4c9c981..09d0475 100644 --- a/R/trend_analysis.R +++ b/R/trend_analysis.R @@ -711,17 +711,17 @@ trend_analysis <- function(dframe, vars_cat = NULL, vars_cont = NULL, subpops = if (error_ind) { error_vec <<- error_vec if (length(error_vec) == 1) { - cat("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") + message("During execution of the program, an error message was generated. The error \nmessage is stored in a vector named 'error_vec'. Enter the following command \nto view the error message: errorprnt()\n") } else { - cat(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) + message(paste("During execution of the program,", length(error_vec), "error messages were generated. The error \nmessages are stored in a vector named 'error_vec'. Enter the following \ncommand to view the error messages: errorprnt()\n")) } if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } stop("See the preceding message(s).") @@ -1727,9 +1727,9 @@ trend_analysis <- function(dframe, vars_cat = NULL, vars_cont = NULL, subpops = if (warn_ind) { warn_df <<- warn_df if (nrow(warn_df) == 1) { - cat("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") + message("During execution of the program, a warning message was generated. The warning \nmessage is stored in a data frame named 'warn_df'. Enter the following command \nto view the warning message: warnprnt()\n") } else { - cat(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) + message(paste("During execution of the program,", nrow(warn_df), "warning messages were generated. The warning \nmessages are stored in a data frame named 'warn_df'. Enter the following \ncommand to view the warning messages: warnprnt() \nTo view a subset of the warning messages (say, messages number 1, 3, and 5), \nenter the following command: warnprnt(m=c(1,3,5))\n")) } } diff --git a/R/warnprnt.R b/R/warnprnt.R index e8d01a2..574cdb6 100644 --- a/R/warnprnt.R +++ b/R/warnprnt.R @@ -37,27 +37,27 @@ warnprnt <- function(warn_df = get("warn_df", envir = .GlobalEnv), for (i in m) { if ("Warning" %in% names(warn_df)) { - cat(paste0("Warning Message ", i, ":\n")) - cat(paste("Stratum:", warn_df$stratum[i], "\n")) - cat(paste("Warning:", warn_df$Warning[i], "\n")) + message(paste0("Warning Message ", i, ":\n")) + message(paste("Stratum:", warn_df$stratum[i], "\n")) + message(paste("Warning:", warn_df$Warning[i], "\n")) } else { - cat(paste0("Warning Message ", i, ":\n")) - # cat(paste("Function:", warn_df$func[i], "\n")) # removed function name output + message(paste0("Warning Message ", i, ":\n")) + # message(paste("Function:", warn_df$func[i], "\n")) # removed function name output if (sum(!is.null(warn_df$subpoptype[i]), is.na(warn_df$subpoptype[i]))) { - cat(paste("Population Type:", warn_df$subpoptype[i], "\n")) + message(paste("Population Type:", warn_df$subpoptype[i], "\n")) } if (sum(!is.null(warn_df$subpop[i]), is.na(warn_df$subpop[i]))) { - cat(paste("Subpopulation:", warn_df$subpop[i], "\n")) + message(paste("Subpopulation:", warn_df$subpop[i], "\n")) } if (sum(!is.null(warn_df$indicator[i]), is.na(warn_df$indicator[i]))) { - cat(paste("Indicator:", warn_df$indicator[i], "\n")) + message(paste("Indicator:", warn_df$indicator[i], "\n")) } if (sum(!is.null(warn_df$stratum[i]), is.na(warn_df$stratum[i]))) { - cat(paste("Stratum:", warn_df$stratum[i], "\n")) + message(paste("Stratum:", warn_df$stratum[i], "\n")) } - cat(paste("Warning:", warn_df$warning[i])) + message(paste("Warning:", warn_df$warning[i])) if (sum(!is.null(warn_df$action[i]), is.na(warn_df$action[i]))) { - cat(paste("Action:", warn_df$action[i], "\n")) + message(paste("Action:", warn_df$action[i], "\n")) } } } diff --git a/tests/testthat/test-attrisk_analysis.R b/tests/testthat/test-attrisk_analysis.R index a94335d..718256c 100644 --- a/tests/testthat/test-attrisk_analysis.R +++ b/tests/testthat/test-attrisk_analysis.R @@ -270,3 +270,14 @@ test_that("Attributable Risk: with finite population correction factor", { expect_equal(attributes(AttRisk_Estimates)$class, "data.frame") expect_equal(nrow(AttRisk_Estimates), 6) }) + +test_that("A warning (in message form) is produced", { + expect_message(expect_error(attrisk_analysis( + dframe = NLA_IN, + vars_response = vars_response, vars_stressor = vars_stressor, + subpops = subpops, siteID = "SITE_ID", weight = "XYZ", xcoord = "XCOORD", + ycoord = "YCOORD", stratumID = "URBN_NLA17", clusterID = "clusterID", + weight1 = "weight1", xcoord1 = "xcoord1", ycoord1 = "ycoord1", + fpc = fpc4a, vartype = "SRS" + ))) +}) diff --git a/tests/testthat/test-cat_analysis.R b/tests/testthat/test-cat_analysis.R index 8893509..e2242f4 100644 --- a/tests/testthat/test-cat_analysis.R +++ b/tests/testthat/test-cat_analysis.R @@ -324,3 +324,11 @@ test_that("Categorical: Unstratified single-stage analysis, YG-HR variance", { expect_equal(attributes(Condition_Estimates)$class, "data.frame") expect_equal(nrow(Condition_Estimates), 9) }) + +test_that("A warning (in message form) is produced", { + expect_message(expect_error(cat_analysis( + dframe = NLA_IN, vars = vars, + subpops = subpops, siteID = "SITE_ID", weight = "XYZ", vartype = "YG", + jointprob = "hr" + ))) +}) diff --git a/tests/testthat/test-change_analysis.R b/tests/testthat/test-change_analysis.R index 5d4a4e9..e2d86ee 100644 --- a/tests/testthat/test-change_analysis.R +++ b/tests/testthat/test-change_analysis.R @@ -302,6 +302,17 @@ test_that("Change: with finite population correction factor", { expect_equal(nrow(Change_Estimates$contsum_median), 2) }) +test_that("A warning (in message form) is produced", { + expect_message(expect_error(change_analysis( + dframe = dframe, vars_cat = vars_cat, + vars_cont = vars_cont, test = c("mean", "total", "median"), subpops = subpops, + surveyID = "YEAR", siteID = "UNIQUE_ID", weight = "XYZ", xcoord = "XCOORD", + ycoord = "YCOORD", stratumID = "LAKE_ORGN", clusterID = "clusterID", + weight1 = "weight1", xcoord1 = "xcoord1", ycoord1 = "ycoord1", + fpc = fpc4b, vartype = "SRS" + ))) +}) + test_that("Change for totals matches cont_analysis()", { set.seed(1) dframe <- data.frame( diff --git a/tests/testthat/test-cont_analysis.R b/tests/testthat/test-cont_analysis.R index a2976d0..10e375f 100644 --- a/tests/testthat/test-cont_analysis.R +++ b/tests/testthat/test-cont_analysis.R @@ -298,3 +298,12 @@ test_that("Continuous: with finite population correction factor", { expect_equal(nrow(CDF_Estimates$Mean), 3) expect_equal(nrow(CDF_Estimates$Total), 3) }) + +test_that("A warning (in message form) is produced", { + expect_message(expect_error(cont_analysis( + dframe = dframe, vars = vars, subpops = subpops, + siteID = "SITE_ID", weight = "XYZ", xcoord = "XCOORD", ycoord = "YCOORD", + stratumID = "URBN_NLA17", clusterID = "clusterID", weight1 = "weight1", + xcoord1 = "xcoord1", ycoord1 = "ycoord1", fpc = fpc4a, vartype = "SRS" + ))) +}) diff --git a/tests/testthat/test-diffrisk_analysis.R b/tests/testthat/test-diffrisk_analysis.R index 44a1c0f..98a50f8 100644 --- a/tests/testthat/test-diffrisk_analysis.R +++ b/tests/testthat/test-diffrisk_analysis.R @@ -270,3 +270,14 @@ test_that("Risk Difference: with finite population correction factor", { expect_equal(attributes(DiffRisk_Estimates)$class, "data.frame") expect_equal(nrow(DiffRisk_Estimates), 6) }) + +test_that("A warning (in message form) is produced", { + expect_message(expect_error(diffrisk_analysis( + dframe = NLA_IN, + vars_response = vars_response, vars_stressor = vars_stressor, + subpops = subpops, siteID = "SITE_ID", weight = "XYZ", xcoord = "XCOORD", + ycoord = "YCOORD", stratumID = "URBN_NLA17", clusterID = "clusterID", + weight1 = "weight1", xcoord1 = "xcoord1", ycoord1 = "ycoord1", + fpc = fpc4a, vartype = "SRS" + ))) +}) \ No newline at end of file diff --git a/tests/testthat/test-grts.R b/tests/testthat/test-grts.R index 2c56984..97d5ae4 100644 --- a/tests/testthat/test-grts.R +++ b/tests/testthat/test-grts.R @@ -238,6 +238,11 @@ if (on_solaris) { expect_equal(NCOL(grts_output$sites_near), 1) }) + test_that("A warning (in message form) is produced", { + n_base <- c(low = 20, high = 30) + expect_message(grts(NE_Lakes, n_base = n_base, stratum_var = "XYZ")) + + }) #-------------------------------------- #-------- Legacy #-------------------------------------- diff --git a/tests/testthat/test-irs.R b/tests/testthat/test-irs.R index 31a2c04..1df551c 100644 --- a/tests/testthat/test-irs.R +++ b/tests/testthat/test-irs.R @@ -238,6 +238,12 @@ if (on_solaris) { expect_equal(NCOL(irs_output$sites_near), 1) }) + test_that("A warning (in message form) is produced", { + n_base <- c(low = 20, high = 30) + expect_message(irs(NE_Lakes, n_base = n_base, stratum_var = "XYZ")) + + }) + #-------------------------------------- #-------- Legacy #-------------------------------------- diff --git a/tests/testthat/test-relrisk_analysis.R b/tests/testthat/test-relrisk_analysis.R index b0afc70..b75f361 100644 --- a/tests/testthat/test-relrisk_analysis.R +++ b/tests/testthat/test-relrisk_analysis.R @@ -270,3 +270,14 @@ test_that("Relative Risk: with finite population correction factor", { expect_equal(attributes(RelRisk_Estimates)$class, "data.frame") expect_equal(nrow(RelRisk_Estimates), 6) }) + +test_that("A warning (in message form) is produced", { + expect_message(expect_error(relrisk_analysis( + dframe = NLA_IN, + vars_response = vars_response, vars_stressor = vars_stressor, + subpops = subpops, siteID = "SITE_ID", weight = "XYZ", xcoord = "XCOORD", + ycoord = "YCOORD", stratumID = "URBN_NLA17", clusterID = "clusterID", + weight1 = "weight1", xcoord1 = "xcoord1", ycoord1 = "ycoord1", + fpc = fpc4a, vartype = "SRS" + ))) +}) \ No newline at end of file diff --git a/tests/testthat/test-trend_analysis.R b/tests/testthat/test-trend_analysis.R index e73820b..5a53261 100644 --- a/tests/testthat/test-trend_analysis.R +++ b/tests/testthat/test-trend_analysis.R @@ -247,3 +247,13 @@ test_that("Trend: with finite population correction factor", { expect_equal(nrow(Trend_Estimates$catsum), 6) expect_equal(nrow(Trend_Estimates$contsum), 3) }) + +test_that("A warning (in message form) is produced", { + expect_message(expect_error(trend_analysis( + dframe = NLA_IN, vars_cat = vars_cat, + vars_cont = vars_cont, model_cont = "SLR", subpops = subpops, + siteID = "UNIQUE_ID", weight = "XYZ", xcoord = "XCOORD", ycoord = "YCOORD", + stratumID = "LAKE_ORGN", clusterID = "clusterID", weight1 = "weight1", + xcoord1 = "xcoord1", ycoord1 = "ycoord1", fpc = fpc4b, vartype = "SRS" + ))) +}) From ccde031def36f981ba82fbe26b99cd85c0441d12 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Thu, 9 Feb 2023 13:10:14 -0800 Subject: [PATCH 06/12] Fixed error in optional tests for new message printing (instead of cat printing) for grts and irs functions --- tests/testthat/test-grts.R | 4 ++-- tests/testthat/test-irs.R | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/testthat/test-grts.R b/tests/testthat/test-grts.R index 97d5ae4..c5c5927 100644 --- a/tests/testthat/test-grts.R +++ b/tests/testthat/test-grts.R @@ -240,9 +240,9 @@ if (on_solaris) { test_that("A warning (in message form) is produced", { n_base <- c(low = 20, high = 30) - expect_message(grts(NE_Lakes, n_base = n_base, stratum_var = "XYZ")) - + expect_message(expect_error(grts(NE_Lakes, n_base = n_base, stratum_var = "XYZ"))) }) + #-------------------------------------- #-------- Legacy #-------------------------------------- diff --git a/tests/testthat/test-irs.R b/tests/testthat/test-irs.R index 1df551c..742c576 100644 --- a/tests/testthat/test-irs.R +++ b/tests/testthat/test-irs.R @@ -240,7 +240,7 @@ if (on_solaris) { test_that("A warning (in message form) is produced", { n_base <- c(low = 20, high = 30) - expect_message(irs(NE_Lakes, n_base = n_base, stratum_var = "XYZ")) + expect_message(expect_error(irs(NE_Lakes, n_base = n_base, stratum_var = "XYZ"))) }) From 010aa5123af0a02baeffa83f4da9ac9b753b55f3 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Thu, 9 Feb 2023 13:10:56 -0800 Subject: [PATCH 07/12] Updated version number to 5.5.0 --- DESCRIPTION | 2 +- NAMESPACE | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 461c195..1637952 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: spsurvey Title: Spatial Sampling Design and Analysis -Version: 5.4.2 +Version: 5.5.0 Authors@R: c( person("Michael", "Dumelle", role=c("aut","cre"), email = "Dumelle.Michael@epa.gov", comment = c(ORCID = "0000-0002-3393-5529")), diff --git a/NAMESPACE b/NAMESPACE index 14b1985..c67f89d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -15,6 +15,7 @@ S3method(sp_summary,sp_design) S3method(summary,sp_design) S3method(summary,sp_frame) export(adjwgt) +export(adjwgtNR) export(ash1_wgt) export(attrisk_analysis) export(cat_analysis) From 96da44cd8eced4c2f669c0aa4e91022b4dcd71a4 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Thu, 9 Feb 2023 13:11:22 -0800 Subject: [PATCH 08/12] added adjwgtNR function for non-response weight adjustments --- NEWS.md | 3 +- R/adjwgtNR.R | 75 ++++++++++++++++++++++++++++++++++ man/adjwgtNR.Rd | 61 +++++++++++++++++++++++++++ tests/testthat/test-adjwgtNR.R | 41 +++++++++++++++++++ 4 files changed, 179 insertions(+), 1 deletion(-) create mode 100644 R/adjwgtNR.R create mode 100644 man/adjwgtNR.Rd create mode 100644 tests/testthat/test-adjwgtNR.R diff --git a/NEWS.md b/NEWS.md index 0a82a49..794b551 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,7 +1,8 @@ -# spsurvey 5.4.2 +# spsurvey 5.5.0 ## Minor Updates +* Added an `adjwgtNR()` function to perform non-response weight adjustments. * Changed default behavior in `attrisk_analysis()`, `diffrisk_analysis()`, and `relrisk_analysis()` regarding the handling of `response_levels` and `stressor_levels`. Previously, if `response_levels` and `stressor_levels` were specified, diff --git a/R/adjwgtNR.R b/R/adjwgtNR.R new file mode 100644 index 0000000..bb82673 --- /dev/null +++ b/R/adjwgtNR.R @@ -0,0 +1,75 @@ +############################################################################ +# Function: adjwgtNR (exported) +# Programmer: Tony Olsen +# Date: April 5, 2022 +# +#' Adjust survey design weights for non-response by categories +#' +#' @description Adjust weights for target sample units that do not respond +#' and are missing at random within categories. The missing at random +#' assumption implies that their sample weight may be assigned to +#' specific categories of units that have responded (i.e., have been +#' sampled). This is a class-based method for non-response adjustment. +#' +#' @param wgt vector of weights for each sample unit that will be adjusted +#' for non-response. Weights must be weights for the design as implemented. +#' All weights must be greater than zero. +#' +#' @param MARClass vector that identifies for each sample unit the category +#' that will be used in non-response weight adjustment for sample units +#' that are known to be target. Within each missing at random (MAR) +#' category, the missing sample units that are not sampled are assumed to +#' be missing at random. +#' +#' @param EvalStatus vector of the evaluation status for each sample unit. +#' Values must include the values given in TNRclass and TRClass. May +#' include other values not required for the non-response adjustment. +#' +#' @param TNRClass subset of values in EvalStatus that identify sample units +#' whose target status is known and that do not respond (i.e., are not +#' sampled). +#' +#' @param TRClass Subset of values in EvalStatus that identify sample units +#' whose target status is known and that respond (i.e., are target and +#' sampled). +#' +#' @return Vector of sample unit weights that are adjusted for non-response +#' and that is the same length of input weights. Weights for sample +#' units that did not response but were known to be eligible are set +#' to zero. Weights for all other sample units are also set to zero. +#' +#' @export +#' +#' @author Tony Olsen \email{olsen.tony@epa.gov} +#' +#' @keywords survey non-response weight adjustment +#' +#' @examples +#' set.seed(5) +#' wgt <- runif(40) +#' MARClass <- rep(c("A", "B"), rep(20, 2)) +#' EvalStatus <- sample(c("Not_Target", "Target_Sampled", "Target_Not_Sampled"), 40, replace = TRUE) +#' TNRClass <- "Target_Not_Sampled" +#' TRClass <- "Target_Sampled" +#' adjwgtNR(wgt, MARClass, EvalStatus, TNRClass, TRClass) +#' # function that has an error check +adjwgtNR <- function(wgt, MARClass, EvalStatus, TNRClass, TRClass){ + tstTNRClass <- EvalStatus %in% c(TNRClass) + tstTRClass <- EvalStatus %in% c(TRClass) + num <- tapply(wgt[tstTNRClass | tstTRClass], + MARClass[tstTNRClass | tstTRClass], sum) + den <- tapply(wgt[tstTRClass], MARClass[tstTRClass], sum) + # error check + # could use any(! unique(MARClass[tstTNRClass]) %in% unique(MARClass[tstTRClass])) + if (length(num) > length(den)) { + stop("At least one level of MARClass does not have any EvalStatus values in + TRClass, so no non-response weight adjustment can be performed. + Consider aggregating categories so that all levels of MARClass are + instead in TRClass.", call. = FALSE) + } + ar <- num/den[match(names(num), names(den))] + wgt[tstTRClass] <- wgt[tstTRClass] * + ar[match(MARClass, names(ar))][tstTRClass] + wgt[!tstTRClass] <- 0 + as.vector(wgt) +} \ No newline at end of file diff --git a/man/adjwgtNR.Rd b/man/adjwgtNR.Rd new file mode 100644 index 0000000..dc2be0b --- /dev/null +++ b/man/adjwgtNR.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/adjwgtNR.R +\name{adjwgtNR} +\alias{adjwgtNR} +\title{Adjust survey design weights for non-response by categories} +\usage{ +adjwgtNR(wgt, MARClass, EvalStatus, TNRClass, TRClass) +} +\arguments{ +\item{wgt}{vector of weights for each sample unit that will be adjusted +for non-response. Weights must be weights for the design as implemented. +All weights must be greater than zero.} + +\item{MARClass}{vector that identifies for each sample unit the category +that will be used in non-response weight adjustment for sample units +that are known to be target. Within each missing at random (MAR) +category, the missing sample units that are not sampled are assumed to +be missing at random.} + +\item{EvalStatus}{vector of the evaluation status for each sample unit. +Values must include the values given in TNRclass and TRClass. May +include other values not required for the non-response adjustment.} + +\item{TNRClass}{subset of values in EvalStatus that identify sample units +whose target status is known and that do not respond (i.e., are not +sampled).} + +\item{TRClass}{Subset of values in EvalStatus that identify sample units +whose target status is known and that respond (i.e., are target and +sampled).} +} +\value{ +Vector of sample unit weights that are adjusted for non-response + and that is the same length of input weights. Weights for sample + units that did not response but were known to be eligible are set + to zero. Weights for all other sample units are also set to zero. +} +\description{ +Adjust weights for target sample units that do not respond +and are missing at random within categories. The missing at random +assumption implies that their sample weight may be assigned to +specific categories of units that have responded (i.e., have been +sampled). This is a class-based method for non-response adjustment. +} +\examples{ +set.seed(5) +wgt <- runif(40) +MARClass <- rep(c("A", "B"), rep(20, 2)) +EvalStatus <- sample(c("Not_Target", "Target_Sampled", "Target_Not_Sampled"), 40, replace = TRUE) +TNRClass <- "Target_Not_Sampled" +TRClass <- "Target_Sampled" +adjwgtNR(wgt, MARClass, EvalStatus, TNRClass, TRClass) +# function that has an error check +} +\author{ +Tony Olsen \email{olsen.tony@epa.gov} +} +\keyword{adjustment} +\keyword{non-response} +\keyword{survey} +\keyword{weight} diff --git a/tests/testthat/test-adjwgtNR.R b/tests/testthat/test-adjwgtNR.R new file mode 100644 index 0000000..3274ce8 --- /dev/null +++ b/tests/testthat/test-adjwgtNR.R @@ -0,0 +1,41 @@ +context("weight adjustment non-response") + +set.seed(5) + +test_that("adjust weight non-response works no extra Eval", { + wgt <- runif(30) + MARClass <- rep(c("A", "B"), rep(15, 2)) + EvalStatus <- rep(c("Target_Sampled", "Target_Not_Sampled"), 15) + TNRClass <- "Target_Not_Sampled" + TRClass <- "Target_Sampled" + wgt_new <- adjwgtNR(wgt, MARClass, EvalStatus, TNRClass, TRClass) + wgtsums <- sum(wgt_new[EvalStatus == "Target_Sampled"]) + wgtzeros <- sum(wgt_new[EvalStatus == "Target_Not_Sampled"]) + expect_equal(sum(wgt), wgtsums) + expect_equal(0, wgtzeros) + + # error returned + EvalStatus[EvalStatus == "Target_Sampled"] <- "Target_Not_Sampled" + expect_error(adjwgtNR(wgt, MARClass, EvalStatus, TNRClass, TRClass)) +}) + +test_that("adjust weight non-response works extra Eval", { + + wgt <- runif(30) + MARClass <- rep(c("A", "B"), rep(15, 2)) + EvalStatus <- rep(c("Not_Target", "Target_Sampled", "Target_Not_Sampled"), 10) + TNRClass <- "Target_Not_Sampled" + TRClass <- "Target_Sampled" + wgt_new <- adjwgtNR(wgt, MARClass, EvalStatus, TNRClass, TRClass) + wgtsums <- sum(wgt_new[EvalStatus == "Target_Sampled"]) + wgtzeros <- sum(wgt_new[EvalStatus == "Target_Not_Sampled"]) + expect_equal(sum(wgt[EvalStatus %in% c("Target_Not_Sampled", "Target_Sampled")]), wgtsums) + expect_equal(0, wgtzeros) + wgtzeros_extra <- sum(wgt_new[!EvalStatus %in% c("Target_Not_Sampled", "Target_Sampled")]) + expect_equal(0, wgtzeros) + + # error returned + EvalStatus[EvalStatus == "Target_Sampled"] <- "Not_Target" + expect_error(adjwgtNR(wgt, MARClass, EvalStatus, TNRClass, TRClass)) +}) + From a44eda0be46dc3d4b7de73856563b9ac41721c51 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Tue, 14 Mar 2023 11:24:43 -0700 Subject: [PATCH 09/12] recycling length one vector in n_over for stratified design with grts() or irs() --- NEWS.md | 1 + R/dsgn_check.R | 11 ++++++++++- R/grts.R | 7 +++++-- man/grts.Rd | 4 +++- man/irs.Rd | 4 +++- 5 files changed, 22 insertions(+), 5 deletions(-) diff --git a/NEWS.md b/NEWS.md index 794b551..a0c7004 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,6 +2,7 @@ ## Minor Updates +* `n_over` is now recycled if the design is stratified and `n_over` is a length-one numeric vector * Added an `adjwgtNR()` function to perform non-response weight adjustments. * Changed default behavior in `attrisk_analysis()`, `diffrisk_analysis()`, and `relrisk_analysis()` regarding the handling of `response_levels` and `stressor_levels`. diff --git a/R/dsgn_check.R b/R/dsgn_check.R index 813da71..74445e6 100644 --- a/R/dsgn_check.R +++ b/R/dsgn_check.R @@ -362,6 +362,15 @@ dsgn_check <- function(sframe, sf_type, legacy_sites, legacy_option, stratum, se stop_mess <- paste0("n_over values must be zero or positive.") stop_df <- rbind(stop_df, data.frame(func = I("n_over"), I(stop_mess))) } + + # fixes a bug downstream in checking sizes of n_base + n_over + if (length(n_over) == 1) { + n_over <- rep(n_over, length(stratum)) + } + if (is.null(names(n_over)) || all(names(n_over) %in% stratum)) { + names(n_over) <- stratum + } + n_over <- as.list(n_over) } if (is.list(n_over)) { if (any(names(n_over) %in% stratum == FALSE)) { @@ -387,7 +396,7 @@ dsgn_check <- function(sframe, sf_type, legacy_sites, legacy_option, stratum, se stop_df <- rbind(stop_df, data.frame(func = I("n_base + n_over"), I(stop_mess))) } } else { - if (any(sapply(stratum, function(x) (n_base[[x]] + sum(n_over[[x]])) > NROW(sframe[sframe[[stratum_var]] == x, , drop = FALSE])))) { + if (any(sapply(stratum, function(x) (n_base[[x]] + sum(n_over[[x]])) > NROW(sframe[sframe[[stratum_var]] == x, , drop = FALSE])))) { stop_ind <- TRUE stop_mess <- paste0("For each stratum, the sum of the base sites and 'Over' replacement sites must be no larger than the number of rows in 'sframe' representing that stratum.") stop_df <- rbind(stop_df, data.frame(func = I("n_base + n_over"), I(stop_mess))) diff --git a/R/grts.R b/R/grts.R index 2ab7ff4..6c4eb8c 100644 --- a/R/grts.R +++ b/R/grts.R @@ -149,9 +149,12 @@ #' If replacement sites are not desired for a particular stratum, then the corresponding #' value in \code{n_over} should be \code{0} or \code{NULL} (which is equivalent to \code{0}). #' If the sampling design is stratified but the number of \code{n_over} sites is the same in each -#' stratum, \code{n_over} can be a vector which is used for each stratum. Note that if the +#' stratum, \code{n_over} can be a vector which is used for each stratum. +#' If \code{n_over} is an unnamed, length-one vector, it's value is recycled +#' and used for each stratum. Note that if the #' sampling design has unequal selection probabilities (\code{seltype = "unequal"}), then \code{n_over} sites -#' are given the same proportion of \code{caty_n} values as \code{n_base}. +#' are given the same proportion of \code{caty_n} values as \code{n_base}. +#' #' #' @param n_near The number of nearest neighbor (nn) replacement sites. #' If the sampling design is unstratified, \code{n_near} is integer from \code{1} diff --git a/man/grts.Rd b/man/grts.Rd index bcb4fd8..c4367e6 100644 --- a/man/grts.Rd +++ b/man/grts.Rd @@ -164,7 +164,9 @@ If the sampling design is stratified, If replacement sites are not desired for a particular stratum, then the corresponding value in \code{n_over} should be \code{0} or \code{NULL} (which is equivalent to \code{0}). If the sampling design is stratified but the number of \code{n_over} sites is the same in each - stratum, \code{n_over} can be a vector which is used for each stratum. Note that if the + stratum, \code{n_over} can be a vector which is used for each stratum. + If \code{n_over} is an unnamed, length-one vector, it's value is recycled + and used for each stratum. Note that if the sampling design has unequal selection probabilities (\code{seltype = "unequal"}), then \code{n_over} sites are given the same proportion of \code{caty_n} values as \code{n_base}.} diff --git a/man/irs.Rd b/man/irs.Rd index 0ebd480..86a32f3 100644 --- a/man/irs.Rd +++ b/man/irs.Rd @@ -164,7 +164,9 @@ If the sampling design is stratified, If replacement sites are not desired for a particular stratum, then the corresponding value in \code{n_over} should be \code{0} or \code{NULL} (which is equivalent to \code{0}). If the sampling design is stratified but the number of \code{n_over} sites is the same in each - stratum, \code{n_over} can be a vector which is used for each stratum. Note that if the + stratum, \code{n_over} can be a vector which is used for each stratum. + If \code{n_over} is an unnamed, length-one vector, it's value is recycled + and used for each stratum. Note that if the sampling design has unequal selection probabilities (\code{seltype = "unequal"}), then \code{n_over} sites are given the same proportion of \code{caty_n} values as \code{n_base}.} From a504476051303de46be5ef2dac94f79e27c2ded2 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Tue, 16 May 2023 12:14:45 -0700 Subject: [PATCH 10/12] minor pre release updates --- NEWS.md | 9 ++++----- cran-comments.md | 4 +--- 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/NEWS.md b/NEWS.md index a0c7004..106db2e 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,8 +2,9 @@ ## Minor Updates -* `n_over` is now recycled if the design is stratified and `n_over` is a length-one numeric vector +* `n_over` is now recycled if the design is stratified and `n_over` is a length-one numeric vector. * Added an `adjwgtNR()` function to perform non-response weight adjustments. +* Warning and error messages from `grts()`, `irs()`, and `*_analysis()` functions now print using `message()` instead of `cat()`. This change makes the resulting output more consistent with standard practice and easier to suppress when desired (#36). * Changed default behavior in `attrisk_analysis()`, `diffrisk_analysis()`, and `relrisk_analysis()` regarding the handling of `response_levels` and `stressor_levels`. Previously, if `response_levels` and `stressor_levels` were specified, @@ -11,8 +12,6 @@ Now, if `response_levels` is specified and its names are `NULL`, then its names are set to `vars_response`, and if `stressor_levels` is specified and its names are `NULL`, then its names are set to `vars_stressor` (#33). -* Warning and error messages from `grts()`, `irs()`, and `*_analysis()` functions now print using `message()` instead of `cat()`. This change makes the resulting output more consistent with standard practices and easier to suppress if desired (#36). - ## Bug Fixes * Fixed a bug that caused an erorr in `grts()` and `irs()` occurred when at least @@ -55,13 +54,13 @@ ## Bug fixes * Fixed a bug that prevented proper printing of the `Indicator` column when using `change_analysis()` with `test = median`. -* Fixed a bug that made `change_analysis` sensititve to the ordering of the levels of variables in `var_cat` if those variables were factors. +* Fixed a bug that made `change_analysis` sensitive to the ordering of the levels of variables in `var_cat` if those variables were factors. * Fixed a bug in `sp_summary()` that incorrectly ordered the `siteuse` variable. * Fixed a bug in `sp_summary()` that failed to summarize data frames that did not have an `sf_column` attribute. * Fixed a bug in `*_analysis()` functions when `popsize` is a list intended for use with `survey::calibrate()`. * Fixed a bug in `*analysis()` functions that returned an error while performing percentile estimation when there was no variability in at least one variable in `vars` for at least one level of one variable in `subpops`. * Fixed a bug in `grts()` that caused an error for some combinations of `n_base` and `n_over`. -* Fixed a bug in `change_analysis()` that returned an error when at least one varible in `vars_cat` has only one unique value. +* Fixed a bug in `change_analysis()` that returned an error when at least one variable in `vars_cat` has only one unique value. # spsurvey 5.3.0 diff --git a/cran-comments.md b/cran-comments.md index 9c19fa5..8366eac 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,6 +1,4 @@ -This is a minor update that adds citation information for a publication in -*The Journal of Statistical Software* (JSS). The DOI in the CITATION for the -new JSS publication will be registered after publication on CRAN. +This is a minor update that adds few new features and bug fixes. ------- From 3309bf1a8e3c0552bde415ee3a44fdbd8854caea Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Tue, 16 May 2023 16:18:01 -0700 Subject: [PATCH 11/12] add cran comments to rbuildignore --- .Rbuildignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.Rbuildignore b/.Rbuildignore index f9eac1f..4c74c56 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -9,3 +9,4 @@ ^_pkgdown\.yml$ ^docs$ ^pkgdown$ +^CRAN-SUBMISSION$ From 7e361c0deee158f34c5cd7654d223e5782d5117c Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Tue, 16 May 2023 16:18:13 -0700 Subject: [PATCH 12/12] website updates --- docs/404.html | 2 +- docs/LICENSE.html | 40 +- docs/articles/EDA.html | 240 +++--- .../figure-html/unnamed-chunk-10-1.png | Bin 122488 -> 122490 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 122488 -> 122490 bytes 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| 212 ++--- docs/reference/cont_analysis.html | 108 +-- docs/reference/cont_cdfplot.html | 88 +-- docs/reference/cont_cdftest.html | 118 +-- docs/reference/cov_panel_dsgn.html | 22 +- docs/reference/diffrisk_analysis.html | 162 ++-- docs/reference/errorprnt.html | 4 +- docs/reference/grts.html | 72 +- docs/reference/index.html | 6 +- docs/reference/irs.html | 72 +- docs/reference/localmean_cov.html | 4 +- docs/reference/localmean_var.html | 4 +- docs/reference/localmean_weight.html | 4 +- docs/reference/pd_summary.html | 26 +- docs/reference/plot.html | 78 +- docs/reference/plot.sp_CDF.html | 114 +-- docs/reference/power_dsgn.html | 60 +- docs/reference/ppd_plot.html | 90 +-- docs/reference/relrisk_analysis.html | 164 ++-- docs/reference/revisit_bibd.html | 68 +- docs/reference/revisit_dsgn.html | 72 +- docs/reference/revisit_rand.html | 108 +-- docs/reference/sp_balance.html | 32 +- docs/reference/sp_frame.html | 16 +- docs/reference/sp_plot.html | 84 +- docs/reference/sp_rbind.html | 14 +- docs/reference/sp_summary.html | 30 +- docs/reference/spsurvey-package.html | 2 +- docs/reference/stopprnt.html | 4 +- docs/reference/summary.html | 26 +- docs/reference/trend_analysis.html | 220 +++--- docs/reference/warnprnt.html | 4 +- docs/sitemap.xml | 3 + 92 files changed, 2330 insertions(+), 2118 deletions(-) create mode 100644 docs/reference/adjwgtNR.html diff --git a/docs/404.html b/docs/404.html index f0ae731..4ed49bb 100644 --- a/docs/404.html +++ b/docs/404.html @@ -32,7 +32,7 @@ spsurvey - 5.4.1 + 5.5.0 diff --git a/docs/LICENSE.html b/docs/LICENSE.html index f2b6b9b..f5cf6e7 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -17,7 +17,7 @@ spsurvey - 5.4.1 + 5.5.0 @@ -234,27 +234,27 @@

17. Interpretation of Sections

How to Apply These Terms to Your New Programs

If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.

To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.

-
<one line to give the program's name and a brief idea of what it does.>
-Copyright (C) <year>  <name of author>
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
<one line to give the program's name and a brief idea of what it does.>
+Copyright (C) <year>  <name of author>
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.

Also add information on how to contact you by electronic and paper mail.

If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:

-
<program>  Copyright (C) <year>  <name of author>
-This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
-This is free software, and you are welcome to redistribute it
-under certain conditions; type 'show c' for details.
+
<program>  Copyright (C) <year>  <name of author>
+This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
+This is free software, and you are welcome to redistribute it
+under certain conditions; type 'show c' for details.

The hypothetical commands show w and show c should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.

You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>.

The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.

diff --git a/docs/articles/EDA.html b/docs/articles/EDA.html index 4859d2e..1830d95 100644 --- a/docs/articles/EDA.html +++ b/docs/articles/EDA.html @@ -33,7 +33,7 @@ spsurvey - 5.4.1 + 5.5.0 @@ -84,7 +84,7 @@ -
+

Summarizing and Visualizing Sampling Frames, Design Sites, and Analysis Data

@@ -100,13 +100,13 @@

Michael Dumelle, Tom Kincaid, Anthony Olsen, an

If you have yet not read the “Start Here” vignette, please do so by running

-vignette("start-here", "spsurvey")
+vignette("start-here", "spsurvey")

Introduction

Before proceeding, we load spsurvey by running

+library(spsurvey)

The summary() and plot() functions in spsurvey are used to summarize and visualize sampling frames, design sites, and analysis data. Both functions use a formula argument that specifies the variables to summarize or visualize. These functions behave differently for one-sided and two-sided formulas. To learn more about formulas in R, run ?formula. Only the core functionality of summary() and plot() will be covered in this vignette, so to learn more about these functions, run ?summary and ?plot. The sp_summary() and sp_plot() functions can equivalently be used in place of plot() and summary(), respectively (sp_summary() and sp_plot() are currently maintained for backwards compatibility with previous spsurvey versions).

The plot() function in spsurvey is built on the plot() function in sf. spsurvey’s plot() function accommodates all the arguments in sf’s plot() function and adds a few additional features. To learn more about the plot() function in sf, run ?plot.sf().

@@ -125,56 +125,56 @@

Sampling frames

Before summarizing or visualizing a sampling frame, turn it into an object using sp_frame():

-NE_Lakes <- sp_frame(NE_Lakes)
+NE_Lakes <- sp_frame(NE_Lakes)

One-sided formulas

One-sided formulas are used to summarize and visualize the distributions of variables. The variables of interest should be placed on the right-hand side of the formula. To summarize the distribution of ELEV, run

-summary(NE_Lakes, formula = ~ ELEV)
-#>    total          ELEV       
-#>  total:195   Min.   :  0.00  
-#>              1st Qu.: 21.93  
-#>              Median : 69.09  
-#>              Mean   :127.39  
-#>              3rd Qu.:203.25  
-#>              Max.   :561.41
+summary(NE_Lakes, formula = ~ ELEV) +#> total ELEV +#> total:195 Min. : 0.00 +#> 1st Qu.: 21.93 +#> Median : 69.09 +#> Mean :127.39 +#> 3rd Qu.:203.25 +#> Max. :561.41

The output contains two columns: total and ELEV. The total column returns the total number of lakes, functioning as an “intercept” to the formula (it can by removed by supplying - 1 to the formula). The ELEV column returns a numerical summary of lake elevation. To visualize ELEV, run

-plot(NE_Lakes, formula = ~ ELEV)
+plot(NE_Lakes, formula = ~ ELEV)

To summarize the distribution of ELEV_CAT, run

-summary(NE_Lakes, formula = ~ ELEV_CAT)
-#>    total     ELEV_CAT  
-#>  total:195   low :112  
-#>              high: 83
+summary(NE_Lakes, formula = ~ ELEV_CAT) +#> total ELEV_CAT +#> total:195 low :112 +#> high: 83

The ELEV_CAT column returns the number of lakes in each elevation category. To visualize ELEV_CAT, run

-plot(NE_Lakes, formula = ~ ELEV_CAT, key.width = lcm(3))
+plot(NE_Lakes, formula = ~ ELEV_CAT, key.width = lcm(3))

The key.width argument extends the plot’s margin to fit the legend text nicely within the plot. The plot’s default title is the formula argument, though this is changed using the main argument to plot().

The formula used by summary() and plot() is quite flexible. Additional variables are included using +:

-summary(NE_Lakes, formula = ~ ELEV_CAT + AREA_CAT)
-#>    total     ELEV_CAT    AREA_CAT  
-#>  total:195   low :112   small:135  
-#>              high: 83   large: 60
+summary(NE_Lakes, formula = ~ ELEV_CAT + AREA_CAT) +#> total ELEV_CAT AREA_CAT +#> total:195 low :112 small:135 +#> high: 83 large: 60

The plot() function returns two plots – one for ELEV_CAT and another for AREA_CAT:

-plot(NE_Lakes, formula = ~ ELEV_CAT + AREA_CAT, key.width = lcm(3))
+plot(NE_Lakes, formula = ~ ELEV_CAT + AREA_CAT, key.width = lcm(3))

Interactions are included using the interaction operator, :. The interaction operator returns the interaction between variables and is most useful when used with categorical variables. To summarize the interaction between ELEV_CAT and AREA_CAT, run

-summary(NE_Lakes, formula = ~ ELEV_CAT:AREA_CAT)
-#>    total      ELEV_CAT:AREA_CAT
-#>  total:195   low:small :82     
-#>              high:small:53     
-#>              low:large :30     
-#>              high:large:30
+summary(NE_Lakes, formula = ~ ELEV_CAT:AREA_CAT) +#> total ELEV_CAT:AREA_CAT +#> total:195 low:small :82 +#> high:small:53 +#> low:large :30 +#> high:large:30

Levels of each variable are separated by :. For example, there are 86 lakes that are in the low elevation category and the small area category. To visualize this interaction, run

-plot(NE_Lakes, formula = ~ ELEV_CAT:AREA_CAT, key.width = lcm(3))
+plot(NE_Lakes, formula = ~ ELEV_CAT:AREA_CAT, key.width = lcm(3))

The formula accommodates the * operator, which combines the + and : operators. For example, ELEV_CAT*AREA_CAT is shorthand for ELEV_CAT + AREA_CAT + ELEV_CAT:AREA_CAT. The formula also accommodates the . operator, which is shorthand for all variables separated by +.

@@ -183,42 +183,42 @@

Two-sided formulas
-summary(NE_Lakes, formula = ELEV ~ AREA_CAT)
-#> ELEV by total: 
-#>       Min. 1st Qu. Median     Mean 3rd Qu.   Max.
-#> total    0  21.925  69.09 127.3862 203.255 561.41
-#> 
-#> ELEV by AREA_CAT: 
-#>       Min. 1st Qu.  Median     Mean  3rd Qu.   Max.
-#> small 0.00   19.64  59.660 117.4473 176.1700 561.41
-#> large 0.01   26.75 102.415 149.7487 241.2025 537.84
+summary(NE_Lakes, formula = ELEV ~ AREA_CAT) +#> ELEV by total: +#> Min. 1st Qu. Median Mean 3rd Qu. Max. +#> total 0 21.925 69.09 127.3862 203.255 561.41 +#> +#> ELEV by AREA_CAT: +#> Min. 1st Qu. Median Mean 3rd Qu. Max. +#> small 0.00 19.64 59.660 117.4473 176.1700 561.41 +#> large 0.01 26.75 102.415 149.7487 241.2025 537.84

To visualize the distribution of ELEV for each level of AREA_CAT, run

-plot(NE_Lakes, formula = ELEV ~ AREA_CAT)
+plot(NE_Lakes, formula = ELEV ~ AREA_CAT)

To only summarize or visualize a particular level of a single right-hand side variable, use the onlyshow argument:

-summary(NE_Lakes, formula = ELEV ~ AREA_CAT, onlyshow = "small")
-#> ELEV by AREA_CAT: 
-#>       Min. 1st Qu. Median     Mean 3rd Qu.   Max.
-#> small    0   19.64  59.66 117.4473  176.17 561.41
+summary(NE_Lakes, formula = ELEV ~ AREA_CAT, onlyshow = "small") +#> ELEV by AREA_CAT: +#> Min. 1st Qu. Median Mean 3rd Qu. Max. +#> small 0 19.64 59.66 117.4473 176.17 561.41 
-plot(NE_Lakes, formula = ELEV ~ AREA_CAT, onlyshow = "small")
+plot(NE_Lakes, formula = ELEV ~ AREA_CAT, onlyshow = "small")

To summarize the distribution of ELEV_CAT for each level of AREA_CAT, run

-summary(NE_Lakes, formula = ELEV_CAT ~ AREA_CAT)
-#> ELEV_CAT by total: 
-#>       low high
-#> total 112   83
-#> 
-#> ELEV_CAT by AREA_CAT: 
-#>       low high
-#> small  82   53
-#> large  30   30
+summary(NE_Lakes, formula = ELEV_CAT ~ AREA_CAT) +#> ELEV_CAT by total: +#> low high +#> total 112 83 +#> +#> ELEV_CAT by AREA_CAT: +#> low high +#> small 82 53 +#> large 30 30

To visualize the distribution of ELEV_CAT for each level of AREA_CAT, run

-plot(NE_Lakes, formula = ELEV_CAT ~ AREA_CAT, key.width = lcm(3))
+plot(NE_Lakes, formula = ELEV_CAT ~ AREA_CAT, key.width = lcm(3))

@@ -235,28 +235,28 @@

Adjusting graphical parameters
-list1 <- list(main = "Elevation Categories", pal = rainbow)
-list2 <- list(main = "Area Categories")
-list3 <- list(levels = c("small", "large"), pch = c(4, 19))
-plot(
-  NE_Lakes,
-  formula = ~ ELEV_CAT + AREA_CAT,
-  var_args = list(ELEV_CAT = list1, AREA_CAT = list2),
-  varlevel_args = list(AREA_CAT = list3),
-  cex = 0.75,
-  key.width = lcm(3)
-)

+list1 <- list(main = "Elevation Categories", pal = rainbow) +list2 <- list(main = "Area Categories") +list3 <- list(levels = c("small", "large"), pch = c(4, 19)) +plot( + NE_Lakes, + formula = ~ ELEV_CAT + AREA_CAT, + var_args = list(ELEV_CAT = list1, AREA_CAT = list2), + varlevel_args = list(AREA_CAT = list3), + cex = 0.75, + key.width = lcm(3) +)

var_args uses list1 to give the ELEV_CAT visualization a new title and color palette; var_args uses list2 to give the AREA_CAT visualization a new title; varlevel_args uses list3 to give the AREA_CAT visualization different shapes for the small and large levels; ... uses cex = 0.75 to reduce the size of all points; and ... uses key.width to adjust legend spacing for all visualizations.

If a two-sided formula is used, it is possible to adjust graphical parameters of the left-hand side variable for all levels of a right-hand side variable. This occurs when a sublist matching the structure of varlevel_args is used as an argument to var_args. In this next example, different shapes are used for the small and large levels of AREA_CAT for all levels of ELEV_CAT:

-sublist <- list(AREA_CAT = list3)
-plot(
-  NE_Lakes,
-  formula = AREA_CAT ~ ELEV_CAT,
-  var_args = list(ELEV_CAT = sublist),
-  key.width = lcm(3)
-)
+sublist <- list(AREA_CAT = list3) +plot( + NE_Lakes, + formula = AREA_CAT ~ ELEV_CAT, + var_args = list(ELEV_CAT = sublist), + key.width = lcm(3) +)

@@ -265,50 +265,50 @@

Design sitesgrts() or irs() functions) can be summarized and visualized using summary() and plot() very similarly to how sampling frames were summarized and visualized in the previous section. Soon you will use the grts() function to select a spatially balanced sample. The grts() function does incorporate randomness, so to match your results with this output exactly you will need to set a reproducible seed by running

+set.seed(51)

First we will obtain some design sites: To select an equal probability GRTS sample of size 50 with 10 reverse hierarchically ordered replacement sites, run

-eqprob_rho <- grts(NE_Lakes, n_base = 50, n_over = 10)
+eqprob_rho <- grts(NE_Lakes, n_base = 50, n_over = 10)

Similar to summary() and plot() for sampling frames, summary() and plot() for design sites uses a formula. The formula should include siteuse, which is the name of the variable in the design sites object that indicates the type of each site. The default formula for summary() and plot() is ~siteuse, which summarizes or visualizes the sites objects in the design sites object. By default, the formula is applied to all non-NULL sites objects (in eqprob_rho, the nonNULL sites objects are sites_base (for the base sites) and sites_over (for the reverse hierarchically ordered replacement sites)).

-summary(eqprob_rho)
-#>    total    siteuse  
-#>  total:60   Base:50  
-#>             Over:10
+summary(eqprob_rho) +#> total siteuse +#> total:60 Base:50 +#> Over:10 
-plot(eqprob_rho, key.width = lcm(3))
+plot(eqprob_rho, key.width = lcm(3))

The sampling frame may be included as an argument to the plot() function:

-plot(eqprob_rho, NE_Lakes, key.width = lcm(3))
+plot(eqprob_rho, NE_Lakes, key.width = lcm(3))

When you include siteuse as a left-hand side variable (siteuse is treated as a categorical variable), you can summarize and visualize the sites object for each level of each right-hand side variable:

-summary(eqprob_rho, formula = siteuse ~ AREA_CAT)
-#> siteuse by total: 
-#>       Base Over
-#> total   50   10
-#> 
-#> siteuse by AREA_CAT: 
-#>       Base Over
-#> small   35    7
-#> large   15    3
+summary(eqprob_rho, formula = siteuse ~ AREA_CAT) +#> siteuse by total: +#> Base Over +#> total 50 10 +#> +#> siteuse by AREA_CAT: +#> Base Over +#> small 35 7 +#> large 15 3 
-plot(eqprob_rho, formula = siteuse ~ AREA_CAT, key.width = lcm(3))
+plot(eqprob_rho, formula = siteuse ~ AREA_CAT, key.width = lcm(3))

You can also summarize and visualize a left-hand side variable for each level of siteuse:

-summary(eqprob_rho, formula = ELEV ~ siteuse)
-#> ELEV by total: 
-#>       Min. 1st Qu. Median    Mean  3rd Qu.   Max.
-#> total 0.03  26.385 65.535 135.364 214.2075 537.84
-#> 
-#> ELEV by siteuse: 
-#>      Min. 1st Qu. Median     Mean 3rd Qu.   Max.
-#> Base 0.68 29.4850  81.76 148.0362 263.640 537.84
-#> Over 0.03 15.1275  54.49  72.0030 119.365 209.25
+summary(eqprob_rho, formula = ELEV ~ siteuse) +#> ELEV by total: +#> Min. 1st Qu. Median Mean 3rd Qu. Max. +#> total 0.03 26.385 65.535 135.364 214.2075 537.84 +#> +#> ELEV by siteuse: +#> Min. 1st Qu. Median Mean 3rd Qu. Max. +#> Base 0.68 29.4850 81.76 148.0362 263.640 537.84 +#> Over 0.03 15.1275 54.49 72.0030 119.365 209.25 
-plot(eqprob_rho, formula = ELEV ~ siteuse)
+plot(eqprob_rho, formula = ELEV ~ siteuse)

@@ -323,31 +323,31 @@

Analysis datasp_frame():

-NLA_PNW <- sp_frame(NLA_PNW)
+NLA_PNW <- sp_frame(NLA_PNW)

To summarize and visualize NITR_COND across all states, run

-summary(NLA_PNW, formula = ~ NITR_COND)
-#>    total    NITR_COND
-#>  total:96   Fair:24  
-#>             Good:38  
-#>             Poor:34
+summary(NLA_PNW, formula = ~ NITR_COND) +#> total NITR_COND +#> total:96 Fair:24 +#> Good:38 +#> Poor:34 
-plot(NLA_PNW, formula = ~ NITR_COND, key.width = lcm(3))
+plot(NLA_PNW, formula = ~ NITR_COND, key.width = lcm(3))

Suppose the sampling design was stratified by STATE. To summarize and visualize NITR_COND by STATE, run

-summary(NLA_PNW, formula = NITR_COND ~ STATE)
-#> NITR_COND by total: 
-#>       Fair Good Poor
-#> total   24   38   34
-#> 
-#> NITR_COND by STATE: 
-#>            Fair Good Poor
-#> California    6    8    5
-#> Oregon        8   26   13
-#> Washington   10    4   16
+summary(NLA_PNW, formula = NITR_COND ~ STATE) +#> NITR_COND by total: +#> Fair Good Poor +#> total 24 38 34 +#> +#> NITR_COND by STATE: +#> Fair Good Poor +#> California 6 8 5 +#> Oregon 8 26 13 +#> Washington 10 4 16 
-plot(NLA_PNW, formula = NITR_COND ~ STATE, key.width = lcm(3))
+plot(NLA_PNW, formula = NITR_COND ~ STATE, key.width = lcm(3))

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+

Analyzing Data

@@ -100,7 +100,7 @@

Michael Dumelle, Tom Kincaid, Anthony Olsen, an

If you have yet not read the “Start Here” vignette, please do so by running

-vignette("start-here", "spsurvey")
+vignette("start-here", "spsurvey")

Introduction

@@ -125,7 +125,7 @@

Introduction
-library(spsurvey)

+library(spsurvey)

Categorical Variable Analysis @@ -136,220 +136,220 @@

Unstratified Analysis

To estimate the proportion of total lakes in each nitrogen condition category and the total number of lakes in each nitrogen condition category, run

-cat_ests <- cat_analysis(
-  NLA_PNW,
-  siteID = "SITE_ID",
-  vars = "NITR_COND",
-  weight = "WEIGHT"
-)
-cat_ests
-#>        Type Subpopulation Indicator Category nResp Estimate.P StdError.P
-#> 1 All_Sites     All Sites NITR_COND     Fair    24   23.69392   6.194024
-#> 2 All_Sites     All Sites NITR_COND     Good    38   51.35111   7.430172
-#> 3 All_Sites     All Sites NITR_COND     Poor    34   24.95496   5.919180
-#> 4 All_Sites     All Sites NITR_COND    Total    96  100.00000   0.000000
-#>   MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U
-#> 1        12.14006   11.55386   35.83399   2530.428   693.5593        1359.351
-#> 2        14.56287   36.78824   65.91398   5484.120  1223.3708        2397.763
-#> 3        11.60138   13.35359   36.55634   2665.103   658.0966        1289.846
-#> 4         0.00000  100.00000  100.00000  10679.652  1416.2707        2775.840
-#>   LCB95Pct.U UCB95Pct.U
-#> 1   1171.077   3889.780
-#> 2   3086.357   7881.883
-#> 3   1375.258   3954.949
-#> 4   7903.812  13455.491
+cat_ests <- cat_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars = "NITR_COND", + weight = "WEIGHT" +) +cat_ests +#> Type Subpopulation Indicator Category nResp Estimate.P StdError.P +#> 1 All_Sites All Sites NITR_COND Fair 24 23.69392 6.194024 +#> 2 All_Sites All Sites NITR_COND Good 38 51.35111 7.430172 +#> 3 All_Sites All Sites NITR_COND Poor 34 24.95496 5.919180 +#> 4 All_Sites All Sites NITR_COND Total 96 100.00000 0.000000 +#> MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U +#> 1 12.14006 11.55386 35.83399 2530.428 693.5593 1359.351 +#> 2 14.56287 36.78824 65.91398 5484.120 1223.3708 2397.763 +#> 3 11.60138 13.35359 36.55634 2665.103 658.0966 1289.846 +#> 4 0.00000 100.00000 100.00000 10679.652 1416.2707 2775.840 +#> LCB95Pct.U UCB95Pct.U +#> 1 1171.077 3889.780 +#> 2 3086.357 7881.883 +#> 3 1375.258 3954.949 +#> 4 7903.812 13455.491

The estimate of the proportion of lakes in Good condition is 51.35% with a 95% confidence interval of (36.8%, 65.9%), while the estimate of the total number of lakes in Good condition is 5484 lakes with a 95% confidence interval of (3086, 7882). In each case, the estimated standard error and margin of error is given. The confidence level can be changed using the conf argument. If more than one categorical variable is of interest, then vars can be a vector of variables and separate analyses are performed for each variable.

Sometimes the goal is to estimate proportions and totals separately for different subsets of the population – these subsets are called subpopulations. To estimate the proportion of total lakes and in each nitrogen condition category the total number of lakes in each nitrogen condition category separately for California, Oregon, and Washington lakes, run

-cat_ests_sp <- cat_analysis(
-  NLA_PNW,
-  siteID = "SITE_ID",
-  vars = "NITR_COND",
-  weight = "WEIGHT",
-  subpop = "STATE"
-)
-cat_ests_sp
-#>     Type Subpopulation Indicator Category nResp Estimate.P StdError.P
-#> 1  STATE    California NITR_COND     Fair     6   8.239162   4.712515
-#> 2  STATE    California NITR_COND     Good     8  73.722638  13.359879
-#> 3  STATE    California NITR_COND     Poor     5  18.038200  11.909638
-#> 4  STATE    California NITR_COND    Total    19 100.000000   0.000000
-#> 5  STATE        Oregon NITR_COND     Fair     8  27.152211   9.763817
-#> 6  STATE        Oregon NITR_COND     Good    26  59.670307   9.717093
-#> 7  STATE        Oregon NITR_COND     Poor    13  13.177483   4.901892
-#> 8  STATE        Oregon NITR_COND    Total    47 100.000000   0.000000
-#> 9  STATE    Washington NITR_COND     Fair    10  30.396139  11.938582
-#> 10 STATE    Washington NITR_COND     Good     4  22.711979  11.878964
-#> 11 STATE    Washington NITR_COND     Poor    16  46.891882  13.041148
-#> 12 STATE    Washington NITR_COND    Total    30 100.000000   0.000000
-#>    MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U
-#> 1         9.236359   0.000000   17.47552   208.4605   87.63415        171.7598
-#> 2        26.184881  47.537757   99.90752  1865.2692  940.12797       1842.6170
-#> 3        23.342462   0.000000   41.38066   456.3876  299.29104        586.5997
-#> 4         0.000000 100.000000  100.00000  2530.1172  978.65831       1918.1350
-#> 5        19.136729   8.015482   46.28894  1298.8470  526.66732       1032.2490
-#> 6        19.045152  40.625155   78.71546  2854.3752  674.02641       1321.0675
-#> 7         9.607532   3.569950   22.78501   630.3551  198.49966        389.0522
-#> 8         0.000000 100.000000  100.00000  4783.5773  706.53216       1384.7776
-#> 9        23.399190   6.996949   53.79533  1023.1210  437.69351        857.8635
-#> 10       23.282341   0.000000   45.99432   764.4755  453.48899        888.8221
-#> 11       25.560181  21.331701   72.45206  1578.3606  556.63044       1090.9756
-#> 12        0.000000 100.000000  100.00000  3365.9571  741.89397       1454.0855
-#>    LCB95Pct.U UCB95Pct.U
-#> 1    36.70069   380.2202
-#> 2    22.65222  3707.8861
-#> 3     0.00000  1042.9873
-#> 4   611.98220  4448.2523
-#> 5   266.59801  2331.0960
-#> 6  1533.30774  4175.4427
-#> 7   241.30288  1019.4072
-#> 8  3398.79970  6168.3549
-#> 9   165.25751  1880.9845
-#> 10    0.00000  1653.2976
-#> 11  487.38500  2669.3362
-#> 12 1911.87165  4820.0426
+cat_ests_sp <- cat_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars = "NITR_COND", + weight = "WEIGHT", + subpop = "STATE" +) +cat_ests_sp +#> Type Subpopulation Indicator Category nResp Estimate.P StdError.P +#> 1 STATE California NITR_COND Fair 6 8.239162 4.712515 +#> 2 STATE California NITR_COND Good 8 73.722638 13.359879 +#> 3 STATE California NITR_COND Poor 5 18.038200 11.909638 +#> 4 STATE California NITR_COND Total 19 100.000000 0.000000 +#> 5 STATE Oregon NITR_COND Fair 8 27.152211 9.763817 +#> 6 STATE Oregon NITR_COND Good 26 59.670307 9.717093 +#> 7 STATE Oregon NITR_COND Poor 13 13.177483 4.901892 +#> 8 STATE Oregon NITR_COND Total 47 100.000000 0.000000 +#> 9 STATE Washington NITR_COND Fair 10 30.396139 11.938582 +#> 10 STATE Washington NITR_COND Good 4 22.711979 11.878964 +#> 11 STATE Washington NITR_COND Poor 16 46.891882 13.041148 +#> 12 STATE Washington NITR_COND Total 30 100.000000 0.000000 +#> MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U +#> 1 9.236359 0.000000 17.47552 208.4605 87.63415 171.7598 +#> 2 26.184881 47.537757 99.90752 1865.2692 940.12797 1842.6170 +#> 3 23.342462 0.000000 41.38066 456.3876 299.29104 586.5997 +#> 4 0.000000 100.000000 100.00000 2530.1172 978.65831 1918.1350 +#> 5 19.136729 8.015482 46.28894 1298.8470 526.66732 1032.2490 +#> 6 19.045152 40.625155 78.71546 2854.3752 674.02641 1321.0675 +#> 7 9.607532 3.569950 22.78501 630.3551 198.49966 389.0522 +#> 8 0.000000 100.000000 100.00000 4783.5773 706.53216 1384.7776 +#> 9 23.399190 6.996949 53.79533 1023.1210 437.69351 857.8635 +#> 10 23.282341 0.000000 45.99432 764.4755 453.48899 888.8221 +#> 11 25.560181 21.331701 72.45206 1578.3606 556.63044 1090.9756 +#> 12 0.000000 100.000000 100.00000 3365.9571 741.89397 1454.0855 +#> LCB95Pct.U UCB95Pct.U +#> 1 36.70069 380.2202 +#> 2 22.65222 3707.8861 +#> 3 0.00000 1042.9873 +#> 4 611.98220 4448.2523 +#> 5 266.59801 2331.0960 +#> 6 1533.30774 4175.4427 +#> 7 241.30288 1019.4072 +#> 8 3398.79970 6168.3549 +#> 9 165.25751 1880.9845 +#> 10 0.00000 1653.2976 +#> 11 487.38500 2669.3362 +#> 12 1911.87165 4820.0426

If more than one type of subpopulation is of interest, then subpop can be a vector of subpopulation variables and separate analyses are performed for each subpopulation. If both vars and subpops are vectors, separate analyses are performed for each variable and subpopulation combination.

Analysis results for all sites (ignoring subpopulations) can be presented alongside the subpopulation analysis results using the All_Sites argument:

-cat_ests_sp <- cat_analysis(
-  NLA_PNW,
-  siteID = "SITE_ID",
-  vars = "NITR_COND",
-  weight = "WEIGHT",
-  subpop = "STATE",
-  All_Sites = TRUE
-)
-cat_ests_sp
-#>         Type Subpopulation Indicator Category nResp Estimate.P StdError.P
-#> 1      STATE    California NITR_COND     Fair     6   8.239162   4.712515
-#> 2      STATE    California NITR_COND     Good     8  73.722638  13.359879
-#> 3      STATE    California NITR_COND     Poor     5  18.038200  11.909638
-#> 4      STATE    California NITR_COND    Total    19 100.000000   0.000000
-#> 5      STATE        Oregon NITR_COND     Fair     8  27.152211   9.763817
-#> 6      STATE        Oregon NITR_COND     Good    26  59.670307   9.717093
-#> 7      STATE        Oregon NITR_COND     Poor    13  13.177483   4.901892
-#> 8      STATE        Oregon NITR_COND    Total    47 100.000000   0.000000
-#> 9      STATE    Washington NITR_COND     Fair    10  30.396139  11.938582
-#> 10     STATE    Washington NITR_COND     Good     4  22.711979  11.878964
-#> 11     STATE    Washington NITR_COND     Poor    16  46.891882  13.041148
-#> 12     STATE    Washington NITR_COND    Total    30 100.000000   0.000000
-#> 13 All_Sites     All Sites NITR_COND     Fair    24  23.693923   6.194024
-#> 14 All_Sites     All Sites NITR_COND     Good    38  51.351112   7.430172
-#> 15 All_Sites     All Sites NITR_COND     Poor    34  24.954965   5.919180
-#> 16 All_Sites     All Sites NITR_COND    Total    96 100.000000   0.000000
-#>    MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U
-#> 1         9.236359   0.000000   17.47552   208.4605   87.63415        171.7598
-#> 2        26.184881  47.537757   99.90752  1865.2692  940.12797       1842.6170
-#> 3        23.342462   0.000000   41.38066   456.3876  299.29104        586.5997
-#> 4         0.000000 100.000000  100.00000  2530.1172  978.65831       1918.1350
-#> 5        19.136729   8.015482   46.28894  1298.8470  526.66732       1032.2490
-#> 6        19.045152  40.625155   78.71546  2854.3752  674.02641       1321.0675
-#> 7         9.607532   3.569950   22.78501   630.3551  198.49966        389.0522
-#> 8         0.000000 100.000000  100.00000  4783.5773  706.53216       1384.7776
-#> 9        23.399190   6.996949   53.79533  1023.1210  437.69351        857.8635
-#> 10       23.282341   0.000000   45.99432   764.4755  453.48899        888.8221
-#> 11       25.560181  21.331701   72.45206  1578.3606  556.63044       1090.9756
-#> 12        0.000000 100.000000  100.00000  3365.9571  741.89397       1454.0855
-#> 13       12.140064  11.553860   35.83399  2530.4285  693.55933       1359.3513
-#> 14       14.562870  36.788242   65.91398  5484.1199 1223.37078       2397.7627
-#> 15       11.601379  13.353585   36.55634  2665.1033  658.09658       1289.8456
-#> 16        0.000000 100.000000  100.00000 10679.6517 1416.27075       2775.8397
-#>    LCB95Pct.U UCB95Pct.U
-#> 1    36.70069   380.2202
-#> 2    22.65222  3707.8861
-#> 3     0.00000  1042.9873
-#> 4   611.98220  4448.2523
-#> 5   266.59801  2331.0960
-#> 6  1533.30774  4175.4427
-#> 7   241.30288  1019.4072
-#> 8  3398.79970  6168.3549
-#> 9   165.25751  1880.9845
-#> 10    0.00000  1653.2976
-#> 11  487.38500  2669.3362
-#> 12 1911.87165  4820.0426
-#> 13 1171.07716  3889.7798
-#> 14 3086.35722  7881.8826
-#> 15 1375.25770  3954.9489
-#> 16 7903.81199 13455.4913
+cat_ests_sp <- cat_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars = "NITR_COND", + weight = "WEIGHT", + subpop = "STATE", + All_Sites = TRUE +) +cat_ests_sp +#> Type Subpopulation Indicator Category nResp Estimate.P StdError.P +#> 1 STATE California NITR_COND Fair 6 8.239162 4.712515 +#> 2 STATE California NITR_COND Good 8 73.722638 13.359879 +#> 3 STATE California NITR_COND Poor 5 18.038200 11.909638 +#> 4 STATE California NITR_COND Total 19 100.000000 0.000000 +#> 5 STATE Oregon NITR_COND Fair 8 27.152211 9.763817 +#> 6 STATE Oregon NITR_COND Good 26 59.670307 9.717093 +#> 7 STATE Oregon NITR_COND Poor 13 13.177483 4.901892 +#> 8 STATE Oregon NITR_COND Total 47 100.000000 0.000000 +#> 9 STATE Washington NITR_COND Fair 10 30.396139 11.938582 +#> 10 STATE Washington NITR_COND Good 4 22.711979 11.878964 +#> 11 STATE Washington NITR_COND Poor 16 46.891882 13.041148 +#> 12 STATE Washington NITR_COND Total 30 100.000000 0.000000 +#> 13 All_Sites All Sites NITR_COND Fair 24 23.693923 6.194024 +#> 14 All_Sites All Sites NITR_COND Good 38 51.351112 7.430172 +#> 15 All_Sites All Sites NITR_COND Poor 34 24.954965 5.919180 +#> 16 All_Sites All Sites NITR_COND Total 96 100.000000 0.000000 +#> MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U +#> 1 9.236359 0.000000 17.47552 208.4605 87.63415 171.7598 +#> 2 26.184881 47.537757 99.90752 1865.2692 940.12797 1842.6170 +#> 3 23.342462 0.000000 41.38066 456.3876 299.29104 586.5997 +#> 4 0.000000 100.000000 100.00000 2530.1172 978.65831 1918.1350 +#> 5 19.136729 8.015482 46.28894 1298.8470 526.66732 1032.2490 +#> 6 19.045152 40.625155 78.71546 2854.3752 674.02641 1321.0675 +#> 7 9.607532 3.569950 22.78501 630.3551 198.49966 389.0522 +#> 8 0.000000 100.000000 100.00000 4783.5773 706.53216 1384.7776 +#> 9 23.399190 6.996949 53.79533 1023.1210 437.69351 857.8635 +#> 10 23.282341 0.000000 45.99432 764.4755 453.48899 888.8221 +#> 11 25.560181 21.331701 72.45206 1578.3606 556.63044 1090.9756 +#> 12 0.000000 100.000000 100.00000 3365.9571 741.89397 1454.0855 +#> 13 12.140064 11.553860 35.83399 2530.4285 693.55933 1359.3513 +#> 14 14.562870 36.788242 65.91398 5484.1199 1223.37078 2397.7627 +#> 15 11.601379 13.353585 36.55634 2665.1033 658.09658 1289.8456 +#> 16 0.000000 100.000000 100.00000 10679.6517 1416.27075 2775.8397 +#> LCB95Pct.U UCB95Pct.U +#> 1 36.70069 380.2202 +#> 2 22.65222 3707.8861 +#> 3 0.00000 1042.9873 +#> 4 611.98220 4448.2523 +#> 5 266.59801 2331.0960 +#> 6 1533.30774 4175.4427 +#> 7 241.30288 1019.4072 +#> 8 3398.79970 6168.3549 +#> 9 165.25751 1880.9845 +#> 10 0.00000 1653.2976 +#> 11 487.38500 2669.3362 +#> 12 1911.87165 4820.0426 +#> 13 1171.07716 3889.7798 +#> 14 3086.35722 7881.8826 +#> 15 1375.25770 3954.9489 +#> 16 7903.81199 13455.4913

Stratified Analysis

To estimate the proportion of total lakes in each nitrogen condition category and the total number of lakes in each nitrogen condition category stratified by URBAN category (whether the lake is classified as Urban or Non-Urban), run

-strat_cat_ests <- cat_analysis(
-  NLA_PNW,
-  siteID = "SITE_ID",
-  vars = "NITR_COND",
-  weight = "WEIGHT",
-  stratumID = "URBAN"
-)
-strat_cat_ests
-#>        Type Subpopulation Indicator Category nResp Estimate.P StdError.P
-#> 1 All_Sites     All Sites NITR_COND     Fair    24   23.69392   6.027083
-#> 2 All_Sites     All Sites NITR_COND     Good    38   51.35111   7.472377
-#> 3 All_Sites     All Sites NITR_COND     Poor    34   24.95496   5.882487
-#> 4 All_Sites     All Sites NITR_COND    Total    96  100.00000   0.000000
-#>   MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U
-#> 1        11.81287   11.88106   35.50679   2530.428   683.8837        1340.387
-#> 2        14.64559   36.70552   65.99670   5484.120  1229.9485        2410.655
-#> 3        11.52946   13.42550   36.48443   2665.103   653.6357        1281.102
-#> 4         0.00000  100.00000  100.00000  10679.652  1440.4796        2823.288
-#>   LCB95Pct.U UCB95Pct.U
-#> 1   1190.041   3870.816
-#> 2   3073.465   7894.775
-#> 3   1384.001   3946.206
-#> 4   7856.363  13502.940
+strat_cat_ests <- cat_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars = "NITR_COND", + weight = "WEIGHT", + stratumID = "URBAN" +) +strat_cat_ests +#> Type Subpopulation Indicator Category nResp Estimate.P StdError.P +#> 1 All_Sites All Sites NITR_COND Fair 24 23.69392 6.027083 +#> 2 All_Sites All Sites NITR_COND Good 38 51.35111 7.472377 +#> 3 All_Sites All Sites NITR_COND Poor 34 24.95496 5.882487 +#> 4 All_Sites All Sites NITR_COND Total 96 100.00000 0.000000 +#> MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U +#> 1 11.81287 11.88106 35.50679 2530.428 683.8837 1340.387 +#> 2 14.64559 36.70552 65.99670 5484.120 1229.9485 2410.655 +#> 3 11.52946 13.42550 36.48443 2665.103 653.6357 1281.102 +#> 4 0.00000 100.00000 100.00000 10679.652 1440.4796 2823.288 +#> LCB95Pct.U UCB95Pct.U +#> 1 1190.041 3870.816 +#> 2 3073.465 7894.775 +#> 3 1384.001 3946.206 +#> 4 7856.363 13502.940

To then compute these estimates separately for California, Oregon, and Washington, run

-strat_cat_ests_sp <- cat_analysis(
-  NLA_PNW,
-  siteID = "SITE_ID",
-  vars = "NITR_COND",
-  weight = "WEIGHT",
-  stratumID = "URBAN",
-  subpop = "STATE"
-)
-strat_cat_ests_sp
-#>     Type Subpopulation Indicator Category nResp Estimate.P StdError.P
-#> 1  STATE    California NITR_COND     Fair     6   8.239162   5.481100
-#> 2  STATE    California NITR_COND     Good     8  73.722638  13.653267
-#> 3  STATE    California NITR_COND     Poor     5  18.038200  12.512366
-#> 4  STATE    California NITR_COND    Total    19 100.000000   0.000000
-#> 5  STATE        Oregon NITR_COND     Fair     8  27.152211   9.592356
-#> 6  STATE        Oregon NITR_COND     Good    26  59.670307   9.845022
-#> 7  STATE        Oregon NITR_COND     Poor    13  13.177483   5.325784
-#> 8  STATE        Oregon NITR_COND    Total    47 100.000000   0.000000
-#> 9  STATE    Washington NITR_COND     Fair    10  30.396139  11.383474
-#> 10 STATE    Washington NITR_COND     Good     4  22.711979   9.039351
-#> 11 STATE    Washington NITR_COND     Poor    16  46.891882  12.109215
-#> 12 STATE    Washington NITR_COND    Total    30 100.000000   0.000000
-#>    MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U
-#> 1         10.74276   0.000000   18.98192   208.4605   87.91849        172.3171
-#> 2         26.75991  46.962726  100.00000  1865.2692  953.91452       1869.6381
-#> 3         24.52379   0.000000   42.56199   456.3876  306.37270        600.4795
-#> 4          0.00000 100.000000  100.00000  2530.1172  994.94678       1950.0599
-#> 5         18.80067   8.351538   45.95288  1298.8470  531.02949       1040.7987
-#> 6         19.29589  40.374417   78.96620  2854.3752  685.86488       1344.2705
-#> 7         10.43835   2.739137   23.61583   630.3551  177.79364        348.4691
-#> 8          0.00000 100.000000  100.00000  4783.5773  737.53853       1445.5490
-#> 9         22.31120   8.084941   52.70734  1023.1210  435.45383        853.4738
-#> 10        17.71680   4.995177   40.42878   764.4755  433.94100        850.5087
-#> 11        23.73363  23.158256   70.62551  1578.3606  554.91047       1087.6045
-#> 12         0.00000 100.000000  100.00000  3365.9571  748.29764       1466.6364
-#>    LCB95Pct.U UCB95Pct.U
-#> 1     36.1434   380.7775
-#> 2      0.0000  3734.9073
-#> 3      0.0000  1056.8671
-#> 4    580.0574  4480.1771
-#> 5    258.0483  2339.6457
-#> 6   1510.1048  4198.6457
-#> 7    281.8859   978.8242
-#> 8   3338.0283  6229.1262
-#> 9    169.6472  1876.5948
-#> 10     0.0000  1614.9842
-#> 11   490.7561  2665.9652
-#> 12  1899.3207  4832.5935
+strat_cat_ests_sp <- cat_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars = "NITR_COND", + weight = "WEIGHT", + stratumID = "URBAN", + subpop = "STATE" +) +strat_cat_ests_sp +#> Type Subpopulation Indicator Category nResp Estimate.P StdError.P +#> 1 STATE California NITR_COND Fair 6 8.239162 5.481100 +#> 2 STATE California NITR_COND Good 8 73.722638 13.653267 +#> 3 STATE California NITR_COND Poor 5 18.038200 12.512366 +#> 4 STATE California NITR_COND Total 19 100.000000 0.000000 +#> 5 STATE Oregon NITR_COND Fair 8 27.152211 9.592356 +#> 6 STATE Oregon NITR_COND Good 26 59.670307 9.845022 +#> 7 STATE Oregon NITR_COND Poor 13 13.177483 5.325784 +#> 8 STATE Oregon NITR_COND Total 47 100.000000 0.000000 +#> 9 STATE Washington NITR_COND Fair 10 30.396139 11.383474 +#> 10 STATE Washington NITR_COND Good 4 22.711979 9.039351 +#> 11 STATE Washington NITR_COND Poor 16 46.891882 12.109215 +#> 12 STATE Washington NITR_COND Total 30 100.000000 0.000000 +#> MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U +#> 1 10.74276 0.000000 18.98192 208.4605 87.91849 172.3171 +#> 2 26.75991 46.962726 100.00000 1865.2692 953.91452 1869.6381 +#> 3 24.52379 0.000000 42.56199 456.3876 306.37270 600.4795 +#> 4 0.00000 100.000000 100.00000 2530.1172 994.94678 1950.0599 +#> 5 18.80067 8.351538 45.95288 1298.8470 531.02949 1040.7987 +#> 6 19.29589 40.374417 78.96620 2854.3752 685.86488 1344.2705 +#> 7 10.43835 2.739137 23.61583 630.3551 177.79364 348.4691 +#> 8 0.00000 100.000000 100.00000 4783.5773 737.53853 1445.5490 +#> 9 22.31120 8.084941 52.70734 1023.1210 435.45383 853.4738 +#> 10 17.71680 4.995177 40.42878 764.4755 433.94100 850.5087 +#> 11 23.73363 23.158256 70.62551 1578.3606 554.91047 1087.6045 +#> 12 0.00000 100.000000 100.00000 3365.9571 748.29764 1466.6364 +#> LCB95Pct.U UCB95Pct.U +#> 1 36.1434 380.7775 +#> 2 0.0000 3734.9073 +#> 3 0.0000 1056.8671 +#> 4 580.0574 4480.1771 +#> 5 258.0483 2339.6457 +#> 6 1510.1048 4198.6457 +#> 7 281.8859 978.8242 +#> 8 3338.0283 6229.1262 +#> 9 169.6472 1876.5948 +#> 10 0.0000 1614.9842 +#> 11 490.7561 2665.9652 +#> 12 1899.3207 4832.5935
@@ -361,89 +361,89 @@

Unstratified Analysis

To estimate the cumulative distribution function (CDF), certain percentiles, and means of BMMI, run

-cont_ests <- cont_analysis(
-  NLA_PNW,
-  siteID = "SITE_ID",
-  vars = "BMMI",
-  weight = "WEIGHT"
-)
+cont_ests <- cont_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars = "BMMI", + weight = "WEIGHT" +)

To view the analysis results for the mean estimates, run

-cont_ests$Mean
-#>        Type Subpopulation Indicator nResp Estimate StdError MarginofError
-#> 1 All_Sites     All Sites      BMMI    96 56.50929 1.782278        3.4932
-#>   LCB95Pct UCB95Pct
-#> 1 53.01609 60.00249
+cont_ests$Mean +#> Type Subpopulation Indicator nResp Estimate StdError MarginofError +#> 1 All_Sites All Sites BMMI 96 56.50929 1.782278 3.4932 +#> LCB95Pct UCB95Pct +#> 1 53.01609 60.00249

Similarly, the CDF and select percentile estimates can be viewed (the output is omitted here) by running

-cont_ests$CDF
-cont_ests$Pct
+cont_ests$CDF +cont_ests$Pct

To visualize the CDF estimates, run

-plot(cont_ests$CDF)
+plot(cont_ests$CDF)

The solid line indicates the CDF estimates, and the dashed lines indicate lower and upper 95% confidence interval bounds for the CDF estimates. cdf_plot() can equivalently be used in place of plot() (cdf_plot() is currently maintained for backwards compatibility with previous spsurvey versions).

To estimate the CDF, certain percentiles, and means of BMMI separately for each state, run

-cont_ests_sp <- cont_analysis(
-  NLA_PNW,
-  siteID = "SITE_ID",
-  vars = "BMMI",
-  weight = "WEIGHT",
-  subpop = "STATE"
-)
+cont_ests_sp <- cont_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars = "BMMI", + weight = "WEIGHT", + subpop = "STATE" +)

To view the analysis results for the mean estimates, run

-cont_ests_sp$Mean
-#>    Type Subpopulation Indicator nResp Estimate StdError MarginofError LCB95Pct
-#> 1 STATE    California      BMMI    19 50.48964 4.049094      7.936079 42.55357
-#> 2 STATE        Oregon      BMMI    47 61.29675 2.581032      5.058730 56.23802
-#> 3 STATE    Washington      BMMI    30 54.23036 3.143924      6.161977 48.06838
-#>   UCB95Pct
-#> 1 58.42572
-#> 2 66.35548
-#> 3 60.39234
+cont_ests_sp$Mean +#> Type Subpopulation Indicator nResp Estimate StdError MarginofError LCB95Pct +#> 1 STATE California BMMI 19 50.48964 4.049094 7.936079 42.55357 +#> 2 STATE Oregon BMMI 47 61.29675 2.581032 5.058730 56.23802 +#> 3 STATE Washington BMMI 30 54.23036 3.143924 6.161977 48.06838 +#> UCB95Pct +#> 1 58.42572 +#> 2 66.35548 +#> 3 60.39234

Stratified Analysis

To estimate the CDF, certain percentiles, and means of BMMI for a design stratified by URBAN category, run

-strat_cont_ests <- cont_analysis(
-  NLA_PNW,
-  siteID = "SITE_ID",
-  vars = "BMMI",
-  weight = "WEIGHT",
-  stratumID = "URBAN"
-)
+strat_cont_ests <- cont_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars = "BMMI", + weight = "WEIGHT", + stratumID = "URBAN" +)

To view the analysis results for the mean estimates, run

-strat_cont_ests$Mean
-#>        Type Subpopulation Indicator nResp Estimate StdError MarginofError
-#> 1 All_Sites     All Sites      BMMI    96 56.50929 1.795959      3.520015
-#>   LCB95Pct UCB95Pct
-#> 1 52.98928 60.02931
+strat_cont_ests$Mean +#> Type Subpopulation Indicator nResp Estimate StdError MarginofError +#> 1 All_Sites All Sites BMMI 96 56.50929 1.795959 3.520015 +#> LCB95Pct UCB95Pct +#> 1 52.98928 60.02931

To then compute these estimates separately for each state, run

-strat_cont_ests_sp <- cont_analysis(
-  NLA_PNW,
-  siteID = "SITE_ID",
-  vars = "BMMI",
-  weight = "WEIGHT",
-  stratumID = "URBAN",
-  subpop = "STATE",
-)
+strat_cont_ests_sp <- cont_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars = "BMMI", + weight = "WEIGHT", + stratumID = "URBAN", + subpop = "STATE", +)

To view the analysis results for the mean estimates, run

-strat_cont_ests_sp$Mean
-#>    Type Subpopulation Indicator nResp Estimate StdError MarginofError LCB95Pct
-#> 1 STATE    California      BMMI    19 50.48964 4.110046      8.055543 42.43410
-#> 2 STATE        Oregon      BMMI    47 61.29675 1.630357      3.195441 58.10131
-#> 3 STATE    Washington      BMMI    30 54.23036 2.930517      5.743708 48.48665
-#>   UCB95Pct
-#> 1 58.54519
-#> 2 64.49219
-#> 3 59.97407
+strat_cont_ests_sp$Mean +#> Type Subpopulation Indicator nResp Estimate StdError MarginofError LCB95Pct +#> 1 STATE California BMMI 19 50.48964 4.110046 8.055543 42.43410 +#> 2 STATE Oregon BMMI 47 61.29675 1.630357 3.195441 58.10131 +#> 3 STATE Washington BMMI 30 54.23036 2.930517 5.743708 48.48665 +#> UCB95Pct +#> 1 58.54519 +#> 2 64.49219 +#> 3 59.97407
@@ -455,64 +455,64 @@

Attributab

The attributable risk is defined as \[1 - \frac{P(Response = Poor | Stressor = Good)}{P(Response = Poor)},\] where \(P(\cdot)\) is a probability and \(P(\cdot | \cdot)\) is a conditional probability. The attributable risk measures the proportion of the response variable in poor condition that could be eliminated if the stressor was always in good condition. To estimate the attributable risk of benthic macroinvertebrates with a phosphorous condition stressor, run

-attrisk_ests <- attrisk_analysis(
-  NLA_PNW, 
-  siteID = "SITE_ID",
-  vars_response = "BMMI_COND",
-  vars_stressor = "PHOS_COND",
-  weight = "WEIGHT"
-)
-attrisk_ests
-#>        Type Subpopulation  Response  Stressor nResp  Estimate StdError_log
-#> 1 All_Sites     All Sites BMMI_COND PHOS_COND    96 0.6201042     0.624808
-#>   MarginofError_log  LCB95Pct  UCB95Pct WeightTotal Count_RespPoor_StressPoor
-#> 1          1.224601 -0.292713 0.8883582    10679.65                         5
-#>   Count_RespPoor_StressGood Count_RespGood_StressPoor Count_RespGood_StressGood
-#> 1                         7                        18                        66
-#>   Prop_RespPoor_StressPoor Prop_RespPoor_StressGood Prop_RespGood_StressPoor
-#> 1               0.03971418               0.01738181                 0.158931
-#>   Prop_RespGood_StressGood
-#> 1                 0.783973
+attrisk_ests <- attrisk_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars_response = "BMMI_COND", + vars_stressor = "PHOS_COND", + weight = "WEIGHT" +) +attrisk_ests +#> Type Subpopulation Response Stressor nResp Estimate StdError_log +#> 1 All_Sites All Sites BMMI_COND PHOS_COND 96 0.6201042 0.624808 +#> MarginofError_log LCB95Pct UCB95Pct WeightTotal Count_RespPoor_StressPoor +#> 1 1.224601 -0.292713 0.8883582 10679.65 5 +#> Count_RespPoor_StressGood Count_RespGood_StressPoor Count_RespGood_StressGood +#> 1 7 18 66 +#> Prop_RespPoor_StressPoor Prop_RespPoor_StressGood Prop_RespGood_StressPoor +#> 1 0.03971418 0.01738181 0.158931 +#> Prop_RespGood_StressGood +#> 1 0.783973

The relative risk is defined as \[\frac{P(Response = Poor | Stressor = Poor)}{P(Response = Poor | Stressor = Good)},\] which measures the risk of the response variable being in poor condition relative to the stressor’s condition. To estimate the relative risk of benthic macroinvertebrates being in poor condition with a phosphorous condition category stressor, run

-relrisk_ests <- relrisk_analysis(
-  NLA_PNW, 
-  siteID = "SITE_ID",
-  vars_response = "BMMI_COND",
-  vars_stressor = "PHOS_COND",
-  weight = "WEIGHT"
-)
-relrisk_ests
-#>        Type Subpopulation  Response  Stressor nResp Estimate Estimate_num
-#> 1 All_Sites     All Sites BMMI_COND PHOS_COND    96 9.217166    0.1999252
-#>   Estimate_denom StdError_log MarginofError_log LCB95Pct UCB95Pct WeightTotal
-#> 1     0.02169053    0.8753855          1.715724 1.657555 51.25389    10679.65
-#>   Count_RespPoor_StressPoor Count_RespPoor_StressGood Count_RespGood_StressPoor
-#> 1                         5                         7                        18
-#>   Count_RespGood_StressGood Prop_RespPoor_StressPoor Prop_RespPoor_StressGood
-#> 1                        66               0.03971418               0.01738181
-#>   Prop_RespGood_StressPoor Prop_RespGood_StressGood
-#> 1                 0.158931                 0.783973
+relrisk_ests <- relrisk_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars_response = "BMMI_COND", + vars_stressor = "PHOS_COND", + weight = "WEIGHT" +) +relrisk_ests +#> Type Subpopulation Response Stressor nResp Estimate Estimate_num +#> 1 All_Sites All Sites BMMI_COND PHOS_COND 96 9.217166 0.1999252 +#> Estimate_denom StdError_log MarginofError_log LCB95Pct UCB95Pct WeightTotal +#> 1 0.02169053 0.8753855 1.715724 1.657555 51.25389 10679.65 +#> Count_RespPoor_StressPoor Count_RespPoor_StressGood Count_RespGood_StressPoor +#> 1 5 7 18 +#> Count_RespGood_StressGood Prop_RespPoor_StressPoor Prop_RespPoor_StressGood +#> 1 66 0.03971418 0.01738181 +#> Prop_RespGood_StressPoor Prop_RespGood_StressGood +#> 1 0.158931 0.783973

The risk difference is defined as \[P(Response = Poor | Stressor = Poor) - P(Response = Poor | Stressor = Good),\] which measures the risk of the response variable being in poor condition differenced by the stressor’s condition. To estimate the risk difference of benthic macroinvertebrates being in poor condition with a phosphorous condition category stressor, run

-diffrisk_ests <- diffrisk_analysis(
-  NLA_PNW, 
-  siteID = "SITE_ID",
-  vars_response = "BMMI_COND",
-  vars_stressor = "PHOS_COND",
-  weight = "WEIGHT"
-)
-diffrisk_ests
-#>        Type Subpopulation  Response  Stressor nResp  Estimate
-#> 1 All_Sites     All Sites BMMI_COND PHOS_COND    96 0.1782347
-#>   Estimate_StressPoor Estimate_StressGood  StdError MarginofError   LCB95Pct
-#> 1           0.1999252          0.02169053 0.1139557      0.223349 -0.0451143
-#>    UCB95Pct WeightTotal Count_RespPoor_StressPoor Count_RespPoor_StressGood
-#> 1 0.4015837    10679.65                         5                         7
-#>   Count_RespGood_StressPoor Count_RespGood_StressGood Prop_RespPoor_StressPoor
-#> 1                        18                        66               0.03971418
-#>   Prop_RespPoor_StressGood Prop_RespGood_StressPoor Prop_RespGood_StressGood
-#> 1               0.01738181                 0.158931                 0.783973
+diffrisk_ests <- diffrisk_analysis( + NLA_PNW, + siteID = "SITE_ID", + vars_response = "BMMI_COND", + vars_stressor = "PHOS_COND", + weight = "WEIGHT" +) +diffrisk_ests +#> Type Subpopulation Response Stressor nResp Estimate +#> 1 All_Sites All Sites BMMI_COND PHOS_COND 96 0.1782347 +#> Estimate_StressPoor Estimate_StressGood StdError MarginofError LCB95Pct +#> 1 0.1999252 0.02169053 0.1139557 0.223349 -0.0451143 +#> UCB95Pct WeightTotal Count_RespPoor_StressPoor Count_RespPoor_StressGood +#> 1 0.4015837 10679.65 5 7 +#> Count_RespGood_StressPoor Count_RespGood_StressGood Prop_RespPoor_StressPoor +#> 1 18 66 0.03971418 +#> Prop_RespPoor_StressGood Prop_RespGood_StressPoor Prop_RespGood_StressGood +#> 1 0.01738181 0.158931 0.783973

By default, the levels of the variables in vars_response and vars_stressor are assumed to equal "Poor" (event occurs) or "Good" (event does not occur). If those default levels do not match the levels of the variables in vars_response and vars_stressor, the levels of vars_response and vars_stressor must be explicitly stated using the response_levels and stressor_levels arguments, respectively. Similar to cat_analysis() and cont_analysis() from the previous sections, subpopulations and stratification are incorporated using subpops and stratumID, respectively. For more on attributable and relative risk in an environmental resource context, see Van Sickle and Paulsen (2008).

@@ -533,65 +533,65 @@

Change and Trend Analysis

To estimate the change between samples (time points) for BMMI (a continuous variable) and NITR_COND (a categorical variable), run

-change_ests <- change_analysis(
-  NRSA_EPA7,
-  siteID = "SITE_ID",
-  vars_cont = "BMMI",
-  vars_cat = "NITR_COND",
-  surveyID = "YEAR",
-  weight = "WEIGHT"
-)
+change_ests <- change_analysis( + NRSA_EPA7, + siteID = "SITE_ID", + vars_cont = "BMMI", + vars_cat = "NITR_COND", + surveyID = "YEAR", + weight = "WEIGHT" +)

The surveyID argument is the variable in the data distinguishing the different samples (YEAR in the previous example).

To view the analysis results for NITR_COND (the categorical variable), run

-change_ests$catsum
-#>   Survey_1 Survey_2      Type Subpopulation Indicator     Category  DiffEst.P
-#> 1  2008-09  2013-14 All_Sites     All Sites NITR_COND         Fair -1.8867976
-#> 2  2008-09  2013-14 All_Sites     All Sites NITR_COND         Good -2.9648182
-#> 3  2008-09  2013-14 All_Sites     All Sites NITR_COND Not Assessed -0.2447633
-#> 4  2008-09  2013-14 All_Sites     All Sites NITR_COND         Poor  5.0963791
-#>   StdError.P MarginofError.P  LCB95Pct.P UCB95Pct.P  DiffEst.U StdError.U
-#> 1  3.5366139       6.9316358  -8.8184334  5.0448382 -2919.8839  4990.4714
-#> 2  4.7633367       9.3359683 -12.3007865  6.3711502 -4597.9365  6772.4175
-#> 3  0.2072643       0.4062305  -0.6509938  0.1614672  -363.1305   306.1656
-#> 4  5.8020788      11.3718655  -6.2754864 16.4682446  6598.4548 19777.9218
-#>   MarginofError.U LCB95Pct.U UCB95Pct.U nResp_1 Estimate.P_1 StdError.P_1
-#> 1       9781.1441 -12701.028   6861.260      37   11.2929433    2.7579622
-#> 2      13273.6943 -17871.631   8675.758      22   18.5076549    3.6712147
-#> 3        600.0735   -963.204    236.943       1    0.2447633    0.2072643
-#> 4      38764.0144 -32165.560  45362.469     119   69.9546385    4.3636869
-#>   MarginofError.P_1 LCB95Pct.P_1 UCB95Pct.P_1 Estimate.U_1 StdError.U_1
-#> 1         5.4055067     5.887437   16.6984500   16754.1954    3998.9767
-#> 2         7.1954486    11.312206   25.7031034   27457.9317    5388.5407
-#> 3         0.4062305     0.000000    0.6509938     363.1305     306.1656
-#> 4         8.5526691    61.401969   78.5073076  103784.6070   12266.4461
-#>   MarginofError.U_1 LCB95Pct.U_1 UCB95Pct.U_1 nResp_2 Estimate.P_2 StdError.P_2
-#> 1         7837.8504     8916.345    24592.046      28     9.406146     2.213884
-#> 2        10561.3456    16896.586    38019.277      34    15.542837     3.035055
-#> 3          600.0735        0.000      963.204       0     0.000000     0.000000
-#> 4        24041.7926    79742.814   127826.400     112    75.051018     3.823919
-#>   MarginofError.P_2 LCB95Pct.P_2 UCB95Pct.P_2 Estimate.U_2 StdError.U_2
-#> 1          4.339133     5.067013     13.74528     13834.31     2985.463
-#> 2          5.948599     9.594238     21.49144     22860.00     4102.349
-#> 3          0.000000     0.000000      0.00000         0.00        0.000
-#> 4          7.494743    67.556274     82.54576    110383.06    15514.525
-#>   MarginofError.U_2 LCB95Pct.U_2 UCB95Pct.U_2
-#> 1          5851.400     7982.911     19685.71
-#> 2          8040.456    14819.539     30900.45
-#> 3             0.000        0.000         0.00
-#> 4         30407.911    79975.151    140790.97
+change_ests$catsum +#> Survey_1 Survey_2 Type Subpopulation Indicator Category DiffEst.P +#> 1 2008-09 2013-14 All_Sites All Sites NITR_COND Fair -1.8867976 +#> 2 2008-09 2013-14 All_Sites All Sites NITR_COND Good -2.9648182 +#> 3 2008-09 2013-14 All_Sites All Sites NITR_COND Not Assessed -0.2447633 +#> 4 2008-09 2013-14 All_Sites All Sites NITR_COND Poor 5.0963791 +#> StdError.P MarginofError.P LCB95Pct.P UCB95Pct.P DiffEst.U StdError.U +#> 1 3.5366139 6.9316358 -8.8184334 5.0448382 -2919.8839 4990.4714 +#> 2 4.7633367 9.3359683 -12.3007865 6.3711502 -4597.9365 6772.4175 +#> 3 0.2072643 0.4062305 -0.6509938 0.1614672 -363.1305 306.1656 +#> 4 5.8020788 11.3718655 -6.2754864 16.4682446 6598.4548 19777.9218 +#> MarginofError.U LCB95Pct.U UCB95Pct.U nResp_1 Estimate.P_1 StdError.P_1 +#> 1 9781.1441 -12701.028 6861.260 37 11.2929433 2.7579622 +#> 2 13273.6943 -17871.631 8675.758 22 18.5076549 3.6712147 +#> 3 600.0735 -963.204 236.943 1 0.2447633 0.2072643 +#> 4 38764.0144 -32165.560 45362.469 119 69.9546385 4.3636869 +#> MarginofError.P_1 LCB95Pct.P_1 UCB95Pct.P_1 Estimate.U_1 StdError.U_1 +#> 1 5.4055067 5.887437 16.6984500 16754.1954 3998.9767 +#> 2 7.1954486 11.312206 25.7031034 27457.9317 5388.5407 +#> 3 0.4062305 0.000000 0.6509938 363.1305 306.1656 +#> 4 8.5526691 61.401969 78.5073076 103784.6070 12266.4461 +#> MarginofError.U_1 LCB95Pct.U_1 UCB95Pct.U_1 nResp_2 Estimate.P_2 StdError.P_2 +#> 1 7837.8504 8916.345 24592.046 28 9.406146 2.213884 +#> 2 10561.3456 16896.586 38019.277 34 15.542837 3.035055 +#> 3 600.0735 0.000 963.204 0 0.000000 0.000000 +#> 4 24041.7926 79742.814 127826.400 112 75.051018 3.823919 +#> MarginofError.P_2 LCB95Pct.P_2 UCB95Pct.P_2 Estimate.U_2 StdError.U_2 +#> 1 4.339133 5.067013 13.74528 13834.31 2985.463 +#> 2 5.948599 9.594238 21.49144 22860.00 4102.349 +#> 3 0.000000 0.000000 0.00000 0.00 0.000 +#> 4 7.494743 67.556274 82.54576 110383.06 15514.525 +#> MarginofError.U_2 LCB95Pct.U_2 UCB95Pct.U_2 +#> 1 5851.400 7982.911 19685.71 +#> 2 8040.456 14819.539 30900.45 +#> 3 0.000 0.000 0.00 +#> 4 30407.911 79975.151 140790.97

Estimates are provided for the difference between the two samples and for each of the two individual samples (the _1 and _2 suffixes).

To view the analysis results for BMMI (the continuous variable), run

-change_ests$contsum_mean
-#>   Survey_1 Survey_2      Type Subpopulation Indicator  DiffEst StdError
-#> 1  2008-09  2013-14 All_Sites     All Sites      BMMI 3.971559 2.561155
-#>   MarginofError  LCB95Pct UCB95Pct nResp_1 Estimate_1 StdError_1
-#> 1      5.019771 -1.048211  8.99133     179   25.88274   1.468744
-#>   MarginofError_1 LCB95Pct_1 UCB95Pct_1 nResp_2 Estimate_2 StdError_2
-#> 1        2.878686   23.00405   28.76142     174    29.8543   2.098166
-#>   MarginofError_2 LCB95Pct_2 UCB95Pct_2
-#> 1        4.112331   25.74196   33.96663
+change_ests$contsum_mean +#> Survey_1 Survey_2 Type Subpopulation Indicator DiffEst StdError +#> 1 2008-09 2013-14 All_Sites All Sites BMMI 3.971559 2.561155 +#> MarginofError LCB95Pct UCB95Pct nResp_1 Estimate_1 StdError_1 +#> 1 5.019771 -1.048211 8.99133 179 25.88274 1.468744 +#> MarginofError_1 LCB95Pct_1 UCB95Pct_1 nResp_2 Estimate_2 StdError_2 +#> 1 2.878686 23.00405 28.76142 174 29.8543 2.098166 +#> MarginofError_2 LCB95Pct_2 UCB95Pct_2 +#> 1 4.112331 25.74196 33.96663

Though we don’t show an example here, the median can be estimated using the test argument in change_anlaysis().

Trend analysis generalizes change analysis to more than two samples (usually time points). Though we omit an example here, the arguments to trend_analysis() are very similar to the arguments for change_analysis(). One difference is that trend_analysis() contains arguments that specify which statistical model to apply to the estimates from each sample.

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+

Spatially Balanced Sampling

@@ -100,7 +100,7 @@

Michael Dumelle, Tom Kincaid, Anthony Olsen, an

If you have yet not read the “Start Here” vignette, please do so by running

-vignette("start-here", "spsurvey")
+vignette("start-here", "spsurvey")

Introduction

@@ -128,8 +128,8 @@

Introduction
-library(spsurvey)
-set.seed(51)

+library(spsurvey) +set.seed(51)

Unstratified sampling @@ -139,18 +139,18 @@

Equal inclusion probabilities
-eqprob <- grts(NE_Lakes, n_base = 50)

+eqprob <- grts(NE_Lakes, n_base = 50)

The first argument to grts() is the sampling frame, which must be an sf object. The second argument is n_base, which specifies the number of sites in the base (main) sample. The sites_base object in eqprob is an sf object and contains the original columns of NE_Lakes as well as a few additional columns such as a site identifier, latitude and longitude coordinates, inclusion probabilities, and design weights (to be used in analyses after collecting data).

To print a summary of site counts, run

-eqprob
-#> Summary of Site Counts: 
-#> 
-#>    total    siteuse  
-#>  total:50   Base:50
+eqprob +#> Summary of Site Counts: +#> +#> total siteuse +#> total:50 Base:50

To visualize the design sites overlain onto the sampling frame, run

-plot(eqprob, NE_Lakes, key.width = lcm(3))
+plot(eqprob, NE_Lakes, key.width = lcm(3))

The key.width argument extends the plot’s margin to fit the legend text nicely within the plot. sp_plot() can equivalently be used in place of plot() (sp_plot() is currently maintained for backwards compatibility with previous spsurvey versions).

@@ -159,24 +159,24 @@

Unequal inclusion probabilities

To select an unstratified GRTS sample where each site in the sampling frame has unequal inclusion probabilities according to some categorical variable, run

-caty_n <- c(small = 40, large = 10)
-uneqprob <- grts(
-  NE_Lakes,
-  n_base = 50,
-  caty_var = "AREA_CAT",
-  caty_n = caty_n
-)
-uneqprob
-#> Summary of Site Counts: 
-#> 
-#> siteuse by total: 
-#>       Base
-#> total   50
-#> 
-#> siteuse by caty: 
-#>       Base
-#> large   10
-#> small   40
+caty_n <- c(small = 40, large = 10) +uneqprob <- grts( + NE_Lakes, + n_base = 50, + caty_var = "AREA_CAT", + caty_n = caty_n +) +uneqprob +#> Summary of Site Counts: +#> +#> siteuse by total: +#> Base +#> total 50 +#> +#> siteuse by caty: +#> Base +#> large 10 +#> small 40

caty_var is the unequal inclusion probability variable ("AREA_CAT" in NE_Lakes), and caty_n is a vector whose names are the levels in caty_var and whose values are the expected sample sizes in each category (the sum of these expected samples sizes must equal n_base). In this sample, inclusion probabilities are adjusted so that on average, there are 40 small lakes and 10 large lakes selected.

@@ -184,11 +184,11 @@

Proportional inclusion probabiliti

To select an unstratified GRTS sample where each site in the sampling frame has inclusion probability proportional to a positive, continuous variable, run

-propprob <- grts(
-  NE_Lakes,
-  n_base = 50,
-  aux_var = "AREA"
-)
+propprob <- grts( + NE_Lakes, + n_base = 50, + aux_var = "AREA" +)

aux_var is the proportional probability (auxiliary) variable ("AREA" in NE_Lakes). Proportional (to size) inclusion probabilities are useful because they can increase the precision of estimates when the response variable is positively correlated with the proportional probability variable.

@@ -201,28 +201,28 @@

Equal inclusion probabilities
-strata_n <- c(low = 25, high = 15)
-strat_eqprob <- grts(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
-strat_eqprob
-#> Summary of Site Counts: 
-#> 
-#> siteuse by total: 
-#>       Base
-#> total   40
-#> 
-#> siteuse by stratum: 
-#>      Base
-#> high   15
-#> low    25
+strata_n <- c(low = 25, high = 15) +strat_eqprob <- grts(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT") +strat_eqprob +#> Summary of Site Counts: +#> +#> siteuse by total: +#> Base +#> total 40 +#> +#> siteuse by stratum: +#> Base +#> high 15 +#> low 25

strata_n is a named vector whose names represent the strata and whose values represent strata-specific sample sizes, and stratum_var is the stratification variable (ELEV_CAT in NE_Lakes).

To visualize the design sites overlain onto the sampling frame (separately for each stratum), run

-plot(
-  strat_eqprob,
-  formula = siteuse ~ ELEV_CAT,
-  NE_Lakes,
-  key.width = lcm(3)
-)
+plot( + strat_eqprob, + formula = siteuse ~ ELEV_CAT, + NE_Lakes, + key.width = lcm(3) +)

@@ -230,40 +230,40 @@

Unequal inclusion probabilities

To select a sample stratified by elevation category with unequal inclusion probabilities for each area category, run

-caty_n <- list(
-  low = c(small = 20, large = 5),
-  high = c(small = 10, large = 5)
-)
-strat_uneqprob <- grts(
-  NE_Lakes,
-  n_base = strata_n,
-  stratum_var = "ELEV_CAT",
-  caty_var = "AREA_CAT",
-  caty_n = caty_n
-)
-strat_uneqprob
-#> Summary of Site Counts: 
-#> 
-#> siteuse by total: 
-#>       Base
-#> total   40
-#> 
-#> siteuse by stratum: 
-#>      Base
-#> high   15
-#> low    25
-#> 
-#> siteuse by caty: 
-#>       Base
-#> large    9
-#> small   31
-#> 
-#> siteuse by stratum:caty: 
-#>            Base
-#> high:large    3
-#> low:large     6
-#> high:small   12
-#> low:small    19
+caty_n <- list( + low = c(small = 20, large = 5), + high = c(small = 10, large = 5) +) +strat_uneqprob <- grts( + NE_Lakes, + n_base = strata_n, + stratum_var = "ELEV_CAT", + caty_var = "AREA_CAT", + caty_n = caty_n +) +strat_uneqprob +#> Summary of Site Counts: +#> +#> siteuse by total: +#> Base +#> total 40 +#> +#> siteuse by stratum: +#> Base +#> high 15 +#> low 25 +#> +#> siteuse by caty: +#> Base +#> large 9 +#> small 31 +#> +#> siteuse by stratum:caty: +#> Base +#> high:large 3 +#> low:large 6 +#> high:small 12 +#> low:small 19

caty_n is now a list: the first element contains the expected sample sizes for each area category in the low elevation stratum, and the second element contains the expected sample sizes for each area category in the high elevation stratum.

@@ -271,12 +271,12 @@

Proportional inclusion probabili

To select a sample stratified by elevation category with probabilities proportional to lake area, run

-strat_propprob <- grts(
-  NE_Lakes,
-  n_base = strata_n,
-  stratum_var = "ELEV_CAT",
-  aux_var = "AREA"
-)
+strat_propprob <- grts( + NE_Lakes, + n_base = strata_n, + stratum_var = "ELEV_CAT", + aux_var = "AREA" +)
@@ -287,16 +287,16 @@

Legacy sampling

Legacy (historical) sites are sites that were selected from a previous sampling design that incorporated randomness into site selection, are part of the sampling frame for the current sampling design, and should be always be selected in the current sample. The NE_Lakes_Legacy data contains some legacy sites. To accommodate these legacy sites while sampling, use the legacy_sites argument:

-legacy <- grts(NE_Lakes, n_base = 50, legacy_sites = NE_Lakes_Legacy)
-legacy
-#> Summary of Site Counts: 
-#> 
-#>    total      siteuse  
-#>  total:50   Legacy: 5  
-#>             Base  :45
+legacy <- grts(NE_Lakes, n_base = 50, legacy_sites = NE_Lakes_Legacy) +legacy +#> Summary of Site Counts: +#> +#> total siteuse +#> total:50 Legacy: 5 +#> Base :45

To visualize the legacy and base sites together, run

-plot(legacy, key.width = lcm(3))
+plot(legacy, key.width = lcm(3))

These points can be overlain onto the sampling frame by including NE_Lakes in the plot() command.

Legacy sites are included in the base (main) sample, so the value for n_base should be equal to the number of legacy sites plus the number of non-legacy sites desired in the sample. If your sampling design uses stratification, unequal inclusion probabilities, or proportional inclusion probabilities, you need to name the column in legacy_sites that represents these values. By default, grts() assumes that the respective columns in sframe and legacy_sites share the same name – if this is not the case, use the legacy_stratum_var, legacy_caty_var or legacy_aux_var arguments. If your population is finite, you may alternatively accommodate legacy sites by including a variable in your sampling frame that indicates whether each site is a legacy site or not and then use the legacy_var argument in grts(). When using legacy_var, you do not need to use legacy_stratum_var, legacy_caty_var or legacy_aux_var.

@@ -307,17 +307,17 @@

Minimum distance sampling

Though the GRTS algorithm selects spatially balanced samples, the algorithm can select sites that are closer together than you may desire. To enforce a minimum distance between sites, run

-mindis <- grts(NE_Lakes, n_base = 50, mindis = 1600)
+mindis <- grts(NE_Lakes, n_base = 50, mindis = 1600)

Here we have specified that sites be no closer together than 1600 meters (meters are the units of the sf object). In some situations, the grts() function will fail to enforce the minimum distance requirement for all sites. When this occurs, the function will enforce the requirement for as many sites as possible and then return a warning.

When stratifying, mindis will apply to all strata if it is a single value. It is possible to set stratum-specific minimum distance requirements by storing them in a named list (the names must match the strata):

-mindis_list <- list(low = 1400, high = 1000)
-strat_mindis <- grts(
-  NE_Lakes,
-  strata_n,
-  stratum_var = "ELEV_CAT",
-  mindis = mindis_list
-)
+mindis_list <- list(low = 1400, high = 1000) +strat_mindis <- grts( + NE_Lakes, + strata_n, + stratum_var = "ELEV_CAT", + mindis = mindis_list +)

Replacement sampling @@ -328,72 +328,72 @@

Reverse hierarchical ordering
-rho_replace <- grts(NE_Lakes, n_base = 50, n_over = 25)
-rho_replace
-#> Summary of Site Counts: 
-#> 
-#>    total    siteuse  
-#>  total:75   Base:50  
-#>             Over:25

+rho_replace <- grts(NE_Lakes, n_base = 50, n_over = 25) +rho_replace +#> Summary of Site Counts: +#> +#> total siteuse +#> total:75 Base:50 +#> Over:25

n_base indicates the desired sample size, and n_over indicates the number of replacement sites. Sites are first selected using the GRTS algorithm for a sample size of n_base + n_over. They are then determined as base sites or replacement sites in a way that preserves as much spatial balance as possible. The spatial balance of the base sites degrades as n_over increases, however, so it is important to choose a realistic value for n_over.

To visualize the base sites and reverse hierarchically ordered replacement sites, run

-plot(rho_replace, key.width = lcm(3))
+plot(rho_replace, key.width = lcm(3))

When stratifying, n_over will apply to all strata if it is a single value. It is possible to set stratum-specific reverse hierarchical ordering requirements by storing them in a named list (the names must match the strata):

-over_list <- list(low = 2, high = 5)
-strat_rho_replace <- grts(
-  NE_Lakes,
-  strata_n,
-  stratum_var = "ELEV_CAT",
-  n_over = over_list
-)
-strat_rho_replace
-#> Summary of Site Counts: 
-#> 
-#> siteuse by total: 
-#>       Base Over
-#> total   40    7
-#> 
-#> siteuse by stratum: 
-#>      Base Over
-#> high   15    5
-#> low    25    2
+over_list <- list(low = 2, high = 5) +strat_rho_replace <- grts( + NE_Lakes, + strata_n, + stratum_var = "ELEV_CAT", + n_over = over_list +) +strat_rho_replace +#> Summary of Site Counts: +#> +#> siteuse by total: +#> Base Over +#> total 40 7 +#> +#> siteuse by stratum: +#> Base Over +#> high 15 5 +#> low 25 2

Nearest neighbor

The second replacement site option is nearest neighbor. To select a base sample with nearest neighbor replacement sites, run

-nn_replace <- grts(NE_Lakes, n_base = 50, n_near = 1)
-nn_replace
-#> Summary of Site Counts: 
-#> 
-#>    total     siteuse  
-#>  total:100   Base:50  
-#>              Near:50
+nn_replace <- grts(NE_Lakes, n_base = 50, n_near = 1) +nn_replace +#> Summary of Site Counts: +#> +#> total siteuse +#> total:100 Base:50 +#> Near:50

n_base indicates the desired sample size, and n_near indicates the number of replacement sites for each base site. For n_near = 1, each site in the base sample has a replacement site associated with it – this replacement site is the closest site (measured by Euclidean distance) to the base site (within a stratum).

When stratifying, n_near will apply to all strata if it is a single value. It is possible to set stratum-specific nearest neighbor requirements by storing them in a named list (the names must match the strata):

-near_list <- list(low = 1, high = 2)
-strat_nn_replace <- grts(
-  NE_Lakes,
-  strata_n,
-  stratum_var = "ELEV_CAT",
-  n_near = near_list
-)
-strat_nn_replace
-#> Summary of Site Counts: 
-#> 
-#> siteuse by total: 
-#>       Base Near
-#> total   40   55
-#> 
-#> siteuse by stratum: 
-#>      Base Near
-#> high   15   30
-#> low    25   25
+near_list <- list(low = 1, high = 2) +strat_nn_replace <- grts( + NE_Lakes, + strata_n, + stratum_var = "ELEV_CAT", + n_near = near_list +) +strat_nn_replace +#> Summary of Site Counts: +#> +#> siteuse by total: +#> Base Near +#> total 40 55 +#> +#> siteuse by stratum: +#> Base Near +#> high 15 30 +#> low 25 25
@@ -401,32 +401,32 @@

Independent Random Samplingirs() function (IRS is for Independent Random Sampling). The function arguments for the irs() function are the same as the function arguments for the grts() function. This means that the flexible sampling design options available for the GRTS algorithm are also available for the IRS algorithm. To select an unstratified IRS sample where each site in the sampling frame has an equal inclusion probability, run

-eqprob_irs <- irs(NE_Lakes, n_base = 50)
-eqprob_irs
-#> Summary of Site Counts: 
-#> 
-#>    total    siteuse  
-#>  total:50   Base:50
+eqprob_irs <- irs(NE_Lakes, n_base = 50) +eqprob_irs +#> Summary of Site Counts: +#> +#> total siteuse +#> total:50 Base:50

To visualize the design sites, run

-plot(eqprob_irs, NE_Lakes, key.width = lcm(3))
+plot(eqprob_irs, NE_Lakes, key.width = lcm(3))

Notice how these IRS design sites appear less spread out than the design sites from the unstratified GRTS sample with equal inclusion probabilities.

To select an IRS sample stratified by elevation category with equal inclusion probabilities in each stratum, run

-strata_n <- c(low = 25, high = 15)
-strat_eqprob_irs <- irs(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
-strat_eqprob_irs
-#> Summary of Site Counts: 
-#> 
-#> siteuse by total: 
-#>       Base
-#> total   40
-#> 
-#> siteuse by stratum: 
-#>      Base
-#> high   15
-#> low    25
+strata_n <- c(low = 25, high = 15) +strat_eqprob_irs <- irs(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT") +strat_eqprob_irs +#> Summary of Site Counts: +#> +#> siteuse by total: +#> Base +#> total 40 +#> +#> siteuse by stratum: +#> Base +#> high 15 +#> low 25
@@ -435,23 +435,23 @@

Measuring spatial balanceSo far we have talked generally about spatial balance but not about how to measure it. Stevens and Olsen (2004) proposed measuring spatial balance using Voronoi polygons. A design site’s Voronoi polygon contains all sites in the sampling frame closer to this design site than any other design site. To measure spatial balance, these Voronoi polygons are combined with the inclusion probabilities of all sites in the sampling frame and a loss metric.

Several spatial balance metrics are available in spsurvey. The default is Pielou’s evenness index. Pielou’s evenness index ranges from 0 (perfect spatial balance) to 1 (a complete lack of spatial balance). Larger values of the Pielou’s evenness index indicate worse spatial balance. To measure the spatial balance of the unstratified GRTS and IRS samples with equal inclusion probabilities, run

-sp_balance(eqprob$sites_base, NE_Lakes) # grts
-#>   stratum metric      value
-#> 1    None pielou 0.02585831
-sp_balance(eqprob_irs$sites_base, NE_Lakes) # irs
-#>   stratum metric      value
-#> 1    None pielou 0.03823327
+sp_balance(eqprob$sites_base, NE_Lakes) # grts +#> stratum metric value +#> 1 None pielou 0.02585831 +sp_balance(eqprob_irs$sites_base, NE_Lakes) # irs +#> stratum metric value +#> 1 None pielou 0.03823327

The GRTS design sites are more spatially balanced than the IRS design sites.

In a stratified sample, spatial balance is calculated separately for each stratum:

-sp_balance(strat_eqprob$sites_base, NE_Lakes, stratum_var = "ELEV_CAT") # grts
-#>   stratum metric      value
-#> 1     low pielou 0.03680620
-#> 2    high pielou 0.04543609
-sp_balance(strat_eqprob_irs$sites_base, NE_Lakes, stratum_var = "ELEV_CAT") # irs
-#>   stratum metric     value
-#> 1     low pielou 0.0415444
-#> 2    high pielou 0.1171086
+sp_balance(strat_eqprob$sites_base, NE_Lakes, stratum_var = "ELEV_CAT") # grts +#> stratum metric value +#> 1 low pielou 0.03680620 +#> 2 high pielou 0.04543609 +sp_balance(strat_eqprob_irs$sites_base, NE_Lakes, stratum_var = "ELEV_CAT") # irs +#> stratum metric value +#> 1 low pielou 0.0415444 +#> 2 high pielou 0.1171086

The GRTS design sites are more spatially balanced than the IRS design sites in both strata.

@@ -459,20 +459,20 @@

Infinite population sampling
-eqprob <- grts(Illinois_River, n_base = 50)

+eqprob <- grts(Illinois_River, n_base = 50)

To visualize the design sites overlain onto the sampling frame, run

-plot(eqprob, Illinois_River, key.width = lcm(3))
+plot(eqprob, Illinois_River, key.width = lcm(3))

To accommodate the Illinois River legacy sites, run:

-legacy <- grts(Illinois_River, n_base = 50, legacy_sites = Illinois_River_Legacy)
+legacy <- grts(Illinois_River, n_base = 50, legacy_sites = Illinois_River_Legacy)

To select a GRTS sample with equal inclusion probabilities from Lake_Ontario, run

-eqprob <- grts(Lake_Ontario, n_base = 50)
+eqprob <- grts(Lake_Ontario, n_base = 50)

To visualize the design sites (with closed circles) overlain onto the sampling frame, run

-plot(eqprob, Lake_Ontario, pch = 19, key.width = lcm(3))
+plot(eqprob, Lake_Ontario, pch = 19, key.width = lcm(3))

@@ -480,10 +480,10 @@

Binding design sitessp_rbind(). For example, to combine the base and reverse hierarchically ordered replacement sites from rho_replace into a single sf object, run

-combined <- sp_rbind(rho_replace)
+combined <- sp_rbind(rho_replace)

Then it is straightforward to write out a single sf object using a function like sf::write_sf(). For example, to save the combined sf object as a shapefile named "file_name.shp" at the location on your machine called "file_path", run

-write_sf(combined, "file_path/file_name.shp")
+write_sf(combined, "file_path/file_name.shp")

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5.5.0

@@ -84,7 +84,7 @@ -
+

Start Here

@@ -103,37 +103,37 @@

Installing and loading spsurvey

If this is your first time using the spsurvey package, run

-install.packages("spsurvey")
+install.packages("spsurvey")

to install the package. You only need to run this code once per version of R.

After the spsurvey package is installed, load it into R each new R session by running

+library(spsurvey)

Citation information

If you used spsurvey in your work, please cite it. You can view the most recent citation by running

-citation("spsurvey")
-#> 
-#> To cite spsurvey in publications use:
-#> 
-#>   Michael Dumelle, Tom Kincaid, Anthony R. Olsen, Marc Weber (2023).
-#>   spsurvey: Spatial Sampling Design and Analysis in R. Journal of
-#>   Statistical Software, 105(3), 1-29. doi:10.18637/jss.v105.i03
-#> 
-#> A BibTeX entry for LaTeX users is
-#> 
-#>   @Article{,
-#>     title = {{spsurvey}: Spatial Sampling Design and Analysis in {R}},
-#>     author = {Michael Dumelle and Tom Kincaid and Anthony R. Olsen and Marc Weber},
-#>     journal = {Journal of Statistical Software},
-#>     year = {2023},
-#>     volume = {105},
-#>     number = {3},
-#>     pages = {1--29},
-#>     doi = {10.18637/jss.v105.i03},
-#>   }
+citation("spsurvey") +#> +#> To cite spsurvey in publications use: +#> +#> Michael Dumelle, Tom Kincaid, Anthony R. Olsen, Marc Weber (2023). +#> spsurvey: Spatial Sampling Design and Analysis in R. Journal of +#> Statistical Software, 105(3), 1-29. doi:10.18637/jss.v105.i03 +#> +#> A BibTeX entry for LaTeX users is +#> +#> @Article{, +#> title = {{spsurvey}: Spatial Sampling Design and Analysis in {R}}, +#> author = {Michael Dumelle and Tom Kincaid and Anthony R. Olsen and Marc Weber}, +#> journal = {Journal of Statistical Software}, +#> year = {2023}, +#> volume = {105}, +#> number = {3}, +#> pages = {1--29}, +#> doi = {10.18637/jss.v105.i03}, +#> }

spsurvey terminology @@ -187,25 +187,25 @@

The sampling functions in spsurvey (grts() and irs()) require that your sampling frame is an sf object. An sf object (shorthand for a “simple features” object) is an R object with a unique structure used to conveniently store spatial data. sf objects are constructed using the sf package (Pebesma, 2018). The sf package is loaded and installed alongside the spsurvey package, so you do not need to run install.packages("sf") or library(sf) to access the sf package if spsurvey is already installed and loaded. For more on the sf package, see here.

Next we discuss a few ways to construct sf objects in R. The first is to read a shapefile directly into R using sf::read_sf(). The second is to use the sf::st_sf() function or the sf::st_as_sf() function to combine an appropriate R object (most commonly a data frame) and an appropriate geometry object into an sf object. To illustrate one approach for turning a data frame into an sf object, we start with NE_Lakes_df, a data frame in spsurvey that contains variables and geographic coordinates (latitude and longitude coordinates) for lakes in the Northeastern United States. To turn NE_Lakes_df into NE_Lakes_geo, an sf object with geographic coordinates, run

-NE_Lakes_geo <- st_as_sf(NE_Lakes_df, coords = c("XCOORD", "YCOORD"), crs = 4326)
-NE_Lakes_geo
-#> Simple feature collection with 195 features and 4 fields
-#> Geometry type: POINT
-#> Dimension:     XY
-#> Bounding box:  xmin: -73.64778 ymin: 41.07065 xmax: -69.96715 ymax: 42.73616
-#> Geodetic CRS:  WGS 84
-#> First 10 features:
-#>         AREA AREA_CAT   ELEV ELEV_CAT                   geometry
-#> 1  10.648825    large 264.69     high POINT (-72.08896 42.55508)
-#> 2   2.504606    small 557.63     high POINT (-73.18199 42.36727)
-#> 3   3.979199    small  28.79      low POINT (-71.14074 42.15596)
-#> 4   1.645657    small 212.60     high   POINT (-73.06726 41.783)
-#> 5   7.489052    small 239.67     high  POINT (-72.2602 42.36255)
-#> 6  86.533725    large 195.37     high POINT (-71.74634 41.87624)
-#> 7   1.926996    small 158.96     high POINT (-73.48408 41.34238)
-#> 8   6.514217    small  29.26      low POINT (-73.25487 41.20551)
-#> 9   3.100221    small 204.62     high POINT (-72.20897 42.12512)
-#> 10  1.868094    small  78.77      low POINT (-72.70233 42.18012)
+NE_Lakes_geo <- st_as_sf(NE_Lakes_df, coords = c("XCOORD", "YCOORD"), crs = 4326) +NE_Lakes_geo +#> Simple feature collection with 195 features and 4 fields +#> Geometry type: POINT +#> Dimension: XY +#> Bounding box: xmin: -73.64778 ymin: 41.07065 xmax: -69.96715 ymax: 42.73616 +#> Geodetic CRS: WGS 84 +#> First 10 features: +#> AREA AREA_CAT ELEV ELEV_CAT geometry +#> 1 10.648825 large 264.69 high POINT (-72.08896 42.55508) +#> 2 2.504606 small 557.63 high POINT (-73.18199 42.36727) +#> 3 3.979199 small 28.79 low POINT (-71.14074 42.15596) +#> 4 1.645657 small 212.60 high POINT (-73.06726 41.783) +#> 5 7.489052 small 239.67 high POINT (-72.2602 42.36255) +#> 6 86.533725 large 195.37 high POINT (-71.74634 41.87624) +#> 7 1.926996 small 158.96 high POINT (-73.48408 41.34238) +#> 8 6.514217 small 29.26 low POINT (-73.25487 41.20551) +#> 9 3.100221 small 204.62 high POINT (-72.20897 42.12512) +#> 10 1.868094 small 78.77 low POINT (-72.70233 42.18012)

The coords argument to sf::st_as_sf specifies the columns in NE_Lakes_df that are the x-coordinates and y-coordinates. The crs argument specifies the coordinate reference system, which we discuss in more detail next.

@@ -214,25 +214,25 @@

Coordinate reference systems. For example, we can transform NE_Lakes_geo (which uses a geographic CRS) to NE_Lakes (which uses a projected CRS) by running

-NE_Lakes <- st_transform(NE_Lakes_geo, crs = 5070)
-NE_Lakes
-#> Simple feature collection with 195 features and 4 fields
-#> Geometry type: POINT
-#> Dimension:     XY
-#> Bounding box:  xmin: 1834001 ymin: 2225021 xmax: 2127632 ymax: 2449985
-#> Projected CRS: NAD83 / Conus Albers
-#> First 10 features:
-#>         AREA AREA_CAT   ELEV ELEV_CAT                geometry
-#> 1  10.648825    large 264.69     high POINT (1930929 2417191)
-#> 2   2.504606    small 557.63     high POINT (1849399 2375085)
-#> 3   3.979199    small  28.79      low POINT (2017323 2393723)
-#> 4   1.645657    small 212.60     high POINT (1874135 2313865)
-#> 5   7.489052    small 239.67     high POINT (1922712 2392868)
-#> 6  86.533725    large 195.37     high POINT (1977163 2350744)
-#> 7   1.926996    small 158.96     high POINT (1852292 2257784)
-#> 8   6.514217    small  29.26      low POINT (1874421 2247388)
-#> 9   3.100221    small 204.62     high POINT (1933352 2368181)
-#> 10  1.868094    small  78.77      low POINT (1892582 2364213)
+NE_Lakes <- st_transform(NE_Lakes_geo, crs = 5070) +NE_Lakes +#> Simple feature collection with 195 features and 4 fields +#> Geometry type: POINT +#> Dimension: XY +#> Bounding box: xmin: 1834001 ymin: 2225021 xmax: 2127632 ymax: 2449985 +#> Projected CRS: NAD83 / Conus Albers +#> First 10 features: +#> AREA AREA_CAT ELEV ELEV_CAT geometry +#> 1 10.648825 large 264.69 high POINT (1930929 2417191) +#> 2 2.504606 small 557.63 high POINT (1849399 2375085) +#> 3 3.979199 small 28.79 low POINT (2017323 2393723) +#> 4 1.645657 small 212.60 high POINT (1874135 2313865) +#> 5 7.489052 small 239.67 high POINT (1922712 2392868) +#> 6 86.533725 large 195.37 high POINT (1977163 2350744) +#> 7 1.926996 small 158.96 high POINT (1852292 2257784) +#> 8 6.514217 small 29.26 low POINT (1874421 2247388) +#> 9 3.100221 small 204.62 high POINT (1933352 2368181) +#> 10 1.868094 small 78.77 low POINT (1892582 2364213)

CRSs in R have traditionally been stored using EPSG codes or proj4string values. This meant that in order to transform your coordinates from one CRS to another, you needed two EPSG codes or proj4string values, one for each CRS. Recent updates to R’s handling of spatial data follow GDAL and PROJ (more information available here), and CRSs in sf objects are stored in R as lists with two components: input, which contains information regarding the EPSG code and proj4string; and wkt, an open geospatial standard format. For more information on CRSs and EPSG codes, see Pebesma (2018) and Lovelace et al. (2019). To search for various CRSs and EPSG codes, see here and here.

spsurvey will use the CRS from your sf object, so it is your responsibility to make sure the sf object has an appropriate CRS. If the CRS is not specified correctly, you may get misleading results.

diff --git a/docs/authors.html b/docs/authors.html index 487b47b..6d59682 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,7 +17,7 @@ spsurvey - 5.4.1 + 5.5.0 diff --git a/docs/index.html b/docs/index.html index a380583..39de1fa 100644 --- a/docs/index.html +++ b/docs/index.html @@ -33,7 +33,7 @@ spsurvey - 5.4.1 + 5.5.0 @@ -97,28 +97,28 @@

Installation
-# install the most recent approved version from CRAN
-install.packages("spsurvey")
-# load the most recent approved version from CRAN
-library(spsurvey)
+# install the most recent approved version from CRAN +install.packages("spsurvey") +# load the most recent approved version from CRAN +library(spsurvey)

You can install and load the most recent development version ofspsurvey from GitHub by running:

-# Installing from GitHub requires you first install the remotes package
-install.packages("remotes")
-
-# install the most recent development version from GitHub
-remotes::install_github("USEPA/spsurvey", ref = "main")
-# load the most recent development version from GitHub
-library(spsurvey)
+# Installing from GitHub requires you first install the remotes package +install.packages("remotes") + +# install the most recent development version from GitHub +remotes::install_github("USEPA/spsurvey", ref = "main") +# load the most recent development version from GitHub +library(spsurvey)

You can install the most recent development version of spsurvey from GitHub with package vignettes by running:

-
install the most recent development version from GitHub with package vignettes
-devtools::install_github("USEPA/spsurvey", build_vignettes=TRUE)
+
install the most recent development version from GitHub with package vignettes
+devtools::install_github("USEPA/spsurvey", build_vignettes=TRUE)

To view the vignettes in RStudio, run

-vignette("start-here", "spsurvey") # start with this vignette for an spsurvey overview
-vignette("EDA", "spsurvey") # for summaries and visualizations (exploratory data analysis)
-vignette("sampling", "spsurvey") # for spatially balanced sampling
-vignette("analysis", "spsurvey") # for analyzing data
+vignette("start-here", "spsurvey") # start with this vignette for an spsurvey overview +vignette("EDA", "spsurvey") # for summaries and visualizations (exploratory data analysis) +vignette("sampling", "spsurvey") # for spatially balanced sampling +vignette("analysis", "spsurvey") # for analyzing data

To view the vignettes in a web format, visit here.

Further detail regarding spsurvey is contained in the package’s documentation manual available for download here.

@@ -127,25 +127,25 @@

Citation

If you used spsurvey in your work, please cite it. You can view the most recent citation by running

-citation(package = "spsurvey")
-
#> To cite spsurvey in publications use:
-#> 
-#>   Michael Dumelle, Tom Kincaid, Anthony R. Olsen, Marc Weber (2023).
-#>   spsurvey: Spatial Sampling Design and Analysis in R. Journal of
-#>   Statistical Software, 105(3), 1-29. doi:10.18637/jss.v105.i03
-#> 
-#> A BibTeX entry for LaTeX users is
-#> 
-#>   @Article{,
-#>     title = {{spsurvey}: Spatial Sampling Design and Analysis in {R}},
-#>     author = {Michael Dumelle and Tom Kincaid and Anthony R. Olsen and Marc Weber},
-#>     journal = {Journal of Statistical Software},
-#>     year = {2023},
-#>     volume = {105},
-#>     number = {3},
-#>     pages = {1--29},
-#>     doi = {10.18637/jss.v105.i03},
-#>   }
+citation(package = "spsurvey") +
#> To cite spsurvey in publications use:
+#> 
+#>   Michael Dumelle, Tom Kincaid, Anthony R. Olsen, Marc Weber (2023).
+#>   spsurvey: Spatial Sampling Design and Analysis in R. Journal of
+#>   Statistical Software, 105(3), 1-29. doi:10.18637/jss.v105.i03
+#> 
+#> A BibTeX entry for LaTeX users is
+#> 
+#>   @Article{,
+#>     title = {{spsurvey}: Spatial Sampling Design and Analysis in {R}},
+#>     author = {Michael Dumelle and Tom Kincaid and Anthony R. Olsen and Marc Weber},
+#>     journal = {Journal of Statistical Software},
+#>     year = {2023},
+#>     volume = {105},
+#>     number = {3},
+#>     pages = {1--29},
+#>     doi = {10.18637/jss.v105.i03},
+#>   }

Package Contributions @@ -208,7 +208,7 @@

Dev status

  • R-CMD-check
  • CRAN
    • -
  • cran checks
    • +
  • cran checks
  • Downloads
diff --git a/docs/news/index.html b/docs/news/index.html index 9be522e..1cde687 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -17,7 +17,7 @@ spsurvey - 5.4.1 + 5.5.0 @@ -67,7 +67,21 @@

Changelog

-

spsurvey 5.4.1

+

spsurvey 5.5.0

+
+

Minor Updates

+
  • +n_over is now recycled if the design is stratified and n_over is a length-one numeric vector.
    • +
  • Added an adjwgtNR() function to perform non-response weight adjustments.
    • +
  • Warning and error messages from grts(), irs(), and *_analysis() functions now print using message() instead of cat(). This change makes the resulting output more consistent with standard practice and easier to suppress when desired (#36).
    • +
  • Changed default behavior in attrisk_analysis(), diffrisk_analysis(), and relrisk_analysis() regarding the handling of response_levels and stressor_levels. Previously, if response_levels and stressor_levels were specified, their elements required names. Now, if response_levels is specified and its names are NULL, then its names are set to vars_response, and if stressor_levels is specified and its names are NULL, then its names are set to vars_stressor (#33).
    • +
+
+

Bug Fixes

+
  • Fixed a bug that caused an erorr in grts() and irs() occurred when at least one variable name in sframe was named "siteID", "siteuse", "replsite", "lon_WGS84", "lat_WGS84", "stratum", "wgt", "ip", "caty", "aux", xcoord, ycoord, or idpts and the name of the geometry column in sframe was not named "geometry" (#32).
+
+
+

spsurvey 5.4.12023-01-16

Minor Updates

@@ -104,13 +118,13 @@

Minor Updates

Bug fixes

  • Fixed a bug that prevented proper printing of the Indicator column when using change_analysis() with test = median.
    • -
  • Fixed a bug that made change_analysis sensititve to the ordering of the levels of variables in var_cat if those variables were factors.
    • +
  • Fixed a bug that made change_analysis sensitive to the ordering of the levels of variables in var_cat if those variables were factors.
  • Fixed a bug in sp_summary() that incorrectly ordered the siteuse variable.
  • Fixed a bug in sp_summary() that failed to summarize data frames that did not have an sf_column attribute.
  • Fixed a bug in *_analysis() functions when popsize is a list intended for use with survey::calibrate().
  • Fixed a bug in *analysis() functions that returned an error while performing percentile estimation when there was no variability in at least one variable in vars for at least one level of one variable in subpops.
  • Fixed a bug in grts() that caused an error for some combinations of n_base and n_over.
    • -
  • Fixed a bug in change_analysis() that returned an error when at least one varible in vars_cat has only one unique value.
    • +
  • Fixed a bug in change_analysis() that returned an error when at least one variable in vars_cat has only one unique value.

diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 7d4039a..a7c3b1c 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,4 +1,4 @@ -pandoc: 2.7.3 +pandoc: 2.11.4 pkgdown: 2.0.6 pkgdown_sha: ~ articles: @@ -6,5 +6,5 @@ articles: EDA: EDA.html sampling: sampling.html start-here: start-here.html -last_built: 2023-01-16T20:20Z +last_built: 2023-05-16T23:11Z diff --git a/docs/reference/Illinois_River.html b/docs/reference/Illinois_River.html index 50dc6e6..eed8dd0 100644 --- a/docs/reference/Illinois_River.html +++ b/docs/reference/Illinois_River.html @@ -18,7 +18,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -74,7 +74,7 @@

Illinois River data

- 
Illinois_River
+ 
Illinois_River
diff --git a/docs/reference/Illinois_River_Legacy.html b/docs/reference/Illinois_River_Legacy.html index 53c18c1..e69238b 100644 --- a/docs/reference/Illinois_River_Legacy.html +++ b/docs/reference/Illinois_River_Legacy.html @@ -18,7 +18,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -74,7 +74,7 @@

Illinois River legacy data

- 
Illinois_River_Legacy
+ 
Illinois_River_Legacy
diff --git a/docs/reference/Lake_Ontario.html b/docs/reference/Lake_Ontario.html index b423093..e832f67 100644 --- a/docs/reference/Lake_Ontario.html +++ b/docs/reference/Lake_Ontario.html @@ -18,7 +18,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -74,7 +74,7 @@

Lake Ontario data

- 
Lake_Ontario
+ 
Lake_Ontario
diff --git a/docs/reference/NE_Lakes.html b/docs/reference/NE_Lakes.html index c50b475..e9d8978 100644 --- a/docs/reference/NE_Lakes.html +++ b/docs/reference/NE_Lakes.html @@ -18,7 +18,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -74,7 +74,7 @@

New England Lakes data

- 
NE_Lakes
+ 
NE_Lakes
diff --git a/docs/reference/NE_Lakes_Legacy.html b/docs/reference/NE_Lakes_Legacy.html index eda8756..bf3c72f 100644 --- a/docs/reference/NE_Lakes_Legacy.html +++ b/docs/reference/NE_Lakes_Legacy.html @@ -17,7 +17,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -72,7 +72,7 @@

New England Lakes legacy data

- 
NE_Lakes_Legacy
+ 
NE_Lakes_Legacy
diff --git a/docs/reference/NE_Lakes_df.html b/docs/reference/NE_Lakes_df.html index 7a8b5c7..6077d0a 100644 --- a/docs/reference/NE_Lakes_df.html +++ b/docs/reference/NE_Lakes_df.html @@ -18,7 +18,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -74,7 +74,7 @@

New England Lakes data (as a data frame)

- 
NE_Lakes_df
+ 
NE_Lakes_df
diff --git a/docs/reference/NLA_PNW.html b/docs/reference/NLA_PNW.html index f98a77a..7c0bf46 100644 --- a/docs/reference/NLA_PNW.html +++ b/docs/reference/NLA_PNW.html @@ -19,7 +19,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -76,7 +76,7 @@

NLA PNW data

- 
NLA_PNW
+ 
NLA_PNW
diff --git a/docs/reference/NRSA_EPA7.html b/docs/reference/NRSA_EPA7.html index 94ca6b9..7889935 100644 --- a/docs/reference/NRSA_EPA7.html +++ b/docs/reference/NRSA_EPA7.html @@ -19,7 +19,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -76,7 +76,7 @@

NRSA EPA7 data

- 
NRSA_EPA7
+ 
NRSA_EPA7
diff --git a/docs/reference/adjwgt.html b/docs/reference/adjwgt.html index a8d1999..9a648fa 100644 --- a/docs/reference/adjwgt.html +++ b/docs/reference/adjwgt.html @@ -20,7 +20,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -78,7 +78,7 @@

Adjust survey design weights by categories

- 
adjwgt(wgt, wgtcat = NULL, framesize, sites = NULL)
+ 
adjwgt(wgt, wgtcat = NULL, framesize, sites = NULL)
@@ -125,20 +125,20 @@

Author

Examples

- 
wgt <- runif(50)
-wgtcat <- rep(c("A", "B"), c(30, 20))
-framesize <- c(A = 15, B = 10)
-sites <- rep(rep(c(TRUE, FALSE), c(9, 1)), 5)
-adjwgt(wgt, wgtcat, framesize, sites)
-#>  [1] 0.300614370 0.235850796 0.720443635 0.449966432 0.211864685 0.705859988
-#>  [7] 0.489414893 1.013659494 0.581508838 0.000000000 1.011888269 0.722010331
-#> [13] 0.550443875 0.806363092 0.002483645 0.436143047 0.685594491 0.970878940
-#> [19] 0.370406902 0.000000000 0.280166439 0.746747302 0.902128606 0.820763554
-#> [25] 0.438965453 0.220665812 0.092562800 0.323913259 0.908691054 0.000000000
-#> [31] 0.785703162 0.511329536 0.146718136 0.527393011 0.558032299 0.401771860
-#> [37] 0.213129011 0.951877905 0.843303504 0.000000000 0.145586305 0.699088549
-#> [43] 0.793258704 0.003576231 0.118941769 0.582050316 0.822761903 0.900746482
-#> [49] 0.994731316 0.000000000
+    
wgt <- runif(50)
+wgtcat <- rep(c("A", "B"), c(30, 20))
+framesize <- c(A = 15, B = 10)
+sites <- rep(rep(c(TRUE, FALSE), c(9, 1)), 5)
+adjwgt(wgt, wgtcat, framesize, sites)
+#>  [1] 0.043236942 0.303467182 1.203465906 0.005329816 0.026742781 0.929493721
+#>  [7] 0.550054450 1.106700002 0.856271032 0.000000000 0.725198992 0.147868667
+#> [13] 0.839568174 0.353124934 0.415406438 0.163085336 0.226615377 0.135662011
+#> [19] 1.041519884 0.000000000 0.675017306 0.233142048 0.366534191 0.306492615
+#> [25] 0.987999204 0.310199240 1.107077620 0.744478986 1.196247145 0.000000000
+#> [31] 0.588051104 0.879216137 1.241887296 0.584546511 0.760906513 0.052847179
+#> [37] 0.245573235 0.282495513 0.168751034 0.000000000 0.483873311 0.922223200
+#> [43] 0.647267182 0.215163713 0.816041558 0.820951051 0.103030270 0.418363640
+#> [49] 0.768811552 0.000000000
diff --git a/docs/reference/adjwgtNR.html b/docs/reference/adjwgtNR.html new file mode 100644 index 0000000..4d9facb --- /dev/null +++ b/docs/reference/adjwgtNR.html @@ -0,0 +1,175 @@ + +Adjust survey design weights for non-response by categories — adjwgtNR • spsurvey + + +
+
+ + + +
+
+
+

Adjust survey design weights for non-response by categories

+ Source: R/adjwgtNR.R + +
+ +
+

Adjust weights for target sample units that do not respond +and are missing at random within categories. The missing at random +assumption implies that their sample weight may be assigned to +specific categories of units that have responded (i.e., have been +sampled). This is a class-based method for non-response adjustment.

+
+ +
+ 
adjwgtNR(wgt, MARClass, EvalStatus, TNRClass, TRClass)
+
+ +
+

Arguments

+
wgt
+

vector of weights for each sample unit that will be adjusted +for non-response. Weights must be weights for the design as implemented. +All weights must be greater than zero.

+ + +
MARClass
+

vector that identifies for each sample unit the category +that will be used in non-response weight adjustment for sample units +that are known to be target. Within each missing at random (MAR) +category, the missing sample units that are not sampled are assumed to +be missing at random.

+ + +
EvalStatus
+

vector of the evaluation status for each sample unit. +Values must include the values given in TNRclass and TRClass. May +include other values not required for the non-response adjustment.

+ + +
TNRClass
+

subset of values in EvalStatus that identify sample units +whose target status is known and that do not respond (i.e., are not +sampled).

+ + +
TRClass
+

Subset of values in EvalStatus that identify sample units +whose target status is known and that respond (i.e., are target and +sampled).

+ +
+
+

Value

+ + +

Vector of sample unit weights that are adjusted for non-response + and that is the same length of input weights. Weights for sample + units that did not response but were known to be eligible are set + to zero. Weights for all other sample units are also set to zero.

+
+
+

Author

+

Tony Olsen olsen.tony@epa.gov

+
+ +
+

Examples

+ 
set.seed(5)
+wgt <- runif(40)
+MARClass <- rep(c("A", "B"), rep(20, 2))
+EvalStatus <- sample(c("Not_Target", "Target_Sampled", "Target_Not_Sampled"), 40, replace = TRUE)
+TNRClass <- "Target_Not_Sampled"
+TRClass <- "Target_Sampled"
+adjwgtNR(wgt, MARClass, EvalStatus, TNRClass, TRClass)
+#>  [1] 0.0000000 1.0531091 1.4091418 0.4370921 0.0000000 1.0774517 0.8114191
+#>  [8] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.4893536 0.0000000
+#> [15] 0.0000000 0.0000000 0.0000000 1.3645626 0.0000000 1.2943413 1.3897962
+#> [22] 0.0000000 0.3299456 0.0000000 0.0000000 0.0000000 0.6828898 0.0000000
+#> [29] 0.0000000 1.4909182 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
+#> [36] 0.0000000 0.0000000 0.9333714 0.6208610 0.0000000
+# function that has an error check
+
+
+
+ +
+ + +
+

Developed by Michael Dumelle, Tom Kincaid, Tony Olsen, Marc Weber.

+
+ +
+

Site built with pkgdown 2.0.6.

+
+ +
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@@ -74,14 +74,14 @@

Compute the average shifted histogram (ASH) for one-dimensional weighted dat
- 
ash1_wgt(
-  x,
-  wgt = rep(1, length(x)),
-  m = 5,
-  nbin = 50,
-  ab = NULL,
-  support = "Continuous"
-)
+ 
ash1_wgt(
+  x,
+  wgt = rep(1, length(x)),
+  m = 5,
+  nbin = 50,
+  ab = NULL,
+  support = "Continuous"
+)
@@ -144,10 +144,10 @@

Author

Examples

- 
x <- rnorm(100, 10, sqrt(10))
-wgt <- runif(100, 10, 100)
-rslt <- ash1_wgt(x, wgt)
-plot(rslt)
+    
x <- rnorm(100, 10, sqrt(10))
+wgt <- runif(100, 10, 100)
+rslt <- ash1_wgt(x, wgt)
+plot(rslt)
diff --git a/docs/reference/attrisk_analysis.html b/docs/reference/attrisk_analysis.html index 005a3bc..5640df3 100644 --- a/docs/reference/attrisk_analysis.html +++ b/docs/reference/attrisk_analysis.html @@ -23,7 +23,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -84,31 +84,31 @@

Attributable risk analysis

- 
attrisk_analysis(
-  dframe,
-  vars_response,
-  vars_stressor,
-  response_levels = NULL,
-  stressor_levels = NULL,
-  subpops = NULL,
-  siteID = NULL,
-  weight = "weight",
-  xcoord = NULL,
-  ycoord = NULL,
-  stratumID = NULL,
-  clusterID = NULL,
-  weight1 = NULL,
-  xcoord1 = NULL,
-  ycoord1 = NULL,
-  sizeweight = FALSE,
-  sweight = NULL,
-  sweight1 = NULL,
-  fpc = NULL,
-  popsize = NULL,
-  vartype = "Local",
-  conf = 95,
-  All_Sites = FALSE
-)
+ 
attrisk_analysis(
+  dframe,
+  vars_response,
+  vars_stressor,
+  response_levels = NULL,
+  stressor_levels = NULL,
+  subpops = NULL,
+  siteID = NULL,
+  weight = "weight",
+  xcoord = NULL,
+  ycoord = NULL,
+  stratumID = NULL,
+  clusterID = NULL,
+  weight1 = NULL,
+  xcoord1 = NULL,
+  ycoord1 = NULL,
+  sizeweight = FALSE,
+  sweight = NULL,
+  sweight1 = NULL,
+  fpc = NULL,
+  popsize = NULL,
+  vartype = "Local",
+  conf = 95,
+  All_Sites = FALSE
+)
@@ -147,7 +147,9 @@

Arguments

contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_response argument and that uses values in the vars_response argument as names -for the list. The default value is NULL.

+for the list. If response_levels is provided without names, +then the names of response_levels are set to vars_response. +The default value is NULL.

stressor_levels
@@ -161,7 +163,9 @@

Arguments

contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_stressor argument and that uses values in the vars_stressor argument as names -for the list. The default value is NULL.

+for the list. If stressor_levels is provided without names, +then the names of stressor_levels are set to vars_stressor. +The default value is NULL.

subpops
@@ -500,62 +504,62 @@

Author

Examples

- 
dframe <- data.frame(
-  siteID = paste0("Site", 1:100),
-  wgt = runif(100, 10, 100),
-  xcoord = runif(100),
-  ycoord = runif(100),
-  stratum = rep(c("Stratum1", "Stratum2"), 50),
-  RespVar1 = sample(c("Poor", "Good"), 100, replace = TRUE),
-  RespVar2 = sample(c("Poor", "Good"), 100, replace = TRUE),
-  StressVar = sample(c("Poor", "Good"), 100, replace = TRUE),
-  All_Sites = rep("All Sites", 100),
-  Resource_Class = rep(c("Agr", "Forest"), c(55, 45))
-)
-myresponse <- c("RespVar1", "RespVar2")
-mystressor <- c("StressVar")
-mysubpops <- c("All_Sites", "Resource_Class")
-attrisk_analysis(dframe,
-  vars_response = myresponse,
-  vars_stressor = mystressor, subpops = mysubpops, siteID = "siteID",
-  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
-  stratumID = "stratum"
-)
+    
dframe <- data.frame(
+  siteID = paste0("Site", 1:100),
+  wgt = runif(100, 10, 100),
+  xcoord = runif(100),
+  ycoord = runif(100),
+  stratum = rep(c("Stratum1", "Stratum2"), 50),
+  RespVar1 = sample(c("Poor", "Good"), 100, replace = TRUE),
+  RespVar2 = sample(c("Poor", "Good"), 100, replace = TRUE),
+  StressVar = sample(c("Poor", "Good"), 100, replace = TRUE),
+  All_Sites = rep("All Sites", 100),
+  Resource_Class = rep(c("Agr", "Forest"), c(55, 45))
+)
+myresponse <- c("RespVar1", "RespVar2")
+mystressor <- c("StressVar")
+mysubpops <- c("All_Sites", "Resource_Class")
+attrisk_analysis(dframe,
+  vars_response = myresponse,
+  vars_stressor = mystressor, subpops = mysubpops, siteID = "siteID",
+  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
+  stratumID = "stratum"
+)
 #>             Type Subpopulation Response  Stressor nResp     Estimate
-#> 1      All_Sites     All Sites RespVar1 StressVar   100 -0.008602746
-#> 2      All_Sites     All Sites RespVar2 StressVar   100 -0.051540581
-#> 3 Resource_Class           Agr RespVar1 StressVar    55 -0.098069000
-#> 4 Resource_Class        Forest RespVar1 StressVar    45  0.086235451
-#> 5 Resource_Class           Agr RespVar2 StressVar    55 -0.047914507
-#> 6 Resource_Class        Forest RespVar2 StressVar    45 -0.060099064
+#> 1      All_Sites     All Sites RespVar1 StressVar   100 -0.146822490
+#> 2      All_Sites     All Sites RespVar2 StressVar   100 -0.029705172
+#> 3 Resource_Class           Agr RespVar1 StressVar    55 -0.220942488
+#> 4 Resource_Class        Forest RespVar1 StressVar    45 -0.072784416
+#> 5 Resource_Class           Agr RespVar2 StressVar    55 -0.044451332
+#> 6 Resource_Class        Forest RespVar2 StressVar    45 -0.006427495
 #>   StdError_log MarginofError_log  LCB95Pct  UCB95Pct WeightTotal
-#> 1    0.3807913         0.7463373 -1.127406 0.5218216    5439.746
-#> 2    0.3550170         0.6958205 -1.108711 0.4756334    5439.746
-#> 3    0.5387972         1.0560232 -2.156855 0.6180517    3028.672
-#> 4    0.5973395         1.1707639 -1.946393 0.7166143    2411.074
-#> 5    0.4827913         0.9462536 -1.699470 0.5932073    3028.672
-#> 6    0.5531554         1.0841646 -2.134680 0.6414913    2411.074
+#> 1    0.3746480         0.7342965 -1.389996 0.4497054    5571.035
+#> 2    0.3825463         0.7497770 -1.179400 0.5134933    5571.035
+#> 3    0.5034165         0.9866781 -2.274945 0.5448167    2944.924
+#> 4    0.5775314         1.1319407 -2.327423 0.6541268    2626.110
+#> 5    0.5252535         1.0294780 -1.924050 0.6269289    2944.924
+#> 6    0.5317098         1.0421320 -1.853479 0.6450311    2626.110
 #>   Count_RespPoor_StressPoor Count_RespPoor_StressGood Count_RespGood_StressPoor
-#> 1                        23                        31                        19
-#> 2                        20                        28                        22
-#> 3                        11                        17                        12
-#> 4                        12                        14                         7
-#> 5                        12                        16                        11
-#> 6                         8                        12                        11
+#> 1                        23                        34                        25
+#> 2                        24                        25                        24
+#> 3                        11                        20                        14
+#> 4                        12                        14                        11
+#> 5                        14                        18                        11
+#> 6                        10                         7                        13
 #>   Count_RespGood_StressGood Prop_RespPoor_StressPoor Prop_RespPoor_StressGood
-#> 1                        27                0.2326256                0.2893811
-#> 2                        30                0.1867886                0.2557994
-#> 3                        15                0.2112064                0.2788079
-#> 4                        12                0.2595314                0.3026626
-#> 5                        16                0.2075605                0.2466110
-#> 6                        14                0.1606958                0.2673414
+#> 1                        18                0.2184026                0.3360798
+#> 2                        27                0.2182399                0.2605832
+#> 3                        10                0.1873002                0.3371157
+#> 4                         8                0.2532809                0.3349180
+#> 5                        12                0.2600568                0.3177389
+#> 6                        15                0.1713464                0.1964886
 #>   Prop_RespGood_StressPoor Prop_RespGood_StressGood
-#> 1                0.2177400                0.2602533
-#> 2                0.2635770                0.2938350
-#> 3                0.2706303                0.2393555
-#> 4                0.1513018                0.2865041
-#> 5                0.2742761                0.2715523
-#> 6                0.2501374                0.3218253
+#> 1                0.2530810                0.1924366
+#> 2                0.2532438                0.2679332
+#> 3                0.2861882                0.1893958
+#> 4                0.2159546                0.1958464
+#> 5                0.2134317                0.2087726
+#> 6                0.2978892                0.3342759
diff --git a/docs/reference/cat_analysis.html b/docs/reference/cat_analysis.html index a672af7..0ce0144 100644 --- a/docs/reference/cat_analysis.html +++ b/docs/reference/cat_analysis.html @@ -22,7 +22,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -82,29 +82,29 @@

Categorical variable analysis

- 
cat_analysis(
-  dframe,
-  vars,
-  subpops = NULL,
-  siteID = NULL,
-  weight = "weight",
-  xcoord = NULL,
-  ycoord = NULL,
-  stratumID = NULL,
-  clusterID = NULL,
-  weight1 = NULL,
-  xcoord1 = NULL,
-  ycoord1 = NULL,
-  sizeweight = FALSE,
-  sweight = NULL,
-  sweight1 = NULL,
-  fpc = NULL,
-  popsize = NULL,
-  vartype = "Local",
-  jointprob = "overton",
-  conf = 95,
-  All_Sites = FALSE
-)
+ 
cat_analysis(
+  dframe,
+  vars,
+  subpops = NULL,
+  siteID = NULL,
+  weight = "weight",
+  xcoord = NULL,
+  ycoord = NULL,
+  stratumID = NULL,
+  clusterID = NULL,
+  weight1 = NULL,
+  xcoord1 = NULL,
+  ycoord1 = NULL,
+  sizeweight = FALSE,
+  sweight = NULL,
+  sweight1 = NULL,
+  fpc = NULL,
+  popsize = NULL,
+  vartype = "Local",
+  jointprob = "overton",
+  conf = 95,
+  All_Sites = FALSE
+)
@@ -423,74 +423,74 @@

Author

Examples

- 
dframe <- data.frame(
-  siteID = paste0("Site", 1:100),
-  wgt = runif(100, 10, 100),
-  xcoord = runif(100),
-  ycoord = runif(100),
-  stratum = rep(c("Stratum1", "Stratum2"), 50),
-  CatVar = rep(c("north", "south", "east", "west"), 25),
-  All_Sites = rep("All Sites", 100),
-  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
-)
-myvars <- c("CatVar")
-mysubpops <- c("All_Sites", "Resource_Class")
-mypopsize <- data.frame(
-  Resource_Class = c("Good", "Poor"),
-  Total = c(4000, 1500)
-)
-cat_analysis(dframe,
-  vars = myvars, subpops = mysubpops, siteID = "siteID",
-  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
-  stratumID = "stratum", popsize = mypopsize
-)
+    
dframe <- data.frame(
+  siteID = paste0("Site", 1:100),
+  wgt = runif(100, 10, 100),
+  xcoord = runif(100),
+  ycoord = runif(100),
+  stratum = rep(c("Stratum1", "Stratum2"), 50),
+  CatVar = rep(c("north", "south", "east", "west"), 25),
+  All_Sites = rep("All Sites", 100),
+  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
+)
+myvars <- c("CatVar")
+mysubpops <- c("All_Sites", "Resource_Class")
+mypopsize <- data.frame(
+  Resource_Class = c("Good", "Poor"),
+  Total = c(4000, 1500)
+)
+cat_analysis(dframe,
+  vars = myvars, subpops = mysubpops, siteID = "siteID",
+  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
+  stratumID = "stratum", popsize = mypopsize
+)
 #>              Type Subpopulation Indicator Category nResp Estimate.P StdError.P
-#> 1       All_Sites     All Sites    CatVar     east    25   23.75497   3.403561
-#> 2       All_Sites     All Sites    CatVar    north    25   26.87107   3.403561
-#> 3       All_Sites     All Sites    CatVar    south    25   24.34974   3.582027
-#> 4       All_Sites     All Sites    CatVar     west    25   25.02422   3.582027
+#> 1       All_Sites     All Sites    CatVar     east    25   24.56407   3.655213
+#> 2       All_Sites     All Sites    CatVar    north    25   26.94039   3.655213
+#> 3       All_Sites     All Sites    CatVar    south    25   25.31730   3.360741
+#> 4       All_Sites     All Sites    CatVar     west    25   23.17824   3.360741
 #> 5       All_Sites     All Sites    CatVar    Total   100  100.00000   0.000000
-#> 6  Resource_Class          Good    CatVar     east    14   22.58904   4.130423
-#> 7  Resource_Class          Good    CatVar    north    14   28.11049   4.130423
-#> 8  Resource_Class          Good    CatVar    south    14   24.51464   4.657531
-#> 9  Resource_Class          Good    CatVar     west    13   24.78583   4.657531
+#> 6  Resource_Class          Good    CatVar     east    14   22.54320   4.807093
+#> 7  Resource_Class          Good    CatVar    north    14   28.93427   4.807093
+#> 8  Resource_Class          Good    CatVar    south    14   25.83591   4.439926
+#> 9  Resource_Class          Good    CatVar     west    13   22.68661   4.439926
 #> 10 Resource_Class          Good    CatVar    Total    55  100.00000   0.000000
-#> 11 Resource_Class          Poor    CatVar     east    11   26.86410   5.316598
-#> 12 Resource_Class          Poor    CatVar    north    11   23.56594   5.316598
-#> 13 Resource_Class          Poor    CatVar    south    11   23.91001   5.362600
-#> 14 Resource_Class          Poor    CatVar     west    12   25.65995   5.362600
+#> 11 Resource_Class          Poor    CatVar     east    11   29.95303   5.373134
+#> 12 Resource_Class          Poor    CatVar    north    11   21.62338   5.373134
+#> 13 Resource_Class          Poor    CatVar    south    11   23.93435   4.086409
+#> 14 Resource_Class          Poor    CatVar     west    12   24.48924   4.086409
 #> 15 Resource_Class          Poor    CatVar    Total    45  100.00000   0.000000
 #>    MarginofError.P LCB95Pct.P UCB95Pct.P Estimate.U StdError.U MarginofError.U
-#> 1         6.670856   17.08411   30.42582  1306.5231  195.87611        383.9101
-#> 2         6.670856   20.20021   33.54192  1477.9087  223.42108        437.8973
-#> 3         7.020643   17.32910   31.37039  1339.2359  223.42450        437.9040
-#> 4         7.020643   18.00358   32.04487  1376.3323  221.72897        434.5808
+#> 1         7.164087   17.39998   31.72815  1351.0237  202.58725        397.0637
+#> 2         7.164087   19.77630   34.10448  1481.7215  252.67329        495.2306
+#> 3         6.586931   18.73037   31.90424  1392.4518  208.32033        408.3003
+#> 4         6.586931   16.59131   29.76517  1274.8030  209.95998        411.5140
 #> 5         0.000000  100.00000  100.00000  5500.0000    0.00000          0.0000
-#> 6         8.095481   14.49356   30.68452   903.5616  163.77389        320.9909
-#> 7         8.095481   20.01501   36.20597  1124.4196  195.87130        383.9007
-#> 8         9.128593   15.38605   33.64324   980.5858  192.77179        377.8258
-#> 9         9.128593   15.65723   33.91442   991.4331  209.33332        410.2858
+#> 6         9.421730   13.12148   31.96493   901.7282  179.01008        350.8533
+#> 7         9.421730   19.51254   38.35600  1157.3708  245.47227        481.1168
+#> 8         8.702095   17.13382   34.53801  1033.4366  194.99544        382.1840
+#> 9         8.702095   13.98452   31.38871   907.4645  184.72902        362.0622
 #> 10        0.000000  100.00000  100.00000  4000.0000    0.00000          0.0000
-#> 11       10.420340   16.44376   37.28444   402.9615   88.26998        173.0060
-#> 12       10.420340   13.14560   33.98628   353.4891   78.55862        153.9721
-#> 13       10.510502   13.39950   34.42051   358.6501   86.64543        169.8219
-#> 14       10.510502   15.14945   36.17045   384.8992   84.80251        166.2099
+#> 11       10.531150   19.42188   40.48418   449.2955   97.12757        190.3665
+#> 12       10.531150   11.09223   32.15453   324.3508   78.92257        154.6854
+#> 13        8.009215   15.92513   31.94356   359.0152   69.13670        135.5054
+#> 14        8.009215   16.48002   32.49845   367.3386   65.75138        128.8703
 #> 15        0.000000  100.00000  100.00000  1500.0000    0.00000          0.0000
 #>    LCB95Pct.U UCB95Pct.U
-#> 1    922.6130  1690.4332
-#> 2   1040.0114  1915.8060
-#> 3    901.3319  1777.1398
-#> 4    941.7515  1810.9131
+#> 1    953.9600  1748.0874
+#> 2    986.4910  1976.9521
+#> 3    984.1514  1800.7521
+#> 4    863.2891  1686.3170
 #> 5   5500.0000  5500.0000
-#> 6    582.5707  1224.5525
-#> 7    740.5189  1508.3203
-#> 8    602.7600  1358.4115
-#> 9    581.1473  1401.7188
+#> 6    550.8749  1252.5815
+#> 7    676.2539  1638.4876
+#> 8    651.2525  1415.6206
+#> 9    545.4022  1269.5267
 #> 10  4000.0000  4000.0000
-#> 11   229.9555   575.9675
-#> 12   199.5171   507.4612
-#> 13   188.8282   528.4720
-#> 14   218.6894   551.1091
+#> 11   258.9289   639.6620
+#> 12   169.6654   479.0362
+#> 13   223.5098   494.5207
+#> 14   238.4682   496.2089
 #> 15  1500.0000  1500.0000
diff --git a/docs/reference/cdf_plot.html b/docs/reference/cdf_plot.html index 905b43b..9c18cee 100644 --- a/docs/reference/cdf_plot.html +++ b/docs/reference/cdf_plot.html @@ -19,7 +19,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -76,25 +76,25 @@

Plot a cumulative distribution function (CDF)

- 
cdf_plot(
-  cdfest,
-  var = NULL,
-  subpop = NULL,
-  subpop_level = NULL,
-  units_cdf = "Percent",
-  type_cdf = "Continuous",
-  log = "",
-  xlab = NULL,
-  ylab = NULL,
-  ylab_r = NULL,
-  main = NULL,
-  legloc = NULL,
-  confcut = 0,
-  conflev = 95,
-  cex.main = 1.2,
-  cex.legend = 1,
-  ...
-)
+ 
cdf_plot(
+  cdfest,
+  var = NULL,
+  subpop = NULL,
+  subpop_level = NULL,
+  units_cdf = "Percent",
+  type_cdf = "Continuous",
+  log = "",
+  xlab = NULL,
+  ylab = NULL,
+  ylab_r = NULL,
+  main = NULL,
+  legloc = NULL,
+  confcut = 0,
+  conflev = 95,
+  cex.main = 1.2,
+  cex.legend = 1,
+  ...
+)
@@ -236,42 +236,42 @@

Author

Examples

- 
if (FALSE) {
-dframe <- data.frame(
-  siteID = paste0("Site", 1:100),
-  wgt = runif(100, 10, 100),
-  xcoord = runif(100),
-  ycoord = runif(100),
-  stratum = rep(c("Stratum1", "Stratum2"), 50),
-  ContVar = rnorm(100, 10, 1),
-  All_Sites = rep("All Sites", 100),
-  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
-)
-myvars <- c("ContVar")
-mysubpops <- c("All_Sites", "Resource_Class")
-mypopsize <- data.frame(
-  Resource_Class = c("Good", "Poor"),
-  Total = c(4000, 1500)
-)
-myanalysis <- cont_analysis(dframe,
-  vars = myvars, subpops = mysubpops,
-  siteID = "siteID", weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
-  stratumID = "stratum", popsize = mypopsize
-)
-keep <- with(myanalysis$CDF, Type == "Resource_Class" &
-  Subpopulation == "Good")
-par(mfrow = c(2, 1))
-cdf_plot(myanalysis$CDF[keep, ],
-  xlab = "ContVar",
-  ylab = "Percent of Stream Length", ylab_r = "Stream Length (km)",
-  main = "Estimates for Resource Class: Good"
-)
-cdf_plot(myanalysis$CDF[keep, ],
-  xlab = "ContVar",
-  ylab = "Percent of Stream Length", ylab_r = "Same",
-  main = "Estimates for Resource Class: Good"
-)
-}
+    
if (FALSE) {
+dframe <- data.frame(
+  siteID = paste0("Site", 1:100),
+  wgt = runif(100, 10, 100),
+  xcoord = runif(100),
+  ycoord = runif(100),
+  stratum = rep(c("Stratum1", "Stratum2"), 50),
+  ContVar = rnorm(100, 10, 1),
+  All_Sites = rep("All Sites", 100),
+  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
+)
+myvars <- c("ContVar")
+mysubpops <- c("All_Sites", "Resource_Class")
+mypopsize <- data.frame(
+  Resource_Class = c("Good", "Poor"),
+  Total = c(4000, 1500)
+)
+myanalysis <- cont_analysis(dframe,
+  vars = myvars, subpops = mysubpops,
+  siteID = "siteID", weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
+  stratumID = "stratum", popsize = mypopsize
+)
+keep <- with(myanalysis$CDF, Type == "Resource_Class" &
+  Subpopulation == "Good")
+par(mfrow = c(2, 1))
+cdf_plot(myanalysis$CDF[keep, ],
+  xlab = "ContVar",
+  ylab = "Percent of Stream Length", ylab_r = "Stream Length (km)",
+  main = "Estimates for Resource Class: Good"
+)
+cdf_plot(myanalysis$CDF[keep, ],
+  xlab = "ContVar",
+  ylab = "Percent of Stream Length", ylab_r = "Same",
+  main = "Estimates for Resource Class: Good"
+)
+}
diff --git a/docs/reference/change_analysis.html b/docs/reference/change_analysis.html index e4a4ae9..685bedd 100644 --- a/docs/reference/change_analysis.html +++ b/docs/reference/change_analysis.html @@ -23,7 +23,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -84,34 +84,34 @@

Change analysis

- 
change_analysis(
-  dframe,
-  vars_cat = NULL,
-  vars_cont = NULL,
-  test = "mean",
-  subpops = NULL,
-  surveyID = "surveyID",
-  survey_names = NULL,
-  siteID = "siteID",
-  weight = "weight",
-  revisitwgt = FALSE,
-  xcoord = NULL,
-  ycoord = NULL,
-  stratumID = NULL,
-  clusterID = NULL,
-  weight1 = NULL,
-  xcoord1 = NULL,
-  ycoord1 = NULL,
-  sizeweight = FALSE,
-  sweight = NULL,
-  sweight1 = NULL,
-  fpc = NULL,
-  popsize = NULL,
-  vartype = "Local",
-  jointprob = "overton",
-  conf = 95,
-  All_Sites = FALSE
-)
+ 
change_analysis(
+  dframe,
+  vars_cat = NULL,
+  vars_cont = NULL,
+  test = "mean",
+  subpops = NULL,
+  surveyID = "surveyID",
+  survey_names = NULL,
+  siteID = "siteID",
+  weight = "weight",
+  revisitwgt = FALSE,
+  xcoord = NULL,
+  ycoord = NULL,
+  stratumID = NULL,
+  clusterID = NULL,
+  weight1 = NULL,
+  xcoord1 = NULL,
+  ycoord1 = NULL,
+  sizeweight = FALSE,
+  sweight = NULL,
+  sweight1 = NULL,
+  fpc = NULL,
+  popsize = NULL,
+  vartype = "Local",
+  jointprob = "overton",
+  conf = 95,
+  All_Sites = FALSE
+)
@@ -816,89 +816,89 @@

Author

Examples

- 
# Categorical variable example for three resource classes
-dframe <- data.frame(
-  surveyID = rep(c("Survey 1", "Survey 2"), c(100, 100)),
-  siteID = paste0("Site", 1:200),
-  wgt = runif(200, 10, 100),
-  xcoord = runif(200),
-  ycoord = runif(200),
-  stratum = rep(rep(c("Stratum 1", "Stratum 2"), c(2, 2)), 50),
-  CatVar = rep(c("North", "South"), 100),
-  All_Sites = rep("All Sites", 200),
-  Resource_Class = sample(c("Good", "Fair", "Poor"), 200, replace = TRUE)
-)
-myvars <- c("CatVar")
-mysubpops <- c("All_Sites", "Resource_Class")
-change_analysis(dframe,
-  vars_cat = myvars, subpops = mysubpops,
-  surveyID = "surveyID", siteID = "siteID", weight = "wgt",
-  xcoord = "xcoord", ycoord = "ycoord", stratumID = "stratum"
-)
+    
# Categorical variable example for three resource classes
+dframe <- data.frame(
+  surveyID = rep(c("Survey 1", "Survey 2"), c(100, 100)),
+  siteID = paste0("Site", 1:200),
+  wgt = runif(200, 10, 100),
+  xcoord = runif(200),
+  ycoord = runif(200),
+  stratum = rep(rep(c("Stratum 1", "Stratum 2"), c(2, 2)), 50),
+  CatVar = rep(c("North", "South"), 100),
+  All_Sites = rep("All Sites", 200),
+  Resource_Class = sample(c("Good", "Fair", "Poor"), 200, replace = TRUE)
+)
+myvars <- c("CatVar")
+mysubpops <- c("All_Sites", "Resource_Class")
+change_analysis(dframe,
+  vars_cat = myvars, subpops = mysubpops,
+  surveyID = "surveyID", siteID = "siteID", weight = "wgt",
+  xcoord = "xcoord", ycoord = "ycoord", stratumID = "stratum"
+)
 #> $catsum
-#>   Survey_1 Survey_2           Type Subpopulation Indicator Category  DiffEst.P
-#> 1 Survey 1 Survey 2      All_Sites     All Sites    CatVar    North   5.687334
-#> 2 Survey 1 Survey 2      All_Sites     All Sites    CatVar    South  -5.687334
-#> 3 Survey 1 Survey 2 Resource_Class          Fair    CatVar    North  10.931715
-#> 4 Survey 1 Survey 2 Resource_Class          Fair    CatVar    South -10.931715
-#> 5 Survey 1 Survey 2 Resource_Class          Good    CatVar    North  -2.184212
-#> 6 Survey 1 Survey 2 Resource_Class          Good    CatVar    South   2.184212
-#> 7 Survey 1 Survey 2 Resource_Class          Poor    CatVar    North  16.026542
-#> 8 Survey 1 Survey 2 Resource_Class          Poor    CatVar    South -16.026542
+#>   Survey_1 Survey_2           Type Subpopulation Indicator Category DiffEst.P
+#> 1 Survey 1 Survey 2      All_Sites     All Sites    CatVar    North -1.474844
+#> 2 Survey 1 Survey 2      All_Sites     All Sites    CatVar    South  1.474844
+#> 3 Survey 1 Survey 2 Resource_Class          Fair    CatVar    North -8.424944
+#> 4 Survey 1 Survey 2 Resource_Class          Fair    CatVar    South  8.424944
+#> 5 Survey 1 Survey 2 Resource_Class          Good    CatVar    North -2.700343
+#> 6 Survey 1 Survey 2 Resource_Class          Good    CatVar    South  2.700343
+#> 7 Survey 1 Survey 2 Resource_Class          Poor    CatVar    North  9.876690
+#> 8 Survey 1 Survey 2 Resource_Class          Poor    CatVar    South -9.876690
 #>   StdError.P MarginofError.P LCB95Pct.P UCB95Pct.P  DiffEst.U StdError.U
-#> 1   6.627503        12.98967  -7.302332  18.677001  366.93366   381.6858
-#> 2   6.627503        12.98967 -18.677001   7.302332 -247.42810   394.0168
-#> 3  11.254737        22.05888 -11.127165  32.990594  458.92014   199.0896
-#> 4  11.254737        22.05888 -32.990594  11.127165  213.47382   244.5832
-#> 5  10.921448        21.40564 -23.589857  19.221432  -28.97260   250.2994
-#> 6  10.921448        21.40564 -19.221432  23.589857   57.59736   238.7439
-#> 7  12.488324        24.47667  -8.450125  40.503208  -63.01389   220.2134
-#> 8  12.488324        24.47667 -40.503208   8.450125 -518.49928   199.6357
-#>   MarginofError.U  LCB95Pct.U UCB95Pct.U nResp_1 Estimate.P_1 StdError.P_1
-#> 1        748.0905  -381.15685  1115.0242      50     46.71029     4.768780
-#> 2        772.2587 -1019.68681   524.8306      50     53.28971     4.768780
-#> 3        390.2084    68.71174   849.1285      11     33.32169     8.441770
-#> 4        479.3743  -265.90044   692.8481      16     66.67831     8.441770
-#> 5        490.5778  -519.55043   461.6052      24     57.90903     7.929511
-#> 6        467.9294  -410.33207   525.5268      14     42.09097     7.929511
-#> 7        431.6103  -494.62420   368.5964      15     44.91151     8.380054
-#> 8        391.2789  -909.77815  -127.2204      20     55.08849     8.380054
+#> 1   6.588741        12.91369  -14.38854   11.43885 -276.59196   399.9584
+#> 2   6.588741        12.91369  -11.43885   14.38854 -110.79795   405.3858
+#> 3  11.629073        22.79256  -31.21751   14.36762 -135.23095   224.1637
+#> 4  11.629073        22.79256  -14.36762   31.21751  187.97610   248.9814
+#> 5  11.535577        22.60932  -25.30966   19.90897  -18.47734   242.7810
+#> 6  11.535577        22.60932  -19.90897   25.30966   91.80035   238.9779
+#> 7  10.718029        21.00695  -11.13026   30.88364 -122.88367   233.7347
+#> 8  10.718029        21.00695  -30.88364   11.13026 -390.57440   200.6060
+#>   MarginofError.U LCB95Pct.U UCB95Pct.U nResp_1 Estimate.P_1 StdError.P_1
+#> 1        783.9040 -1060.4959 507.312026      50     50.85132     4.735320
+#> 2        794.5416  -905.3396 683.743675      50     49.14868     4.735320
+#> 3        439.3527  -574.5837 304.121778      18     52.86449     8.718695
+#> 4        487.9946  -300.0185 675.970720      14     47.13551     8.718695
+#> 5        475.8419  -494.3193 457.364609      14     46.31958     8.760600
+#> 6        468.3881  -376.5877 560.188433      18     53.68042     8.760600
+#> 7        458.1116  -580.9953 335.227975      18     53.15233     7.543663
+#> 8        393.1805  -783.7549   2.606108      18     46.84767     7.543663
 #>   MarginofError.P_1 LCB95Pct.P_1 UCB95Pct.P_1 Estimate.U_1 StdError.U_1
-#> 1          9.346637     37.36366     56.05693    2499.3557     264.4515
-#> 2          9.346637     43.94307     62.63634    2851.4041     288.4971
-#> 3         16.545564     16.77613     49.86725     491.8611     117.6043
-#> 4         16.545564     50.13275     83.22387     984.2378     161.4159
-#> 5         15.541555     42.36747     73.45058    1191.0416     172.3842
-#> 6         15.541555     26.54942     57.63253     865.7043     179.8425
-#> 7         16.424604     28.48690     61.33611     816.4530     166.3135
-#> 8         16.424604     38.66389     71.51310    1001.4619     163.2339
+#> 1          9.281057     41.57026     60.13238    2941.4984     306.2000
+#> 2          9.281057     39.86762     58.42974    2843.0090     286.4790
+#> 3         17.088327     35.77616     69.95282     995.6200     167.0792
+#> 4         17.088327     30.04718     64.22384     887.7237     180.9941
+#> 5         17.170460     29.14912     63.49004     865.5563     186.1827
+#> 6         17.170460     36.50996     70.85088    1003.1055     165.3294
+#> 7         14.785308     38.36702     67.93764    1080.3222     185.9011
+#> 8         14.785308     32.06236     61.63298     952.1798     147.9496
 #>   MarginofError.U_1 LCB95Pct.U_1 UCB95Pct.U_1 nResp_2 Estimate.P_2 StdError.P_2
-#> 1          518.3154    1981.0402    3017.6711      50     52.39763     4.602448
-#> 2          565.4438    2285.9602    3416.8479      50     47.60237     4.602448
-#> 3          230.5002     261.3610     722.3613      17     44.25340     7.443496
-#> 4          316.3694     667.8684    1300.6073      21     55.74660     7.443496
-#> 5          337.8668     853.1748    1528.9084      18     55.72482     7.510052
-#> 6          352.4849     513.2194    1218.1892      17     44.27518     7.510052
-#> 7          325.9685     490.4845    1142.4215      15     60.93805     9.259208
-#> 8          319.9326     681.5293    1321.3946      12     39.06195     9.259208
+#> 1          600.1409    2341.3575     3541.639      50     49.37648     4.581293
+#> 2          561.4884    2281.5206     3404.497      50     50.62352     4.581293
+#> 3          327.4692     668.1508     1323.089      14     44.43954     7.695434
+#> 4          354.7419     532.9818     1242.466      18     55.56046     7.695434
+#> 5          364.9115     500.6448     1230.468      17     43.61924     7.504761
+#> 6          324.0397     679.0658     1327.145      22     56.38076     7.504761
+#> 7          364.3594     715.9628     1444.682      19     63.02902     7.613757
+#> 8          289.9759     662.2039     1242.156      10     36.97098     7.613757
 #>   MarginofError.P_2 LCB95Pct.P_2 UCB95Pct.P_2 Estimate.U_2 StdError.U_2
-#> 1          9.020633     43.37700     61.41826    2866.2893     275.2262
-#> 2          9.020633     38.58174     56.62300    2603.9760     268.3630
-#> 3         14.588984     29.66442     58.84239     950.7813     160.6421
-#> 4         14.588984     41.15761     70.33558    1197.7117     183.7548
-#> 5         14.719432     41.00538     70.44425    1162.0690     181.4759
-#> 6         14.719432     29.55575     58.99462     923.3017     157.0201
-#> 7         18.147715     42.79033     79.08576     753.4391     144.3390
-#> 8         18.147715     20.91424     57.20967     482.9627     114.9309
+#> 1          8.979169     40.39731     58.35565    2664.9065     257.3097
+#> 2          8.979169     41.64435     59.60269    2732.2111     286.8231
+#> 3         15.082774     29.35677     59.52232     860.3891     149.4453
+#> 4         15.082774     40.47768     70.64323    1075.6998     170.9763
+#> 5         14.709060     28.91018     58.32830     847.0789     155.8159
+#> 6         14.709060     41.67170     71.08982    1094.9058     172.5590
+#> 7         14.922689     48.10633     77.95171     957.4385     141.6782
+#> 8         14.922689     22.04829     51.89367     561.6054     135.4757
 #>   MarginofError.U_2 LCB95Pct.U_2 UCB95Pct.U_2
-#> 1          539.4335    2326.8558    3405.7228
-#> 2          525.9817    2077.9943    3129.9577
-#> 3          314.8528     635.9285    1265.6340
-#> 4          360.1528     837.5588    1557.8645
-#> 5          355.6861     806.3828    1517.7551
-#> 6          307.7537     615.5480    1231.0554
-#> 7          282.8993     470.5398    1036.3383
-#> 8          225.2605     257.7022     708.2231
+#> 1          504.3177    2160.5888     3169.224
+#> 2          562.1629    2170.0482     3294.374
+#> 3          292.9074     567.4816     1153.296
+#> 4          335.1073     740.5925     1410.807
+#> 5          305.3935     541.6855     1152.472
+#> 6          338.2095     756.6963     1433.115
+#> 7          277.6842     679.7543     1235.123
+#> 8          265.5276     296.0779      827.133
 #> 
 #> $contsum_mean
 #> NULL
diff --git a/docs/reference/cont_analysis.html b/docs/reference/cont_analysis.html
index 5b3b28e..0e0240d 100644
--- a/docs/reference/cont_analysis.html
+++ b/docs/reference/cont_analysis.html
@@ -23,7 +23,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -84,31 +84,31 @@ 
Continuous variable analysis
- 
cont_analysis(
-  dframe,
-  vars,
-  subpops = NULL,
-  siteID = NULL,
-  weight = "weight",
-  xcoord = NULL,
-  ycoord = NULL,
-  stratumID = NULL,
-  clusterID = NULL,
-  weight1 = NULL,
-  xcoord1 = NULL,
-  ycoord1 = NULL,
-  sizeweight = FALSE,
-  sweight = NULL,
-  sweight1 = NULL,
-  fpc = NULL,
-  popsize = NULL,
-  vartype = "Local",
-  jointprob = "overton",
-  conf = 95,
-  pctval = c(5, 10, 25, 50, 75, 90, 95),
-  statistics = c("CDF", "Pct", "Mean", "Total"),
-  All_Sites = FALSE
-)
+ 
cont_analysis(
+  dframe,
+  vars,
+  subpops = NULL,
+  siteID = NULL,
+  weight = "weight",
+  xcoord = NULL,
+  ycoord = NULL,
+  stratumID = NULL,
+  clusterID = NULL,
+  weight1 = NULL,
+  xcoord1 = NULL,
+  ycoord1 = NULL,
+  sizeweight = FALSE,
+  sweight = NULL,
+  sweight1 = NULL,
+  fpc = NULL,
+  popsize = NULL,
+  vartype = "Local",
+  jointprob = "overton",
+  conf = 95,
+  pctval = c(5, 10, 25, 50, 75, 90, 95),
+  statistics = c("CDF", "Pct", "Mean", "Total"),
+  All_Sites = FALSE
+)
@@ -548,27 +548,27 @@

Author

Examples

- 
dframe <- data.frame(
-  siteID = paste0("Site", 1:100),
-  wgt = runif(100, 10, 100),
-  xcoord = runif(100),
-  ycoord = runif(100),
-  stratum = rep(c("Stratum1", "Stratum2"), 50),
-  ContVar = rnorm(100, 10, 1),
-  All_Sites = rep("All Sites", 100),
-  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
-)
-myvars <- c("ContVar")
-mysubpops <- c("All_Sites", "Resource_Class")
-mypopsize <- data.frame(
-  Resource_Class = c("Good", "Poor"),
-  Total = c(4000, 1500)
-)
-cont_analysis(dframe,
-  vars = myvars, subpops = mysubpops, siteID = "siteID",
-  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
-  stratumID = "stratum", popsize = mypopsize, statistics = "Mean"
-)
+    
dframe <- data.frame(
+  siteID = paste0("Site", 1:100),
+  wgt = runif(100, 10, 100),
+  xcoord = runif(100),
+  ycoord = runif(100),
+  stratum = rep(c("Stratum1", "Stratum2"), 50),
+  ContVar = rnorm(100, 10, 1),
+  All_Sites = rep("All Sites", 100),
+  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
+)
+myvars <- c("ContVar")
+mysubpops <- c("All_Sites", "Resource_Class")
+mypopsize <- data.frame(
+  Resource_Class = c("Good", "Poor"),
+  Total = c(4000, 1500)
+)
+cont_analysis(dframe,
+  vars = myvars, subpops = mysubpops, siteID = "siteID",
+  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
+  stratumID = "stratum", popsize = mypopsize, statistics = "Mean"
+)
 #> $CDF
 #> NULL
 #> 
@@ -576,14 +576,14 @@ 
Examples

 #> NULL
 #> 
 #> $Mean
-#>             Type Subpopulation Indicator nResp  Estimate  StdError
-#> 1      All_Sites     All Sites   ContVar   100  9.963611 0.1034540
-#> 2 Resource_Class          Good   ContVar    55  9.906697 0.1300741
-#> 3 Resource_Class          Poor   ContVar    45 10.115384 0.1581813
+#>             Type Subpopulation Indicator nResp Estimate   StdError
+#> 1      All_Sites     All Sites   ContVar   100 10.09394 0.09919356
+#> 2 Resource_Class          Good   ContVar    55 10.15428 0.12675491
+#> 3 Resource_Class          Poor   ContVar    45  9.93302 0.14953279
 #>   MarginofError LCB95Pct UCB95Pct
-#> 1     0.2027661 9.760845 10.16638
-#> 2     0.2549405 9.651756 10.16164
-#> 3     0.3100297 9.805354 10.42541
+#> 1     0.1944158 9.899522 10.28835
+#> 2     0.2484351 9.905846 10.40272
+#> 3     0.2930789 9.639941 10.22610
 #> 
 #> $Total
 #> NULL
diff --git a/docs/reference/cont_cdfplot.html b/docs/reference/cont_cdfplot.html
index 65a9d27..5e479ef 100644
--- a/docs/reference/cont_cdfplot.html
+++ b/docs/reference/cont_cdfplot.html
@@ -21,7 +21,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -80,24 +80,24 @@ 
Create a PDF file containing cumulative distribution functions (CDF) plots
 
     

-    
cont_cdfplot(
-  pdffile = "cdf2x2.pdf",
-  cdfest,
-  units_cdf = "Percent",
-  ind_type = rep("Continuous", nind),
-  log = rep("", nind),
-  xlab = NULL,
-  ylab = NULL,
-  ylab_r = NULL,
-  legloc = NULL,
-  cdf_page = 4,
-  width = 10,
-  height = 8,
-  confcut = 0,
-  cex.main = 1.2,
-  cex.legend = 1,
-  ...
-)

+    
cont_cdfplot(
+  pdffile = "cdf2x2.pdf",
+  cdfest,
+  units_cdf = "Percent",
+  ind_type = rep("Continuous", nind),
+  log = rep("", nind),
+  xlab = NULL,
+  ylab = NULL,
+  ylab_r = NULL,
+  legloc = NULL,
+  cdf_page = 4,
+  width = 10,
+  height = 8,
+  confcut = 0,
+  cex.main = 1.2,
+  cex.legend = 1,
+  ...
+)

     

 
     

@@ -225,31 +225,31 @@ 
Author

 
     

     
Examples

-    
if (FALSE) {
-dframe <- data.frame(
-  siteID = paste0("Site", 1:100),
-  wgt = runif(100, 10, 100),
-  xcoord = runif(100),
-  ycoord = runif(100),
-  stratum = rep(c("Stratum1", "Stratum2"), 50),
-  ContVar = rnorm(100, 10, 1),
-  All_Sites = rep("All Sites", 100),
-  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
-)
-myvars <- c("ContVar")
-mysubpops <- c("All_Sites", "Resource_Class")
-mypopsize <- data.frame(
-  Resource_Class = c("Good", "Poor"),
-  Total = c(4000, 1500)
-)
-myanalysis <- cont_analysis(dframe,
-  vars = myvars, subpops = mysubpops,
-  siteID = "siteID", weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
-  stratumID = "stratum", popsize = mypopsize
-)
-cont_cdfplot("myanalysis.pdf", myanalysis$CDF, ylab_r = "Stream Length (km)")
-}
-
+    
if (FALSE) {
+dframe <- data.frame(
+  siteID = paste0("Site", 1:100),
+  wgt = runif(100, 10, 100),
+  xcoord = runif(100),
+  ycoord = runif(100),
+  stratum = rep(c("Stratum1", "Stratum2"), 50),
+  ContVar = rnorm(100, 10, 1),
+  All_Sites = rep("All Sites", 100),
+  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
+)
+myvars <- c("ContVar")
+mysubpops <- c("All_Sites", "Resource_Class")
+mypopsize <- data.frame(
+  Resource_Class = c("Good", "Poor"),
+  Total = c(4000, 1500)
+)
+myanalysis <- cont_analysis(dframe,
+  vars = myvars, subpops = mysubpops,
+  siteID = "siteID", weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
+  stratumID = "stratum", popsize = mypopsize
+)
+cont_cdfplot("myanalysis.pdf", myanalysis$CDF, ylab_r = "Stream Length (km)")
+}
+
 

     

   

diff --git a/docs/reference/cont_cdftest.html b/docs/reference/cont_cdftest.html
index 185fc95..b1fbfba 100644
--- a/docs/reference/cont_cdftest.html
+++ b/docs/reference/cont_cdftest.html
@@ -34,7 +34,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -106,30 +106,30 @@ 
Cumulative distribution function (CDF) inference for a probability survey
 
     

-    
cont_cdftest(
-  dframe,
-  vars,
-  subpops = NULL,
-  surveyID = NULL,
-  siteID = "siteID",
-  weight = "weight",
-  xcoord = NULL,
-  ycoord = NULL,
-  stratumID = NULL,
-  clusterID = NULL,
-  weight1 = NULL,
-  xcoord1 = NULL,
-  ycoord1 = NULL,
-  sizeweight = FALSE,
-  sweight = NULL,
-  sweight1 = NULL,
-  fpc = NULL,
-  popsize = NULL,
-  vartype = "Local",
-  jointprob = "overton",
-  testname = "adjWald",
-  nclass = 3
-)

+    
cont_cdftest(
+  dframe,
+  vars,
+  subpops = NULL,
+  surveyID = NULL,
+  siteID = "siteID",
+  weight = "weight",
+  xcoord = NULL,
+  ycoord = NULL,
+  stratumID = NULL,
+  clusterID = NULL,
+  weight1 = NULL,
+  xcoord1 = NULL,
+  ycoord1 = NULL,
+  sizeweight = FALSE,
+  sweight = NULL,
+  sweight1 = NULL,
+  fpc = NULL,
+  popsize = NULL,
+  vartype = "Local",
+  jointprob = "overton",
+  testname = "adjWald",
+  nclass = 3
+)

     

 
     

@@ -402,44 +402,44 @@ 
Author

 
     

     
Examples

-    
n <- 200
-mysiteID <- paste("Site", 1:n, sep = "")
-dframe <- data.frame(
-  siteID = mysiteID,
-  wgt = runif(n, 10, 100),
-  xcoord = runif(n),
-  ycoord = runif(n),
-  stratum = rep(c("Stratum1", "Stratum2"), n / 2),
-  Resource_Class = sample(c("Agr", "Forest", "Urban"), n, replace = TRUE)
-)
-ContVar <- numeric(n)
-tst <- dframe$Resource_Class == "Agr"
-ContVar[tst] <- rnorm(sum(tst), 10, 1)
-tst <- dframe$Resource_Class == "Forest"
-ContVar[tst] <- rnorm(sum(tst), 10.1, 1)
-tst <- dframe$Resource_Class == "Urban"
-ContVar[tst] <- rnorm(sum(tst), 10.5, 1)
-dframe$ContVar <- ContVar
-myvars <- c("ContVar")
-mysubpops <- c("Resource_Class")
-mypopsize <- data.frame(
-  Resource_Class = rep(c("Agr", "Forest", "Urban"), rep(2, 3)),
-  stratum = rep(c("Stratum1", "Stratum2"), 3),
-  Total = c(2500, 1500, 1000, 500, 600, 450)
-)
-cont_cdftest(dframe,
-  vars = myvars, subpops = mysubpops, siteID = "siteID",
-  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
-  stratumID = "stratum", popsize = mypopsize, testname = "RaoScott_First"
-)
+    
n <- 200
+mysiteID <- paste("Site", 1:n, sep = "")
+dframe <- data.frame(
+  siteID = mysiteID,
+  wgt = runif(n, 10, 100),
+  xcoord = runif(n),
+  ycoord = runif(n),
+  stratum = rep(c("Stratum1", "Stratum2"), n / 2),
+  Resource_Class = sample(c("Agr", "Forest", "Urban"), n, replace = TRUE)
+)
+ContVar <- numeric(n)
+tst <- dframe$Resource_Class == "Agr"
+ContVar[tst] <- rnorm(sum(tst), 10, 1)
+tst <- dframe$Resource_Class == "Forest"
+ContVar[tst] <- rnorm(sum(tst), 10.1, 1)
+tst <- dframe$Resource_Class == "Urban"
+ContVar[tst] <- rnorm(sum(tst), 10.5, 1)
+dframe$ContVar <- ContVar
+myvars <- c("ContVar")
+mysubpops <- c("Resource_Class")
+mypopsize <- data.frame(
+  Resource_Class = rep(c("Agr", "Forest", "Urban"), rep(2, 3)),
+  stratum = rep(c("Stratum1", "Stratum2"), 3),
+  Total = c(2500, 1500, 1000, 500, 600, 450)
+)
+cont_cdftest(dframe,
+  vars = myvars, subpops = mysubpops, siteID = "siteID",
+  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
+  stratumID = "stratum", popsize = mypopsize, testname = "RaoScott_First"
+)
 #>             Type Subpopulation_1 Subpopulation_2 Indicator
 #> 1 Resource_Class             Agr          Forest   ContVar
 #> 2 Resource_Class             Agr           Urban   ContVar
 #> 3 Resource_Class          Forest           Urban   ContVar
-#>   Rao-Scott First Order Statistic Degrees_of_Freedom      p_Value
-#> 1                        2.178944                  2 0.3708324911
-#> 2                        7.020577                  2 0.0180998017
-#> 3                       20.586927                  2 0.0001627683
+#>   Rao-Scott First Order Statistic Degrees_of_Freedom     p_Value
+#> 1                        0.512239                  2 0.822142193
+#> 2                        8.691382                  2 0.009796616
+#> 3                       14.275884                  2 0.005918207
 

     

   

diff --git a/docs/reference/cov_panel_dsgn.html b/docs/reference/cov_panel_dsgn.html
index 1b6ba17..a608f6c 100644
--- a/docs/reference/cov_panel_dsgn.html
+++ b/docs/reference/cov_panel_dsgn.html
@@ -21,7 +21,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -80,16 +80,16 @@ 
Create a covariance matrix for a panel design
- 
cov_panel_dsgn(
-  paneldsgn = matrix(50, 1, 10),
-  nrepeats = 1,
-  unit_var = NULL,
-  period_var = NULL,
-  unitperiod_var = NULL,
-  index_var = NULL,
-  unit_rho = 1,
-  period_rho = 0
-)
+ 
cov_panel_dsgn(
+  paneldsgn = matrix(50, 1, 10),
+  nrepeats = 1,
+  unit_var = NULL,
+  period_var = NULL,
+  unitperiod_var = NULL,
+  index_var = NULL,
+  unit_rho = 1,
+  period_rho = 0
+)
diff --git a/docs/reference/diffrisk_analysis.html b/docs/reference/diffrisk_analysis.html index da783f8..403dafe 100644 --- a/docs/reference/diffrisk_analysis.html +++ b/docs/reference/diffrisk_analysis.html @@ -23,7 +23,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -84,31 +84,31 @@

Risk difference analysis

- 
diffrisk_analysis(
-  dframe,
-  vars_response,
-  vars_stressor,
-  response_levels = NULL,
-  stressor_levels = NULL,
-  subpops = NULL,
-  siteID = NULL,
-  weight = "weight",
-  xcoord = NULL,
-  ycoord = NULL,
-  stratumID = NULL,
-  clusterID = NULL,
-  weight1 = NULL,
-  xcoord1 = NULL,
-  ycoord1 = NULL,
-  sizeweight = FALSE,
-  sweight = NULL,
-  sweight1 = NULL,
-  fpc = NULL,
-  popsize = NULL,
-  vartype = "Local",
-  conf = 95,
-  All_Sites = FALSE
-)
+ 
diffrisk_analysis(
+  dframe,
+  vars_response,
+  vars_stressor,
+  response_levels = NULL,
+  stressor_levels = NULL,
+  subpops = NULL,
+  siteID = NULL,
+  weight = "weight",
+  xcoord = NULL,
+  ycoord = NULL,
+  stratumID = NULL,
+  clusterID = NULL,
+  weight1 = NULL,
+  xcoord1 = NULL,
+  ycoord1 = NULL,
+  sizeweight = FALSE,
+  sweight = NULL,
+  sweight1 = NULL,
+  fpc = NULL,
+  popsize = NULL,
+  vartype = "Local",
+  conf = 95,
+  All_Sites = FALSE
+)
@@ -147,7 +147,9 @@

Arguments

contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_response argument and that uses values in the vars_response argument as names -for the list. The default value is NULL.

+for the list. If response_levels is provided without names, +then the names of response_levels are set to vars_response. +The default value is NULL.

stressor_levels
@@ -161,7 +163,9 @@

Arguments

contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_stressor argument and that uses values in the vars_stressor argument as names -for the list. The default value is NULL.

+for the list. If stressor_levels is provided without names, +then the names of stressor_levels are set to vars_stressor. +The default value is NULL.

subpops
@@ -500,62 +504,62 @@

Author

Examples

- 
dframe <- data.frame(
-  siteID = paste0("Site", 1:100),
-  wgt = runif(100, 10, 100),
-  xcoord = runif(100),
-  ycoord = runif(100),
-  stratum = rep(c("Stratum1", "Stratum2"), 50),
-  RespVar1 = sample(c("Poor", "Good"), 100, replace = TRUE),
-  RespVar2 = sample(c("Poor", "Good"), 100, replace = TRUE),
-  StressVar = sample(c("Poor", "Good"), 100, replace = TRUE),
-  All_Sites = rep("All Sites", 100),
-  Resource_Class = rep(c("Agr", "Forest"), c(55, 45))
-)
-myresponse <- c("RespVar1", "RespVar2")
-mystressor <- c("StressVar")
-mysubpops <- c("All_Sites", "Resource_Class")
-diffrisk_analysis(dframe,
-  vars_response = myresponse,
-  vars_stressor = mystressor, subpops = mysubpops, siteID = "siteID",
-  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
-  stratumID = "stratum"
-)
+    
dframe <- data.frame(
+  siteID = paste0("Site", 1:100),
+  wgt = runif(100, 10, 100),
+  xcoord = runif(100),
+  ycoord = runif(100),
+  stratum = rep(c("Stratum1", "Stratum2"), 50),
+  RespVar1 = sample(c("Poor", "Good"), 100, replace = TRUE),
+  RespVar2 = sample(c("Poor", "Good"), 100, replace = TRUE),
+  StressVar = sample(c("Poor", "Good"), 100, replace = TRUE),
+  All_Sites = rep("All Sites", 100),
+  Resource_Class = rep(c("Agr", "Forest"), c(55, 45))
+)
+myresponse <- c("RespVar1", "RespVar2")
+mystressor <- c("StressVar")
+mysubpops <- c("All_Sites", "Resource_Class")
+diffrisk_analysis(dframe,
+  vars_response = myresponse,
+  vars_stressor = mystressor, subpops = mysubpops, siteID = "siteID",
+  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
+  stratumID = "stratum"
+)
 #>             Type Subpopulation Response  Stressor nResp     Estimate
-#> 1      All_Sites     All Sites RespVar1 StressVar   100  0.018690392
-#> 2      All_Sites     All Sites RespVar2 StressVar   100  0.021324043
-#> 3 Resource_Class           Agr RespVar1 StressVar    55 -0.002991139
-#> 4 Resource_Class        Forest RespVar1 StressVar    45  0.044662377
-#> 5 Resource_Class           Agr RespVar2 StressVar    55  0.050662698
-#> 6 Resource_Class        Forest RespVar2 StressVar    45 -0.027243258
+#> 1      All_Sites     All Sites RespVar1 StressVar   100 -0.037973184
+#> 2      All_Sites     All Sites RespVar2 StressVar   100  0.080765266
+#> 3 Resource_Class           Agr RespVar1 StressVar    55  0.077373530
+#> 4 Resource_Class        Forest RespVar1 StressVar    45 -0.160101785
+#> 5 Resource_Class           Agr RespVar2 StressVar    55 -0.002120854
+#> 6 Resource_Class        Forest RespVar2 StressVar    45  0.220818943
 #>   Estimate_StressPoor Estimate_StressGood   StdError MarginofError   LCB95Pct
-#> 1           0.4773413           0.4586509 0.09473713     0.1856814 -0.1669910
-#> 2           0.5245306           0.5032066 0.09773530     0.1915577 -0.1702336
-#> 3           0.4779974           0.4809885 0.13677727     0.2680785 -0.2710697
-#> 4           0.4763957           0.4317333 0.13537327     0.2653267 -0.2206643
-#> 5           0.5804472           0.5297845 0.13583216     0.2662261 -0.2155634
-#> 6           0.4439360           0.4711793 0.14921609     0.2924582 -0.3197014
+#> 1           0.4277709           0.4657441 0.09165693     0.1796443 -0.2176175
+#> 2           0.5730444           0.4922791 0.09514689     0.1864845 -0.1057192
+#> 3           0.4477905           0.3704169 0.12564736     0.2462643 -0.1688908
+#> 4           0.3925336           0.5526354 0.14062332     0.2756167 -0.4357184
+#> 5           0.4970375           0.4991584 0.13590982     0.2663783 -0.2684992
+#> 6           0.7068276           0.4860086 0.13624365     0.2670326 -0.0462137
 #>    UCB95Pct WeightTotal Count_RespPoor_StressPoor Count_RespPoor_StressGood
-#> 1 0.2043718    5527.073                        29                        21
-#> 2 0.2128817    5527.073                        30                        24
-#> 3 0.2650874    3155.809                        17                        11
-#> 4 0.3099891    2371.264                        12                        10
-#> 5 0.3168888    3155.809                        19                        12
-#> 6 0.2652149    2371.264                        11                        12
+#> 1 0.1416711    5404.906                        23                        23
+#> 2 0.2672497    5404.906                        26                        22
+#> 3 0.3236378    2997.766                        15                         9
+#> 4 0.1155149    2407.140                         8                        14
+#> 5 0.2642575    2997.766                        15                        10
+#> 6 0.4878516    2407.140                        11                        12
 #>   Count_RespGood_StressPoor Count_RespGood_StressGood Prop_RespPoor_StressPoor
-#> 1                        26                        24                0.2662085
-#> 2                        25                        21                0.2925256
-#> 3                        14                        13                0.2756391
-#> 4                        12                        11                0.2536578
-#> 5                        12                        12                0.3347172
-#> 6                        13                         9                0.2363746
+#> 1                        26                        28                0.2068686
+#> 2                        23                        29                0.2771224
+#> 3                        16                        15                0.2489805
+#> 4                        10                        13                0.1544240
+#> 5                        16                        14                0.2763628
+#> 6                         7                        15                0.2780682
 #>   Prop_RespPoor_StressGood Prop_RespGood_StressPoor Prop_RespGood_StressGood
-#> 1                0.2028658                0.2914816                0.2394440
-#> 2                0.2225732                0.2651646                0.2197366
-#> 3                0.2036245                0.3010149                0.2197214
-#> 4                0.2018560                0.2787942                0.2656920
-#> 5                0.2242821                0.2419368                0.1990638
-#> 6                0.2202989                0.2960774                0.2472491
+#> 1                0.2405118                0.2767281                0.2758915
+#> 2                0.2542146                0.2064743                0.2621887
+#> 3                0.1644577                0.3070396                0.2795222
+#> 4                0.3352269                0.2389792                0.2713699
+#> 5                0.2216163                0.2796572                0.2223636
+#> 6                0.2948113                0.1153350                0.3117855
diff --git a/docs/reference/errorprnt.html b/docs/reference/errorprnt.html index b4465da..bfd74c9 100644 --- a/docs/reference/errorprnt.html +++ b/docs/reference/errorprnt.html @@ -17,7 +17,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -72,7 +72,7 @@

Print errors from analysis functions

- 
errorprnt(error_vec = get("error_vec", envir = .GlobalEnv))
+ 
errorprnt(error_vec = get("error_vec", envir = .GlobalEnv))
diff --git a/docs/reference/grts.html b/docs/reference/grts.html index b37356d..8c78413 100644 --- a/docs/reference/grts.html +++ b/docs/reference/grts.html @@ -25,7 +25,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -88,30 +88,30 @@

Select a generalized random tessellation stratified (GRTS) sample

- 
grts(
-  sframe,
-  n_base,
-  stratum_var = NULL,
-  seltype = NULL,
-  caty_var = NULL,
-  caty_n = NULL,
-  aux_var = NULL,
-  legacy_var = NULL,
-  legacy_sites = NULL,
-  legacy_stratum_var = NULL,
-  legacy_caty_var = NULL,
-  legacy_aux_var = NULL,
-  mindis = NULL,
-  maxtry = 10,
-  n_over = NULL,
-  n_near = NULL,
-  wgt_units = NULL,
-  pt_density = NULL,
-  DesignID = "Site",
-  SiteBegin = 1,
-  sep = "-",
-  projcrs_check = TRUE
-)
+ 
grts(
+  sframe,
+  n_base,
+  stratum_var = NULL,
+  seltype = NULL,
+  caty_var = NULL,
+  caty_n = NULL,
+  aux_var = NULL,
+  legacy_var = NULL,
+  legacy_sites = NULL,
+  legacy_stratum_var = NULL,
+  legacy_caty_var = NULL,
+  legacy_aux_var = NULL,
+  mindis = NULL,
+  maxtry = 10,
+  n_over = NULL,
+  n_near = NULL,
+  wgt_units = NULL,
+  pt_density = NULL,
+  DesignID = "Site",
+  SiteBegin = 1,
+  sep = "-",
+  projcrs_check = TRUE
+)
@@ -279,7 +279,9 @@

Arguments

If replacement sites are not desired for a particular stratum, then the corresponding value in n_over should be 0 or NULL (which is equivalent to 0). If the sampling design is stratified but the number of n_over sites is the same in each - stratum, n_over can be a vector which is used for each stratum. Note that if the + stratum, n_over can be a vector which is used for each stratum. + If n_over is an unnamed, length-one vector, it's value is recycled + and used for each stratum. Note that if the sampling design has unequal selection probabilities (seltype = "unequal"), then n_over sites are given the same proportion of caty_n values as n_base.

@@ -479,15 +481,15 @@

Author

Examples

- 
if (FALSE) {
-samp <- grts(NE_Lakes, n_base = 100)
-print(samp)
-strata_n <- c(low = 25, high = 30)
-samp_strat <- grts(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
-print(samp_strat)
-samp_over <- grts(NE_Lakes, n_base = 30, n_over = 5)
-print(samp_over)
-}
+    
if (FALSE) {
+samp <- grts(NE_Lakes, n_base = 100)
+print(samp)
+strata_n <- c(low = 25, high = 30)
+samp_strat <- grts(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
+print(samp_strat)
+samp_over <- grts(NE_Lakes, n_base = 30, n_over = 5)
+print(samp_over)
+}
diff --git a/docs/reference/index.html b/docs/reference/index.html index c3277dc..d3dc8c9 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -73,6 +73,10 @@

All functions adjwgt()

Adjust survey design weights by categories

+ +

adjwgtNR()

+ +

Adjust survey design weights for non-response by categories

ash1_wgt()

diff --git a/docs/reference/irs.html b/docs/reference/irs.html index 79e685b..0952113 100644 --- a/docs/reference/irs.html +++ b/docs/reference/irs.html @@ -24,7 +24,7 @@ spsurvey - 5.4.1 + 5.5.0

@@ -86,30 +86,30 @@

Select an independent random sample (IRS)

- 
irs(
-  sframe,
-  n_base,
-  stratum_var = NULL,
-  seltype = NULL,
-  caty_var = NULL,
-  caty_n = NULL,
-  aux_var = NULL,
-  legacy_var = NULL,
-  legacy_sites = NULL,
-  legacy_stratum_var = NULL,
-  legacy_caty_var = NULL,
-  legacy_aux_var = NULL,
-  mindis = NULL,
-  maxtry = 10,
-  n_over = NULL,
-  n_near = NULL,
-  wgt_units = NULL,
-  pt_density = NULL,
-  DesignID = "Site",
-  SiteBegin = 1,
-  sep = "-",
-  projcrs_check = TRUE
-)
+ 
irs(
+  sframe,
+  n_base,
+  stratum_var = NULL,
+  seltype = NULL,
+  caty_var = NULL,
+  caty_n = NULL,
+  aux_var = NULL,
+  legacy_var = NULL,
+  legacy_sites = NULL,
+  legacy_stratum_var = NULL,
+  legacy_caty_var = NULL,
+  legacy_aux_var = NULL,
+  mindis = NULL,
+  maxtry = 10,
+  n_over = NULL,
+  n_near = NULL,
+  wgt_units = NULL,
+  pt_density = NULL,
+  DesignID = "Site",
+  SiteBegin = 1,
+  sep = "-",
+  projcrs_check = TRUE
+)
@@ -277,7 +277,9 @@

Arguments

If replacement sites are not desired for a particular stratum, then the corresponding value in n_over should be 0 or NULL (which is equivalent to 0). If the sampling design is stratified but the number of n_over sites is the same in each - stratum, n_over can be a vector which is used for each stratum. Note that if the + stratum, n_over can be a vector which is used for each stratum. + If n_over is an unnamed, length-one vector, it's value is recycled + and used for each stratum. Note that if the sampling design has unequal selection probabilities (seltype = "unequal"), then n_over sites are given the same proportion of caty_n values as n_base.

@@ -472,15 +474,15 @@

Author

Examples

- 
if (FALSE) {
-samp <- irs(NE_Lakes, n_base = 100)
-print(samp)
-strata_n <- c(low = 25, high = 30)
-samp_strat <- irs(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
-print(samp_strat)
-samp_over <- irs(NE_Lakes, n_base = 30, n_over = 5)
-print(samp_over)
-}
+    
if (FALSE) {
+samp <- irs(NE_Lakes, n_base = 100)
+print(samp)
+strata_n <- c(low = 25, high = 30)
+samp_strat <- irs(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
+print(samp_strat)
+samp_over <- irs(NE_Lakes, n_base = 30, n_over = 5)
+print(samp_over)
+}
diff --git a/docs/reference/localmean_cov.html b/docs/reference/localmean_cov.html index 89ef0cd..d2c2862 100644 --- a/docs/reference/localmean_cov.html +++ b/docs/reference/localmean_cov.html @@ -18,7 +18,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -74,7 +74,7 @@

Internal Function: Variance-Covariance Matrix Based on Local Mean Estimator<
- 
localmean_cov(zmat, weight_1st)
+ 
localmean_cov(zmat, weight_1st)
diff --git a/docs/reference/localmean_var.html b/docs/reference/localmean_var.html index 01b17aa..339f6a0 100644 --- a/docs/reference/localmean_var.html +++ b/docs/reference/localmean_var.html @@ -17,7 +17,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -72,7 +72,7 @@

Internal Function: Local Mean Variance Estimator

- 
localmean_var(z, weight_1st)
+ 
localmean_var(z, weight_1st)
diff --git a/docs/reference/localmean_weight.html b/docs/reference/localmean_weight.html index 9a2f472..bebc451 100644 --- a/docs/reference/localmean_weight.html +++ b/docs/reference/localmean_weight.html @@ -18,7 +18,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -74,7 +74,7 @@

Internal Function: Local Mean Variance Neighbors and Weights

- 
localmean_weight(x, y, prb, nbh = 4)
+ 
localmean_weight(x, y, prb, nbh = 4)
diff --git a/docs/reference/pd_summary.html b/docs/reference/pd_summary.html index 44945ee..fa9db6b 100644 --- a/docs/reference/pd_summary.html +++ b/docs/reference/pd_summary.html @@ -21,7 +21,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -80,7 +80,7 @@

Summary characteristics of a panel revisit design

- 
pd_summary(object, visitdsgn = NULL, ...)
+ 
pd_summary(object, visitdsgn = NULL, ...)
@@ -151,12 +151,12 @@

Author

Examples

- 
# Serially alternating panel revisit design summary
-sa_dsgn <- revisit_dsgn(20, panels = list(SA60N = list(
-  n = 60, pnl_dsgn = c(1, 4),
-  pnl_n = NA, start_option = "None"
-)), begin = 1)
-pd_summary(sa_dsgn)
+    
# Serially alternating panel revisit design summary
+sa_dsgn <- revisit_dsgn(20, panels = list(SA60N = list(
+  n = 60, pnl_dsgn = c(1, 4),
+  pnl_n = NA, start_option = "None"
+)), begin = 1)
+pd_summary(sa_dsgn)
 #> $n_panel
 #> [1] 5
 #> 
@@ -182,11 +182,11 @@ 
Examples

 #>   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
 #>  60 120 180 240 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 
 #> 
-# Add visit design where first panel is sampled twice at every time period
-sa_visit <- sa_dsgn
-sa_visit[sa_visit > 0] <- 1
-sa_visit[1, sa_visit[1, ] > 0] <- 2
-pd_summary(sa_dsgn, sa_visit)
+# Add visit design where first panel is sampled twice at every time period
+sa_visit <- sa_dsgn
+sa_visit[sa_visit > 0] <- 1
+sa_visit[1, sa_visit[1, ] > 0] <- 2
+pd_summary(sa_dsgn, sa_visit)
 #> $n_panel
 #> [1] 5
 #> 
diff --git a/docs/reference/plot.html b/docs/reference/plot.html
index 2825878..e4a8568 100644
--- a/docs/reference/plot.html
+++ b/docs/reference/plot.html
@@ -25,7 +25,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -81,41 +81,41 @@ 
Plot sampling frames, design sites, and analysis data.

 are of the distributions of the right-hand side variables. If the left-hand side
 of the variable contains a variable, plots are of the left-hand size variable
 for each level of each right-hand side variable.
-This function is largely built on plot.sf(), and all spsurvey plotting
-methods can supply additional arguments to plot.sf(). For more information on
+This function is largely built on plot.sf(), and all spsurvey plotting
+methods can supply additional arguments to plot.sf(). For more information on
 plotting in sf, run ?sf::plot.sf(). Equivalent to sp_plot(); both
 are currently maintained for backwards compatibility.
- 
# S3 method for sp_frame
-plot(
-  x,
-  formula = ~1,
-  xcoord,
-  ycoord,
-  crs,
-  var_args = NULL,
-  varlevel_args = NULL,
-  geom = FALSE,
-  onlyshow = NULL,
-  fix_bbox = TRUE,
-  ...
-)
-
-# S3 method for sp_design
-plot(
-  x,
-  sframe = NULL,
-  formula = ~siteuse,
-  siteuse = NULL,
-  var_args = NULL,
-  varlevel_args = NULL,
-  geom = FALSE,
-  onlyshow = NULL,
-  fix_bbox = TRUE,
-  ...
-)
+ 
# S3 method for sp_frame
+plot(
+  x,
+  formula = ~1,
+  xcoord,
+  ycoord,
+  crs,
+  var_args = NULL,
+  varlevel_args = NULL,
+  geom = FALSE,
+  onlyshow = NULL,
+  fix_bbox = TRUE,
+  ...
+)
+
+# S3 method for sp_design
+plot(
+  x,
+  sframe = NULL,
+  formula = ~siteuse,
+  siteuse = NULL,
+  var_args = NULL,
+  varlevel_args = NULL,
+  geom = FALSE,
+  onlyshow = NULL,
+  fix_bbox = TRUE,
+  ...
+)
@@ -198,7 +198,7 @@

Arguments

...
-

Additional arguments to pass to plot.sf().

+

Additional arguments to pass to plot.sf().

sframe
@@ -225,13 +225,13 @@

Author

Examples

- 
if (FALSE) {
-data("NE_Lakes")
-NE_Lakes <- sp_frame(NE_Lakes)
-plot(NE_Lakes, formula = ~ELEV_CAT)
-sample <- grts(NE_Lakes, 30)
-plot(sample, NE_Lakes)
-}
+    
if (FALSE) {
+data("NE_Lakes")
+NE_Lakes <- sp_frame(NE_Lakes)
+plot(NE_Lakes, formula = ~ELEV_CAT)
+sample <- grts(NE_Lakes, 30)
+plot(sample, NE_Lakes)
+}
diff --git a/docs/reference/plot.sp_CDF.html b/docs/reference/plot.sp_CDF.html index b26f9c5..224d42c 100644 --- a/docs/reference/plot.sp_CDF.html +++ b/docs/reference/plot.sp_CDF.html @@ -20,7 +20,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -78,26 +78,26 @@

Plot a cumulative distribution function (CDF)

- 
# S3 method for sp_CDF
-plot(
-  x,
-  var = NULL,
-  subpop = NULL,
-  subpop_level = NULL,
-  units_cdf = "Percent",
-  type_cdf = "Continuous",
-  log = "",
-  xlab = NULL,
-  ylab = NULL,
-  ylab_r = NULL,
-  main = NULL,
-  legloc = NULL,
-  confcut = 0,
-  conflev = 95,
-  cex.main = 1.2,
-  cex.legend = 1,
-  ...
-)
+ 
# S3 method for sp_CDF
+plot(
+  x,
+  var = NULL,
+  subpop = NULL,
+  subpop_level = NULL,
+  units_cdf = "Percent",
+  type_cdf = "Continuous",
+  log = "",
+  xlab = NULL,
+  ylab = NULL,
+  ylab_r = NULL,
+  main = NULL,
+  legloc = NULL,
+  confcut = 0,
+  conflev = 95,
+  cex.main = 1.2,
+  cex.legend = 1,
+  ...
+)
@@ -239,42 +239,42 @@

Author

Examples

- 
if (FALSE) {
-dframe <- data.frame(
-  siteID = paste0("Site", 1:100),
-  wgt = runif(100, 10, 100),
-  xcoord = runif(100),
-  ycoord = runif(100),
-  stratum = rep(c("Stratum1", "Stratum2"), 50),
-  ContVar = rnorm(100, 10, 1),
-  All_Sites = rep("All Sites", 100),
-  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
-)
-myvars <- c("ContVar")
-mysubpops <- c("All_Sites", "Resource_Class")
-mypopsize <- data.frame(
-  Resource_Class = c("Good", "Poor"),
-  Total = c(4000, 1500)
-)
-myanalysis <- cont_analysis(dframe,
-  vars = myvars, subpops = mysubpops,
-  siteID = "siteID", weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
-  stratumID = "stratum", popsize = mypopsize
-)
-keep <- with(myanalysis$CDF, Type == "Resource_Class" &
-  Subpopulation == "Good")
-par(mfrow = c(2, 1))
-plot(myanalysis$CDF[keep, ],
-  xlab = "ContVar",
-  ylab = "Percent of Stream Length", ylab_r = "Stream Length (km)",
-  main = "Estimates for Resource Class: Good"
-)
-plot(myanalysis$CDF[keep, ],
-  xlab = "ContVar",
-  ylab = "Percent of Stream Length", ylab_r = "Same",
-  main = "Estimates for Resource Class: Good"
-)
-}
+    
if (FALSE) {
+dframe <- data.frame(
+  siteID = paste0("Site", 1:100),
+  wgt = runif(100, 10, 100),
+  xcoord = runif(100),
+  ycoord = runif(100),
+  stratum = rep(c("Stratum1", "Stratum2"), 50),
+  ContVar = rnorm(100, 10, 1),
+  All_Sites = rep("All Sites", 100),
+  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
+)
+myvars <- c("ContVar")
+mysubpops <- c("All_Sites", "Resource_Class")
+mypopsize <- data.frame(
+  Resource_Class = c("Good", "Poor"),
+  Total = c(4000, 1500)
+)
+myanalysis <- cont_analysis(dframe,
+  vars = myvars, subpops = mysubpops,
+  siteID = "siteID", weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
+  stratumID = "stratum", popsize = mypopsize
+)
+keep <- with(myanalysis$CDF, Type == "Resource_Class" &
+  Subpopulation == "Good")
+par(mfrow = c(2, 1))
+plot(myanalysis$CDF[keep, ],
+  xlab = "ContVar",
+  ylab = "Percent of Stream Length", ylab_r = "Stream Length (km)",
+  main = "Estimates for Resource Class: Good"
+)
+plot(myanalysis$CDF[keep, ],
+  xlab = "ContVar",
+  ylab = "Percent of Stream Length", ylab_r = "Same",
+  main = "Estimates for Resource Class: Good"
+)
+}
diff --git a/docs/reference/power_dsgn.html b/docs/reference/power_dsgn.html index 363f10c..77df7ab 100644 --- a/docs/reference/power_dsgn.html +++ b/docs/reference/power_dsgn.html @@ -21,7 +21,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -80,23 +80,23 @@

Power calculation for multiple panel designs

- 
power_dsgn(
-  ind_names,
-  ind_values,
-  unit_var,
-  period_var,
-  unitperiod_var,
-  index_var,
-  unit_rho = 1,
-  period_rho = 0,
-  paneldsgn,
-  nrepeats = NULL,
-  trend_type = "mean",
-  ind_pct = NULL,
-  ind_tail = NULL,
-  trend = 2,
-  alpha = 0.05
-)
+ 
power_dsgn(
+  ind_names,
+  ind_values,
+  unit_var,
+  period_var,
+  unitperiod_var,
+  index_var,
+  unit_rho = 1,
+  period_rho = 0,
+  paneldsgn,
+  nrepeats = NULL,
+  trend_type = "mean",
+  ind_pct = NULL,
+  ind_tail = NULL,
+  trend = 2,
+  alpha = 0.05
+)
@@ -237,18 +237,18 @@

Author

Examples

- 
# Power for rotating panel with sample size 60
-power_dsgn("Variable_Name",
-  ind_values = 43, unit_var = 280, period_var = 4,
-  unitperiod_var = 40, index_var = 90, unit_rho = 1, period_rho = 0,
-  paneldsgn = list(NoR60 = revisit_dsgn(20,
-    panels = list(NoR60 = list(
-      n = 60, pnl_dsgn = c(1, NA),
-      pnl_n = NA, start_option = "None"
-    )), begin = 1
-  )),
-  nrepeats = NULL, trend_type = "mean", trend = 1.0, alpha = 0.05
-)
+    
# Power for rotating panel with sample size 60
+power_dsgn("Variable_Name",
+  ind_values = 43, unit_var = 280, period_var = 4,
+  unitperiod_var = 40, index_var = 90, unit_rho = 1, period_rho = 0,
+  paneldsgn = list(NoR60 = revisit_dsgn(20,
+    panels = list(NoR60 = list(
+      n = 60, pnl_dsgn = c(1, NA),
+      pnl_n = NA, start_option = "None"
+    )), begin = 1
+  )),
+  nrepeats = NULL, trend_type = "mean", trend = 1.0, alpha = 0.05
+)
 #> $design
 #> [1] "NoR60"
 #> 
diff --git a/docs/reference/ppd_plot.html b/docs/reference/ppd_plot.html
index d71a13b..464e403 100644
--- a/docs/reference/ppd_plot.html
+++ b/docs/reference/ppd_plot.html
@@ -23,7 +23,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -84,19 +84,19 @@ 
Plot power curves for panel designs
- 
ppd_plot(
-  object,
-  plot_type = "standard",
-  trend_type = "mean",
-  xaxis_type = "period",
-  comp_type = "design",
-  dsgns = NULL,
-  indicator = NULL,
-  trend = NULL,
-  period = NULL,
-  alpha = NULL,
-  ...
-)
+ 
ppd_plot(
+  object,
+  plot_type = "standard",
+  trend_type = "mean",
+  xaxis_type = "period",
+  comp_type = "design",
+  dsgns = NULL,
+  indicator = NULL,
+  trend = NULL,
+  period = NULL,
+  alpha = NULL,
+  ...
+)
@@ -224,37 +224,37 @@

Author

Examples

- 
if (FALSE) {
-# Construct a rotating panel design with sample size of 60
-R60N <- revisit_dsgn(20, panels = list(R60N = list(
-  n = 60, pnl_dsgn = c(1, NA),
-  pnl_n = NA, start_option = "None"
-)), begin = 1)
-
-# Construct a fixed panel design with sample size of 60
-F60 <- revisit_dsgn(20, panels = list(F60 = list(
-  n = 60, pnl_dsgn = c(1, 0),
-  pnl_n = NA, start_option = "None"
-)), begin = 1)
-
-# Power for rotating panel with sample size 60
-Power_tst <- power_dsgn("Variable_Name",
-  ind_values = 43, unit_var = 280,
-  period_var = 4, unitperiod_var = 40, index_var = 90,
-  unit_rho = 1, period_rho = 0, paneldsgn = list(
-    R60N = R60N, F60 = F60
-  ), nrepeats = NULL,
-  trend_type = "mean", trend = c(1.0, 2.0), alpha = 0.05
-)
-ppd_plot(Power_tst)
-ppd_plot(Power_tst, dsgns = c("F60", "R60N"))
-ppd_plot(Power_tst, dsgns = c("F60", "R60N"), trend = 1.0)
-ppd_plot(Power_tst,
-  plot_type = "relative", comp_type = "design",
-  trend_type = "mean", trend = c(1, 2), dsgns = c("R60N", "F60"),
-  indicator = "Variable_Name"
-)
-}
+    
if (FALSE) {
+# Construct a rotating panel design with sample size of 60
+R60N <- revisit_dsgn(20, panels = list(R60N = list(
+  n = 60, pnl_dsgn = c(1, NA),
+  pnl_n = NA, start_option = "None"
+)), begin = 1)
+
+# Construct a fixed panel design with sample size of 60
+F60 <- revisit_dsgn(20, panels = list(F60 = list(
+  n = 60, pnl_dsgn = c(1, 0),
+  pnl_n = NA, start_option = "None"
+)), begin = 1)
+
+# Power for rotating panel with sample size 60
+Power_tst <- power_dsgn("Variable_Name",
+  ind_values = 43, unit_var = 280,
+  period_var = 4, unitperiod_var = 40, index_var = 90,
+  unit_rho = 1, period_rho = 0, paneldsgn = list(
+    R60N = R60N, F60 = F60
+  ), nrepeats = NULL,
+  trend_type = "mean", trend = c(1.0, 2.0), alpha = 0.05
+)
+ppd_plot(Power_tst)
+ppd_plot(Power_tst, dsgns = c("F60", "R60N"))
+ppd_plot(Power_tst, dsgns = c("F60", "R60N"), trend = 1.0)
+ppd_plot(Power_tst,
+  plot_type = "relative", comp_type = "design",
+  trend_type = "mean", trend = c(1, 2), dsgns = c("R60N", "F60"),
+  indicator = "Variable_Name"
+)
+}
diff --git a/docs/reference/relrisk_analysis.html b/docs/reference/relrisk_analysis.html index f05a385..3584a52 100644 --- a/docs/reference/relrisk_analysis.html +++ b/docs/reference/relrisk_analysis.html @@ -23,7 +23,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -84,31 +84,31 @@

Relative risk analysis

- 
relrisk_analysis(
-  dframe,
-  vars_response,
-  vars_stressor,
-  response_levels = NULL,
-  stressor_levels = NULL,
-  subpops = NULL,
-  siteID = NULL,
-  weight = "weight",
-  xcoord = NULL,
-  ycoord = NULL,
-  stratumID = NULL,
-  clusterID = NULL,
-  weight1 = NULL,
-  xcoord1 = NULL,
-  ycoord1 = NULL,
-  sizeweight = FALSE,
-  sweight = NULL,
-  sweight1 = NULL,
-  fpc = NULL,
-  popsize = NULL,
-  vartype = "Local",
-  conf = 95,
-  All_Sites = FALSE
-)
+ 
relrisk_analysis(
+  dframe,
+  vars_response,
+  vars_stressor,
+  response_levels = NULL,
+  stressor_levels = NULL,
+  subpops = NULL,
+  siteID = NULL,
+  weight = "weight",
+  xcoord = NULL,
+  ycoord = NULL,
+  stratumID = NULL,
+  clusterID = NULL,
+  weight1 = NULL,
+  xcoord1 = NULL,
+  ycoord1 = NULL,
+  sizeweight = FALSE,
+  sweight = NULL,
+  sweight1 = NULL,
+  fpc = NULL,
+  popsize = NULL,
+  vartype = "Local",
+  conf = 95,
+  All_Sites = FALSE
+)
@@ -147,7 +147,9 @@

Arguments

contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_response argument and that uses values in the vars_response argument as names -for the list. The default value is NULL.

+for the list. If response_levels is provided without names, +then the names of response_levels are set to vars_response. +The default value is NULL.

stressor_levels
@@ -161,7 +163,9 @@

Arguments

contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_stressor argument and that uses values in the vars_stressor argument as names -for the list. The default value is NULL.

+for the list. If stressor_levels is provided without names, +then the names of stressor_levels are set to vars_stressor. +The default value is NULL.

subpops
@@ -499,62 +503,62 @@

Author

Examples

- 
dframe <- data.frame(
-  siteID = paste0("Site", 1:100),
-  wgt = runif(100, 10, 100),
-  xcoord = runif(100),
-  ycoord = runif(100),
-  stratum = rep(c("Stratum1", "Stratum2"), 50),
-  RespVar1 = sample(c("Poor", "Good"), 100, replace = TRUE),
-  RespVar2 = sample(c("Poor", "Good"), 100, replace = TRUE),
-  StressVar = sample(c("Poor", "Good"), 100, replace = TRUE),
-  All_Sites = rep("All Sites", 100),
-  Resource_Class = rep(c("Agr", "Forest"), c(55, 45))
-)
-myresponse <- c("RespVar1", "RespVar2")
-mystressor <- c("StressVar")
-mysubpops <- c("All_Sites", "Resource_Class")
-relrisk_analysis(dframe,
-  vars_response = myresponse,
-  vars_stressor = mystressor, subpops = mysubpops, siteID = "siteID",
-  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
-  stratumID = "stratum"
-)
+    
dframe <- data.frame(
+  siteID = paste0("Site", 1:100),
+  wgt = runif(100, 10, 100),
+  xcoord = runif(100),
+  ycoord = runif(100),
+  stratum = rep(c("Stratum1", "Stratum2"), 50),
+  RespVar1 = sample(c("Poor", "Good"), 100, replace = TRUE),
+  RespVar2 = sample(c("Poor", "Good"), 100, replace = TRUE),
+  StressVar = sample(c("Poor", "Good"), 100, replace = TRUE),
+  All_Sites = rep("All Sites", 100),
+  Resource_Class = rep(c("Agr", "Forest"), c(55, 45))
+)
+myresponse <- c("RespVar1", "RespVar2")
+mystressor <- c("StressVar")
+mysubpops <- c("All_Sites", "Resource_Class")
+relrisk_analysis(dframe,
+  vars_response = myresponse,
+  vars_stressor = mystressor, subpops = mysubpops, siteID = "siteID",
+  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
+  stratumID = "stratum"
+)
 #>             Type Subpopulation Response  Stressor nResp  Estimate Estimate_num
-#> 1      All_Sites     All Sites RespVar1 StressVar   100 1.0343622    0.4978175
-#> 2      All_Sites     All Sites RespVar2 StressVar   100 0.9579892    0.4873652
-#> 3 Resource_Class           Agr RespVar1 StressVar    55 0.6719372    0.3791104
-#> 4 Resource_Class        Forest RespVar1 StressVar    45 1.7657859    0.6670747
-#> 5 Resource_Class           Agr RespVar2 StressVar    55 1.1234606    0.4540808
-#> 6 Resource_Class        Forest RespVar2 StressVar    45 0.8366566    0.5348233
-#>   Estimate_denom StdError_log MarginofError_log  LCB95Pct UCB95Pct WeightTotal
-#> 1      0.4812797    0.1926776         0.3776412 0.7090309 1.508968    5341.368
-#> 2      0.5087377    0.1903992         0.3731756 0.6596180 1.391325    5341.368
-#> 3      0.5642051    0.2750361         0.5390608 0.3919381 1.151966    3052.033
-#> 4      0.3777778    0.3068663         0.6014469 0.9676827 3.222130    2289.335
-#> 5      0.4041804    0.2937975         0.5758326 0.6316506 1.998199    3052.033
-#> 6      0.6392388    0.2527632         0.4954068 0.5097941 1.373092    2289.335
+#> 1      All_Sites     All Sites RespVar1 StressVar   100 0.7052158    0.4270103
+#> 2      All_Sites     All Sites RespVar2 StressVar   100 0.7917668    0.4033665
+#> 3 Resource_Class           Agr RespVar1 StressVar    55 0.6514748    0.3697156
+#> 4 Resource_Class        Forest RespVar1 StressVar    45 0.7623695    0.4959350
+#> 5 Resource_Class           Agr RespVar2 StressVar    55 0.6760754    0.3183989
+#> 6 Resource_Class        Forest RespVar2 StressVar    45 0.9108577    0.5055812
+#>   Estimate_denom StdError_log MarginofError_log  LCB95Pct  UCB95Pct WeightTotal
+#> 1      0.6055031    0.1666798         0.3266864 0.5086791 0.9776877    5562.359
+#> 2      0.5094512    0.2053415         0.4024620 0.5294321 1.1840889    5562.359
+#> 3      0.5675056    0.2917605         0.5718401 0.3677482 1.1541031    3026.685
+#> 4      0.6505179    0.2201228         0.4314328 0.4952182 1.1736386    2535.673
+#> 5      0.4709518    0.3317126         0.6501447 0.3528912 1.2952374    3026.685
+#> 6      0.5550606    0.2651067         0.5195997 0.5417404 1.5314746    2535.673
 #>   Count_RespPoor_StressPoor Count_RespPoor_StressGood Count_RespGood_StressPoor
-#> 1                        25                        25                        24
-#> 2                        25                        28                        24
-#> 3                        12                        15                        15
-#> 4                        13                        10                         9
-#> 5                        13                        12                        14
-#> 6                        12                        16                        10
+#> 1                        23                        29                        27
+#> 2                        21                        25                        29
+#> 3                        10                        17                        15
+#> 4                        13                        12                        12
+#> 5                         9                        15                        16
+#> 6                        12                        10                        13
 #>   Count_RespGood_StressGood Prop_RespPoor_StressPoor Prop_RespPoor_StressGood
-#> 1                        26                0.2476353                0.2418710
-#> 2                        23                0.2424358                0.2556702
-#> 3                        13                0.1939902                0.2755022
-#> 4                        13                0.3191524                0.1970354
-#> 5                        16                0.2323524                0.1973619
-#> 6                         7                0.2558786                0.3334041
+#> 1                        21                0.2099824                0.3077469
+#> 2                        25                0.1983555                0.2589285
+#> 3                        13                0.1824536                0.2874433
+#> 4                         8                0.2428419                0.3319822
+#> 5                        15                0.1571289                0.2385385
+#> 6                        10                0.2475653                0.2832670
 #>   Prop_RespGood_StressPoor Prop_RespGood_StressGood
-#> 1                0.2498066                0.2606871
-#> 2                0.2550060                0.2468879
-#> 3                0.3177083                0.2127993
-#> 4                0.1592833                0.3245289
-#> 5                0.2793460                0.2909396
-#> 6                0.2225571                0.1881602
+#> 1                0.2817677                0.2005030
+#> 2                0.2933945                0.2493214
+#> 3                0.3110435                0.2190597
+#> 4                0.2468229                0.1783530
+#> 5                0.3363681                0.2679645
+#> 6                0.2420995                0.2270683
diff --git a/docs/reference/revisit_bibd.html b/docs/reference/revisit_bibd.html index 82cd7d1..bd44b99 100644 --- a/docs/reference/revisit_bibd.html +++ b/docs/reference/revisit_bibd.html @@ -22,7 +22,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -82,16 +82,16 @@

Create a balanced incomplete block panel revisit design

- 
revisit_bibd(
-  n_period,
-  n_pnl,
-  n_visit,
-  nsamp,
-  panel_name = "BIB",
-  begin = 1,
-  skip = 1,
-  iter = 30
-)
+ 
revisit_bibd(
+  n_period,
+  n_pnl,
+  n_visit,
+  nsamp,
+  panel_name = "BIB",
+  begin = 1,
+  skip = 1,
+  iter = 30
+)
@@ -177,30 +177,30 @@

Author

Examples

- 
# Balanced incomplete block design with 20 sample occasions, 20 panels,
-# 3 visits to each unit, and 20 units in each panel.
-revisit_bibd(n_period = 20, n_pnl = 20, n_visit = 3, nsamp = 20)
+    
# Balanced incomplete block design with 20 sample occasions, 20 panels,
+# 3 visits to each unit, and 20 units in each panel.
+revisit_bibd(n_period = 20, n_pnl = 20, n_visit = 3, nsamp = 20)
 #>         1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
-#> BIB_01  0 20  0  0  0  0  0 20  0  0  0  0  0  0 20  0  0  0  0  0
-#> BIB_02  0  0 20  0  0  0  0  0  0  0 20  0  0  0  0  0 20  0  0  0
-#> BIB_03  0  0  0  0  0 20  0 20  0  0  0  0  0  0  0  0  0 20  0  0
-#> BIB_04  0  0  0  0 20  0  0  0 20  0  0  0  0  0  0 20  0  0  0  0
-#> BIB_05  0 20  0 20  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0
-#> BIB_06 20  0  0  0  0  0 20  0  0  0  0  0 20  0  0  0  0  0  0  0
-#> BIB_07  0  0  0  0  0  0  0  0  0 20  0 20  0  0  0  0 20  0  0  0
-#> BIB_08  0 20  0  0 20  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0
-#> BIB_09 20  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0
-#> BIB_10  0  0  0  0  0  0  0 20  0  0 20  0 20  0  0  0  0  0  0  0
-#> BIB_11  0  0  0 20  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0 20
-#> BIB_12  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0  0 20 20
-#> BIB_13  0  0  0  0 20 20  0  0  0 20  0  0  0  0  0  0  0  0  0  0
-#> BIB_14  0  0  0  0  0  0  0  0  0 20  0  0 20 20  0  0  0  0  0  0
-#> BIB_15  0  0 20  0  0  0  0  0  0  0  0  0  0 20  0 20  0  0  0  0
-#> BIB_16  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0 20  0 20  0  0
-#> BIB_17 20  0  0  0  0  0  0  0 20  0  0  0  0  0 20  0  0  0  0  0
-#> BIB_18  0  0 20  0  0  0 20  0  0  0  0  0  0  0  0  0  0  0 20  0
-#> BIB_19  0  0  0  0  0  0  0  0 20  0 20  0  0  0  0  0  0  0  0 20
-#> BIB_20  0  0  0  0  0  0  0  0  0  0  0 20  0  0 20  0  0  0 20  0
+#> BIB_01  0  0  0  0  0 20  0  0 20  0  0 20  0  0  0  0  0  0  0  0
+#> BIB_02 20  0  0  0  0  0 20 20  0  0  0  0  0  0  0  0  0  0  0  0
+#> BIB_03  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0 20  0  0  0 20
+#> BIB_04  0  0  0  0  0  0  0 20  0  0  0  0 20  0  0 20  0  0  0  0
+#> BIB_05  0  0 20  0  0  0 20  0 20  0  0  0  0  0  0  0  0  0  0  0
+#> BIB_06  0  0  0 20  0 20  0  0  0  0  0  0  0  0  0  0  0 20  0  0
+#> BIB_07  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0 20  0  0 20
+#> BIB_08  0  0  0  0  0  0  0  0  0  0 20  0 20  0  0  0 20  0  0  0
+#> BIB_09  0 20 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0
+#> BIB_10  0  0  0  0  0  0  0  0  0  0 20  0  0 20 20  0  0  0  0  0
+#> BIB_11 20  0  0 20  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0
+#> BIB_12 20  0  0  0  0  0  0  0  0 20  0 20  0  0  0  0  0  0  0  0
+#> BIB_13  0 20  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0 20  0  0
+#> BIB_14  0  0  0  0 20  0 20  0  0  0  0  0  0  0  0  0 20  0  0  0
+#> BIB_15  0  0  0  0 20  0  0  0  0 20  0  0  0  0  0  0  0  0 20  0
+#> BIB_16  0  0 20  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0 20
+#> BIB_17  0  0  0  0  0  0  0  0 20  0  0  0  0 20  0 20  0  0  0  0
+#> BIB_18  0 20  0  0  0  0  0  0  0  0 20 20  0  0  0  0  0  0  0  0
+#> BIB_19  0  0  0  0 20  0  0  0  0  0  0  0  0 20  0  0  0 20  0  0
+#> BIB_20  0  0  0 20  0  0  0  0  0  0  0  0 20  0  0  0  0  0 20  0
 #> attr(,"class")
 #> [1] "paneldesign"
 

diff --git a/docs/reference/revisit_dsgn.html b/docs/reference/revisit_dsgn.html
index 5b5d131..cfddfcf 100644
--- a/docs/reference/revisit_dsgn.html
+++ b/docs/reference/revisit_dsgn.html
@@ -20,7 +20,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
@@ -78,7 +78,7 @@

Create a panel revisit design

- 
revisit_dsgn(n_period, panels, begin = 1, skip = 1)
+ 
revisit_dsgn(n_period, panels, begin = 1, skip = 1)
@@ -226,26 +226,26 @@

Author

Examples

- 
# One panel of  60 sample units sampled at every time period: [1-0]
-revisit_dsgn(20, panels = list(
-  Annual = list(
-    n = 60, pnl_dsgn = c(1, 0), pnl.n = NA,
-    start_option = "None"
-  )
-), begin = 1)
+    
# One panel of  60 sample units sampled at every time period: [1-0]
+revisit_dsgn(20, panels = list(
+  Annual = list(
+    n = 60, pnl_dsgn = c(1, 0), pnl.n = NA,
+    start_option = "None"
+  )
+), begin = 1)
 #>         1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
 #> Annual 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
 #> attr(,"class")
 #> [1] "paneldesign"
-
-# Rotating panels of 60 units sampled once and never again: [1-n].  Number
-# of panels equal n_period.
-revisit_dsgn(20,
-  panels = list(
-    R60N = list(n = 60, pnl_dsgn = c(1, NA), pnl_n = NA, start_option = "None")
-  ),
-  begin = 1
-)
+
+# Rotating panels of 60 units sampled once and never again: [1-n].  Number
+# of panels equal n_period.
+revisit_dsgn(20,
+  panels = list(
+    R60N = list(n = 60, pnl_dsgn = c(1, NA), pnl_n = NA, start_option = "None")
+  ),
+  begin = 1
+)
 #>          1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
 #> R60N_01 60  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
 #> R60N_02  0 60  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
@@ -269,15 +269,15 @@ 
Examples

 #> R60N_20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 60
 #> attr(,"class")
 #> [1] "paneldesign"
-
-# Serially alternating panel with three visits to sample unit then skip
-# next two time periods: [3-2]
-revisit_dsgn(20, panels = list(
-  SA60PE = list(
-    n = 20, pnl_dsgn = c(3, 2), pnl_n = NA,
-    start_option = "Partial_End"
-  )
-), begin = 1)
+
+# Serially alternating panel with three visits to sample unit then skip
+# next two time periods: [3-2]
+revisit_dsgn(20, panels = list(
+  SA60PE = list(
+    n = 20, pnl_dsgn = c(3, 2), pnl_n = NA,
+    start_option = "Partial_End"
+  )
+), begin = 1)
 #>           1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
 #> SA60PE_1 20 20 20  0  0 20 20 20  0  0 20 20 20  0  0 20 20 20  0  0
 #> SA60PE_2  0 20 20 20  0  0 20 20 20  0  0 20 20 20  0  0 20 20 20  0
@@ -286,15 +286,15 @@ 
Examples

 #> SA60PE_5 20 20  0  0 20 20 20  0  0 20 20 20  0  0 20 20 20  0  0 20
 #> attr(,"class")
 #> [1] "paneldesign"
-
-# Split panel of sample units combining above two panel designs: [1-0, 1-n]
-revisit_dsgn(n_period = 20, begin = 2017, panels = list(
-  Annual = list(
-    n = 60, pnl_dsgn = c(1, 0), pnl.n = NA,
-    start_option = "None"
-  ),
-  R60N = list(n = 60, pnl_dsgn = c(1, NA), pnl_n = NA, start_option = "None")
-))
+
+# Split panel of sample units combining above two panel designs: [1-0, 1-n]
+revisit_dsgn(n_period = 20, begin = 2017, panels = list(
+  Annual = list(
+    n = 60, pnl_dsgn = c(1, 0), pnl.n = NA,
+    start_option = "None"
+  ),
+  R60N = list(n = 60, pnl_dsgn = c(1, NA), pnl_n = NA, start_option = "None")
+))
 #>         2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
 #> R60N_01   60    0    0    0    0    0    0    0    0    0    0    0    0    0
 #> R60N_02    0   60    0    0    0    0    0    0    0    0    0    0    0    0
diff --git a/docs/reference/revisit_rand.html b/docs/reference/revisit_rand.html
index 66f5bbb..9c189ed 100644
--- a/docs/reference/revisit_rand.html
+++ b/docs/reference/revisit_rand.html
@@ -22,7 +22,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -82,16 +82,16 @@ 
Create a revisit design with random assignment to panels and time periods
 
     

-    
revisit_rand(
-  n_period,
-  n_pnl,
-  rand_control = "period",
-  n_visit,
-  nsamp,
-  panel_name = "Random",
-  begin = 1,
-  skip = 1
-)

+    
revisit_rand(
+  n_period,
+  n_pnl,
+  rand_control = "period",
+  n_visit,
+  nsamp,
+  panel_name = "Random",
+  begin = 1,
+  skip = 1
+)

     

 
     

@@ -176,55 +176,55 @@ 
Author

 
     

     
Examples

-    
revisit_rand(
-  n_period = 20, n_pnl = 10, rand_control = "none", n_visit = 50,
-  nsamp = 20
-)
-#>           1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
-#> Random_01 0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0 20 20  0 20
-#> Random_02 0  0  0  0 20  0  0  0  0 20  0 20  0  0  0  0 20 20 20 20
-#> Random_03 0 20  0  0  0  0  0 20  0 20  0 20  0  0  0  0  0  0 20  0
-#> Random_04 0  0  0 20 20  0 20 20 20 20  0 20  0  0  0  0  0 20 20  0
-#> Random_05 0  0  0  0  0  0  0 20  0  0 20  0  0  0  0 20 20  0  0  0
-#> Random_06 0  0 20  0  0  0  0 20  0  0 20 20  0  0  0  0 20  0  0 20
-#> Random_07 0  0 20  0  0 20  0 20  0  0  0  0 20 20  0  0 20  0  0  0
-#> Random_08 0  0  0 20  0  0 20  0  0  0  0  0  0 20  0  0  0  0  0 20
-#> Random_09 0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0 20
-#> Random_10 0  0  0  0  0  0  0 20 20  0  0  0  0  0  0  0  0  0 20  0
+    
revisit_rand(
+  n_period = 20, n_pnl = 10, rand_control = "none", n_visit = 50,
+  nsamp = 20
+)
+#>            1  2  3  4  5  6 7  8  9 10 11 12 13 14 15 16 17 18 19 20
+#> Random_01  0  0  0 20 20  0 0  0  0  0  0  0  0 20  0 20  0 20  0  0
+#> Random_02  0  0  0  0  0 20 0  0  0  0  0 20  0  0  0  0 20  0  0  0
+#> Random_03  0 20  0 20 20  0 0  0  0 20 20  0  0  0  0 20 20  0  0  0
+#> Random_04  0 20  0  0 20  0 0  0 20  0 20  0  0  0  0  0  0 20  0  0
+#> Random_05 20 20  0  0  0 20 0  0  0 20  0 20  0  0  0 20 20 20  0  0
+#> Random_06  0  0 20  0  0  0 0 20  0  0  0  0  0 20  0  0  0 20  0  0
+#> Random_07 20  0 20  0  0  0 0  0  0  0  0 20  0  0  0  0  0 20  0  0
+#> Random_08 20  0  0 20  0 20 0  0  0  0  0  0  0  0  0 20  0 20  0 20
+#> Random_09  0  0  0 20  0  0 0  0  0  0  0  0  0 20  0  0  0  0  0  0
+#> Random_10  0  0 20  0  0  0 0  0  0 20  0 20  0 20  0 20  0 20  0  0
 #> attr(,"class")
 #> [1] "paneldesign"
-revisit_rand(
-  n_period = 20, n_pnl = 10, rand_control = "panel", n_visit = 5,
-  nsamp = 10
-)
+revisit_rand(
+  n_period = 20, n_pnl = 10, rand_control = "panel", n_visit = 5,
+  nsamp = 10
+)
 #>            1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
-#> Random_01  0  0  0  0  0  0  0  0  0  0  0 10 10 10 10 10  0  0  0  0
-#> Random_02 10 10  0  0  0  0  0 10  0  0  0  0  0  0  0  0 10  0 10  0
-#> Random_03  0  0  0  0  0 10 10  0  0  0  0 10 10 10  0  0  0  0  0  0
-#> Random_04  0  0  0  0 10  0  0  0  0 10  0  0 10 10  0  0 10  0  0  0
-#> Random_05  0 10  0  0  0  0  0 10 10  0  0 10  0  0  0  0  0 10  0  0
-#> Random_06  0  0  0  0 10 10  0  0  0 10  0  0  0  0 10 10  0  0  0  0
-#> Random_07 10  0 10  0  0  0  0  0  0  0  0 10 10  0  0  0  0  0 10  0
-#> Random_08  0  0 10 10  0  0  0 10 10  0  0  0  0  0  0  0  0  0 10  0
-#> Random_09  0  0 10  0 10 10  0  0  0 10  0  0  0  0  0  0  0  0 10  0
-#> Random_10  0  0  0  0  0  0  0 10  0  0  0 10  0 10  0 10  0  0 10  0
+#> Random_01 10  0 10  0  0  0 10  0 10  0  0 10  0  0  0  0  0  0  0  0
+#> Random_02  0  0  0  0  0  0  0 10  0 10  0  0 10 10 10  0  0  0  0  0
+#> Random_03  0 10 10  0  0 10  0  0  0  0  0  0  0  0 10  0  0  0  0 10
+#> Random_04  0 10  0  0  0 10  0 10 10  0  0 10  0  0  0  0  0  0  0  0
+#> Random_05 10 10  0  0  0  0  0  0  0  0  0  0 10 10  0  0  0  0  0 10
+#> Random_06  0 10  0 10  0  0  0 10  0  0 10 10  0  0  0  0  0  0  0  0
+#> Random_07  0  0 10 10  0 10  0  0  0  0 10 10  0  0  0  0  0  0  0  0
+#> Random_08  0  0  0  0 10 10 10 10  0  0  0  0  0  0  0  0  0  0 10  0
+#> Random_09  0 10  0  0 10  0 10  0 10  0  0  0  0 10  0  0  0  0  0  0
+#> Random_10  0  0  0  0  0  0  0 10  0 10  0  0  0 10  0  0  0  0 10 10
 #> attr(,"class")
 #> [1] "paneldesign"
-revisit_rand(
-  n_period = 20, n_pnl = 10, rand_control = "period",
-  n_visit = 5, nsamp = 10
-)
+revisit_rand(
+  n_period = 20, n_pnl = 10, rand_control = "period",
+  n_visit = 5, nsamp = 10
+)
 #>            1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
-#> Random_01  0  0 10 10 10  0 10 10 10 10  0 10 10  0 10 10  0 10 10 10
-#> Random_02 10 10  0 10  0 10 10  0  0  0  0 10  0 10 10  0 10  0 10  0
-#> Random_03  0  0  0 10  0 10  0  0 10 10 10  0  0 10 10 10 10 10  0 10
-#> Random_04  0 10  0 10 10 10  0 10  0  0  0  0 10  0  0 10  0  0 10 10
-#> Random_05 10 10  0 10 10  0  0 10 10  0  0  0  0 10  0  0  0  0  0  0
-#> Random_06 10  0 10  0  0  0 10  0 10  0 10  0 10  0  0 10 10 10 10  0
-#> Random_07  0 10  0  0  0 10 10  0  0 10 10 10 10  0  0  0 10 10  0 10
-#> Random_08 10  0 10  0  0  0 10 10  0 10 10 10 10  0 10  0  0  0 10  0
-#> Random_09 10  0 10  0 10 10  0 10 10  0  0  0  0 10  0  0 10  0  0  0
-#> Random_10  0 10 10  0 10  0  0  0  0 10 10 10  0 10 10 10  0 10  0 10
+#> Random_01  0 10  0 10  0  0 10 10 10  0  0 10 10 10 10 10 10 10  0 10
+#> Random_02  0 10  0  0  0 10  0  0  0 10 10  0 10 10 10  0  0  0 10  0
+#> Random_03 10 10  0  0  0 10 10 10  0  0 10 10 10  0  0 10  0 10  0 10
+#> Random_04 10  0 10 10 10 10 10  0 10 10  0 10  0 10  0  0 10  0 10 10
+#> Random_05 10  0  0 10 10 10 10 10  0  0  0  0 10  0 10 10  0  0  0 10
+#> Random_06 10  0 10 10 10  0 10 10 10  0 10  0  0  0 10  0 10 10  0  0
+#> Random_07  0  0  0  0 10  0  0  0  0 10 10  0  0 10  0  0  0 10 10  0
+#> Random_08  0  0 10  0 10 10  0  0  0 10  0  0  0  0  0 10 10  0 10 10
+#> Random_09 10 10 10 10  0  0  0 10 10  0  0 10 10  0  0  0  0  0 10  0
+#> Random_10  0 10 10  0  0  0  0  0 10 10 10 10  0 10 10 10 10 10  0  0
 #> attr(,"class")
 #> [1] "paneldesign"
 

diff --git a/docs/reference/sp_balance.html b/docs/reference/sp_balance.html
index 19cd5ef..2dba24a 100644
--- a/docs/reference/sp_balance.html
+++ b/docs/reference/sp_balance.html
@@ -19,7 +19,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -76,14 +76,14 @@ 
Calculate spatial balance metrics

     

 
     

-    
sp_balance(
-  object,
-  sframe,
-  stratum_var = NULL,
-  ip = NULL,
-  metrics = "pielou",
-  extents = FALSE
-)

+    
sp_balance(
+  object,
+  sframe,
+  stratum_var = NULL,
+  ip = NULL,
+  metrics = "pielou",
+  extents = FALSE
+)

     

 
     

@@ -167,13 +167,13 @@ 
Author

 
     

     
Examples

-    
if (FALSE) {
-sample <- grts(NE_Lakes, 30)
-sp_balance(sample$sites_base, NE_Lakes)
-strata_n <- c(low = 25, high = 30)
-sample_strat <- grts(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
-sp_balance(sample_strat$sites_base, NE_Lakes, stratum_var = "ELEV_CAT", metric = "rmse")
-}
+    
if (FALSE) {
+sample <- grts(NE_Lakes, 30)
+sp_balance(sample$sites_base, NE_Lakes)
+strata_n <- c(low = 25, high = 30)
+sample_strat <- grts(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
+sp_balance(sample_strat$sites_base, NE_Lakes, stratum_var = "ELEV_CAT", metric = "rmse")
+}
 

     

   

diff --git a/docs/reference/sp_frame.html b/docs/reference/sp_frame.html
index 84ddfea..9db45bd 100644
--- a/docs/reference/sp_frame.html
+++ b/docs/reference/sp_frame.html
@@ -18,7 +18,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -74,9 +74,9 @@ 
sp_frame objects

     

 
     

-    
sp_frame(frame)
-
-sp_unframe(sp_frame)

+    
sp_frame(frame)
+
+sp_unframe(sp_frame)

     

 
     

@@ -105,11 +105,11 @@ 
Details

 
     

     
Examples

-    
NE_Lakes <- sp_frame(NE_Lakes)
-class(NE_Lakes)
+    
NE_Lakes <- sp_frame(NE_Lakes)
+class(NE_Lakes)
 #> [1] "sp_frame"   "sf"         "data.frame"
-NE_Lakes <- sp_unframe(NE_Lakes)
-class(NE_Lakes)
+NE_Lakes <- sp_unframe(NE_Lakes)
+class(NE_Lakes)
 #> [1] "sf"         "data.frame"
 

     

diff --git a/docs/reference/sp_plot.html b/docs/reference/sp_plot.html
index 411a693..3b1ce31 100644
--- a/docs/reference/sp_plot.html
+++ b/docs/reference/sp_plot.html
@@ -25,7 +25,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -81,43 +81,43 @@ 
Plot sampling frames, design sites, and analysis data.

 are of the distributions of the right-hand side variables. If the left-hand side
 of the variable contains a variable, plots are of the left-hand size variable
 for each level of each right-hand side variable.
-This function is largely built on plot.sf(), and all spsurvey plotting
-methods can supply additional arguments to plot.sf(). For more information on
+This function is largely built on plot.sf(), and all spsurvey plotting
+methods can supply additional arguments to plot.sf(). For more information on
 plotting in sf, run ?sf::plot.sf(). Equivalent to spsurvey::plot(); both
 are currently maintained for backwards compatibility.

     

 
     

-    
sp_plot(object, ...)
-
-# S3 method for default
-sp_plot(
-  object,
-  formula = ~1,
-  xcoord,
-  ycoord,
-  crs,
-  var_args = NULL,
-  varlevel_args = NULL,
-  geom = FALSE,
-  onlyshow = NULL,
-  fix_bbox = TRUE,
-  ...
-)
-
-# S3 method for sp_design
-sp_plot(
-  object,
-  sframe = NULL,
-  formula = ~siteuse,
-  siteuse = NULL,
-  var_args = NULL,
-  varlevel_args = NULL,
-  geom = FALSE,
-  onlyshow = NULL,
-  fix_bbox = TRUE,
-  ...
-)

+    
sp_plot(object, ...)
+
+# S3 method for default
+sp_plot(
+  object,
+  formula = ~1,
+  xcoord,
+  ycoord,
+  crs,
+  var_args = NULL,
+  varlevel_args = NULL,
+  geom = FALSE,
+  onlyshow = NULL,
+  fix_bbox = TRUE,
+  ...
+)
+
+# S3 method for sp_design
+sp_plot(
+  object,
+  sframe = NULL,
+  formula = ~siteuse,
+  siteuse = NULL,
+  var_args = NULL,
+  varlevel_args = NULL,
+  geom = FALSE,
+  onlyshow = NULL,
+  fix_bbox = TRUE,
+  ...
+)

     

 
     

@@ -129,7 +129,7 @@ 
Arguments

 
 
 
...

-
Additional arguments to pass to plot.sf().

+
Additional arguments to pass to plot.sf().

 
 
 
formula

@@ -225,14 +225,14 @@ 
Author

 
     

     
Examples

-    
if (FALSE) {
-data("NE_Lakes")
-sp_plot(NE_Lakes, formula = ~ELEV_CAT)
-sample <- grts(NE_Lakes, 30)
-sp_plot(sample, NE_Lakes)
-data("NLA_PNW")
-sp_plot(NLA_PNW, formula = ~BMMI)
-}
+    
if (FALSE) {
+data("NE_Lakes")
+sp_plot(NE_Lakes, formula = ~ELEV_CAT)
+sample <- grts(NE_Lakes, 30)
+sp_plot(sample, NE_Lakes)
+data("NLA_PNW")
+sp_plot(NLA_PNW, formula = ~BMMI)
+}
 

     

   

diff --git a/docs/reference/sp_rbind.html b/docs/reference/sp_rbind.html
index d0d9553..e5a2b21 100644
--- a/docs/reference/sp_rbind.html
+++ b/docs/reference/sp_rbind.html
@@ -21,7 +21,7 @@
       
       
         spsurvey
-        5.4.1
+        5.5.0
       
     

 
@@ -80,7 +80,7 @@ 
Combine rows from GRTS or IRS samples.
- 
sp_rbind(object, siteuse = NULL)
+ 
sp_rbind(object, siteuse = NULL)
@@ -111,11 +111,11 @@

Author

Examples

- 
if (FALSE) {
-sample <- grts(NE_Lakes, 50, n_over = 10)
-sample <- sp_rbind(sample)
-write_sf(sample, "mypath/sample.shp")
-}
+    
if (FALSE) {
+sample <- grts(NE_Lakes, 50, n_over = 10)
+sample <- sp_rbind(sample)
+write_sf(sample, "mypath/sample.shp")
+}
diff --git a/docs/reference/sp_summary.html b/docs/reference/sp_summary.html index d42bf90..6f4f2ca 100644 --- a/docs/reference/sp_summary.html +++ b/docs/reference/sp_summary.html @@ -23,7 +23,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -84,13 +84,13 @@

Summarize sampling frames, design sites, and analysis data.

- 
sp_summary(object, ...)
-
-# S3 method for default
-sp_summary(object, formula = ~1, onlyshow = NULL, ...)
-
-# S3 method for sp_design
-sp_summary(object, formula = ~siteuse, siteuse = NULL, onlyshow = NULL, ...)
+ 
sp_summary(object, ...)
+
+# S3 method for default
+sp_summary(object, formula = ~1, onlyshow = NULL, ...)
+
+# S3 method for sp_design
+sp_summary(object, formula = ~siteuse, siteuse = NULL, onlyshow = NULL, ...)
@@ -156,13 +156,13 @@

Author

Examples

- 
if (FALSE) {
-data("NE_Lakes")
-sp_summary(NE_Lakes, ELEV ~ 1)
-sp_summary(NE_Lakes, ~ ELEV_CAT * AREA_CAT)
-sample <- grts(NE_Lakes, 100)
-sp_summary(sample, ~ ELEV_CAT * AREA_CAT)
-}
+    
if (FALSE) {
+data("NE_Lakes")
+sp_summary(NE_Lakes, ELEV ~ 1)
+sp_summary(NE_Lakes, ~ ELEV_CAT * AREA_CAT)
+sample <- grts(NE_Lakes, 100)
+sp_summary(sample, ~ ELEV_CAT * AREA_CAT)
+}
diff --git a/docs/reference/spsurvey-package.html b/docs/reference/spsurvey-package.html index a03d4eb..70b8be5 100644 --- a/docs/reference/spsurvey-package.html +++ b/docs/reference/spsurvey-package.html @@ -37,7 +37,7 @@ spsurvey - 5.4.1 + 5.5.0
diff --git a/docs/reference/stopprnt.html b/docs/reference/stopprnt.html index e1a6946..48d8143 100644 --- a/docs/reference/stopprnt.html +++ b/docs/reference/stopprnt.html @@ -18,7 +18,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -74,7 +74,7 @@

Print grts() and irs() errors.

- 
stopprnt(stop_df = get("stop_df", envir = .GlobalEnv), m = 1:nrow(stop_df))
+ 
stopprnt(stop_df = get("stop_df", envir = .GlobalEnv), m = 1:nrow(stop_df))
diff --git a/docs/reference/summary.html b/docs/reference/summary.html index 4acc172..6593700 100644 --- a/docs/reference/summary.html +++ b/docs/reference/summary.html @@ -23,7 +23,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -84,11 +84,11 @@

Summarize sampling frames, design sites, and analysis data.

- 
# S3 method for sp_frame
-summary(object, formula = ~1, onlyshow = NULL, ...)
-
-# S3 method for sp_design
-summary(object, formula = ~siteuse, siteuse = NULL, onlyshow = NULL, ...)
+ 
# S3 method for sp_frame
+summary(object, formula = ~1, onlyshow = NULL, ...)
+
+# S3 method for sp_design
+summary(object, formula = ~siteuse, siteuse = NULL, onlyshow = NULL, ...)
@@ -155,13 +155,13 @@

Author

Examples

- 
if (FALSE) {
-data("NE_Lakes")
-summary(NE_Lakes, ELEV ~ 1)
-summary(NE_Lakes, ~ ELEV_CAT * AREA_CAT)
-sample <- grts(NE_Lakes, 100)
-summary(sample, ~ ELEV_CAT * AREA_CAT)
-}
+    
if (FALSE) {
+data("NE_Lakes")
+summary(NE_Lakes, ELEV ~ 1)
+summary(NE_Lakes, ~ ELEV_CAT * AREA_CAT)
+sample <- grts(NE_Lakes, 100)
+summary(sample, ~ ELEV_CAT * AREA_CAT)
+}
diff --git a/docs/reference/trend_analysis.html b/docs/reference/trend_analysis.html index 58c29eb..0d1a2e6 100644 --- a/docs/reference/trend_analysis.html +++ b/docs/reference/trend_analysis.html @@ -29,7 +29,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -96,37 +96,37 @@

Trend analysis

- 
trend_analysis(
-  dframe,
-  vars_cat = NULL,
-  vars_cont = NULL,
-  subpops = NULL,
-  model_cat = "SLR",
-  cat_rhs = NULL,
-  model_cont = "LMM",
-  cont_rhs = NULL,
-  siteID = "siteID",
-  yearID = "year",
-  weight = "weight",
-  xcoord = NULL,
-  ycoord = NULL,
-  stratumID = NULL,
-  clusterID = NULL,
-  weight1 = NULL,
-  xcoord1 = NULL,
-  ycoord1 = NULL,
-  sizeweight = FALSE,
-  sweight = NULL,
-  sweight1 = NULL,
-  fpc = NULL,
-  popsize = NULL,
-  invprboot = TRUE,
-  nboot = 1000,
-  vartype = "Local",
-  jointprob = "overton",
-  conf = 95,
-  All_Sites = FALSE
-)
+ 
trend_analysis(
+  dframe,
+  vars_cat = NULL,
+  vars_cont = NULL,
+  subpops = NULL,
+  model_cat = "SLR",
+  cat_rhs = NULL,
+  model_cont = "LMM",
+  cont_rhs = NULL,
+  siteID = "siteID",
+  yearID = "year",
+  weight = "weight",
+  xcoord = NULL,
+  ycoord = NULL,
+  stratumID = NULL,
+  clusterID = NULL,
+  weight1 = NULL,
+  xcoord1 = NULL,
+  ycoord1 = NULL,
+  sizeweight = FALSE,
+  sweight = NULL,
+  sweight1 = NULL,
+  fpc = NULL,
+  popsize = NULL,
+  invprboot = TRUE,
+  nboot = 1000,
+  vartype = "Local",
+  jointprob = "overton",
+  conf = 95,
+  All_Sites = FALSE
+)
@@ -637,94 +637,94 @@

Author

Examples

- 
# Example using a categorical variable with three resource classes and a
-# continuous variable
-mydframe <- data.frame(
-  siteID = rep(paste0("Site", 1:40), rep(5, 40)),
-  yearID = rep(seq(2000, 2020, by = 5), 40),
-  wgt = rep(runif(40, 10, 100), rep(5, 40)),
-  xcoord = rep(runif(40), rep(5, 40)),
-  ycoord = rep(runif(40), rep(5, 40)),
-  All_Sites = rep("All Sites", 200),
-  Region = sample(c("North", "South"), 200, replace = TRUE),
-  Resource_Class = sample(c("Good", "Fair", "Poor"), 200, replace = TRUE),
-  ContVar = rnorm(200, 10, 1)
-)
-myvars_cat <- c("Resource_Class")
-myvars_cont <- c("ContVar")
-mysubpops <- c("All_Sites", "Region")
-trend_analysis(
-  dframe = mydframe,
-  vars_cat = myvars_cat,
-  vars_cont = myvars_cont,
-  subpops = mysubpops,
-  model_cat = "WLR",
-  model_cont = "SLR",
-  siteID = "siteID",
-  yearID = "yearID",
-  weight = "wgt",
-  xcoord = "xcoord",
-  ycoord = "ycoord"
-)
+    
# Example using a categorical variable with three resource classes and a
+# continuous variable
+mydframe <- data.frame(
+  siteID = rep(paste0("Site", 1:40), rep(5, 40)),
+  yearID = rep(seq(2000, 2020, by = 5), 40),
+  wgt = rep(runif(40, 10, 100), rep(5, 40)),
+  xcoord = rep(runif(40), rep(5, 40)),
+  ycoord = rep(runif(40), rep(5, 40)),
+  All_Sites = rep("All Sites", 200),
+  Region = sample(c("North", "South"), 200, replace = TRUE),
+  Resource_Class = sample(c("Good", "Fair", "Poor"), 200, replace = TRUE),
+  ContVar = rnorm(200, 10, 1)
+)
+myvars_cat <- c("Resource_Class")
+myvars_cont <- c("ContVar")
+mysubpops <- c("All_Sites", "Region")
+trend_analysis(
+  dframe = mydframe,
+  vars_cat = myvars_cat,
+  vars_cont = myvars_cont,
+  subpops = mysubpops,
+  model_cat = "WLR",
+  model_cont = "SLR",
+  siteID = "siteID",
+  yearID = "yearID",
+  weight = "wgt",
+  xcoord = "xcoord",
+  ycoord = "ycoord"
+)
 #> $catsum
 #>        Type Subpopulation      Indicator Category Trend_Estimate
-#> 1 All_Sites     All Sites Resource_Class     Fair      1.1727221
-#> 2 All_Sites     All Sites Resource_Class     Good     -0.5558858
-#> 3 All_Sites     All Sites Resource_Class     Poor     -0.6716191
-#> 4    Region         North Resource_Class     Fair      1.5434959
-#> 5    Region         North Resource_Class     Good     -0.7465316
-#> 6    Region         North Resource_Class     Poor     -0.4799649
-#> 7    Region         South Resource_Class     Fair      0.9671572
-#> 8    Region         South Resource_Class     Good     -0.2454305
-#> 9    Region         South Resource_Class     Poor     -0.9889771
+#> 1 All_Sites     All Sites Resource_Class     Fair     0.04523685
+#> 2 All_Sites     All Sites Resource_Class     Good    -0.40839357
+#> 3 All_Sites     All Sites Resource_Class     Poor     0.38870400
+#> 4    Region         North Resource_Class     Fair     0.61223475
+#> 5    Region         North Resource_Class     Good    -1.22616241
+#> 6    Region         North Resource_Class     Poor     0.66950061
+#> 7    Region         South Resource_Class     Fair    -1.41616234
+#> 8    Region         South Resource_Class     Good     0.68421557
+#> 9    Region         South Resource_Class     Poor    -0.10773624
 #>   Trend_Std_Error Trend_LCB95Pct Trend_UCB95Pct Trend_p_Value
-#> 1       0.6594097     -0.9258139      3.2712582    0.17338878
-#> 2       0.5133497     -2.1895937      1.0778220    0.35813863
-#> 3       0.3966571     -1.9339590      0.5907208    0.18899286
-#> 4       1.0513476     -1.8023613      4.8893530    0.23837914
-#> 5       0.7654575     -3.1825589      1.6894958    0.40135282
-#> 6       0.5353186     -2.1835877      1.2236579    0.43600464
-#> 7       0.3538078     -0.1588171      2.0931315    0.07172515
-#> 8       0.3104826     -1.2335248      0.7426638    0.48697219
-#> 9       0.3450930     -2.0872172      0.1092629    0.06426940
+#> 1       0.3660183     -1.1195967      1.2100704     0.9094540
+#> 2       0.3384541     -1.4855054      0.6687183     0.3140403
+#> 3       0.3990614     -0.8812874      1.6586954     0.4018741
+#> 4       0.6544452     -1.4705021      2.6949716     0.4185408
+#> 5       0.9024478     -4.0981542      1.6458293     0.2673743
+#> 6       0.3746057     -0.5226620      1.8616632     0.1718685
+#> 7       0.9588723     -4.4677220      1.6353973     0.2362015
+#> 8       0.4157430     -0.6388643      2.0072955     0.1983640
+#> 9       0.8545475     -2.8272879      2.6118154     0.9076481
 #>   Intercept_Estimate Intercept_Std_Error Intercept_LCB95Pct Intercept_UCB95Pct
-#> 1           25.21329            7.535505           1.231948           49.19463
-#> 2           40.51672            6.082999          21.157905           59.87554
-#> 3           32.76657            5.390329          15.612133           49.92100
-#> 4           17.25723           12.159497         -21.439711           55.95418
-#> 5           40.78678            9.883249           9.333868           72.23968
-#> 6           32.61545            7.446874           8.916170           56.31472
-#> 7           29.46509            3.737211          17.571611           41.35856
-#> 8           39.13325            3.265790          28.740050           49.52645
-#> 9           32.43999            4.555849          17.941247           46.93874
-#>   Intercept_p_Value R_Squared Adj_R_Squared
-#> 1       0.044192162 0.5132128    0.35095038
-#> 2       0.006898524 0.2810214    0.04136183
-#> 3       0.008938233 0.4886585    0.31821132
-#> 4       0.250889296 0.4180805    0.22410738
-#> 5       0.025802595 0.2407296   -0.01236059
-#> 6       0.022033324 0.2113328   -0.05155632
-#> 7       0.004252007 0.7135323    0.61804313
-#> 8       0.001250302 0.1723817   -0.10349103
-#> 9       0.005700706 0.7324526    0.64327017
+#> 1           34.17562            4.458670         19.9861447           48.36510
+#> 2           37.36444            4.169962         24.0937551           50.63512
+#> 3           27.66571            4.923735         11.9961868           43.33523
+#> 4           29.25989            7.928626          4.0274576           54.49231
+#> 5           50.01256           10.936958         15.2062788           84.81884
+#> 6           18.52645            4.481118          4.2655363           32.78737
+#> 7           43.46842           13.852895         -0.6176701           87.55452
+#> 8           21.01016            4.830891          5.6361042           36.38421
+#> 9           38.66653           10.060016          6.6510671           70.68199
+#>   Intercept_p_Value   R_Squared Adj_R_Squared
+#> 1       0.004612606 0.005065849   -0.32657887
+#> 2       0.002933281 0.326748926    0.10233190
+#> 3       0.011145479 0.240268774   -0.01297497
+#> 4       0.034506712 0.225839176   -0.03221443
+#> 5       0.019623920 0.380943639    0.17459152
+#> 6       0.025679670 0.515670797    0.35422773
+#> 7       0.051749281 0.420988626    0.22798484
+#> 8       0.022450110 0.474471974    0.29929597
+#> 9       0.031074264 0.005270296   -0.32630627
 #> 
 #> $contsum
 #>        Type Subpopulation Indicator Trend_Estimate Trend_Std_Error
-#> 1 All_Sites     All Sites   ContVar   -0.012484552     0.003006216
-#> 2    Region         North   ContVar    0.002436174     0.006329977
-#> 3    Region         South   ContVar   -0.030487781     0.013240318
+#> 1 All_Sites     All Sites   ContVar   -0.004842711      0.01030439
+#> 2    Region         North   ContVar   -0.013031361      0.02167446
+#> 3    Region         South   ContVar    0.015256704      0.01759278
 #>   Trend_LCB95Pct Trend_UCB95Pct Trend_p_Value Intercept_Estimate
-#> 1    -0.02205167   -0.002917431    0.02537758          10.041141
-#> 2    -0.01770864    0.022580986    0.72600223           9.849657
-#> 3    -0.07262438    0.011648819    0.10473389          10.253830
+#> 1    -0.03763588     0.02795046     0.6704321          10.118495
+#> 2    -0.08200918     0.05594645     0.5900801          10.279755
+#> 3    -0.04073137     0.07124478     0.4496260           9.849746
 #>   Intercept_Std_Error Intercept_LCB95Pct Intercept_UCB95Pct Intercept_p_Value
-#> 1          0.03681848           9.923969           10.15831      1.087171e-07
-#> 2          0.07752607           9.602934           10.09638      1.075112e-06
-#> 3          0.16216011           9.737764           10.76990      8.714724e-06
+#> 1           0.1262025           9.716862           10.52013      4.276453e-06
+#> 2           0.2654569           9.434953           11.12456      3.788461e-05
+#> 3           0.2154667           9.164035           10.53546      2.304561e-05
 #>    R_Squared Adj_R_Squared
-#> 1 0.85182757     0.8024368
-#> 2 0.04705017    -0.2705998
-#> 3 0.63864931     0.5181991
+#> 1 0.06857402   -0.24190130
+#> 2 0.10753566   -0.18995246
+#> 3 0.20043923   -0.06608102
 #>
diff --git a/docs/reference/warnprnt.html b/docs/reference/warnprnt.html index 1e57725..dcca243 100644 --- a/docs/reference/warnprnt.html +++ b/docs/reference/warnprnt.html @@ -18,7 +18,7 @@ spsurvey - 5.4.1 + 5.5.0
@@ -74,7 +74,7 @@

Print grts(), irs()), and analysis function warnings

- 
warnprnt(warn_df = get("warn_df", envir = .GlobalEnv), m = 1:nrow(warn_df))
+ 
warnprnt(warn_df = get("warn_df", envir = .GlobalEnv), m = 1:nrow(warn_df))
diff --git a/docs/sitemap.xml b/docs/sitemap.xml index a86f916..88d7dd0 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -33,6 +33,9 @@ /reference/adjwgt.html + + /reference/adjwgtNR.html + /reference/ash1_wgt.html