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plotGenerator.R
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plotGenerator.R
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library(colorspace)
library(mgcv)
# makes plots as well as parameter estimate tables for the example applications in the manuscript
makeAllPlots = function(dat=ed, meanRange, meanRange2, meanTicks, meanTicks2, meanTickLabels, meanTickLabels2,
meanRangeSPDE, meanTicksSPDE, meanTickLabelsSPDE, sdRange, sdRange2,
sdTicks, sdTicks2, sdTicksSPDE, sdTickLabels, sdTickLabels2, sdTickLabelsSPDE,
meanRangeND, meanTicksND, meanTickLabelsND, sdRangeND, sdTicksND, sdTickLabelsND,
meanRangeBYM2, meanTicksBYM2, meanTickLabelsBYM2, sdTicksBYM2, sdTickLabelsBYM2,
varName="SEP", plotNameRoot="Education", resultNameRoot="Ed", meanCols=makeRedBlueDivergingColors(64),
sdCols=makeBlueYellowSequentialColors(64), popCols=makeBlueSequentialColors(64),
ncols=29, relativeCols=makeRedGreenDivergingColors(ncols), urbCols=makeGreenBlueSequentialColors(ncols),
plotUrbanMap=FALSE, kenyaLatRange=c(-4.6, 5), kenyaLonRange=c(33.5, 42.0),
makeScreenSplitPlot=FALSE, sharedPredictionScale=TRUE, widthRange, widthTicks,
widthTickLabels, widthRangeSPDE, widthTicksSPDE, widthTickLabelsSPDE) {
plotNameRootLower = tolower(plotNameRoot)
resultNameRootLower = tolower(resultNameRoot)
plotDataVisualizations(dat, meanRange, meanRange2, meanTicks, meanTicks2, meanTickLabels, meanTickLabels2,
meanRangeSPDE, meanTicksSPDE, meanTickLabelsSPDE, sdRange, sdRange2,
sdTicks, sdTicks2, sdTicksSPDE, sdTickLabels, sdTickLabels2, sdTickLabelsSPDE,
meanRangeND, meanTicksND, meanTickLabelsND, sdRangeND, sdTicksND, sdTickLabelsND,
meanRangeBYM2, meanTicksBYM2, meanTickLabelsBYM2, sdTicksBYM2, sdTickLabelsBYM2,
varName, plotNameRoot, resultNameRoot, meanCols, sdCols, popCols, ncols,
relativeCols, urbCols, plotUrbanMap, kenyaLatRange, kenyaLonRange)
plotModelPredictions(dat, meanRange, meanRange2, meanTicks, meanTicks2, meanTickLabels, meanTickLabels2,
meanRangeSPDE, meanTicksSPDE, meanTickLabelsSPDE, sdRange, sdRange2,
sdTicks, sdTicks2, sdTicksSPDE, sdTickLabels, sdTickLabels2, sdTickLabelsSPDE,
meanRangeND, meanTicksND, meanTickLabelsND, sdRangeND, sdTicksND, sdTickLabelsND,
meanRangeBYM2, meanTicksBYM2, meanTickLabelsBYM2, sdTicksBYM2, sdTickLabelsBYM2,
varName, plotNameRoot, resultNameRoot, meanCols, sdCols, popCols, ncols,
relativeCols, urbCols, plotUrbanMap, kenyaLatRange, kenyaLonRange, widthRange, widthTicks,
widthTickLabels, widthRangeSPDE, widthTicksSPDE, widthTickLabelsSPDE)
plotModelPredictionsPart2(dat, meanRange, meanRange2, meanTicks, meanTicks2, meanTickLabels, meanTickLabels2,
meanRangeSPDE, meanTicksSPDE, meanTickLabelsSPDE, sdRange, sdRange2,
sdTicks, sdTicks2, sdTicksSPDE, sdTickLabels, sdTickLabels2, sdTickLabelsSPDE,
meanRangeND, meanTicksND, meanTickLabelsND, sdRangeND, sdTicksND, sdTickLabelsND,
meanRangeBYM2, meanTicksBYM2, meanTickLabelsBYM2, sdTicksBYM2, sdTickLabelsBYM2,
varName, plotNameRoot, resultNameRoot, meanCols, sdCols, popCols, ncols,
relativeCols, urbCols, plotUrbanMap, kenyaLatRange, kenyaLonRange,
sharedPredictionScale=sharedPredictionScale, widthRange, widthTicks,
widthTickLabels, widthRangeSPDE, widthTicksSPDE, widthTickLabelsSPDE)
makePairPlots(dat, meanRange, meanRange2, meanTicks, meanTicks2, meanTickLabels, meanTickLabels2,
meanRangeSPDE, meanTicksSPDE, meanTickLabelsSPDE, sdRange, sdRange2,
sdTicks, sdTicks2, sdTicksSPDE, sdTickLabels, sdTickLabels2, sdTickLabelsSPDE,
meanRangeND, meanTicksND, meanTickLabelsND, sdRangeND, sdTicksND, sdTickLabelsND,
meanRangeBYM2, meanTicksBYM2, meanTickLabelsBYM2, sdTicksBYM2, sdTickLabelsBYM2,
varName, plotNameRoot, resultNameRoot, meanCols, sdCols, popCols, ncols,
relativeCols, urbCols, plotUrbanMap, kenyaLatRange, kenyaLonRange,
makeScreenSplitPlot=makeScreenSplitPlot)
##### now print the parameter estimates:
print("printing parameter estimates...")
# BYM2
argList = list(list(includeUrbanRural = FALSE, includeCluster = FALSE),
list(includeUrbanRural = FALSE, includeCluster = TRUE),
list(includeUrbanRural = TRUE, includeCluster = FALSE),
list(includeUrbanRural = TRUE, includeCluster = TRUE))
for(i in 1:length(argList)) {
args = argList[[i]]
includeUrban = args$includeUrbanRural
includeCluster = args$includeCluster
clusterText = ifelse(includeCluster, "", "NoClust")
nameRoot = paste0('bym2', resultNameRoot, 'UrbRur',includeUrban, 'Cluster', includeCluster)
out = load(paste0(nameRoot, '.RData'))
urbanText = ifelse(includeUrban, "", "noUrb")
clusterText = ifelse(includeCluster, "", "NoClust")
both = includeUrban && includeCluster
notBothText = ifelse(both, "", " ")
# typeText = romanText(i)
if(i == 1)
typeText = "uc"
else if(i == 2)
typeText = "uC"
else if(i == 3)
typeText = "Uc"
else if(i == 4)
typeText = "UC"
# add on interval width
designRes$parameters$width = designRes$parameters$Q90 - designRes$parameters$Q10
# reorder the columns TODO: fix the BYM2 code so this is necessary, so that variance and medians are included, and so that urban effect CI and points are switched
designRes$parameters = designRes$parameters[,c(3, 4, 1, 2, 5)]
# rename the columns
colnames(designRes$parameters) = c("Est", "SD", "Q10", "Q90", "80% CI Width")
print(paste0("Parameter summary table for BYM2 ", typeText, " model:"))
print(xtable(designRes$parameters, digits=3))
}
# SPDE
argList = list(list(clustDat = dat, includeClustEffect = FALSE, urbanEffect = FALSE),
list(clustDat = dat, includeClustEffect = TRUE, urbanEffect = FALSE),
list(clustDat = dat, includeClustEffect = FALSE, urbanEffect = TRUE),
list(clustDat = dat, includeClustEffect = TRUE, urbanEffect = TRUE))
for(i in 1:length(argList)) {
args = argList[[i]]
includeUrban = args$urbanEffect
includeCluster = args$includeClustEffect
clusterText = ifelse(includeCluster, "", "NoClust")
nameRoot = paste0("SPDE", resultNameRootLower, "_includeClustEffect", includeCluster,
"_urbanEffect", includeUrban)
out = load(paste0("results", nameRoot, '.RData'))
urbanText = ifelse(includeUrban, "", "noUrb")
clusterText = ifelse(includeCluster, "", "NoClust")
both = includeUrban && includeCluster
notBothText = ifelse(both, "", " ")
# typeText = romanText(i)
if(i == 1)
typeText = "uc"
else if(i == 2)
typeText = "uC"
else if(i == 3)
typeText = "Uc"
else if(i == 4)
typeText = "UC"
parameters = spdeResults
parameters$resultsPixel = NULL
parameters$resultsCounty = NULL
parameters$resultsRegion = NULL
parameters$resultsCluster = NULL
parameters$pixelDraws = NULL
parameters$fit = NULL
parameters = do.call("rbind", parameters)
parameters = parameters[,-3]
# modify the row names do not include the word "Summary"
allNames = rownames(parameters)
rownames(parameters) = unlist(sapply(allNames, strsplit, split="Summary"))
# rename the columns
colnames(parameters) = c("Est", "SD", "Q10", "Q50", "Q90", "80% CI Width")
print(paste0("Parameter summary table for SPDE ", typeText, " model:"))
print(xtable(parameters, digit=3))
}
invisible(NULL)
}
plotDataVisualizations = function(dat=ed, meanRange, meanRange2, meanTicks, meanTicks2, meanTickLabels, meanTickLabels2,
meanRangeSPDE, meanTicksSPDE, meanTickLabelsSPDE, sdRange, sdRange2,
sdTicks, sdTicks2, sdTicksSPDE, sdTickLabels, sdTickLabels2, sdTickLabelsSPDE,
meanRangeND, meanTicksND, meanTickLabelsND, sdRangeND, sdTicksND, sdTickLabelsND,
meanRangeBYM2, meanTicksBYM2, meanTickLabelsBYM2, sdTicksBYM2, sdTickLabelsBYM2,
varName="SEP", plotNameRoot="Education", resultNameRoot="Ed", meanCols=makeRedBlueDivergingColors(64),
sdCols=makeBlueYellowSequentialColors(64), popCols=makeBlueSequentialColors(64),
ncols=29, relativeCols=makeRedGreenDivergingColors(ncols), urbCols=makeGreenBlueSequentialColors(ncols),
plotUrbanMap=FALSE, kenyaLatRange=c(-4.6, 5), kenyaLonRange=c(33.5, 42.0)) {
plotNameRootLower = tolower(plotNameRoot)
resultNameRootLower = tolower(resultNameRoot)
print("generating data visualizations...")
# plot the actual data
png(file=paste0("figures/", resultNameRoot, "/clustersUrban", plotNameRoot, ".png"), width=500, height=500)
par(oma=c( 0,0,0,0), mar=c(5.1, 4.1, 4.1, 4.1))
urban = dat$urban
plot(dat$lon[!urban], dat$lat[!urban], pch=19, col="green", main=paste0("Urban vs. rural clusters"), xlim=kenyaLonRange,
ylim=kenyaLatRange, xlab="Longitude", ylab="Latitude", cex=.2, asp=1)
points(dat$lon[urban], dat$lat[urban], pch=19, col="blue", cex=.2)
# world(add=TRUE)
plotMapDat(adm1)
dev.off()
# plot a map of urbanicity if requested (this can take ~10 minutes)
if(plotUrbanMap) {
# inside this if statement since it takes around ten minutes to run
makeUrbanMap(kmres=1, savePlot=TRUE, lonLim=kenyaLonRange, latLim=kenyaLatRange)
}
png(file=paste0("figures/", resultNameRoot, "/empirical", plotNameRoot, ".png"), width=500, height=500)
par(oma=c( 0,0,0,2), mar=c(5.1, 4.1, 4.1, 6))
# plot(cbind(dat$lon, dat$lat), type="n", ylim=kenyaLatRange, xlim=kenyaLonRange,
# xlab="Longitude", ylab="Latitude", main=paste0("Empirical ", varName), asp=1)
plot(cbind(dat$lon, dat$lat), type="n", ylim=kenyaLatRange, xlim=kenyaLonRange,
xlab="Longitude", ylab="Latitude", main="", asp=1)
quilt.plot(dat$lon, dat$lat, dat$y / dat$n, nx=150, ny=150, col=meanCols, add=TRUE)
# world(add=TRUE)
plotMapDat(adm1)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/empirical", plotNameRoot, "Logit.png"), width=500, height=500)
par(oma=c( 0,0,0,2), mar=c(5.1, 4.1, 4.1, 6))
ticks = pretty(seq(0, max(dat$y / dat$n), l=10), n=10)
ticks = logit(ticks[-c(1, 11)])
varRange = expit(range(ticks))
# par( oma=c( 0,0,0,5)) # save some room for the legend
plot(cbind(dat$lon, dat$lat), type="n", main=paste0("Kenya empirical ", varName), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(dat$lon, dat$lat), logit(dat$y / dat$n), col=meanCols,
nx=100, ny=100, add.legend=FALSE, add=TRUE, zlim=logit(varRange))
plotMapDat(adm1)
# world(add=TRUE)
# par( oma=c(0,0,0,2))
image.plot(zlim=logit(varRange), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=ticks, labels=expit(ticks)))
dev.off()
png(file=paste0("figures/", resultNameRoot, "/empirical", plotNameRoot, "Discrete.png"), width=500, height=500)
par(oma=c( 0,0,0,0), mar=c(5.1, 4.1, 4.1, 4.1))
totals = aggregate(dat$n, list(dat$admin1), FUN=sum)
counts = aggregate(dat$y, list(dat$admin1), FUN=sum)
# plotMapDat(adm1, plotVar=counts$x / totals$x, new = TRUE, main=paste0("Empirical ", varName), cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=counts$x / totals$x, new = TRUE, main="", cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/denominatorsDiscrete", plotNameRoot, ".png"), width=500, height=500)
par(oma=c( 0,0,0,0), mar=c(5.1, 4.1, 4.1, 4.1))
plotMapDat(adm1, plotVar=totals$x, new = TRUE, main=paste0("Sample size"), cols=popCols, zlim=c(0, max(totals$x)), xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/empirical", varName, "DiscreteLogit.png"), width=500, height=500)
par(oma=c( 0,0,0,0), mar=c(5.1, 4.1, 4.1, 4.1))
totals = aggregate(dat$n, list(dat$admin1), FUN=sum)
counts = aggregate(dat$y, list(dat$admin1), FUN=sum)
plotMapDat(adm1, plotVar=counts$x / totals$x, new = TRUE, main=paste0("Empirical ", varName), col=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels)
dev.off()
png(file="figures/populationDiscrete.png", width=500, height=500)
countyPops = poppc$popTotal
sortI = sort(poppc$County, index.return=TRUE)$ix
countyPops = countyPops[sortI]
par(oma=c( 0,0,0,0), mar=c(5.1, 4.1, 4.1, 4.1))
zlim = range(countyPops)
plotMapDat(adm1, plotVar=countyPops, new = TRUE, main=paste0("Total population"), col=popCols, zlim=log(zlim), scaleFun=log, scaleFunInverse=exp, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
# entirely for testing the extendData function
out = load(paste0("data4direct", resultNameRoot, ".RData"))
png(file=paste0("figures/", resultNameRoot, "/extended", varName, "Discrete.png"), width=500, height=500)
testY = aggregate(resDat$y, list(resDat$admin1), FUN=sum)
testN = aggregate(rep(1, nrow(resDat)), list(resDat$admin1), FUN=sum)
plotMapDat(adm1, plotVar=testY$x / testN$x, new = TRUE, main=paste0("Extended ", varName), cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
popGrid = makeInterpPopGrid(kmRes=5, adjustPopSurface = TRUE)
thisPop = popGrid$popOrig / 5^2
png(file=paste0("figures/populationDensityAdjusted.png"), width=550, height=500)
par(oma=c( 0,0,0,5), mar=c(5.1, 5.1, 4.1, 4), lab.cex=2.0, cex.lab=1.5, cex.axis=1.5, cex.main=2)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=TeX("Adjusted population density (people/$km^2$)"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
popRange = log(c(.1, max(thisPop)))
popTicks = log(pretty(exp(popRange)))
popTicks = c(log(.1), log(1), log(10), log(100), log(1000), popTicks[-c(1, 2, 4, 5, 6)])
tickLabels = as.character(exp(popTicks))
# tickLabels[7] = ""
quilt.plot(cbind(popGrid$lon, popGrid$lat, log(thisPop)), nx=150, ny=150, add.legend=FALSE, add=TRUE,
zlim=range(popTicks), col=popCols)
plotMapDat(adm1, lwd=.5)
image.plot(zlim=popRange, nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=popCols, add = TRUE, axis.args=list(at=popTicks, labels=tickLabels), legend.mar = 0)
dev.off()
popGrid = makeInterpPopGrid(kmRes=5)
thisPop = popGrid$popOrig
totalPop = 43*10^6 # from DHS 2014 survey final report page 2
thisPop = totalPop * thisPop / sum(thisPop) / 5^2 # population density per km^2
png(file=paste0("figures/populationDensity.png"), width=550, height=500)
par(oma=c( 0,0,0,5), mar=c(5.1, 5.1, 4.1, 4), lab.cex=2.0, cex.lab=1.5, cex.axis=1.5, cex.main=2)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=TeX("Population density (people/$km^2$)"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
popRange = log(c(1, max(thisPop)))
popTicks = log(pretty(exp(popRange)))
popTicks = c(log(1), log(10), log(100), log(1000), popTicks[-c(1, 2, 4, 6)])
tickLabels = as.character(exp(popTicks))
tickLabels[7] = ""
quilt.plot(cbind(popGrid$lon, popGrid$lat, log(thisPop)), nx=150, ny=150, add.legend=FALSE, add=TRUE,
zlim=range(popTicks), col=popCols)
plotMapDat(adm1, lwd=.5)
image.plot(zlim=popRange, nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=popCols, add = TRUE, axis.args=list(at=popTicks, labels=tickLabels), legend.mar = 0)
dev.off()
png(file=paste0("figures/populationDensityDiscrete.png"), width=500, height=500)
test = aggregate(thisPop*5^2, list(popGrid$admin1), FUN=sum)
zlim = range(test$x)
ticks = c(100000, 500000, 1000000, 2000000, 3000000, 4000000)
par(mar=c(5.1, 5.1, 4.1, 4), lab.cex=2.0, cex.lab=1.5, cex.axis=1.5, cex.main=2)
plotMapDat(adm1, plotVar=test$x, new = TRUE, main=TeX("Integrated population density"),
col=popCols, zlim=log(zlim), scaleFun=log, scaleFunInverse=exp, ticks = ticks, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
# plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " SD of logit predictions"), ylim=kenyaLatRange,
# xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude")
# quilt.plot(cbind(popGrid$lon, popGrid$lat), log(spdeResults$resultsPixel$sds),
# nx=150, ny=150, add.legend=FALSE, add=TRUE, zlim=range(log(sdRange2)))
# plotMapDat(adm1, lwd=.5)
# points(dat$lon, dat$lat, pch=".")
# image.plot(zlim=range(log(sdRange2)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
# col=cols, add = TRUE, axis.args=list(at=log(sdTicks2), labels=sdTickLabels2), legend.mar = 0)
tab = totals
tab$x = counts$x / totals$x
tab
}
plotModelPredictions = function(dat=ed, meanRange, meanRange2, meanTicks, meanTicks2, meanTickLabels, meanTickLabels2,
meanRangeSPDE, meanTicksSPDE, meanTickLabelsSPDE, sdRange, sdRange2,
sdTicks, sdTicks2, sdTicksSPDE, sdTickLabels, sdTickLabels2, sdTickLabelsSPDE,
meanRangeND, meanTicksND, meanTickLabelsND, sdRangeND, sdTicksND, sdTickLabelsND,
meanRangeBYM2, meanTicksBYM2, meanTickLabelsBYM2, sdTicksBYM2, sdTickLabelsBYM2,
varName="SEP", plotNameRoot="Education", resultNameRoot="Ed", meanCols=makeRedBlueDivergingColors(64),
sdCols=makeBlueYellowSequentialColors(64), popCols=makeBlueSequentialColors(64),
ncols=29, relativeCols=makeRedGreenDivergingColors(ncols), urbCols=makeGreenBlueSequentialColors(ncols),
plotUrbanMap=FALSE, kenyaLatRange=c(-4.6, 5), kenyaLonRange=c(33.5, 42.0), widthRange, widthTicks,
widthTickLabels, widthRangeSPDE, widthTicksSPDE, widthTickLabelsSPDE) {
plotNameRootLower = tolower(plotNameRoot)
resultNameRootLower = tolower(resultNameRoot)
##### Naive and direct estimates
print("plotting direct and naive estimates...")
# save(directEstEd, naiveEd, file="resultsDirectNaiveEd.RData")
out = load(paste0("resultsDirectNaive", resultNameRoot, ".RData"))
plotName = paste0(resultNameRoot, "Preds")
png(file=paste0("figures/", resultNameRoot, "/naive", plotName, ".png"), width=1000, height=1200)
par(mfrow=c(2,2))
zlim = range(c(expit(naiveResults$upper),expit(naiveResults$lower) ))
plotMapDat(adm1, plotVar=naiveResults$est, new = TRUE, main=paste0("Naive ", varName, " estimates"), zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=sqrt(naiveResults$var.est), new = TRUE, main=paste0("Naive SD of logit predictions"), zlim=sdRange, cols=sdCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(naiveResults$upper), new = TRUE, main=paste0("Naive 10th percentile"), zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(naiveResults$lower), new = TRUE, main=paste0("Naive 90th percentile"), zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/naive", plotName, "Logit.png"), width=1000, height=1200)
par(mfrow=c(2,2))
zlim = range(c(expit(naiveResults$upper),expit(naiveResults$lower) ))
plotMapDat(adm1, plotVar=naiveResults$est, new = TRUE, main=paste0("Naive ", varName, " estimates"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels)
plotMapDat(adm1, plotVar=sqrt(naiveResults$var.est), new = TRUE, main=paste0("Naive SD of logit predictions"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=log(sdRange), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks, tickLabels=sdTickLabels, cols=sdCols)
plotMapDat(adm1, plotVar=expit(naiveResults$upper), new = TRUE, main=paste0("Naive 10th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
plotMapDat(adm1, plotVar=expit(naiveResults$lower), new = TRUE, main=paste0("Naive 90th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/direct", plotName, ".png"), width=800, height=1000)
par(mfrow=c(2,2))
zlim = range(c(expit(directEstResults$upper),expit(directEstResults$lower) ))
plotMapDat(adm1, plotVar=directEstResults$est, new = TRUE, main=paste0("Direct ", varName, " estimates"),
zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=sqrt(directEstResults$var.est), new = TRUE, main=paste0("Direct SD of logit predictions"),
zlim=sdRange, cols=sdCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(directEstResults$upper), new = TRUE, main=paste0("Direct 10th percentile"),
zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(directEstResults$lower), new = TRUE, main=paste0("Direct 90th percentile"),
zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/direct", plotName, "Logit.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=directEstResults$est, new = TRUE, main=paste0("Direct ", varName, " estimates"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
plotMapDat(adm1, plotVar=sqrt(directEstResults$var.est), new = TRUE, main=paste0("Direct SD of logit predictions"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=log(sdRange), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks, tickLabels=sdTickLabels, cols=sdCols)
plotMapDat(adm1, plotVar=expit(directEstResults$upper), new = TRUE, main=paste0("Direct 10th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
plotMapDat(adm1, plotVar=expit(directEstResults$lower), new = TRUE, main=paste0("Direct 90th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
dev.off()
##### Mercer et al. estimates
print("plotting mercer estimates...")
# save(mercerEd, file=paste0("resultsMercerEd.RData"))
plotName = paste0("mercer", resultNameRoot, "Preds")
out = load(paste0("resultsMercer", resultNameRoot, ".RData" ))
zlim = range(c(expit(mercerResults$lower.mercer),expit(mercerResults$upper.mercer)))
png(file=paste0("figures/", resultNameRoot, "/", plotName, ".png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=mercerResults$est.mercer, new = TRUE, main=paste0("Smoothed direct ", varName, " estimates"),
zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=sqrt(mercerResults$var.est.mercer), new = TRUE, main=paste0("Smoothed direct SD of logit predictions"),
zlim=sdRange, cols=sdCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(mercerResults$lower.mercer), new = TRUE, main=paste0("Smoothed direct 10th percentile"),
zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(mercerResults$upper.mercer), new = TRUE, main=paste0("Smoothed direct 90th percentile"),
zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/", plotName, "Logit.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=mercerResults$est.mercer, new = TRUE, main=paste0("Smoothed direct ", varName, " estimates"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
plotMapDat(adm1, plotVar=sqrt(mercerResults$var.est.mercer), new = TRUE, main=paste0("Smoothed direct SD of logit predictions"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=log(sdRange), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks, tickLabels=sdTickLabels, cols=sdCols)
plotMapDat(adm1, plotVar=expit(mercerResults$lower.mercer), new = TRUE, main=paste0("Smoothed direct 10th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
plotMapDat(adm1, plotVar=expit(mercerResults$upper.mercer), new = TRUE, main=paste0("Smoothed direct 90th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
dev.off()
zlim = range(c(expit(mercerResults$lower.mercer),expit(mercerResults$upper.mercer)))
png(file=paste0("figures/", resultNameRoot, "/", plotName, "2.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=mercerResults$est.mercer, new = TRUE, main=paste0("Smoothed direct ", varName, " estimates"),
zlim=meanRange2, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=sqrt(mercerResults$var.est.mercer), new = TRUE, main=paste0("Smoothed direct SD of logit predictions"),
zlim=sdRange2, cols=sdCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(mercerResults$lower.mercer), new = TRUE, main=paste0("Smoothed direct 10th percentile"),
zlim=meanRange2, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(mercerResults$upper.mercer), new = TRUE, main=paste0("Smoothed direct 90th percentile"),
zlim=meanRange2, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/", plotName, "Logit2.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=mercerResults$est.mercer, new = TRUE, main=paste0("Smoothed direct ", varName, " estimates"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, cols=meanCols)
plotMapDat(adm1, plotVar=sqrt(mercerResults$var.est.mercer), new = TRUE, main=paste0("Smoothed direct SD of logit predictions"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=log(sdRange2), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks2, tickLabels=sdTickLabels2, cols=sdCols)
plotMapDat(adm1, plotVar=expit(mercerResults$lower.mercer), new = TRUE, main=paste0("Smoothed direct 10th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, cols=meanCols)
plotMapDat(adm1, plotVar=expit(mercerResults$upper.mercer), new = TRUE, main=paste0("Smoothed direct 90th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, cols=meanCols)
dev.off()
##### BYM2 estimates
print("plotting BYM2 estimates...")
# save(file = paste0('bym2EdUrbRur',includeUrbanRural, 'Cluster', includeCluster, '.RData'),
# designRes = designRes)
# if(includeCluster) {
# save(file = paste0('bym2EdUrbRur',includeUrbanRural, 'Cluster', includeCluster, 'debiased.RData'),
# designRes = designRes)
# }
argList = list(list(includeUrbanRural = FALSE, includeCluster = FALSE),
list(includeUrbanRural = FALSE, includeCluster = TRUE),
list(includeUrbanRural = TRUE, includeCluster = FALSE),
list(includeUrbanRural = TRUE, includeCluster = TRUE))
for(i in 1:length(argList)) {
args = argList[[i]]
includeUrban = args$includeUrbanRural
includeCluster = args$includeCluster
nameRoot = paste0('bym2', resultNameRoot, 'UrbRur',includeUrban, 'Cluster', includeCluster)
out = load(paste0(nameRoot, '.RData'))
# typeText = romanText(i)
if(i == 1)
typeText = "uc"
else if(i == 2)
typeText = "uC"
else if(i == 3)
typeText = "Uc"
else if(i == 4)
typeText = "UC"
zlim = range(c(expit(designRes$predictions$Q10),expit(designRes$predictions$Q90)))
png(file=paste0("figures/", resultNameRoot, "/", nameRoot, ".png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=expit(designRes$predictions$mean), new = TRUE, main=paste0("BYM2 ", typeText, " ", varName, " estimates"),
zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=designRes$predictions$stddev, new = TRUE, main=paste0("BYM2 ", typeText, " SD of logit predictions"),
zlim=sdRange, cols=sdCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q10), new = TRUE, main=paste0("BYM2 ", typeText, " 10th percentile"),
zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q90), new = TRUE, main=paste0("BYM2 ", typeText, " 90th percentile"),
zlim=meanRange, cols=meanCols, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/", nameRoot, "Logit.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=expit(designRes$predictions$mean), new = TRUE, main=paste0("BYM2 ", typeText, " ", varName, " estimates"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
plotMapDat(adm1, plotVar=designRes$predictions$stddev, new = TRUE, main=paste0("BYM2 ", typeText, " SD of logit predictions"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=log(sdRange), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks, tickLabels=sdTickLabels, cols=sdCols)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q10), new = TRUE, main=paste0("BYM2 ", typeText, " 10th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q90), new = TRUE, main=paste0("BYM2 ", typeText, " 90th percentile"), xlim=kenyaLonRange, ylim=kenyaLatRange,
zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, cols=meanCols)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/", nameRoot, "2.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=expit(designRes$predictions$mean), new = TRUE, main=paste0("BYM2 ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=meanRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=designRes$predictions$stddev, new = TRUE, main=paste0("BYM2 ", typeText, " SD of logit predictions"), cols=sdCols, zlim=sdRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q10), new = TRUE, main=paste0("BYM2 ", typeText, " 10th percentile"), cols=meanCols, zlim=meanRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q90), new = TRUE, main=paste0("BYM2 ", typeText, " 90th percentile"), cols=meanCols, zlim=meanRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/", nameRoot, "Logit2.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=expit(designRes$predictions$mean), new = TRUE, main=paste0("BYM2 ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=designRes$predictions$stddev, new = TRUE, main=paste0("BYM2 ", typeText, " SD of logit predictions"), cols=sdCols, zlim=log(sdRange2), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks2, tickLabels=sdTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q10), new = TRUE, main=paste0("BYM2 ", typeText, " 10th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q90), new = TRUE, main=paste0("BYM2 ", typeText, " 90th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
if(includeCluster) {
# also gather and plot the debiased results
nameRoot = paste0('bym2', resultNameRoot, 'UrbRur',includeUrban, 'Cluster', includeCluster, "debiased")
out = load(paste0(nameRoot, '.RData'))
# typeText = paste0(typeText, "'")
png(file=paste0("figures/", resultNameRoot, "/", nameRoot, ".png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=expit(designRes$predictions$mean), new = TRUE, main=paste0("BYM2 ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=meanRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=designRes$predictions$stddev, new = TRUE, main=paste0("BYM2 ", typeText, " SD of logit predictions"), cols=sdCols, zlim=sdRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q10), new = TRUE, main=paste0("BYM2 ", typeText, " 10th percentile"), cols=meanCols, zlim=meanRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q90), new = TRUE, main=paste0("BYM2 ", typeText, " 90th percentile"), cols=meanCols, zlim=meanRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/", nameRoot, "Logit.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=expit(designRes$predictions$mean), new = TRUE, main=paste0("BYM2 ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=designRes$predictions$stddev, new = TRUE, main=paste0("BYM2 ", typeText, " SD of logit predictions"), cols=sdCols, zlim=log(sdRange), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks, tickLabels=sdTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q10), new = TRUE, main=paste0("BYM2 ", typeText, " 10th percentile"), cols=meanCols, zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q90), new = TRUE, main=paste0("BYM2 ", typeText, " 90th percentile"), cols=meanCols, zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/", nameRoot, "2.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=expit(designRes$predictions$mean), new = TRUE, main=paste0("BYM2 ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=meanRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=designRes$predictions$stddev, new = TRUE, main=paste0("BYM2 ", typeText, " SD of logit predictions"), cols=sdCols, zlim=sdRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q10), new = TRUE, main=paste0("BYM2 ", typeText, " 10th percentile"), cols=meanCols, zlim=meanRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q90), new = TRUE, main=paste0("BYM2 ", typeText, " 90th percentile"), cols=meanCols, zlim=meanRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/", nameRoot, "Logit2.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=expit(designRes$predictions$mean), new = TRUE, main=paste0("BYM2 ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=designRes$predictions$stddev, new = TRUE, main=paste0("BYM2 ", typeText, " SD of logit predictions"), cols=sdCols, zlim=log(sdRange2), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks2, tickLabels=sdTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q10), new = TRUE, main=paste0("BYM2 ", typeText, " 10th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q90), new = TRUE, main=paste0("BYM2 ", typeText, " 90th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
}
}
##### SPDE estimates
print("plotting SPDE estimates...")
argList = list(list(clustDat = dat, includeClustEffect = FALSE, urbanEffect = FALSE),
list(clustDat = dat, includeClustEffect = TRUE, urbanEffect = FALSE),
list(clustDat = dat, includeClustEffect = FALSE, urbanEffect = TRUE),
list(clustDat = dat, includeClustEffect = TRUE, urbanEffect = TRUE))
for(i in 1:length(argList)) {
args = argList[[i]]
includeUrban = args$urbanEffect
includeCluster = args$includeClustEffect
clusterText = ifelse(includeCluster, "", "NoClust")
nameRoot = paste0("SPDE", resultNameRootLower, "_includeClustEffect", includeCluster,
"_urbanEffect", includeUrban)
out = load(paste0("results", nameRoot, '.RData'))
urbanText = ifelse(includeUrban, "", "noUrb")
clusterText = ifelse(includeCluster, "", "NoClust")
both = includeUrban && includeCluster
notBothText = ifelse(both, "", " ")
# typeText = romanText(i)
if(i == 1)
typeText = "uc"
else if(i == 2)
typeText = "uC"
else if(i == 3)
typeText = "Uc"
else if(i == 4)
typeText = "UC"
zlim = range(c(spdeResults$resultsCounty$lower,spdeResults$resultsCounty$upper))
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, ".png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$pred, new = TRUE, main=paste0("SPDE ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=meanRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$sds, new = TRUE, main=paste0("SPDE ", typeText, " SD of logit predictions"), cols=sdCols, zlim=sdRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$lower, new = TRUE, main=paste0("SPDE ", typeText, " 10th percentile"), cols=meanCols, zlim=meanRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$upper, new = TRUE, main=paste0("SPDE ", typeText, " 90th percentile"), cols=meanCols, zlim=meanRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "Logit.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$pred, new = TRUE, main=paste0("SPDE ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$sds, new = TRUE, main=paste0("SPDE ", typeText, " SD of logit predictions"), cols=sdCols, zlim=log(sdRange), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks, tickLabels=sdTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$lower, new = TRUE, main=paste0("SPDE ", typeText, " 10th percentile"), cols=meanCols, zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$upper, new = TRUE, main=paste0("SPDE ", typeText, " 90th percentile"), cols=meanCols, zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "2.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$pred, new = TRUE, main=paste0("SPDE ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=meanRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$sds, new = TRUE, main=paste0("SPDE ", typeText, " SD of logit predictions"), cols=sdCols, zlim=sdRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$lower, new = TRUE, main=paste0("SPDE ", typeText, " 10th percentile"), cols=meanCols, zlim=meanRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$upper, new = TRUE, main=paste0("SPDE ", typeText, " 90th percentile"), cols=meanCols, zlim=meanRange2, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "Logit2.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$pred, new = TRUE, main=paste0("SPDE ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$sds, new = TRUE, main=paste0("SPDE ", typeText, " SD of logit predictions"), cols=sdCols, zlim=log(sdRange2), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks2, tickLabels=sdTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$lower, new = TRUE, main=paste0("SPDE ", typeText, " 10th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$upper, new = TRUE, main=paste0("SPDE ", typeText, " 90th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
## plot continuous prediction surface
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, ".png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$pred, new = TRUE, main=paste0("SPDE ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=meanRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$sds, new = TRUE, main=paste0("SPDE ", typeText, " SD of logit predictions"), cols=sdCols, zlim=sdRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$lower, new = TRUE, main=paste0("SPDE ", typeText, " 10th percentile"), cols=meanCols, zlim=meanRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$upper, new = TRUE, main=paste0("SPDE ", typeText, " 90th percentile"), cols=meanCols, zlim=meanRange, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "Logit.png"), width=800, height=1000)
par(mfrow=c(2,2))
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$pred, new = TRUE, main=paste0("SPDE ", typeText, " ", varName, " estimates"), cols=meanCols, zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$sds, new = TRUE, main=paste0("SPDE ", typeText, " SD of logit predictions"), cols=sdCols, zlim=log(sdRange), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks, tickLabels=sdTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$lower, new = TRUE, main=paste0("SPDE ", typeText, " 10th percentile"), cols=meanCols, zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$upper, new = TRUE, main=paste0("SPDE ", typeText, " 90th percentile"), cols=meanCols, zlim=logit(meanRange), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks, tickLabels=meanTickLabels, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "ContinuousLogit.png"), width=800, height=1000)
par(mfrow=c(2,2), oma=c( 0,0,0,1.5), mar=c(5.1, 4.1, 4.1, 6))
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " ", varName, " estimates"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$pred),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRange)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(logit(meanRange)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicks), labels=meanTickLabels), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " SD of logit predictions"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), log(spdeResults$resultsPixel$sds),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=sdCols, zlim=range(log(sdRange)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(log(sdRange)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=sdCols, add = TRUE, axis.args=list(at=log(sdTicks), labels=sdTickLabels), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " 10th percentile"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$lower),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRange)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(logit(meanRange)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicks), labels=meanTickLabels), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " 90th percentile"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$upper),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRange)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(logit(meanRange)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicks), labels=meanTickLabels), legend.mar = 5)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "ContinuousLogit2.png"), width=800, height=1000)
par(mfrow=c(2,2), oma=c( 0,0,0,1.5), mar=c(5.1, 4.1, 4.1, 6))
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " ", varName, " estimates"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$pred),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRange2)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(logit(meanRange2)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicks2), labels=meanTickLabels2), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " SD of logit predictions"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), log(spdeResults$resultsPixel$sds),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=sdCols, zlim=range(log(sdRange2)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(log(sdRange2)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=sdCols, add = TRUE, axis.args=list(at=log(sdTicks2), labels=sdTickLabels2), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " 10th percentile"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$lower),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRange2)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(logit(meanRange2)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicks2), labels=meanTickLabels2), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " 90th percentile"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$upper),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRange2)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(logit(meanRange2)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicks2), labels=meanTickLabels2), legend.mar = 5)
dev.off()
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "ContinuousLogitSelf.png"), width=800, height=1000)
par(mfrow=c(2,2), oma=c( 0,0,0,1.5), mar=c(5.1, 4.1, 4.1, 6))
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " ", varName, " estimates"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$pred),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRangeSPDE)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(logit(meanRangeSPDE)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicksSPDE), labels=meanTickLabelsSPDE), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " SD of logit predictions"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), log(spdeResults$resultsPixel$sds),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=sdCols, zlim=range(log(sdRangeSPDE)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(log(sdRangeSPDE)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=sdCols, add = TRUE, axis.args=list(at=log(sdTicksSPDE), labels=sdTickLabelsSPDE), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " 10th percentile"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$lower),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRangeSPDE)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(logit(meanRangeSPDE)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicksSPDE), labels=meanTickLabelsSPDE), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " 90th percentile"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$upper),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRangeSPDE)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
image.plot(zlim=range(logit(meanRangeSPDE)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicksSPDE), labels=meanTickLabelsSPDE), legend.mar = 5)
dev.off()
}
##### now put the predictions from each together in the same plot when relevant
# 1: put naive and direct together with standard deviations and credible intervals
## make two by four plot with these two models and lower, mean, and upper bounds, and SDs
# 2: put values from the smooth models including all effects together:
## mercer, BYM2 with urban and cluster effects, SPDE with urban and cluster effects
## make three by four plot with these three models and lower, mean, and upper bounds, and SDs
# 3: plot test spde models together
## 4 x 4 plot showing estimates, sds, 10% and 90% CIs
# 4: plot relative differences between the models, relative to the BYM2 model including urban and cluster effects that has been debiased
## 2 x 2 plot
# 5: plot the BYM2 models together
## 4 x 6 plot
# 6: plot relative differences between the BYM2 models
## 3 x 2 plot (one of the subplots will be empty)
# 7: plot relative differences between the SPDE models on a continuous scale
## 2 x 2 plot, all relative to the model with both urban and cluster effects
# 8: plot relative differences between the SPDE models on a discrete scale
## 2 x 2 plot
# 9: make pairs plot of the full models with respect to each other
## Plot 1: direct and naive models
print("plotting direct and naive estimates together...")
out = load(paste0("resultsDirectNaive", resultNameRoot, ".RData" ))
png(file=paste0("figures/", resultNameRoot, "/fullDirectNaive", plotNameRoot, ".png"), width=800, height=1200)
par(mfrow=c(4,2))
plotMapDat(adm1, plotVar=naiveResults$est, new = TRUE, main=paste0("Naive ", varName, " estimates"), cols=meanCols, zlim=logit(meanRangeND), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicksND, tickLabels=meanTickLabelsND, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=directEstResults$est, new = TRUE, main=paste0("Direct ", varName, " estimates"), cols=meanCols, zlim=logit(meanRangeND), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicksND, tickLabels=meanTickLabelsND, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=sqrt(naiveResults$var.est), new = TRUE, main=paste0("Naive SD of logit predictions"), cols=sdCols, zlim=log(sdRangeND), scaleFun=log, scaleFunInverse=exp, ticks=sdTicksND, tickLabels=sdTickLabelsND, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=sqrt(directEstResults$var.est), new = TRUE, main=paste0("Direct SD of logit predictions"), cols=sdCols, zlim=log(sdRangeND), scaleFun=log, scaleFunInverse=exp, ticks=sdTicksND, tickLabels=sdTickLabelsND, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(naiveResults$upper), new = TRUE, main=paste0("Naive 10th percentile"), cols=meanCols, zlim=logit(meanRangeND), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicksND, tickLabels=meanTickLabelsND, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(directEstResults$upper), new = TRUE, main=paste0("Direct 10th percentile"), cols=meanCols, zlim=logit(meanRangeND), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicksND, tickLabels=meanTickLabelsND, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(naiveResults$lower), new = TRUE, main=paste0("Naive 90th percentile"), cols=meanCols, zlim=logit(meanRangeND), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicksND, tickLabels=meanTickLabelsND, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(directEstResults$lower), new = TRUE, main=paste0("Direct 90th percentile"), cols=meanCols, zlim=logit(meanRangeND), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicksND, tickLabels=meanTickLabelsND, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
## Plot 2: mercer, BYM2 with urban and cluster effects, SPDE with urban and cluster effects
print("plotting smoothed estimates together...")
png(file=paste0("figures/", resultNameRoot, "/fullSmoothed", plotNameRoot, ".png"), width=1000, height=1200)
par(mfrow=c(4,3))
includeUrban = TRUE
includeCluster = TRUE
clusterText = ifelse(includeCluster, "", "NoClust")
nameRoot = paste0('bym2', resultNameRoot, 'UrbRur',includeUrban, 'Cluster', includeCluster, "debiased")
out = load(paste0(nameRoot, '.RData'))
urbanText = ifelse(includeUrban, "", "noUrb")
clusterText = ifelse(includeCluster, "", "NoClust")
both = includeUrban && includeCluster
debiasedText = "Debiased"
# typeTextBYM = "UC"
typeTextBYM = "UC"
includeUrban = TRUE
includeCluster = TRUE
clusterText = ifelse(includeCluster, "", "NoClust")
nameRoot = paste0("SPDE", resultNameRootLower, "_includeClustEffect", includeCluster,
"_urbanEffect", includeUrban)
out = load(paste0("results", nameRoot, '.RData'))
urbanText = ifelse(includeUrban, "", "noUrb")
clusterText = ifelse(includeCluster, "", "NoClust")
both = includeUrban && includeCluster
notBothText = ifelse(both, "", " ")
# typeTextSPDE = "UC"
typeTextSPDE = "UC"
plotMapDat(adm1, plotVar=mercerResults$est.mercer, new = TRUE, main=paste0("Smoothed direct ", varName, " estimates"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$mean), new = TRUE, main=paste0("BYM2 ", typeTextBYM, " ", varName, " estimates"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$pred, new = TRUE, main=paste0("SPDE ", typeTextSPDE, " ", varName, " estimates"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=sqrt(mercerResults$var.est.mercer), new = TRUE, main=paste0("Smoothed direct SD of logit predictions"), cols=sdCols, zlim=log(sdRange2), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks2, tickLabels=sdTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=designRes$predictions$stddev, new = TRUE, main=paste0("BYM2 ", typeTextBYM, " SD of logit predictions"), cols=sdCols, zlim=log(sdRange2), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks2, tickLabels=sdTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$sds, new = TRUE, main=paste0("SPDE ", typeTextSPDE, " SD of logit predictions"), cols=sdCols, zlim=log(sdRange2), scaleFun=log, scaleFunInverse=exp, ticks=sdTicks2, tickLabels=sdTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(mercerResults$lower.mercer), new = TRUE, main=paste0("Smoothed direct 10th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q10), new = TRUE, main=paste0("BYM2 ", typeTextBYM, " 10th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$lower, new = TRUE, main=paste0("SPDE ", typeTextSPDE, " 10th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(mercerResults$upper.mercer), new = TRUE, main=paste0("Smoothed direct 90th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=expit(designRes$predictions$Q90), new = TRUE, main=paste0("BYM2 ", typeTextBYM, " 90th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
plotMapDat(adm1, plotVar=spdeResults$resultsCounty$upper, new = TRUE, main=paste0("SPDE ", typeTextSPDE, " 90th percentile"), cols=meanCols, zlim=logit(meanRange2), scaleFun=logit, scaleFunInverse=expit, ticks=meanTicks2, tickLabels=meanTickLabels2, xlim=kenyaLonRange, ylim=kenyaLatRange)
dev.off()
## Plot 3: plot all of the SPDE plots together (4 x 4 plot)
print("plotting SPDE models together...")
argList = list(list(clustDat = dat, includeClustEffect = FALSE, urbanEffect = FALSE),
list(clustDat = dat, includeClustEffect = TRUE, urbanEffect = FALSE),
list(clustDat = dat, includeClustEffect = FALSE, urbanEffect = TRUE),
list(clustDat = dat, includeClustEffect = TRUE, urbanEffect = TRUE))
# png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "ContinuousTogetherSelf.png"), width=1400, height=1400)
# par(mfrow=c(4,4), oma=c( 0,0,0,1.5), mar=c(5.1, 4.1, 4.1, 6))
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "ContinuousTogetherSelf.png"), width=1400, height=700)
par(mfrow=c(2,4), oma=c( 0,0,0,1.5), mar=c(5.1, 4.1, 4.1, 6))
for(i in 1:length(argList)) {
args = argList[[i]]
includeUrban = args$urbanEffect
includeCluster = args$includeClustEffect
clusterText = ifelse(includeCluster, "", "NoClust")
nameRoot = paste0("SPDE", resultNameRootLower, "_includeClustEffect", includeCluster,
"_urbanEffect", includeUrban)
out = load(paste0("results", nameRoot, '.RData'))
urbanText = ifelse(includeUrban, "", "noUrb")
clusterText = ifelse(includeCluster, "", "NoClust")
both = includeUrban && includeCluster
notBothText = ifelse(both, "", " ")
# typeText = romanText(i)
if(i == 1)
typeText = "uc"
else if(i == 2)
typeText = "uC"
else if(i == 3)
typeText = "Uc"
else if(i == 4)
typeText = "UC"
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " ", varName, " estimates"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$pred),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRangeSPDE)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
if(i == 4) {
image.plot(zlim=range(logit(meanRangeSPDE)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicksSPDE), labels=meanTickLabelsSPDE), legend.mar = 0)
}
}
for(i in 1:length(argList)) {
args = argList[[i]]
includeUrban = args$urbanEffect
includeCluster = args$includeClustEffect
clusterText = ifelse(includeCluster, "", "NoClust")
nameRoot = paste0("SPDE", resultNameRootLower, "_includeClustEffect", includeCluster,
"_urbanEffect", includeUrban)
out = load(paste0("results", nameRoot, '.RData'))
urbanText = ifelse(includeUrban, "", "noUrb")
clusterText = ifelse(includeCluster, "", "NoClust")
both = includeUrban && includeCluster
notBothText = ifelse(both, "", " ")
# typeText = romanText(i)
if(i == 1)
typeText = "uc"
else if(i == 2)
typeText = "uC"
else if(i == 3)
typeText = "Uc"
else if(i == 4)
typeText = "UC"
# don't plot the SDs. Instead plut 80% interval widths
# plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " SD of logit predictions"), ylim=kenyaLatRange,
# xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
# quilt.plot(cbind(popGrid$lon, popGrid$lat), log(spdeResults$resultsPixel$sds),
# nx=150, ny=150, add.legend=FALSE, add=TRUE, col=sdCols, zlim=range(log(sdRangeSPDE)))
# plotMapDat(adm1, lwd=.5)
# points(dat$lon, dat$lat, pch=".")
# image.plot(zlim=range(log(sdRangeSPDE)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
# col=sdCols, add = TRUE, axis.args=list(at=log(sdTicksSPDE), labels=sdTickLabelsSPDE), legend.mar = 0)
plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " 80% credible interval width"), ylim=kenyaLatRange,
xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
quilt.plot(cbind(popGrid$lon, popGrid$lat), log(spdeResults$resultsPixel$upper-spdeResults$resultsPixel$lower),
nx=150, ny=150, add.legend=FALSE, add=TRUE, col=sdCols, zlim=range(log(widthRangeSPDE)))
plotMapDat(adm1, lwd=.5)
points(dat$lon, dat$lat, pch=".")
if(i == 4) {
image.plot(zlim=range(log(widthRangeSPDE)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
col=sdCols, add = TRUE, axis.args=list(at=log(widthTicksSPDE), labels=widthTickLabelsSPDE), legend.mar = 0)
}
}
# don't plot the actual credible interval:
# for(i in 1:length(argList)) {
# args = argList[[i]]
# includeUrban = args$urbanEffect
# includeCluster = args$includeClustEffect
# clusterText = ifelse(includeCluster, "", "NoClust")
#
# nameRoot = paste0("SPDE", resultNameRootLower, "_includeClustEffect", includeCluster,
# "_urbanEffect", includeUrban)
# out = load(paste0("results", nameRoot, '.RData'))
#
# urbanText = ifelse(includeUrban, "", "noUrb")
# clusterText = ifelse(includeCluster, "", "NoClust")
# both = includeUrban && includeCluster
# notBothText = ifelse(both, "", " ")
# # typeText = romanText(i)
# if(i == 1)
# typeText = "uc"
# else if(i == 2)
# typeText = "uC"
# else if(i == 3)
# typeText = "Uc"
# else if(i == 4)
# typeText = "UC"
#
# plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " 10th percentile"), ylim=kenyaLatRange,
# xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
# quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$lower),
# nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRangeSPDE)))
# plotMapDat(adm1, lwd=.5)
# points(dat$lon, dat$lat, pch=".")
# image.plot(zlim=range(logit(meanRangeSPDE)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
# col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicksSPDE), labels=meanTickLabelsSPDE), legend.mar = 0)
# }
#
# for(i in 1:length(argList)) {
# args = argList[[i]]
# includeUrban = args$urbanEffect
# includeCluster = args$includeClustEffect
# clusterText = ifelse(includeCluster, "", "NoClust")
#
# nameRoot = paste0("SPDE", resultNameRootLower, "_includeClustEffect", includeCluster,
# "_urbanEffect", includeUrban)
# out = load(paste0("results", nameRoot, '.RData'))
#
# urbanText = ifelse(includeUrban, "", "noUrb")
# clusterText = ifelse(includeCluster, "", "NoClust")
# both = includeUrban && includeCluster
# notBothText = ifelse(both, "", " ")
# # typeText = romanText(i)
# if(i == 1)
# typeText = "uc"
# else if(i == 2)
# typeText = "uC"
# else if(i == 3)
# typeText = "Uc"
# else if(i == 4)
# typeText = "UC"
#
# plot(cbind(popGrid$lon, popGrid$lat), type="n", main=paste0("SPDE ", typeText, " 90th percentile"), ylim=kenyaLatRange,
# xlim=kenyaLonRange, xlab="Longitude", ylab="Latitude", asp=1)
# quilt.plot(cbind(popGrid$lon, popGrid$lat), logit(spdeResults$resultsPixel$upper),
# nx=150, ny=150, add.legend=FALSE, add=TRUE, col=meanCols, zlim=range(logit(meanRangeSPDE)))
# plotMapDat(adm1, lwd=.5)
# points(dat$lon, dat$lat, pch=".")
# image.plot(zlim=range(logit(meanRangeSPDE)), nlevel=length(cols), legend.only=TRUE, horizontal=FALSE,
# col=meanCols, add = TRUE, axis.args=list(at=logit(meanTicksSPDE), labels=meanTickLabelsSPDE), legend.mar = 5)
# }
dev.off()
##### Plot 3.5: plot continuous cluster models together
argList = list(list(clustDat = dat, includeClustEffect = TRUE, urbanEffect = FALSE),
list(clustDat = dat, includeClustEffect = TRUE, urbanEffect = TRUE))
# png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "ContinuousTogetherSelf.png"), width=1400, height=1400)
# par(mfrow=c(4,4), oma=c( 0,0,0,1.5), mar=c(5.1, 4.1, 4.1, 6))
png(file=paste0("figures/", resultNameRoot, "/preds", nameRoot, "ContinuousTogetherSelf.png"), width=1400, height=700)
par(mfrow=c(2,2), oma=c( 0,0,0,1.5), mar=c(5.1, 4.1, 4.1, 6))
for(i in 1:length(argList)) {
args = argList[[i]]