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validation.R
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library(kableExtra)
# validate the smoothing models by leaving out data from one county at a time
validateExample = function(dat=NULL, leaveOutRegion=FALSE,
startI=1, loadPreviousFit=FALSE, verbose=TRUE, endI=Inf, loadPreviousResults=FALSE) {
targetPop = match.arg(targetPop)
family = match.arg(family)
# load in relevant data for the given example
resultNameRoot="Mort"
if(is.null(dat)) {
out = load("../U5MR/kenyaData.RData")
dat = mort
}
load("../U5MR/popGridAdjusted.RData")
resultNameRootLower = tolower(resultNameRoot)
dataType = resultNameRootLower
clusterEffect = TRUE
# get region names
regions = sort(unique(countyToRegion(as.character(dat$admin1))))
# get the truth aggregated to the pixel level
pixelTruth = getPixelLevelTruth(dat, popMat=NULL)
##### Do validation
resultsListSPDE = list()
# resultsListLKINLA = list()
##### run SPDE
argList = list(list(dat = dat))
otherArguments = list(urbanEffect = TRUE, verbose=verbose,
loadPreviousFit=loadPreviousFit,
loadPreviousResults=loadPreviousResults, stratifiedValidation=!leaveOutRegion)
modelNames = c()
for(i in 1:length(argList)) {
args = argList[[i]]
separateRanges = args$separateRanges
urbanEffect = args$urbanEffect
fileName = paste0("savedOutput/validation/resultsSPDE", resultNameRootLower, "_LORegion", leaveOutRegion)
if(urbanEffect)
urbanText = "U"
else
urbanText = "u"
modelNames = c(modelNames, paste0("spde", urbanText, "C"))
if(i > endI)
return(invisible(NULL))
if(startI <= i) {
print(paste0("Fitting SPDE model with urbanEffect=", urbanEffect, "..."))
results = do.call("validateSPDEKenyaDat", c(args, otherArguments))
results = list(fit=results)
# save(results, file=paste0(fileName, ".RData")) # too much space
results$fit$fullModelFit = NULL
save(results, file=paste0(fileName, "compact.RData"))
} else {
print(paste0("Loading SPDE model results with urbanEffect=", urbanEffect, "..."))
if(file.exists(paste0(fileName, "compact.RData")))
load(paste0(fileName, "compact.RData"))
else {
load(paste0(fileName, ".RData"))
results$fit$fullModelFit = NULL
save(results, file=paste0(fileName, "compact.RData"))
}
}
resultsListSPDE = c(resultsListSPDE, list(results))
}
names(resultsListSPDE) = modelNames
#
# ##### run LK-INLA
# argList = list(list(dat = dat, separateRanges = FALSE, urbanEffect = FALSE),
# list(dat = dat, separateRanges = FALSE, urbanEffect = TRUE),
# list(dat = dat, separateRanges = TRUE, urbanEffect = FALSE),
# list(dat = dat, separateRanges = TRUE, urbanEffect = TRUE))
# otherArguments = list(dataType=dataType, loadPreviousFit=loadPreviousFit, family=family,
# loadPreviousResults=loadPreviousResults, stratifiedValidation=!leaveOutRegion)
#
# for(i in 1:length(argList)) {
# args = argList[[i]]
# separateRanges = args$separateRanges
# urbanEffect = args$urbanEffect
#
# urbanPriorText = ""
# if(!urbanPrior && separateRanges)
# urbanPriorText = "_noUrbanPrior"
#
# fileName = paste0("savedOutput/validation/resultsLKINLA", resultNameRootLower, "_urbanEffect", urbanEffect,
# "_separateRanges", separateRanges, familyText, urbanPriorText, "_LORegion", leaveOutRegion)
# if(separateRanges)
# separateText = "S"
# else
# separateText = "s"
# if(urbanEffect)
# urbanText = "U"
# else
# urbanText = "u"
# modelNames = c(modelNames, paste0("lkinla", urbanText, separateText))
#
# if(i+2 > endI)
# return(invisible(NULL))
#
# if(startI <= i + 2) {
# print(paste0("Fitting LK-INLA model with separateRanges=", separateRanges, " and urbanEffect=", urbanEffect, "..."))
# results = do.call("validateLKINLAKenyaDat", c(args, otherArguments))
#
# results = list(fit=results)
# # save(results, file=paste0(fileName, ".RData")) # too much space
#
# results$fit$fullModelFit = NULL
# save(results, file=paste0(fileName, "compact.RData"))
# } else {
# print(paste0("Loading LK-INLA model results with separateRanges=", separateRanges, " and urbanEffect=", urbanEffect, "..."))
# if(file.exists(paste0(fileName, "compact.RData")))
# load(paste0(fileName, "compact.RData"))
# else {
# load(paste0(fileName, ".RData"))
# results$fit$fullModelFit = NULL
# save(results, file=paste0(fileName, "compact.RData"))
# }
#
# if(names(results$fit)[34] == "") {
# # leftover error from previous run that we can fix without rerunning the whole thing
# names(results$fit)[33:34] = c("singleScores", "singleScoresBinomial")
# save(results, file=paste0(fileName, "compact.RData"))
# }
# }
#
# resultsListLKINLA = c(resultsListLKINLA, list(results))
# }
# names(resultsListLKINLA) = modelNames[3:6]
print("Finished validating models, now concatenating results...")
##### concatenate all scoring rules into a single table (one for in sample, and one for out of sample)
allModelNames = c(names(resultsListSPDE), names(resultsListLKINLA))
## concatenate all in sample results
scoresInSample = rbind(t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$inSamplePooledScores)})),
t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$inSampleUrbanScores)})),
t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$inSampleRuralScores, na.rm=TRUE)})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$inSamplePooledScores)})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$inSampleUrbanScores)})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$inSampleRuralScores, na.rm=TRUE)})))
# reorder them in order to group them by model rather than by urbanicity
tempModelISPDE = seq(1, 1+4, by=2)
tempModelILKINLA = seq(7, 7+4*2, by=4)
scoresInSample = scoresInSample[c(tempModelISPDE, tempModelISPDE+1,
tempModelILKINLA, tempModelILKINLA+1, tempModelILKINLA+2, tempModelILKINLA+3),]
scoresInSample = data.frame(scoresInSample)
# add in column saying how the scores were aggregated (across all clusters, urban clusters, or rural clusters).
# Also add in the model name as the name of the row
scoresInSample = cbind("Subset"=rep(c("Avg", "Urban", "Rural"), 6), scoresInSample)
# rownames(scoresInSample) = rep(allModelNames, each=3)
## concatenate all in sample results (accounting for binomial variation)
scoresInSampleBinomial = rbind(t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$inSamplePooledScoresBinomial)})),
t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$inSampleUrbanScoresBinomial)})),
t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$inSampleRuralScoresBinomial, na.rm=TRUE)})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$inSamplePooledScoresBinomial)})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$inSampleUrbanScoresBinomial)})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$inSampleRuralScoresBinomial, na.rm=TRUE)})))
# reorder them in order to group them by model rather than by urbanicity
tempModelISPDE = seq(1, 1+4, by=2)
tempModelILKINLA = seq(7, 7+4*2, by=4)
scoresInSampleBinomial = scoresInSampleBinomial[c(tempModelISPDE, tempModelISPDE+1,
tempModelILKINLA, tempModelILKINLA+1, tempModelILKINLA+2, tempModelILKINLA+3),]
scoresInSampleBinomial = data.frame(scoresInSampleBinomial)
# add in column saying how the scores were aggregated (across all clusters, urban clusters, or rural clusters).
# Also add in the model name as the name of the row
scoresInSampleBinomial = cbind("Subset"=rep(c("Avg", "Urban", "Rural"), 6), scoresInSampleBinomial)
# rownames(scoresInSample) = rep(allModelNames, each=3)
## concatenate all leave out region results
scoresLeaveOutRegion = rbind(t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$pooledScoreTable[,-1])})),
t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$urbanScoreTable[,-1])})),
t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$ruralScoreTable[,-1], na.rm=TRUE)})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$pooledScoreTable[,-1])})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$urbanScoreTable[,-1])})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$ruralScoreTable[,-1], na.rm=TRUE)})))
# reorder them in order to group them by model rather than by urbanicity
tempModelISPDE = seq(1, 1+4, by=2)
tempModelILKINLA = seq(7, 7+4*2, by=4)
scoresLeaveOutRegion = scoresLeaveOutRegion[c(tempModelISPDE, tempModelISPDE+1,
tempModelILKINLA, tempModelILKINLA+1, tempModelILKINLA+2, tempModelILKINLA+3),]
scoresLeaveOutRegion = data.frame(scoresLeaveOutRegion)
# add in column saying how the scores were aggregated (across all clusters, urban clusters, or rural clusters).
# Also add in the model name as the name of the row
scoresLeaveOutRegion = cbind("Subset"=rep(c("Avg", "Urban", "Rural"), 6), scoresLeaveOutRegion)
# rownames(scoresInSample) = rep(allModelNames, each=3)
## concatenate all leave out region results (accounting for binomial variation)
scoresLeaveOutRegionBinomial = rbind(t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$pooledScoreTableBinomial[,-1])})),
t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$urbanScoreTableBinomial[,-1])})),
t(sapply(resultsListSPDE, function(x) {colMeans(x$fit$ruralScoreTableBinomial[,-1], na.rm=TRUE)})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$pooledScoreTableBinomial[,-1])})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$urbanScoreTableBinomial[,-1])})),
t(sapply(resultsListLKINLA, function(x) {colMeans(x$fit$ruralScoreTableBinomial[,-1], na.rm=TRUE)})))
# reorder them in order to group them by model rather than by urbanicity
tempModelISPDE = seq(1, 1+4, by=2)
tempModelILKINLA = seq(7, 7+4*2, by=4)
scoresLeaveOutRegionBinomial = scoresLeaveOutRegionBinomial[c(tempModelISPDE, tempModelISPDE+1,
tempModelILKINLA, tempModelILKINLA+1, tempModelILKINLA+2, tempModelILKINLA+3),]
scoresLeaveOutRegionBinomial = data.frame(scoresLeaveOutRegionBinomial)
# add in column saying how the scores were aggregated (across all clusters, urban clusters, or rural clusters).
# Also add in the model name as the name of the row
scoresLeaveOutRegionBinomial = cbind("Subset"=rep(c("Avg", "Urban", "Rural"), 6), scoresLeaveOutRegionBinomial)
# rownames(scoresInSample) = rep(allModelNames, each=3)
## get all leave one out results, remove them from the end sample results
leaveOneOutI = sapply(c("CPO", "WAIC", "DIC"), function(x) {which(grepl(x, names(scoresInSample)))})
scoresLeaveOneOut = scoresInSample[,c(1, leaveOneOutI)]
scoresInSample = scoresInSample[,-leaveOneOutI]
scoresLeaveOneOutBinomial = scoresInSampleBinomial[,c(1, leaveOneOutI)]
scoresInSampleBinomial = scoresInSampleBinomial[,-leaveOneOutI]
##### concatenate binned and single scoring rule results
# binnedScoringRulesuuAll=averageBinnedScores(binnedScoringRulesuuAll), binnedScoringRulesuUAll=averageBinnedScores(binnedScoringRulesuUAll),
# binnedScoringRulesUuAll=averageBinnedScores(binnedScoringRulesUuAll), binnedScoringRulesUUAll=averageBinnedScores(binnedScoringRulesUUAll),
# binnedScoringRulesAuAll=averageBinnedScores(binnedScoringRulesAuAll), binnedScoringRulesAUAll=averageBinnedScores(binnedScoringRulesAUAll),
# binnedScoringRulesuAAll=averageBinnedScores(binnedScoringRulesuAAll), binnedScoringRulesUAAll=averageBinnedScores(binnedScoringRulesUAAll),
# binnedScoringRulesAAAll=averageBinnedScores(binnedScoringRulesAAAll),
# binnedScoringRulesuuBinomialAll=averageBinnedScores(binnedScoringRulesuuBinomialAll), binnedScoringRulesuUBinomialAll=averageBinnedScores(binnedScoringRulesuUBinomialAll),
# binnedScoringRulesUuBinomialAll=averageBinnedScores(binnedScoringRulesUuBinomialAll), binnedScoringRulesUUBinomialAll=averageBinnedScores(binnedScoringRulesUUBinomialAll),
# binnedScoringRulesAuBinomialAll=averageBinnedScores(binnedScoringRulesAuBinomialAll), binnedScoringRulesAUBinomialAll=averageBinnedScores(binnedScoringRulesAUBinomialAll),
# binnedScoringRulesuABinomialAll=averageBinnedScores(binnedScoringRulesuABinomialAll), binnedScoringRulesUABinomialAll=averageBinnedScores(binnedScoringRulesUABinomialAll),
# binnedScoringRulesAABinomialAll=averageBinnedScores(binnedScoringRulesAABinomialAll),
#
# singleScores=singleScores, singleScoresBinomial=singleScoresBinomial
binnedScoringRulesuuAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesuuAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesuuAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesuUAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesuUAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesuUAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesUuAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesUuAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesUuAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesUUAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesUUAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesUUAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesAuAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesAuAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesAuAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesAUAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesAUAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesAUAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesuAAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesuAAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesuAAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesUAAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesUAAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesUAAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesAAAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesAAAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesAAAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesuuBinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesuuBinomialAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesuuBinomialAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesuUBinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesuUBinomialAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesuUBinomialAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesUuBinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesUuBinomialAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesUuBinomialAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesUUBinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesUUBinomialAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesUUBinomialAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesAuBinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesAuBinomialAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesAuBinomialAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesAUBinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesAUBinomialAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesAUBinomialAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesuABinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesuABinomialAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesuABinomialAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesUABinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesUABinomialAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesUABinomialAll}))
names(binnedScoringRulesuuAll) = modelNames
binnedScoringRulesAABinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$binnedScoringRulesAABinomialAll}), lapply(resultsListLKINLA, function(x) {x$fit$binnedScoringRulesAABinomialAll}))
names(binnedScoringRulesuuAll) = modelNames
singleScoresAll = c(lapply(resultsListSPDE, function(x) {x$fit$singleScores}), lapply(resultsListLKINLA, function(x) {x$fit$singleScores}))
names(singleScoresAll) = modelNames
singleScoresBinomialAll = c(lapply(resultsListSPDE, function(x) {x$fit$singleScoresBinomial}), lapply(resultsListLKINLA, function(x) {x$fit$singleScoresBinomial}))
names(singleScoresBinomialAll) = modelNames
##### Save all scoring rule tables
urbanPriorText = ""
if(!urbanPrior)
urbanPriorText = "_noUrbanPrior"
fileName = paste0("savedOutput/validation/validationResults", resultNameRoot, familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".RData")
allScores = list(scoresInSample=scoresInSample, scoresLeaveOneOut=scoresLeaveOneOut, scoresLeaveOutRegion=scoresLeaveOutRegion,
scoresInSampleBinomial=scoresInSampleBinomial, scoresLeaveOneOutBinomial=scoresLeaveOneOutBinomial, scoresLeaveOutRegionBinomial=scoresLeaveOutRegionBinomial,
binnedScoringRulesuuAll=binnedScoringRulesuuAll,
binnedScoringRulesuUAll=binnedScoringRulesuUAll,
binnedScoringRulesUuAll=binnedScoringRulesUuAll,
binnedScoringRulesUUAll=binnedScoringRulesUUAll,
binnedScoringRulesAuAll=binnedScoringRulesAuAll,
binnedScoringRulesAUAll=binnedScoringRulesAUAll,
binnedScoringRulesuAAll=binnedScoringRulesuAAll,
binnedScoringRulesUAAll=binnedScoringRulesUAAll,
binnedScoringRulesAAAll=binnedScoringRulesAAAll,
binnedScoringRulesuuBinomialAll=binnedScoringRulesuuBinomialAll,
binnedScoringRulesuUBinomialAll=binnedScoringRulesuUBinomialAll,
binnedScoringRulesUuBinomialAll=binnedScoringRulesUuBinomialAll,
binnedScoringRulesUUBinomialAll=binnedScoringRulesUUBinomialAll,
binnedScoringRulesAuBinomialAll=binnedScoringRulesAuBinomialAll,
binnedScoringRulesAUBinomialAll=binnedScoringRulesAUBinomialAll,
binnedScoringRulesuABinomialAll=binnedScoringRulesuABinomialAll,
binnedScoringRulesUABinomialAll=binnedScoringRulesUABinomialAll,
binnedScoringRulesAABinomialAll=binnedScoringRulesAABinomialAll,
singleScoresAll=singleScoresAll,
singleScoresBinomialAll=singleScoresBinomialAll)
save(allScores, file=fileName)
allScores
}
# print out validation results and plot the PITs
printValidationResults = function(dat=NULL, targetPop=c("women", "children"), family=c("betabinomial", "binomial"), leaveOutRegion=TRUE, urbanPrior=FALSE,
doShortenedTable=TRUE) {
require(stringr)
require(dplyr)
require(kableExtra)
targetPop = match.arg(targetPop)
family = match.arg(family)
familyText=""
if(family == "binomial")
familyText = "_LgtN"
urbanPriorText = ""
if(!urbanPrior)
urbanPriorText = "_noUrbanPrior"
# load in relevant data for the given example
if(targetPop == "women") {
resultNameRoot="Ed"
if(is.null(dat)) {
out = load("../U5MR/kenyaDataEd.RData")
dat = ed
}
load("../U5MR/popGridAdjustedWomen.RData")
} else if(targetPop == "children") {
resultNameRoot="Mort"
if(is.null(dat)) {
out = load("../U5MR/kenyaData.RData")
dat = mort
}
load("../U5MR/popGridAdjusted.RData")
}
resultNameRootLower = tolower(resultNameRoot)
dataType = resultNameRootLower
# get region names
regions = sort(unique(countyToRegion(as.character(dat$admin1))))
# Load all scoring rule tables
fileName = paste0("savedOutput/validation/validationResults", resultNameRoot, familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".RData")
# first load the validation results
out = load(fileName)
# remove the three layer model
threeLayerModel = grepl("lkinlaus", rownames(allScores$scoresLeaveOutRegionBinomial)) | grepl("lkinlaUs", rownames(allScores$scoresLeaveOutRegionBinomial))
allScores$scoresLeaveOutRegionBinomial = allScores$scoresLeaveOutRegionBinomial[!threeLayerModel,]
modelNames = rownames(allScores$scoresLeaveOutRegionBinomial)
# change the modelNames to the adjusted ones:
badNames = c("lkinla", "spde", "uS", "US")
goodNames = c("ELK", "SPDE", "u", "U")
for(i in 1:length(badNames)) {
badName = badNames[i]
goodName = goodNames[i]
whichI = grepl(badName, modelNames)
modelNames[whichI] = gsub(badName, goodName, modelNames[whichI])
}
rownames(allScores$scoresLeaveOutRegionBinomial) = modelNames
allScores$scoresLeaveOutRegionBinomial$Coverage = allScores$scoresLeaveOutRegionBinomial$Coverage * 100
allScores$scoresLeaveOutRegionBinomial$Time.elapsed = allScores$scoresLeaveOutRegionBinomial$Time.elapsed / 60
## print out the scoring rule tables
print("")
print("Leave out county results:")
# scalingPower = c(MSE=2, Var=3, Bias=3)
# displayRow = "s"
# displayMSE = rep("f", 3)
# displayVar = rep("f", 3)
# displayBias = rep("f", 3)
# displayCPO = "f"
# displayCRPS = "f"
# display = c(displayRow, displayMSE, displayVar, displayBias, displayCPO, displayCRPS)
# colScaleMSE = rep(10^scalingPower[1], 3)
# colScaleVar = rep(10^scalingPower[2], 3)
# colScaleBias = rep(10^scalingPower[3], 3)
# colScaleCPO = 1
# colScaleCRPS = 1
# colScale = c(colScaleMSE, colScaleVar, colScaleBias, colScaleCPO, colScaleCRPS)
# colUnitsMSE = rep(paste0(" ($\\times 10^{-", as.character(scalingPower[1]), "}$)"), 3)
# colUnitsVar = rep(paste0(" ($\\times 10^{-", as.character(scalingPower[2]), "}$)"), 3)
# colUnitsBias = rep(paste0(" ($\\times 10^{-", as.character(scalingPower[3]), "}$)"), 3)
# colUnitsCPO = ""
# colUnitsCRPS = ""
# colUnits = c(colUnitsMSE, colUnitsVar, colUnitsBias, colUnitsCPO, colUnitsCRPS)
# digitsMSE = rep(1, 3)
# digitsVar = rep(1, 3)
# digitsBias = rep(1, 3)
# digitsCPO = 2
# digitsCRPS = 2
# if(resultNameRoot == "Mort") {
# digitsCPO = 3
# digitsCRPS = 3
# }
# colDigits = c(digitsMSE, digitsVar, digitsBias, digitsCPO, digitsCRPS)
tab = data.frame(allScores$scoresLeaveOutRegionBinomial)
# for(i in 1:ncol(tab)) {
# tab[,i] = as.numeric(round(unlist(tab[,i]) * colScale[i], digits=colDigits[i]))
# colnames(tab)[i] = paste0(colnames(tab)[i], colUnits[i])
# }
# colnames(tab) = gsub("Urban", "Urb", colnames(tab), fixed=TRUE)
# colnames(tab) = gsub("Rural", "Rur", colnames(tab), fixed=TRUE)
# print(xtable(tab, digits=c(1,colDigits), display=display),
# include.colnames=TRUE,
# hline.after=0,
# math.style.exponents=TRUE,
# sanitize.text.function=function(x){x},
# scalebox=0.5)
# print(xtable(tab, digits=c(1,colDigits), display=display),
# include.colnames=TRUE,
# hline.after=0,
# math.style.exponents=TRUE,
# sanitize.text.function=function(x){x},
# scalebox=0.5)
# tab = t(tab)
# temp = xtable2kable(xtable(tab),
# include.colnames=TRUE,
# hline.after=0,
# math.style.exponents=TRUE,
# sanitize.text.function=function(x){x})
# print(add_header_above(kable_styling(temp), c(" " = 1, "Smoothed Direct" = 1, "BYM2"=4, "SPDE"=4)))
# remove the smoothed direct results
subsets = tab[,1]
tab = tab[,-c(1, 2, 4)]
tab = tab[,c(2,1,3:ncol(tab))]
temp = cbind(round(tab$RMSE, digits=3), round(tab$Var, digits=3), round(tab$CRPS, digits=3), round(tab$Coverage, digits=0), round(tab$Width, digits=2), round(tab$Time.elapsed, digits=1))
rownames(temp) = rownames(tab)
colnames(temp) = colnames(tab)
tab = data.frame(temp)
colnames(tab) = gsub(".", " ", colnames(tab), fixed=TRUE)
if(doShortenedTable) {
# shorten the table by removing results stratified by urban and rural
tab = tab[seq(1, nrow(tab), by=3),]
}
# bold the best entries of each row, italicize worst entries of each row
centers = c(rep(0, 3), 80, 0, 0)
colWorst = apply(abs(sweep(tab, 2, centers, "-")), 2, max, na.rm=TRUE)
colBest = apply(abs(sweep(tab, 2, centers, "-")), 2, min, na.rm=TRUE)
boldFun = function(i) {
vals = tab[,i]
isNA = is.na(vals)
out = rep(FALSE, length(vals))
out[!isNA] = abs(tab[!isNA,i] - centers[i]) == colBest[i]
out
}
italicFun = function(i) {
vals = tab[,i]
isNA = is.na(vals)
out = rep(FALSE, length(vals))
out[!isNA] = abs(tab[!isNA,i] - centers[i]) == colWorst[i]
out
}
if(resultNameRoot == "Ed") {
# bold the best model for each scoring rule, italicize the worst
test = sapply(1:ncol(tab), function(i) {cell_spec(tab[,i], "latex", bold=boldFun(i), italic=italicFun(i),
monospace=FALSE, underline=FALSE, strikeout=FALSE)})
}
else {
# don't bother bolding or italicizing, since all models perform essentially equally
test = tab
}
# revert the column names to their true values
colnames(test) = colnames(tab)
if(!doShortenedTable) {
scoreVariations = c("Avg", "Urban", "Rural")
scoreVariations = rep(scoreVariations, 4)
test = cbind(" "=scoreVariations, test)
rownames(test)=NULL
} else {
rownames(test)=rownames(tab)
}
uniqueModelNames = modelNames[seq(1, length(modelNames), by=3)]
browser()
# group the rows by urbanicity
fullTab = test %>%
kable("latex", escape = F, booktabs = T, format.args=list(drop0trailing=FALSE, scientific=FALSE),
align=c("l", rep("r", ncol(test) - 1))) %>% kable_styling()
if(!doShortenedTable) {
for(i in 1:length(uniqueModelNames)) {
startR = 1 + (i-1) * 3
endR = 3 * i
fullTab = fullTab %>% pack_rows(uniqueModelNames[i], startR, endR, latex_gap_space = "0.3em", escape=FALSE)
}
}
# print(add_header_above(fullTab, c(" " = 1, "BYM2"=4, "SPDE"=4), italic=TRUE, bold=TRUE, escape=FALSE))
print(fullTab)
browser()
print("")
print("Leave out cluster results:")
colScale = colScaleCPO
colDigits = digitsCPO
colUnits = colUnitsCPO
display = c(displayRow, displayCPO)
tab = data.frame(allScores$scoresLeaveOutCluster)
for(i in 1:ncol(tab)) {
tab[,i] = as.numeric(round(unlist(tab[,i]) * colScale[i], digits=colDigits[i]))
colnames(tab)[i] = paste0(colnames(tab)[i], colUnits[i])
}
# print(xtable(tab, digits=c(1,colDigits), display=display),
# include.colnames=TRUE,
# hline.after=0,
# math.style.exponents=TRUE,
# sanitize.text.function=function(x){x},
# scalebox=0.5)
tab = t(tab)
# bold the best entries of each row, italicize worst entries of each row
centers = c(rep(0, nrow(tab)))
rowWorst = apply(abs(sweep(tab, 1, centers, "-")), 1, min, na.rm=TRUE)
rowBest = apply(abs(sweep(tab, 1, centers, "-")), 1, max, na.rm=TRUE)
boldFun = function(i) {
vals = tab[i,]
isNA = is.na(vals)
out = rep(FALSE, length(vals))
out[!isNA] = abs(tab[i,!isNA] - centers[i]) == rowBest[i]
out
}
italicFun = function(i) {
vals = tab[i,]
isNA = is.na(vals)
out = rep(FALSE, length(vals))
out[!isNA] = abs(tab[i,!isNA] - centers[i]) == rowWorst[i]
out
}
test = t(sapply(1:nrow(tab), function(i) {cell_spec(tab[i,], "latex", bold=boldFun(i), italic=italicFun(i),
monospace=FALSE, underline=FALSE, strikeout=FALSE)}))
# revert the column names to their true values
colnames(test) = word(colnames(tab), 2)
rownames(test)= "CPO"
# construct the kable version of the table
fullTab = test %>%
kable("latex", escape = F, booktabs = T, format.args=list(drop0trailing=FALSE, scientific=FALSE),
align=c("l", rep("r", ncol(test) - 1))) %>% kable_styling()
# group the rows by urbanicity
print(add_header_above(fullTab, c(" "=1, "BYM2"=4, "SPDE"=4), italic=TRUE, bold=TRUE, escape=FALSE))
}
plotValidationResults = function(dat=NULL, targetPop=c("women", "children"), leaveOutRegion=FALSE,
family = c("betabinomial", "binomial"), urbanPrior=TRUE) {
targetPop = match.arg(targetPop)
family = match.arg(family)
familyText=""
if(family == "binomial")
familyText = "_LgtN"
urbanPriorText = ""
if(!urbanPrior)
urbanPriorText = "_noUrbanPrior"
# load in relevant data for the given example
if(targetPop == "women") {
resultNameRoot="Ed"
if(is.null(dat)) {
out = load("../U5MR/kenyaDataEd.RData")
dat = ed
}
load("../U5MR/popGridAdjustedWomen.RData")
} else if(targetPop == "children") {
resultNameRoot="Mort"
if(is.null(dat)) {
out = load("../U5MR/kenyaData.RData")
dat = mort
}
load("../U5MR/popGridAdjusted.RData")
}
resultNameRootLower = tolower(resultNameRoot)
dataType = resultNameRootLower
# get region names
regions = sort(unique(countyToRegion(as.character(dat$admin1))))
# Load all scoring rule tables
fileName = paste0("savedOutput/validation/validationResults", resultNameRoot, familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".RData")
# allScores = list(scoresInSample=scoresInSample, scoresLeaveOneOut=scoresLeaveOneOut, scoresLeaveOutRegion=scoresLeaveOutRegion,
# scoresInSampleBinomial=scoresInSampleBinomial, scoresLeaveOneOutBinomial=scoresLeaveOneOutBinomial, scoresLeaveOutRegionBinomial=scoresLeaveOutRegionBinomial,
#
# binnedScoringRulesuuAll=binnedScoringRulesuuAll,
# binnedScoringRulesuUAll=binnedScoringRulesuUAll,
# binnedScoringRulesUuAll=binnedScoringRulesUuAll,
# binnedScoringRulesUUAll=binnedScoringRulesUUAll,
# binnedScoringRulesAuAll=binnedScoringRulesAuAll,
# binnedScoringRulesAUAll=binnedScoringRulesAUAll,
# binnedScoringRulesuAAll=binnedScoringRulesuAAll,
# binnedScoringRulesUAAll=binnedScoringRulesUAAll,
# binnedScoringRulesAAAll=binnedScoringRulesAAAll,
# binnedScoringRulesuuBinomialAll=binnedScoringRulesuuBinomialAll,
# binnedScoringRulesuUBinomialAll=binnedScoringRulesuUBinomialAll,
# binnedScoringRulesUuBinomialAll=binnedScoringRulesUuBinomialAll,
# binnedScoringRulesUUBinomialAll=binnedScoringRulesUUBinomialAll,
# binnedScoringRulesAuBinomialAll=binnedScoringRulesAuBinomialAll,
# binnedScoringRulesAUBinomialAll=binnedScoringRulesAUBinomialAll,
# binnedScoringRulesuABinomialAll=binnedScoringRulesuABinomialAll,
# binnedScoringRulesUABinomialAll=binnedScoringRulesUABinomialAll,
# binnedScoringRulesAABinomialAll=binnedScoringRulesAABinomialAll,
#
# singleScoresAll=singleScoresAll,
# singleScoresBinomialAll=singleScoresBinomialAll)
# save(allScores, file=fileName)
out = load(fileName)
# modelNames = names(allScores$singleScoresBinomialAll)
modelNames = c(expression("SPDE"[u]), expression("SPDE"[U]),
expression("ELK"[ui]), expression("ELK"[Ui]),
expression("ELK"[uI]), expression("ELK"[UI]))
browser()
##### Start with single observation scoring rules
# construct information for separating out prediction types
sortI = sort(allScores$singleScoresBinomialAll$spdeu$dataI, index.return=TRUE)$ix
## pair plots
# plot prediction standard deviations
my_line <- function(x,y,...){
# if(diff(range(x)) >= .04)
xlim = zlim
# else
# xlim = zlim2
# if(diff(range(y)) >= .04)
ylim = zlim
# else
# ylim = zlim2
# if(diff(range(c(x, y))) > 0.04)
# par(usr = c(zlim, zlim))
# else
# par(usr = c(zlim2, zlim2))
# par(usr = c(xlim, ylim))
# points(x,y,..., col="blue")
abline(a = 0,b = 1,...)
points(x[!dat$urban],y[!dat$urban],..., col="green", pch=19, cex=.1)
points(x[dat$urban],y[dat$urban],..., col="blue", pch=19, cex=.1)
}
values = data.frame(allScores$singleScoresBinomialAll$spdeu$Width[sortI], allScores$singleScoresBinomialAll$lkinlaus$Width[sortI], allScores$singleScoresBinomialAll$lkinlauS$Width[sortI])
zlim = range(c(as.matrix(values)))
lims = rep(list(zlim), length(values))
myPairs(values,
labels=modelNames,
lower.panel=my_line, upper.panel = my_line,
main=paste0("80% CI Width"), log="xy",
lims=lims, oma=c(3,3,6,7))
my_line <- function(x,y,...){
# if(diff(range(x)) >= .04)
xlim = zlim
# else
# xlim = zlim2
# if(diff(range(y)) >= .04)
ylim = zlim
# else
# ylim = zlim2
# if(diff(range(c(x, y))) > 0.04)
# par(usr = c(zlim, zlim))
# else
# par(usr = c(zlim2, zlim2))
# par(usr = c(xlim, ylim))
# points(x,y,..., col="blue")
abline(a = 0,b = 1,...)
points(x[!dat$urban],y[!dat$urban],..., col="green", pch=19, cex=.1)
points(x[dat$urban],y[dat$urban],..., col="blue", pch=19, cex=.1)
}
values = data.frame(allScores$singleScoresAll$spdeu$Width[sortI], allScores$singleScoresAll$lkinlaus$Width[sortI], allScores$singleScoresAll$lkinlauS$Width[sortI])
zlim = range(c(as.matrix(values)))
lims = rep(list(zlim), length(values))
myPairs(values,
labels=modelNames,
lower.panel=my_line, upper.panel = my_line,
main=paste0("80% CI Width"),
lims=lims, oma=c(3,3,6,7))
## plot scores versus nearest neighbor distance
plot(allScores$singleScoresBinomialAll$spdeu$NNDist[sortI][!dat$urban],
allScores$singleScoresBinomialAll$spdeu$RMSE[sortI][!dat$urban],
pch=19, cex=.1, col="green",
main="RMSE vs. NN Distance", xlab="Distance (km)", ylab="RMSE", ylim=zlim)
points(allScores$singleScoresBinomialAll$spdeu$NNDist[sortI][dat$urban],
allScores$singleScoresBinomialAll$spdeu$RMSE[sortI][dat$urban],
pch=19, cex=.1, col="blue")
plot(allScores$singleScoresBinomialAll$lkinlaus$NNDist[sortI][!dat$urban],
allScores$singleScoresBinomialAll$lkinlaus$RMSE[sortI][!dat$urban],
pch=19, cex=.1, col="green",
main="RMSE vs. NN Distance", xlab="Distance (km)", ylab="RMSE", ylim=zlim)
points(allScores$singleScoresBinomialAll$lkinlaus$NNDist[sortI][dat$urban],
allScores$singleScoresBinomialAll$lkinlaus$RMSE[sortI][dat$urban],
pch=19, cex=.1, col="blue")
## plot scores spatially
out = load("../U5MR/adminMapData.RData")
kenyaMap = adm0
countyMap = adm1
latRange=c(-4.6, 5)
lonRange=c(33.5, 42.0)
coords = cbind(dat$lon, dat$lat)
popCoords = cbind(popGrid$lon, popGrid$lat)
popUrban = popGrid$urban
values = data.frame(allScores$singleScoresBinomialAll$spdeu$RMSE[sortI],
allScores$singleScoresBinomialAll$lkinlaus$RMSE[sortI],
allScores$singleScoresBinomialAll$lkinlauS$RMSE[sortI])
zlim = range(c(as.matrix(values)))
modelNames = c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI]))
width = 1000
png(file=paste0("Figures/", resultNameRoot, "/spatialRMSE", familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".png"), width=width, height=1000)
par(mfrow=c(2,2), oma=c( 0,0,0,2), mar=c(5.1, 4.1, 4.1, 6))
for(i in 1:ncol(values)) {
quilt.plot(coords, values[,i], xlim=lonRange, ylim=latRange, main=bquote(.(modelNames[[i]]) ~ " RMSE"),
nx=150, ny=150, col=makeRedGrayBlueDivergingColors(n=29, valRange=zlim, center=0), zlim=zlim)
plotMapDat(mapDat=countyMap, lwd=.5)
}
dev.off()
values = data.frame(allScores$singleScoresBinomialAll$spdeu$Bias[sortI],
allScores$singleScoresBinomialAll$lkinlaus$Bias[sortI],
allScores$singleScoresBinomialAll$lkinlauS$Bias[sortI])
zlim = range(c(as.matrix(values)))
modelNames = c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI]))
png(file=paste0("Figures/", resultNameRoot, "/spatialBias", familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".png"), width=width, height=1000)
par(mfrow=c(2,2), oma=c( 0,0,0,2), mar=c(5.1, 4.1, 4.1, 6))
for(i in 1:ncol(values)) {
quilt.plot(coords, values[,i], xlim=lonRange, ylim=latRange, main=bquote(.(modelNames[[i]]) ~ " Bias"),
nx=150, ny=150, col=makeRedGrayBlueDivergingColors(n=29, valRange=zlim, center=0), zlim=zlim)
plotMapDat(mapDat=countyMap, lwd=.5)
}
dev.off()
values = data.frame(allScores$singleScoresBinomialAll$spdeu$Width[sortI],
allScores$singleScoresBinomialAll$lkinlaus$Width[sortI],
allScores$singleScoresBinomialAll$lkinlauS$Width[sortI])
zlim = range(c(as.matrix(values)))
modelNames = c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI]))
png(file=paste0("Figures/", resultNameRoot, "/spatialWidthBinomial", familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".png"), width=width, height=1000)
par(mfrow=c(2,2), oma=c( 0,0,0,2), mar=c(5.1, 4.1, 4.1, 6))
for(i in 1:ncol(values)) {
quilt.plot(coords, values[,i], xlim=lonRange, ylim=latRange, main=bquote(.(modelNames[[i]]) ~ " Width"),
nx=120, ny=120, col=makeBlueYellowSequentialColors(n=64), zlim=zlim)
plotMapDat(mapDat=countyMap, lwd=.5)
}
dev.off()
values = data.frame(allScores$singleScoresAll$spdeu$Width[sortI],
allScores$singleScoresAll$lkinlaus$Width[sortI],
allScores$singleScoresAll$lkinlauS$Width[sortI])
zlim = range(c(as.matrix(values)))
modelNames = c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI]))
png(file=paste0("Figures/", resultNameRoot, "/spatialWidth", familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".png"), width=width, height=1000)
par(mfrow=c(2,2), oma=c( 0,0,0,2), mar=c(5.1, 4.1, 4.1, 6))
quilt.plot(popCoords, popUrban, xlim=lonRange, ylim=latRange, main="Urbanicity",
nx=170, ny=170, col=c(rgb(0,0,0,0), "blue"), add.legend=FALSE)
plotMapDat(mapDat=countyMap, lwd=.5)
for(i in 1:ncol(values)) {
quilt.plot(coords, values[,i], xlim=lonRange, ylim=latRange, main=bquote(.(modelNames[[i]]) ~ " Width"),
nx=120, ny=120, col=makeBlueYellowSequentialColors(n=64), zlim=zlim)
plotMapDat(mapDat=countyMap, lwd=.5)
}
dev.off()
png(file=paste0("Figures/", resultNameRoot, "/womenPerCluster", familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".png"), width=500, height=500)
quilt.plot(coords, dat$n, xlim=lonRange, ylim=latRange, main="Women Per Cluster",
nx=120, ny=120, col=makeBlueYellowSequentialColors(n=64))
plotMapDat(mapDat=countyMap, lwd=.5)
dev.off()
png(file=paste0("Figures/", resultNameRoot, "/womenPerClusterLog", familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".png"), width=500, height=500)
quilt.plot(coords, dat$n, xlim=lonRange, ylim=latRange, main="Women Per Cluster",
nx=120, ny=120, col=makeBlueYellowSequentialColors(n=64), FUN=function(x){mean(log10(x))})
plotMapDat(mapDat=countyMap, lwd=.5)
dev.off()
png(file=paste0("Figures/", resultNameRoot, "/womenPerCell", familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".png"), width=500, height=500)
quilt.plot(coords, dat$n, xlim=lonRange, ylim=latRange, main="Women Per Grid Cell",
nx=120, ny=120, col=makeBlueYellowSequentialColors(n=64), FUN = function(x) {log10(sum(x)+1)})
plotMapDat(mapDat=countyMap, lwd=.5)
dev.off()
png(file=paste0("Figures/", resultNameRoot, "/womenPerCellLog", familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".png"), width=500, height=500)
quilt.plot(coords, dat$n, xlim=lonRange, ylim=latRange, main="Women Per Grid Cell",
nx=120, ny=120, col=makeBlueYellowSequentialColors(n=64), FUN = function(x) {log10(sum(x)+1)})
plotMapDat(mapDat=countyMap, lwd=.5)
dev.off()
png(file=paste0("Figures/", resultNameRoot, "/Variance", familyText, urbanPriorText, "_LORegion", leaveOutRegion, ".png"), width=500, height=500)
quilt.plot(coords, dat$y/dat$n, xlim=lonRange, ylim=latRange, main="Variance",
nx=50, ny=50, col=makeBlueYellowSequentialColors(n=64), FUN = function(x) {var(x)})
plotMapDat(mapDat=countyMap, lwd=.5)
dev.off()
##### Binned scoring rules versus distance (overall)
## RMSE
# AA
values = data.frame(allScores$binnedScoringRulesAABinomialAll$spdeu$RMSE, allScores$binnedScoringRulesAABinomialAll$lkinlaus$RMSE, allScores$binnedScoringRulesAABinomialAll$lkinlauS$RMSE)
ns = allScores$binnedScoringRulesAABinomialAll$spdeu$nPerBin
sizes = sqrt(ns) / 5
zlim = range(c(as.matrix(values)))
cols = rainbow(3)
d = allScores$binnedScoringRulesAABinomialAll$spdeu$NNDist
for(i in 1:ncol(values)) {
if(i == 1) {
plot(d,
values[,i],
cex = 1/sizes, col=cols[i],
main="RMSE vs. Distance From Observation", xlab="Distance (km)", ylab="RMSE", ylim=zlim)
} else
points(d, values[,i], col=cols[i], cex = 1/sizes)
lines(d, values[,i], col=cols[i])
}
legend("topright", c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI])), col=cols, lty=1, pch=1)
# Au
values = data.frame(allScores$binnedScoringRulesAuBinomialAll$spdeu$RMSE, allScores$binnedScoringRulesAuBinomialAll$lkinlaus$RMSE, allScores$binnedScoringRulesAuBinomialAll$lkinlauS$RMSE)
ns = allScores$binnedScoringRulesAuBinomialAll$spdeu$nPerBin
sizes = sqrt(ns) / 5
zlim = range(c(as.matrix(values)))
cols = rainbow(3)
d = allScores$binnedScoringRulesAuBinomialAll$spdeu$NNDist
for(i in 1:ncol(values)) {
if(i == 1) {
plot(d,
values[,i],
cex = 1/sizes, col=cols[i],
main="RMSE vs. Distance From Observation to Rural Pt.", xlab="Distance (km)", ylab="RMSE", ylim=zlim)
} else
points(d, values[,i], col=cols[i], cex = 1/sizes)
lines(d, values[,i], col=cols[i])
}
legend("topright", c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI])), col=cols, lty=1, pch=1)
# Uu
values = data.frame(allScores$binnedScoringRulesUuBinomialAll$spdeu$RMSE, allScores$binnedScoringRulesUuBinomialAll$lkinlaus$RMSE, allScores$binnedScoringRulesUuBinomialAll$lkinlauS$RMSE)
ns = allScores$binnedScoringRulesUuBinomialAll$spdeu$nPerBin
sizes = sqrt(ns) / 5
zlim = range(c(as.matrix(values)))
cols = rainbow(3)
d = allScores$binnedScoringRulesUuBinomialAll$spdeu$NNDist
for(i in 1:ncol(values)) {
if(i == 1) {
plot(d,
values[,i],
cex = 1/sizes, col=cols[i],
main="RMSE vs. Distance From Urban to Rural Pt.", xlab="Distance (km)", ylab="RMSE", ylim=zlim)
} else
points(d, values[,i], col=cols[i], cex = 1/sizes)
lines(d, values[,i], col=cols[i])
}
legend("topright", c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI])), col=cols, lty=1, pch=1)
# uu
values = data.frame(allScores$binnedScoringRulesuuBinomialAll$spdeu$RMSE, allScores$binnedScoringRulesuuBinomialAll$lkinlaus$RMSE, allScores$binnedScoringRulesuuBinomialAll$lkinlauS$RMSE)
ns = allScores$binnedScoringRulesuuBinomialAll$spdeu$nPerBin
sizes = sqrt(ns) / 5
zlim = range(c(as.matrix(values)))
cols = rainbow(3)
d = allScores$binnedScoringRulesuuBinomialAll$spdeu$NNDist
for(i in 1:ncol(values)) {
if(i == 1) {
plot(d,
values[,i],
cex = 1/sizes, col=cols[i],
main="RMSE vs. Distance From Rural to Rural", xlab="Distance (km)", ylab="RMSE", ylim=zlim)
} else
points(d, values[,i], col=cols[i], cex = 1/sizes)
lines(d, values[,i], col=cols[i])
}
legend("topright", c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI])), col=cols, lty=1, pch=1)
## CRPS
# AA
values = data.frame(allScores$binnedScoringRulesAABinomialAll$spdeu$CRPS, allScores$binnedScoringRulesAABinomialAll$lkinlaus$CRPS, allScores$binnedScoringRulesAABinomialAll$lkinlauS$CRPS)
ns = allScores$binnedScoringRulesAABinomialAll$spdeu$nPerBin
sizes = sqrt(ns) / 5
zlim = range(c(as.matrix(values)))
cols = rainbow(3)
d = allScores$binnedScoringRulesAABinomialAll$spdeu$NNDist
for(i in 1:ncol(values)) {
if(i == 1) {
plot(d,
values[,i],
cex = 1/sizes, col=cols[i],
main="CRPS vs. Distance From Observation", xlab="Distance (km)", ylab="CRPS", ylim=zlim)
} else
points(d, values[,i], col=cols[i], cex = 1/sizes)
lines(d, values[,i], col=cols[i])
}
legend("topright", c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI])), col=cols, lty=1, pch=1)
# Au
values = data.frame(allScores$binnedScoringRulesAuBinomialAll$spdeu$CRPS, allScores$binnedScoringRulesAuBinomialAll$lkinlaus$CRPS, allScores$binnedScoringRulesAuBinomialAll$lkinlauS$CRPS)
ns = allScores$binnedScoringRulesAuBinomialAll$spdeu$nPerBin
sizes = sqrt(ns) / 5
zlim = range(c(as.matrix(values)))
cols = rainbow(3)
d = allScores$binnedScoringRulesAuBinomialAll$spdeu$NNDist
for(i in 1:ncol(values)) {
if(i == 1) {
plot(d,
values[,i],
cex = 1/sizes, col=cols[i],
main="CRPS vs. Distance From Observation to Rural Pt.", xlab="Distance (km)", ylab="CRPS", ylim=zlim)
} else
points(d, values[,i], col=cols[i], cex = 1/sizes)
lines(d, values[,i], col=cols[i])
}
legend("topright", c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI])), col=cols, lty=1, pch=1)
# Uu
values = data.frame(allScores$binnedScoringRulesUuBinomialAll$spdeu$CRPS, allScores$binnedScoringRulesUuBinomialAll$lkinlaus$CRPS, allScores$binnedScoringRulesUuBinomialAll$lkinlauS$CRPS)
ns = allScores$binnedScoringRulesUuBinomialAll$spdeu$nPerBin
sizes = sqrt(ns) / 5
zlim = range(c(as.matrix(values)))
cols = rainbow(3)
d = allScores$binnedScoringRulesUuBinomialAll$spdeu$NNDist
for(i in 1:ncol(values)) {
if(i == 1) {
plot(d,
values[,i],
cex = 1/sizes, col=cols[i],
main="CRPS vs. Distance From Urban to Rural Pt.", xlab="Distance (km)", ylab="CRPS", ylim=zlim)
} else
points(d, values[,i], col=cols[i], cex = 1/sizes)
lines(d, values[,i], col=cols[i])
}
legend("topright", c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI])), col=cols, lty=1, pch=1)
# uu
values = data.frame(allScores$binnedScoringRulesuuBinomialAll$spdeu$CRPS, allScores$binnedScoringRulesuuBinomialAll$lkinlaus$CRPS, allScores$binnedScoringRulesuuBinomialAll$lkinlauS$CRPS)
ns = allScores$binnedScoringRulesuuBinomialAll$spdeu$nPerBin
sizes = sqrt(ns) / 5
zlim = range(c(as.matrix(values)))
cols = rainbow(3)
d = allScores$binnedScoringRulesuuBinomialAll$spdeu$NNDist
for(i in 1:ncol(values)) {
if(i == 1) {
plot(d,
values[,i],
cex = 1/sizes, col=cols[i],
main="CRPS vs. Distance From Rural to Rural", xlab="Distance (km)", ylab="CRPS", ylim=zlim)
} else
points(d, values[,i], col=cols[i], cex = 1/sizes)
lines(d, values[,i], col=cols[i])
}
legend("topright", c(expression("SPDE"[u]), expression("ELK"[ui]), expression("ELK"[uI])), col=cols, lty=1, pch=1)