From 0436f2ebd92b42472d4e735d6d8b207e63ba83d2 Mon Sep 17 00:00:00 2001 From: Manny Gimond Date: Mon, 17 Jun 2024 20:41:32 -0400 Subject: [PATCH] release version 0.2.5 --- DESCRIPTION | 2 +- NEWS.md | 4 + R/eda_bipow.R | 6 +- R/eda_re.R | 2 +- R/eda_rline.R | 4 +- R/eda_unipow.R | 6 +- README.Rmd | 1 + README.md | 1 + docs/404.html | 5 +- docs/LICENSE-text.html | 5 +- docs/articles/RLine.html | 7 +- docs/articles/RLine_files/figure-html/r-1.png | Bin 42748 -> 46140 bytes docs/articles/index.html | 9 +- docs/articles/polish.html | 7 +- docs/articles/qq.html | 80 ++++--- docs/articles/symqq.html | 220 ++++++++++++++++++ .../figure-html/unnamed-chunk-2-1.png | Bin 0 -> 55988 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 0 -> 23723 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 0 -> 28437 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 0 -> 27650 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 0 -> 29023 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 0 -> 32635 bytes docs/authors.html | 5 +- docs/index.html | 6 +- docs/news/index.html | 10 +- docs/pkgdown.yml | 3 +- docs/reference/Rplot001.png | Bin 1011 -> 20221 bytes docs/reference/Rplot002.png | Bin 29219 -> 19540 bytes docs/reference/Rplot003.png | Bin 24084 -> 13460 bytes docs/reference/Rplot004.png | Bin 87900 -> 16101 bytes docs/reference/Rplot005.png | Bin 86764 -> 15735 bytes docs/reference/Rplot006.png | Bin 96775 -> 14246 bytes docs/reference/Rplot007.png | Bin 21856 -> 14502 bytes docs/reference/Rplot008.png | Bin 21171 -> 16590 bytes docs/reference/Rplot011.png | Bin 21480 -> 12577 bytes docs/reference/age_height.html | 5 +- docs/reference/eda_3pt.html | 5 +- docs/reference/eda_add.html | 5 +- docs/reference/eda_bipow-1.png | Bin 263397 -> 261560 bytes docs/reference/eda_bipow-2.png | Bin 261117 -> 259569 bytes docs/reference/eda_bipow-3.png | Bin 259569 -> 261117 bytes docs/reference/eda_bipow.html | 11 +- docs/reference/eda_boxls.html | 5 +- docs/reference/eda_dens.html | 5 +- docs/reference/eda_lm.html | 5 +- docs/reference/eda_lsum.html | 5 +- docs/reference/eda_normfit.html | 5 +- docs/reference/eda_pol.html | 5 +- docs/reference/eda_qq-1.png | Bin 83732 -> 54531 bytes docs/reference/eda_qq-11.png | Bin 0 -> 42398 bytes docs/reference/eda_qq-2.png | Bin 69143 -> 54433 bytes docs/reference/eda_qq-3.png | Bin 66559 -> 55692 bytes docs/reference/eda_qq-4.png | Bin 68619 -> 52854 bytes docs/reference/eda_qq.html | 67 ++++-- docs/reference/eda_re.html | 9 +- docs/reference/eda_rline.html | 7 +- docs/reference/eda_sl.html | 5 +- docs/reference/eda_trim.html | 5 +- docs/reference/eda_unipow.html | 11 +- docs/reference/index.html | 7 +- docs/reference/neoplasms.html | 5 +- docs/reference/nine_point.html | 5 +- docs/reference/plot.eda_polish.html | 5 +- docs/reference/plot.eda_rline.html | 5 +- docs/reference/tukeyedar-package.html | 5 +- docs/reference/wat05.html | 5 +- docs/reference/wat95.html | 5 +- docs/search.json | 2 +- docs/sitemap.xml | 3 + man/eda_bipow.Rd | 6 +- man/eda_qq.Rd | 36 ++- man/eda_re.Rd | 2 +- man/eda_rline.Rd | 2 +- man/eda_unipow.Rd | 6 +- vignettes/qq.Rmd | 4 +- 75 files changed, 502 insertions(+), 144 deletions(-) create mode 100644 docs/articles/symqq.html create mode 100644 docs/articles/symqq_files/figure-html/unnamed-chunk-2-1.png create mode 100644 docs/articles/symqq_files/figure-html/unnamed-chunk-4-1.png create mode 100644 docs/articles/symqq_files/figure-html/unnamed-chunk-5-1.png create mode 100644 docs/articles/symqq_files/figure-html/unnamed-chunk-6-1.png create mode 100644 docs/articles/symqq_files/figure-html/unnamed-chunk-7-1.png create mode 100644 docs/articles/symqq_files/figure-html/unnamed-chunk-8-1.png create mode 100644 docs/reference/eda_qq-11.png diff --git a/DESCRIPTION b/DESCRIPTION index c21b65b..a1cbf22 100755 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,5 +1,5 @@ Package: tukeyedar -Version: 0.2.4 +Version: 0.2.5 Type: Package Title: Tukey Inspired Exploratory Data Analysis Functions Authors@R: person(given = "Manuel", diff --git a/NEWS.md b/NEWS.md index 930750e..20afd20 100755 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +# tukeyedar 0.2.5 +* Added symmetry QQ option to `eda_qq` function. +* Ensure that all re-expressions default to Box-Cox method. + # tukeyedar 0.2.4 * Added polynomial option to `eda_lm` function. * Added robust fitting option to `eda_lm` function (uses `MASS::rlm`) diff --git a/R/eda_bipow.R b/R/eda_bipow.R index 4d97f54..efd6bfd 100755 --- a/R/eda_bipow.R +++ b/R/eda_bipow.R @@ -35,11 +35,11 @@ #' eda_bipow(dat = cars, x = speed, y = dist) #' # Custom powers #' eda_bipow(dat = cars, x = speed, y = dist, p = c(-1, -0.5, 0, 0.5, 1)) -#' # Adopt box-cox transformation -#' eda_bipow(dat = cars, x = speed, y = dist, tukey = FALSE, p = c(-1, -0.5, 0, 0.5, 1)) +#' # Adopt Tukey transformation +#' eda_bipow(dat = cars, x = speed, y = dist, tukey = TRUE, p = c(-1, -0.5, 0, 0.5, 1)) #' -eda_bipow <- function(dat, x, y, p = c(-1, 0, 0.5, 1, 2), tukey=TRUE, ...) { +eda_bipow <- function(dat, x, y, p = c(-1, 0, 0.5, 1, 2), tukey = FALSE, ...) { if(!missing(dat)) { diff --git a/R/eda_re.R b/R/eda_re.R index 74e4a75..fc8f7c0 100755 --- a/R/eda_re.R +++ b/R/eda_re.R @@ -19,7 +19,7 @@ #' x <- c(15, 28, 17, 73, 8, 83, 2) #' eda_re(x, p=-1/3) -eda_re <- function(x, p=0, tukey=TRUE){ +eda_re <- function(x, p=0, tukey=FALSE){ if(p == 0) { z <- ifelse(!is.na(x), log(x), NA) } else if(tukey == FALSE & p != 1) { diff --git a/R/eda_rline.R b/R/eda_rline.R index ff98c4d..a9e7a40 100755 --- a/R/eda_rline.R +++ b/R/eda_rline.R @@ -75,7 +75,7 @@ #' -eda_rline <- function(dat, x, y, px = 1, py = 1, tukey = TRUE, maxiter = 20){ +eda_rline <- function(dat, x, y, px = 1, py = 1, tukey = FALSE, maxiter = 20){ if(!missing(dat)) { @@ -168,7 +168,7 @@ eda_rline <- function(dat, x, y, px = 1, py = 1, tukey = TRUE, maxiter = 20){ } -# eda_rline <- function(dat, x, y, px = 1, py = 1, tukey = TRUE, iter = 20){ +# eda_rline <- function(dat, x, y, px = 1, py = 1, tukey = FALSE, iter = 20){ # # if(!missing(dat)) # { diff --git a/R/eda_unipow.R b/R/eda_unipow.R index 1e7b997..22204ef 100755 --- a/R/eda_unipow.R +++ b/R/eda_unipow.R @@ -8,7 +8,7 @@ #' #' @param x Vector #' @param p Vector of powers -#' @param tukey If TRUE (default), apply Tukey's power transformation, if FALSE +#' @param tukey If TRUE, apply Tukey's power transformation. If FALSE #' adopt Box-Cox transformation #' @param bins Number of histogram bins #' @param cex.main Histogram title size (assigned to each histogram plot) @@ -30,11 +30,11 @@ #' #' @examples #' data(mtcars) -#' eda_unipow(mtcars$mpg, bins=6) +#' eda_unipow(mtcars$mpg, bins=6, tukey = TRUE) eda_unipow <- function(x, p = c(2, 1, 1/2, 0.33, 0, -0.33, -1/2, -1, -2), - tukey = TRUE, + tukey = FALSE, bins=5, cex.main=1.3, col="#DDDDDD",border="#AAAAAA", title="Re-expressed data via ladder of powers", diff --git a/README.Rmd b/README.Rmd index 870f680..69930a1 100755 --- a/README.Rmd +++ b/README.Rmd @@ -58,6 +58,7 @@ It’s strongly recommended that you read the vignettes. These can be accessed f - [A detailed rundown of the resistant line function](https://mgimond.github.io/tukeyedar/articles/RLine.html) - [The median polish](https://mgimond.github.io/tukeyedar/articles/polish.html) - [The empirical QQ plot](https://mgimond.github.io/tukeyedar/articles/qq.html) +- [The symmetry QQ plot](https://mgimond.github.io/tukeyedar/articles/symqq.html) If you chose to have the vignettes locally created when you installed the package, then you can view them locally via `vignette("RLine", package = "tukeyedar")`. If you use a dark themed IDE, the vignettes may not render very well so you might opt to view them in a web browser via the functions `RShowDoc("RLine", package = "tukeyedar")`. diff --git a/README.md b/README.md index 6cb7af5..00c3ea6 100755 --- a/README.md +++ b/README.md @@ -52,6 +52,7 @@ accessed from this website: polish](https://mgimond.github.io/tukeyedar/articles/polish.html) - [The empirical QQ plot](https://mgimond.github.io/tukeyedar/articles/qq.html) +- [The symmetry QQ plot](https://mgimond.github.io/tukeyedar/articles/symqq.html) If you chose to have the vignettes locally created when you installed the package, then you can view them locally via diff --git a/docs/404.html b/docs/404.html index 62cca89..c9d428b 100755 --- a/docs/404.html +++ b/docs/404.html @@ -24,7 +24,7 @@ tukeyedar - 0.2.4 + 0.2.5 + + + + + +
+ + + + +
+
+ + + +
+

Introduction +

+

The symmetry QQ plot is inspired by Chambers et al.’s +symmetry plot which pairs the quantiles of the lower +half of a batch of values with matching quantiles of the batch’s upper +half of values. The median value is used to define the halves as +follows:

+

\[ +lower\ half = median - y_i\\ +upper\ half = x_{n+1-i} - median +\]

+

where \(n\) is the number of values +in \(x\), \(i\) = 1 to \(n/2\) if \(n\) is even or \(i\) = 1 to \((n+1)/2\) if \(n\) is odd.

+

The plot is interpreted no differently than a QQ plot. If the data +are symmetrical about the batch’s median value, the points will hug the +1:1 line. For example, given a batch of 1000 normally distributed values +shown in the left density plot, we would expect the symmetry QQ plot to +show the points very close to the 1:1 line as shown in the right +plot.

+

+

The axes show the distance of each observation in the batch to that +batch’s median value. The units are those of the batch. Points that are +close to 0 are those observations closest to the median. Points that are +furthest from 0 are those that are at both tail ends of the +distribution.

+
+
+

The symmetry QQ plot function +

+

The symmetry QQ plot is generated using the eda_qq() +function with the sym = TRUE argument.

+

Before exploring the function and its output, let’s first generate +some data. Here, we’ll create a slightly skewed dataset (one that is +skewed towards large values).

+
+set.seed(123) 
+x <- rgamma(100, shape = 2, scale = 1)  
+

Next, let’s generate the symmetry QQ plot.

+
+library(tukeyedar)
+
+eda_qq(x, sym = TRUE)
+

+

If you are familiar with the use of eda_qq as an +empirical QQ plot function, you are familiar with the grey boxes and +dashed lines that highlight the IQR and mid 75 percent of the batch, +respectively. Here, given that the x and y axes are mapping the lower +and upper halves of the batch, the lower and upper parts of the IQR are +bounded by 0 given that 0 is the central value +of x. The dashed lines are to be interpreted similarly–75% +of the values are to the left of the vertical dashed line and below the +horizontal dashed line.

+

In this example, the points do not hug the 1:1 line, especially +outside of the IQR region–they curve upward away from the 1:1 line. This +is to be expected given that we generated a right skewed dataset. For +example, a point in the lower half of x that is about +1 unit away of the median has a matching quantile in the +upper half of x that is about 1.4 units away +of the median placing it further away from the median than its lower +half counterpart. This skew becomes more pronounced as we move closer to +the tails. The furthest point away from the median is about +1.4 units for the lower half and a little less than +4 units away for the upper half.

+

The eda_qq function allows the use of a re-expression. +This feature can be helpful if one seeks to symmetrize a batch of values +using a power transformation. For example, if we wanted to render +x more symmetrical, we could try a power of +0.5 (i.e. the square root) by setting the argument +p = 0.5.

+
+eda_qq(x, sym = TRUE, p = 0.5)
+

+

Here, the square root transformation does a good job in rendering +x more symmetrical. Note that the points do not hug the 1:1 +exactly–this is fine. What we don’t want to see is a systematic bend in +the points away from the 1:1 line. For example, if we were too +aggressive with the power transformation and chose a log transformation +(p = 0), we would end up with a left skewed batch of +values.

+
+eda_qq(x, sym = TRUE, p = 0)
+

+

The symmetry QQ plot can leverage eda_qq’s built-in +Tukey mean-difference plot (md = TRUE) if a finer grain +resolution of the points vis-a-vis the 1:1 line is desired. Note that +with the Tukey mean-difference plot, the x and y axes values are +different, but this need not matter since we are simply leveraging this +plot to help identify a power transformation that will give us a +symmetrical distribution.

+

Here is the original (untransformed) data in a Tukey mean-difference +plot:

+
+eda_qq(x, sym = TRUE, md = TRUE)
+

+

The 1:1 line is now the horizontal black line centered on 0.

+

Here’s the transformed version of the data:

+
+eda_qq(x, sym = TRUE, md = TRUE, p = 0.5)
+

+
+
+

Reference +

+
    +
  • John M. Chambers, William S. Cleveland, Beat Kleiner, Paul A. Tukey. +Graphical Methods for Data Analysis. 1983.
  • +
+
+
+
+ + + +
+ + + +
+
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