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designBased.R
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designBased.R
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##################################################################################
# designBased.R #
# Produce county level estimates using pixel-wise spatial model with #
# aggregation using true population and BYM spatial model #
##################################################################################
# same as runBYM, except fits the BYM2 reparameterized model (iid component in the BYM2 is that the county level)
runBYM2 = function(tausq=0.1^2, test=FALSE, includeUrbanRural=TRUE, includeCluster=TRUE, maxDataSets=NULL,
aggregateByPopulation=FALSE, margVar=0.15^2, gamma=-1, plotPriorPost=FALSE, strictPrior=FALSE,
seed=NULL, effRange=150) {
if(!is.null(seed))
set.seed(seed)
# load and relevant data
rangeText = ifelse(effRange == 150, "", "Range50")
if(!test)
load(paste0("simDataMultiBeta-1.75margVar", round(margVar, 4), "tausq", round(tausq, 4), "gamma", round(gamma, 4),
"HHoldVar0urbanOverSamplefrac0", rangeText, ".RData"))
else
load(paste0("simDataMultiBeta-1.75margVar", round(margVar, 4), "tausq", round(tausq, 4), "gamma", round(gamma, 4),
"HHoldVar0urbanOverSamplefrac0Test", rangeText, ".RData"))
# Get true ratios of urban/rural
urbRatio = vector('numeric', length = 47)
counties = sort(unique(as.character(SRSDat$eaDat$admin1)))
if(!aggregateByPopulation) {
for(i in 1:47){
# compute the exact proportion of children that are in urban areas
idx = which(as.numeric(factor(SRSDat$eaDat$admin1)) == i)
urbanI = idx[SRSDat$eaDat$urban[idx]]
urbRatio[i] = sum(SRSDat$eaDat$numChildren[urbanI])/sum(SRSDat$eaDat$numChildren[idx])
}
urbRatioAdjusted = urbRatio
} else {
# now compute the proportion of the population that is urban (possibly adjusting for
# the number of children per unit population being different in urban and rural areas)
getUrbanRatios = function(aggregationWeightTable) {
urbRatio = aggregationWeightTable$popUrb / aggregationWeightTable$popTotal
sortI = matchMultiple(counties, aggregationWeightTable$County)
urbRatio = urbRatio[sortI]
}
urbRatio = getUrbanRatios(poppc)
urbRatioAdjusted = getUrbanRatios(adjustPopulationPerCountyTable())
}
sortI = matchMultiple(counties, easpc$County)
clustersPerUrban = easpc$EAUrb[sortI]
clustersPerRural = easpc$EARur[sortI]
clustersPerCounty = rowSums(cbind(clustersPerUrban, clustersPerRural))
# Define formula
if(!strictPrior)
hyperList = list(param=c(1, 0.01), prior="pc.prec")
else
hyperList = list(param=c(.15, 0.01), prior="pc.prec")
if(!strictPrior)
clusterList = list(param=c(1, 0.01), prior="pc.prec")
else
clusterList = list(param=c(.15, 0.01), prior="pc.prec")
if(includeUrbanRural) {
if(includeCluster) {
formula = y ~ urban +
f(idx, model="bym2",
graph="Kenyaadm1.graph", scale.model=TRUE, constr=TRUE,
hyper=list(prec=hyperList, phi=list(param=c(0.5, 2/3), prior="pc"))) +
f(idxEps, model = "iid",
hyper = list(prec = clusterList))
} else {
formula = y ~ urban +
f(idx, model="bym2",
graph="Kenyaadm1.graph", scale.model=TRUE, constr=TRUE,
hyper=list(prec=hyperList, phi=list(param=c(0.5, 2/3), prior="pc")))
}
} else {
if(includeCluster) {
formula = y ~ f(idx, model="bym2",
graph="Kenyaadm1.graph", scale.model=TRUE, constr=TRUE,
hyper=list(prec=hyperList,
phi=list(param=c(0.5, 2/3), prior="pc"))) +
f(idxEps, model = "iid",
hyper = list(prec = clusterList))
} else {
formula = y ~
f(idx, model="bym2",
graph="Kenyaadm1.graph", scale.model=TRUE, constr=TRUE,
hyper=list(prec=clusterList, phi=list(param=c(0.5, 2/3), prior="pc")))
}
}
# Number of simulations for producing results
Nsim = 10000
# Help functions
logit = function(x){
return(log(x/(1-x)))
}
expit = function(x){
return(1/(1+exp(-x)))
}
if(includeCluster)
parNames = c("Intercept", "Urban", "Cluster Var", "BYM2 Phi", "BYM2 Tot. Var", "BYM2 Spatial Var", "BYM2 iid Var", "Cluster SD", "BYM2 Tot. SD", "BYM2 Spatial SD", "BYM2 iid SD")
else
parNames = c("Intercept", "Urban", "BYM2 Phi", "BYM2 Tot. Var", "BYM2 Spatial Var", "BYM2 iid Var", "BYM2 Tot. SD", "BYM2 Spatial SD", "BYM2 iid SD")
includeI = c(1, rep(2, includeUrbanRural), 3:length(parNames))
parNames = parNames[includeI]
# set the maximum number of data sets to analyze
if(is.null(maxDataSets)) {
maxDataSets = length(SRSDat$clustDat)
}
# Go through datasets for SRSDat
sampCountySRSDat = array(NA, dim = c(47, Nsim, maxDataSets))
sampCountySRSDatAdjusted = array(NA, dim = c(47, Nsim, maxDataSets))
sampCountySRSDatMod = array(NA, dim = c(47, Nsim, maxDataSets))
sampClusterSRSDatUrban = array(NA, dim = c(47, Nsim, maxDataSets))
sampClusterSRSDatRural = array(NA, dim = c(47, Nsim, maxDataSets))
sampPixelSRSDatUrban = array(NA, dim = c(47, Nsim, maxDataSets))
sampPixelSRSDatRural = array(NA, dim = c(47, Nsim, maxDataSets))
sampPixelSRSDatUrbanMod = array(NA, dim = c(47, Nsim, maxDataSets))
sampPixelSRSDatRuralMod = array(NA, dim = c(47, Nsim, maxDataSets))
# for the final parameters to store, 2 fixed effects, 2 + includeCluster estimated
# hyperparameters, and 2 hyperparameters we will get via transformation
sampCountySRSDatPar = matrix(NA, length(parNames), ncol=maxDataSets)
sampCountySRSDatSD = matrix(NA, length(parNames), ncol=maxDataSets)
sampCountySRSDat10 = matrix(NA, length(parNames), ncol=maxDataSets)
sampCountySRSDat50 = matrix(NA, length(parNames), ncol=maxDataSets)
sampCountySRSDat90 = matrix(NA, length(parNames), ncol=maxDataSets)
rownames(sampCountySRSDatPar) = parNames
rownames(sampCountySRSDatSD) = parNames
rownames(sampCountySRSDat10) = parNames
rownames(sampCountySRSDat50) = parNames
rownames(sampCountySRSDat90) = parNames
for(i in 1:maxDataSets){
# Extract data
currData = SRSDat$clustDat[[i]]
currData$admin1 = factor(currData$admin1)
# INLA data
dat = list(y = currData$died,
Ntrials = currData$numChildren,
urban = currData$urban,
idx = as.numeric(currData$admin1),
idxEps = 1:length(currData$died))
# Add unobserved data to make sampling easier
dat$y = c(rep(NA, 47*2), dat$y)
dat$Ntrials = c(rep(1, 47*2), dat$Ntrials)
dat$urban = c(rep(c(1,0), each = 47), dat$urban)
dat$idx = c(rep(1:47, 2), dat$idx)
dat$idxEps = c(rep(NA, 47*2), dat$idxEps)
# Run model
print(paste0("fitting SRS model for dataset ", i, "/", maxDataSets))
result = inla(formula = formula,
family="binomial",
Ntrials = Ntrials,
data=dat,
control.compute = list(config = TRUE),
quantiles=c(0.1, 0.5, 0.9))
if(plotPriorPost) {
# maxX = max(result$marginals.hyperpar[[1]][,1]) * 1.1
maxX = inla.qmarginal(0.8, result$marginals.hyperpar[[1]])
xs = exp(seq(-5, log(maxX), l=1500))
ys = inla.pc.dprec(xs, 1, 0.01)
maxY = max(max(ys), max(result$marginals.hyperpar[[1]][,2]))
plot(xs, ys, type="l", col="blue", xlab="BYM2 Precision", ylab="Density", xlim=c(0, maxX),
ylim=c(0, maxY), main="BYM2 Precision Prior vs Posterior")
lines(result$marginals.hyperpar[[1]], col="red")
legend("topright", c("Prior", "Posterior"), col=c("blue", "red"), lty=1)
}
## include parameter estimates in the table
# fixed effects
sampCountySRSDatPar[1:(1 + includeUrbanRural), i] = result$summary.fixed[,1]
sampCountySRSDatSD[1:(1 + includeUrbanRural), i] = result$summary.fixed[,2]
sampCountySRSDat10[1:(1 + includeUrbanRural), i] = result$summary.fixed[,3]
sampCountySRSDat50[1:(1 + includeUrbanRural), i] = result$summary.fixed[,4]
sampCountySRSDat90[1:(1 + includeUrbanRural), i] = result$summary.fixed[,5]
## include parameter estimates in the table
# fixed effects
sampCountySRSDatPar[1:(1 + includeUrbanRural), i] = result$summary.fixed[,1]
sampCountySRSDatSD[1:(1 + includeUrbanRural), i] = result$summary.fixed[,2]
sampCountySRSDat10[1:(1 + includeUrbanRural), i] = result$summary.fixed[,3]
sampCountySRSDat50[1:(1 + includeUrbanRural), i] = result$summary.fixed[,3]
sampCountySRSDat90[1:(1 + includeUrbanRural), i] = result$summary.fixed[,5]
# BYM2 hyperparameter phi
sampCountySRSDatPar[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,1]
sampCountySRSDatSD[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,2]
sampCountySRSDat10[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,3]
sampCountySRSDat50[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,3]
sampCountySRSDat90[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,5]
## transformed hyperparameters
# sample the hyperparameters, using the marginals to improve the sampling
out = inla.hyperpar.sample(1000, result, improve.marginals=TRUE)
transformFunction = function(x) {c(1/x[3], x[2], 1/x[1], 1/x[1]*x[2], 1/x[1]*(1-x[2]), sqrt(1/x[3]), sqrt(1/x[1]), sqrt(1/x[1]*x[2]), sqrt(1/x[1]*(1-x[2])))}
if(!includeCluster)
transformFunction = function(x) {c(1/x[1], x[2], 1/x[1]*x[2], 1/x[1]*(1-x[2]), sqrt(1/x[1]), sqrt(1/x[1]*x[2]), sqrt(1/x[1]*(1-x[2])))}
transformedOut = apply(out, 1, transformFunction)
# now calculate the summary statistics of the transformed BYM2 hyperparameters
sampCountySRSDatPar[(2 + includeUrbanRural):length(parNames), i] = rowMeans(transformedOut)
sampCountySRSDatSD[(2 + includeUrbanRural):length(parNames), i] = apply(transformedOut, 1, sd)
sampCountySRSDat10[(2 + includeUrbanRural):length(parNames), i] = apply(transformedOut, 1, quantile, probs=.1)
sampCountySRSDat50[(2 + includeUrbanRural):length(parNames), i] = apply(transformedOut, 1, quantile, probs=.5)
sampCountySRSDat90[(2 + includeUrbanRural):length(parNames), i] = apply(transformedOut, 1, quantile, probs=.9)
# Simulate from posterior
if(includeUrbanRural) {
samp = inla.posterior.sample(n = Nsim, result = result)
sampRural = matrix(NA, nrow = 47, ncol = Nsim)
sampUrban = matrix(NA, nrow = 47, ncol = Nsim)
sampRuralMod = matrix(NA, nrow = 47, ncol = Nsim)
sampUrbanMod = matrix(NA, nrow = 47, ncol = Nsim)
for(j in 1:Nsim){
sampRural[, j] = samp[[j]]$latent[47 + (1:47)]
sampUrban[, j] = samp[[j]]$latent[1:47]
if(includeCluster) {
# if cluster effect is included, must debias predictions in each modeled strata
clusterSigma = sqrt(1/samp[[j]]$hyperpar[3])
# muSigmaMatRural = cbind(sampRural[, j], clusterSigma)
# muSigmaMatUrban = cbind(sampUrban[, j], clusterSigma)
# sampRuralMod[, j] = logitNormMean(muSigmaMat = muSigmaMatRural)
# sampUrbanMod[, j] = logitNormMean(muSigmaMat = muSigmaMatUrban)
sampRuralMod[, j] = sapply(1:length(clustersPerRural), function(i) {mean(expit(sampRural[i, j] + rnorm(clustersPerRural[i], sd=clusterSigma)))})
sampRuralMod[clustersPerRural == 0,] = 0 # we just need to set these values to something other than NaN
sampUrbanMod[, j] = sapply(1:length(clustersPerUrban), function(i) {mean(expit(sampUrban[i, j] + rnorm(clustersPerUrban[i], sd=clusterSigma)))})
sampClusterSRSDatRural[,j,i] = sampRural[, j] + rnorm(47, sd=clusterSigma)
sampClusterSRSDatUrban[,j,i] = sampUrban[, j] + rnorm(47, sd=clusterSigma)
}
}
sampCountySRSDat[,,i] = logit(expit(sampUrban)*urbRatio + expit(sampRural)*(1-urbRatio))
sampCountySRSDatAdjusted[,,i] = logit(expit(sampUrban)*urbRatioAdjusted + expit(sampRural)*(1-urbRatioAdjusted))
sampCountySRSDatMod[,,i] = logit(sampUrbanMod*urbRatioAdjusted + sampRuralMod*(1-urbRatioAdjusted))
if(!includeCluster) {
sampClusterSRSDatRural[,,i] = sampRural
sampClusterSRSDatUrban[,,i] = sampUrban
} else {
sampPixelSRSDatUrbanMod[,,i] = sampUrbanMod
sampPixelSRSDatRuralMod[,,i] = sampRuralMod
}
sampPixelSRSDatUrban[,,i] = sampUrban
sampPixelSRSDatRural[,,i] = sampRural
} else {
samp = inla.posterior.sample(n = Nsim, result = result)
sampCounty = matrix(NA, nrow = 47, ncol = Nsim)
sampCountyMod = matrix(NA, nrow = 47, ncol = Nsim)
for(j in 1:Nsim){
sampCounty[, j] = samp[[j]]$latent[1:47]
if(includeCluster) {
# if cluster effect is included, must debias predictions in each modeled strata
clusterSigma = sqrt(1/samp[[j]]$hyperpar[3])
# muSigmaMat = cbind(sampCounty[, j], clusterSigma)
# sampCountyMod[, j] = logitNormMean(muSigmaMat = muSigmaMat
sampCountyMod[, j] = sapply(1:length(clustersPerCounty), function(i) {mean(expit(sampCounty[i, j] + rnorm(clustersPerCounty[i], sd=clusterSigma)))})
sampClusterSRSDatRural[,j,i] = expit(sampCounty[, j] + rnorm(47, sd=clusterSigma))
sampClusterSRSDatUrban[,j,i] = sampClusterSRSDatRural[,j,i]
}
}
sampCountySRSDat[,,i] = sampCounty
sampCountySRSDatAdjusted[,,i] = sampCounty
sampCountySRSDatMod[,,i] = logit(sampCountyMod)
sampPixelSRSDatUrban[,,i] = sampCounty
sampPixelSRSDatRural[,,i] = sampCounty
if(!includeCluster) {
sampClusterSRSDatRural[,,i] = sampCounty
sampClusterSRSDatUrban[,,i] = sampCounty
} else {
sampPixelSRSDatUrbanMod[,,i] = sampCountyMod
sampPixelSRSDatRuralMod[,,i] = sampCountyMod
}
}
}
# Go through datasets for overSampDat
sampCountyOverSampDat = array(NA, dim = c(47, Nsim, maxDataSets))
sampCountyOverSampDatAdjusted = array(NA, dim = c(47, Nsim, maxDataSets))
sampCountyOverSampDatMod = array(NA, dim = c(47, Nsim, maxDataSets))
sampClusterOverSampDatUrban = array(NA, dim = c(47, Nsim, maxDataSets))
sampClusterOverSampDatRural = array(NA, dim = c(47, Nsim, maxDataSets))
sampPixelOverSampDatUrban = array(NA, dim = c(47, Nsim, maxDataSets))
sampPixelOverSampDatRural = array(NA, dim = c(47, Nsim, maxDataSets))
sampPixelOverSampDatUrbanMod = array(NA, dim = c(47, Nsim, maxDataSets))
sampPixelOverSampDatRuralMod = array(NA, dim = c(47, Nsim, maxDataSets))
# for the final parameters to store, 2 fixed effects, 2 + includeCluster estimated
# hyperparameters, and 2 hyperparameters we will get via transformation
sampCountyOverSampDatPar = matrix(NA, length(parNames), ncol=maxDataSets)
sampCountyOverSampDatSD = matrix(NA, length(parNames), ncol=maxDataSets)
sampCountyOverSampDat10 = matrix(NA, length(parNames), ncol=maxDataSets)
sampCountyOverSampDat50 = matrix(NA, length(parNames), ncol=maxDataSets)
sampCountyOverSampDat90 = matrix(NA, length(parNames), ncol=maxDataSets)
rownames(sampCountyOverSampDatPar) = parNames
rownames(sampCountyOverSampDatSD) = parNames
rownames(sampCountyOverSampDat10) = parNames
rownames(sampCountyOverSampDat50) = parNames
rownames(sampCountyOverSampDat90) = parNames
for(i in 1:maxDataSets) {
# Extract data
currData = overSampDat$clustDat[[i]]
currData$admin1 = factor(currData$admin1)
# INLA data
dat = list(y = currData$died,
Ntrials = currData$numChildren,
urban = currData$urban,
idx = as.numeric(currData$admin1),
idx2 = as.numeric(currData$admin1),
idxEps = 1:length(currData$died))
# Add unobserved data to make sampling easier
dat$y = c(rep(NA, 47*2), dat$y)
dat$Ntrials = c(rep(1, 47*2), dat$Ntrials)
dat$urban = c(rep(c(1,0), each = 47), dat$urban)
dat$idx = c(rep(1:47, 2), dat$idx)
dat$idx2 = c(rep(1:47, 2), dat$idx2)
dat$idxEps = c(rep(NA, 47*2), dat$idxEps)
# Run model
print(paste0("fitting urban oversampled model for dataset ", i, "/", maxDataSets))
result = inla(formula = formula,
family="binomial",
Ntrials = Ntrials,
data=dat, quantiles = c(0.1, 0.5, 0.9),
control.compute = list(config = TRUE))
# fixed effects
sampCountyOverSampDatPar[1:(1 + includeUrbanRural), i] = result$summary.fixed[,1]
sampCountyOverSampDatSD[1:(1 + includeUrbanRural), i] = result$summary.fixed[,2]
sampCountyOverSampDat10[1:(1 + includeUrbanRural), i] = result$summary.fixed[,3]
sampCountyOverSampDat50[1:(1 + includeUrbanRural), i] = result$summary.fixed[,3]
sampCountyOverSampDat90[1:(1 + includeUrbanRural), i] = result$summary.fixed[,5]
## include parameter estimates in the table
# fixed effects
sampCountyOverSampDatPar[1:(1 + includeUrbanRural), i] = result$summary.fixed[,1]
sampCountyOverSampDatSD[1:(1 + includeUrbanRural), i] = result$summary.fixed[,2]
sampCountyOverSampDat10[1:(1 + includeUrbanRural), i] = result$summary.fixed[,3]
sampCountyOverSampDat50[1:(1 + includeUrbanRural), i] = result$summary.fixed[,3]
sampCountyOverSampDat90[1:(1 + includeUrbanRural), i] = result$summary.fixed[,5]
# BYM2 hyperparameter phi
sampCountyOverSampDatPar[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,1]
sampCountyOverSampDatSD[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,2]
sampCountyOverSampDat10[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,3]
sampCountyOverSampDat50[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,3]
sampCountyOverSampDat90[(2 + includeCluster + includeUrbanRural), i] = result$summary.hyperpar[2,5]
## transformed hyperparameters
# sample the hyperparameters, using the marginals to improve the sampling
out = inla.hyperpar.sample(1000, result, improve.marginals=TRUE)
transformFunction = function(x) {c(1/x[3], x[2], 1/x[1], 1/x[1]*x[2], 1/x[1]*(1-x[2]), sqrt(1/x[3]), sqrt(1/x[1]), sqrt(1/x[1]*x[2]), sqrt(1/x[1]*(1-x[2])))}
if(!includeCluster)
transformFunction = function(x) {c(1/x[1], x[2], 1/x[1]*x[2], 1/x[1]*(1-x[2]), sqrt(1/x[1]), sqrt(1/x[1]*x[2]), sqrt(1/x[1]*(1-x[2])))}
transformedOut = apply(out, 1, transformFunction)
# now calculate the summary statistics of the transformed BYM2 hyperparameters
sampCountyOverSampDatPar[(2 + includeUrbanRural):length(parNames), i] = rowMeans(transformedOut)
sampCountyOverSampDatSD[(2 + includeUrbanRural):length(parNames), i] = apply(transformedOut, 1, sd)
sampCountyOverSampDat10[(2 + includeUrbanRural):length(parNames), i] = apply(transformedOut, 1, quantile, probs=.1)
sampCountyOverSampDat50[(2 + includeUrbanRural):length(parNames), i] = apply(transformedOut, 1, quantile, probs=.5)
sampCountyOverSampDat90[(2 + includeUrbanRural):length(parNames), i] = apply(transformedOut, 1, quantile, probs=.9)
# Simulate from posterior
if(includeUrbanRural) {
samp = inla.posterior.sample(n = Nsim, result = result)
sampRural = matrix(NA, nrow = 47, ncol = Nsim)
sampUrban = matrix(NA, nrow = 47, ncol = Nsim)
sampRuralMod = matrix(NA, nrow = 47, ncol = Nsim)
sampUrbanMod = matrix(NA, nrow = 47, ncol = Nsim)
for(j in 1:Nsim){
sampRural[, j] = samp[[j]]$latent[47 + (1:47)]
sampUrban[, j] = samp[[j]]$latent[1:47]
if(includeCluster) {
# if cluster effect is included, must debias predictions in each modeled strata
clusterSigma = sqrt(1/samp[[j]]$hyperpar[3])
# muSigmaMatRural = cbind(sampRural[, j], clusterSigma)
# muSigmaMatUrban = cbind(sampUrban[, j], clusterSigma)
# sampRuralMod[, j] = logitNormMean(muSigmaMat = muSigmaMatRural)
# sampUrbanMod[, j] = logitNormMean(muSigmaMat = muSigmaMatUrban)
sampRuralMod[, j] = sapply(1:length(clustersPerRural), function(i) {mean(expit(sampRural[i, j] + rnorm(clustersPerRural[i], sd=clusterSigma)))})
sampRuralMod[clustersPerRural == 0,] = 0 # we just need to set these values to something other than NaN
sampUrbanMod[, j] = sapply(1:length(clustersPerUrban), function(i) {mean(expit(sampUrban[i, j] + rnorm(clustersPerUrban[i], sd=clusterSigma)))})
sampClusterOverSampDatRural[,j,i] = sampRural[, j] + rnorm(47, sd=clusterSigma)
sampClusterOverSampDatUrban[,j,i] = sampUrban[, j] + rnorm(47, sd=clusterSigma)
}
}
sampCountyOverSampDat[,,i] = logit(expit(sampUrban)*urbRatio + expit(sampRural)*(1-urbRatio))
sampCountyOverSampDatAdjusted[,,i] = logit(expit(sampUrban)*urbRatioAdjusted + expit(sampRural)*(1-urbRatioAdjusted))
sampCountyOverSampDatMod[,,i] = logit(sampUrbanMod*urbRatioAdjusted + sampRuralMod*(1-urbRatioAdjusted))
if(!includeCluster) {
sampClusterOverSampDatRural[,,i] = sampRural
sampClusterOverSampDatUrban[,,i] = sampUrban
} else {
sampPixelOverSampDatUrbanMod[,,i] = sampUrbanMod
sampPixelOverSampDatRuralMod[,,i] = sampRuralMod
}
sampPixelOverSampDatUrban[,,i] = sampUrban
sampPixelOverSampDatRural[,,i] = sampRural
} else {
samp = inla.posterior.sample(n = Nsim, result = result)
sampCounty = matrix(NA, nrow = 47, ncol = Nsim)
sampCountyMod = matrix(NA, nrow = 47, ncol = Nsim)
for(j in 1:Nsim){
sampCounty[, j] = samp[[j]]$latent[1:47]
if(includeCluster) {
# if cluster effect is included, must debias predictions in each modeled strata
clusterSigma = sqrt(1/samp[[j]]$hyperpar[3])
# muSigmaMat = cbind(sampCounty[, j], clusterSigma)
# sampCountyMod[, j] = logitNormMean(muSigmaMat = muSigmaMat
sampCountyMod[, j] = sapply(1:length(clustersPerCounty), function(i) {mean(expit(sampCounty[i, j] + rnorm(clustersPerCounty[i], sd=clusterSigma)))})
sampClusterOverSampDatRural[,j,i] = sampCounty[, j] + rnorm(47, sd=clusterSigma)
sampClusterOverSampDatUrban[,j,i] = sampClusterOverSampDatRural[,j,i]
}
}
sampCountyOverSampDat[,,i] = sampCounty
sampCountyOverSampDatAdjusted[,,i] = sampCounty
sampCountyOverSampDatMod[,,i] = logit(sampCountyMod)
sampPixelOverSampDatUrban[,,i] = sampCounty
sampPixelOverSampDatRural[,,i] = sampCounty
if(!includeCluster) {
sampClusterOverSampDatRural[,,i] = sampCounty
sampClusterOverSampDatUrban[,,i] = sampCounty
} else {
sampPixelOverSampDatUrbanMod[,,i] = sampCountyMod
sampPixelOverSampDatRuralMod[,,i] = sampCountyMod
}
}
}
processSamples = function(samp){
# 80% credible intervals
CI = t(apply(X = samp, MARGIN = 1, FUN = quantile, probs = c(0.1, 0.5, 0.9)))
mm = rowMeans(samp)
ss = apply(X = samp, MARGIN = 1, FUN = sd)
return(list(logit = list(CI = CI,
mean = mm,
stddev = ss),
prob = list(CI = exp(CI))))
}
## SRSdata
Q10 = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDat)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDat)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDat)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDat)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDat)[3])
for(i in 1:dim(sampCountySRSDat)[3]){
tmp = processSamples(sampCountySRSDat[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resSRSdat = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatAdjusted)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatAdjusted)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatAdjusted)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatAdjusted)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatAdjusted)[3])
for(i in 1:dim(sampCountySRSDatAdjusted)[3]){
tmp = processSamples(sampCountySRSDatAdjusted[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resSRSdatAdjusted = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
# calculate summary statistics for cluster and pixel predictions
Q10 = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatRural)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatRural)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatRural)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatRural)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatRural)[3])
for(i in 1:dim(sampClusterSRSDatRural)[3]) {
tmp = processSamples(sampClusterSRSDatRural[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatSRSClusterRural = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatUrban)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatUrban)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatUrban)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatUrban)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampClusterSRSDatUrban)[3])
for(i in 1:dim(sampClusterSRSDatUrban)[3]) {
tmp = processSamples(sampClusterSRSDatUrban[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatSRSClusterUrban = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrban)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrban)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrban)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrban)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrban)[3])
for(i in 1:dim(sampPixelSRSDatUrban)[3]) {
tmp = processSamples(sampPixelSRSDatUrban[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatSRSPixelUrban = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRural)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRural)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRural)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRural)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRural)[3])
for(i in 1:dim(sampPixelSRSDatRural)[3]) {
tmp = processSamples(sampPixelSRSDatRural[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatSRSPixelRural = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
if(includeCluster) {
Q10 = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatMod)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatMod)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatMod)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatMod)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampCountySRSDatMod)[3])
for(i in 1:dim(sampCountySRSDatMod)[3]){
tmp = processSamples(sampCountySRSDatMod[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resSRSdatMod = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrbanMod)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrbanMod)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrbanMod)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrbanMod)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatUrbanMod)[3])
for(i in 1:dim(sampPixelSRSDatUrbanMod)[3]) {
tmp = processSamples(sampPixelSRSDatUrbanMod[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatSRSPixelUrbanMod = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRuralMod)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRuralMod)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRuralMod)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRuralMod)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampPixelSRSDatRuralMod)[3])
for(i in 1:dim(sampPixelSRSDatRuralMod)[3]) {
tmp = processSamples(sampPixelSRSDatRuralMod[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatSRSPixelRuralMod = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
} else {
resSRSdatMod = NULL
resDatSRSPixelUrbanMod = NULL
resDatSRSPixelRuralMod = NULL
}
## now collect the parameters
# make rural parameter the urban parameter
# if(includeUrbanRural) {
# sampCountySRSDatPar[2,] = -sampCountySRSDatPar[2,]
# sampCountySRSDat10[2,] = -sampCountySRSDat10[2,]
# sampCountySRSDat50[2,] = -sampCountySRSDat50[2,]
# sampCountySRSDat90[2,] = -sampCountySRSDat90[2,]
# }
mm = rowMeans(sampCountySRSDatPar)
ss = rowMeans(sampCountySRSDatSD)
Q10 = rowMeans(sampCountySRSDat10)
Q50 = rowMeans(sampCountySRSDat50)
Q90 = rowMeans(sampCountySRSDat90)
resSRSdatPar = data.frame(list(Est = mm,
SD = ss,
Q10 = Q10,
Q50 = Q50,
Q90 = Q90))
## overSampDat
Q10 = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDat)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDat)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDat)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDat)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDat)[3])
for(i in 1:dim(sampCountyOverSampDat)[3]){
tmp = processSamples(sampCountyOverSampDat[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatOverSamp = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatAdjusted)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatAdjusted)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatAdjusted)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatAdjusted)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatAdjusted)[3])
for(i in 1:dim(sampCountyOverSampDatAdjusted)[3]){
tmp = processSamples(sampCountyOverSampDatAdjusted[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatOverSampAdjusted = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
# calculate summary statistics for cluster and pixel predictions
Q10 = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatRural)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatRural)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatRural)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatRural)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatRural)[3])
for(i in 1:dim(sampClusterOverSampDatRural)[3]) {
tmp = processSamples(sampClusterOverSampDatRural[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatOverSampClusterRural = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatUrban)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatUrban)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatUrban)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatUrban)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampClusterOverSampDatUrban)[3])
for(i in 1:dim(sampClusterOverSampDatUrban)[3]) {
tmp = processSamples(sampClusterOverSampDatUrban[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatOverSampClusterUrban = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrban)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrban)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrban)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrban)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrban)[3])
for(i in 1:dim(sampPixelOverSampDatUrban)[3]) {
tmp = processSamples(sampPixelOverSampDatUrban[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatOverSampPixelUrban = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRural)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRural)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRural)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRural)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRural)[3])
for(i in 1:dim(sampPixelOverSampDatRural)[3]) {
tmp = processSamples(sampPixelOverSampDatRural[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatOverSampPixelRural = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
if(includeCluster) {
Q10 = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatMod)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatMod)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatMod)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatMod)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampCountyOverSampDatMod)[3])
for(i in 1:dim(sampCountyOverSampDatMod)[3]){
tmp = processSamples(sampCountyOverSampDatMod[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatOverSampMod = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrbanMod)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrbanMod)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrbanMod)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrbanMod)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatUrbanMod)[3])
for(i in 1:dim(sampPixelOverSampDatUrbanMod)[3]) {
tmp = processSamples(sampPixelOverSampDatUrbanMod[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatOverSampPixelUrbanMod = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
Q10 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRuralMod)[3])
Q50 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRuralMod)[3])
Q90 = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRuralMod)[3])
mm = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRuralMod)[3])
ss = matrix(NA, nrow = 47, ncol = dim(sampPixelOverSampDatRuralMod)[3])
for(i in 1:dim(sampPixelOverSampDatRuralMod)[3]) {
tmp = processSamples(sampPixelOverSampDatRuralMod[,,i])
Q10[,i] = tmp$logit$CI[,1]
Q50[,i] = tmp$logit$CI[,2]
Q90[,i] = tmp$logit$CI[,3]
mm[,i] = tmp$logit$mean
ss[,i] = tmp$logit$stddev
}
resDatOverSampPixelRuralMod = list(Q10 = Q10,
Q50 = Q50,
Q90 = Q90,
mean = mm,
stddev = ss)
} else {
resDatOverSampMod = NULL
resDatOverSampPixelUrbanMod = NULL
resDatOverSampPixelRuralMod = NULL
}
## now collect the parameters
# make rural parameter the urban parameter
# if(includeUrbanRural) {
# sampCountyOverSampDatPar[2,] = -sampCountyOverSampDatPar[2,]
# sampCountyOverSampDat10[2,] = -sampCountyOverSampDat10[2,]
# sampCountyOverSampDat50[2,] = -sampCountyOverSampDat50[2,]
# sampCountyOverSampDat90[2,] = -sampCountyOverSampDat90[2,]
# }
mm = rowMeans(sampCountyOverSampDatPar)
ss = rowMeans(sampCountyOverSampDatSD)
Q10 = rowMeans(sampCountyOverSampDat10)
Q50 = rowMeans(sampCountyOverSampDat50)
Q90 = rowMeans(sampCountyOverSampDat90)
resDatOverSampPar = data.frame(list(Est = mm,
SD = ss,
Q10 = Q10,
Q50 = Q50,
Q90 = Q90))
# Full result
designRes = list(SRSdat = resSRSdatAdjusted,
overSampDat = resDatOverSampAdjusted,
SRSdatUnadjusted = resSRSdat,
overSampDatUnadjusted = resDatOverSamp,
SRSdatPixelRural = resDatSRSPixelRural,
overSampDatPixelRural = resDatOverSampPixelRural,
SRSdatPixelUrban = resDatSRSPixelUrban,
overSampDatPixelUrban = resDatOverSampPixelUrban,
SRSdatClusterRural = resDatSRSClusterRural,
overSampDatClusterRural = resDatOverSampClusterRural,
SRSdatClusterUrban = resDatSRSClusterUrban,
overSampDatClusterUrban = resDatOverSampClusterUrban,
overSampDatPar = resDatOverSampPar,
SRSdatPar = resSRSdatPar)
# save(file = 'kenyaSpatialDesignResultNew.RData', designRes = designRes)
# save(file = paste0('kenyaSpatialDesignResultNewTausq0UrbRur',
# includeUrbanRural, '.RData'), designRes = designRes)
testText = ifelse(test, "Test", "")
strictPriorText = ifelse(strictPrior, "strictPrior", "")
save(file = paste0('bym2Beta-1.75margVar', round(margVar, 4), "tausq", round(tausq, 4), "gamma", round(gamma, 4), 'UrbRur',
includeUrbanRural, 'Cluster', includeCluster, "aggByPop", aggregateByPopulation, "maxDataSets",
maxDataSets, strictPriorText, testText, rangeText, '.RData'),
designRes = designRes)
# include the debiased results if cluster effect is included
if(includeCluster) {
designRes = list(SRSdat = resSRSdatMod,
overSampDat = resDatOverSampMod,
SRSdatAdjusted = resSRSdatAdjusted,
overSampDatAdjusted = resDatOverSampAdjusted,
SRSdatUnadjusted = resSRSdat,
overSampDatUnadjusted = resDatOverSamp,
SRSdatPixelRural = resDatSRSPixelRuralMod,
overSampDatPixelRural = resDatOverSampPixelRuralMod,
SRSdatPixelUrban = resDatSRSPixelRuralMod,
overSampDatPixelUrban = resDatOverSampPixelRuralMod,
SRSdatClusterRural = resDatSRSClusterRural,
overSampDatClusterRural = resDatOverSampClusterRural,
SRSdatClusterUrban = resDatSRSClusterUrban,
overSampDatClusterUrban = resDatOverSampClusterUrban,
overSampDatPar = resDatOverSampPar,
SRSdatPar = resSRSdatPar)
save(file = paste0('bym2Beta-1.75margVar', round(margVar, 4), "tausq", round(tausq, 4), "gamma", round(gamma, 4), 'UrbRur',
includeUrbanRural, 'Cluster', includeCluster, "aggByPop", aggregateByPopulation, 'debiasedMaxDataSets',
maxDataSets, strictPriorText, testText, rangeText, '.RData'),
designRes = designRes)
}
invisible(designRes)
}
# same as runBYM2, except fits a single data set (the ed global data frame)
# doPredsAtPostMean: if TRUE, fix all model hyperparameters at the posterior mean
# getPosteriorDensity: EXPERIMENTAL: evaluate the posterior density of direct estimates using multivariate normal approximation
runBYM2Dat = function(dat=ed, includeUrbanRural=TRUE, includeCluster=TRUE, saveResults=TRUE, fileNameRoot="Ed",
previousResult=NULL, doValidation=FALSE, predCountyI=NULL, counties=sort(unique(poppc$County)),
doPredsAtPostMean=FALSE, directLogitEsts=NULL, fixedParameters=FALSE, getPosteriorDensity=FALSE) {
# remove data from the given county for validation if necessary
if(!is.null(predCountyI))
dat$y[as.character(dat$admin1)==counties[predCountyI]] = NA
includeUrban = includeUrbanRural
# Get true ratios of urban/rural
if(fileNameRoot == "Ed")
thisPopTable = adjustPopulationPerCountyTable("women")
else
thisPopTable = adjustPopulationPerCountyTable("children")
urbRatio = vector('numeric', length = 47)
counties = sort(unique(as.character(dat$admin1)))
urbRatio = thisPopTable$popUrb / thisPopTable$popTotal
sortI = matchMultiple(counties, thisPopTable$County)
urbRatio = urbRatio[sortI]
# Define formula
if(includeUrbanRural) {
if(includeCluster) {
formula = y ~ urban +
f(idx, model="bym2",
graph="Kenyaadm1.graph", scale.model=TRUE, constr=TRUE,
hyper=list(prec=list(param=c(1, 0.01), prior="pc.prec"), phi=list(param=c(0.5, 2/3), prior="pc"))) +
f(idxEps, model = "iid",
hyper = list(prec = list(prior = "pc.prec", param = c(1,0.01))))
} else {
formula = y ~ urban +
f(idx, model="bym2",
graph="Kenyaadm1.graph", scale.model=TRUE, constr=TRUE,
hyper=list(prec=list(param=c(1, 0.01), prior="pc.prec"), phi=list(param=c(0.5, 2/3), prior="pc")))
}
} else {
if(includeCluster) {
formula = y ~ f(idx, model="bym2",
graph="Kenyaadm1.graph", scale.model=TRUE, constr=TRUE,
hyper=list(prec=list(param=c(1, 0.01), prior="pc.prec"),
phi=list(param=c(0.5, 2/3), prior="pc"))) +
f(idxEps, model = "iid",
hyper = list(prec = list(prior = "pc.prec", param = c(1,0.01))))
} else {
formula = y ~
f(idx, model="bym2",
graph="Kenyaadm1.graph", scale.model=TRUE, constr=TRUE,
hyper=list(prec=list(param=c(1, 0.01), prior="pc.prec"), phi=list(param=c(0.5, 2/3), prior="pc")))
}
}
# Number of simulations for producing results
Nsim = 1000
# Help functions
logit = function(x){
return(log(x/(1-x)))
}
expit = function(x){
return(1/(1+exp(-x)))
}
if(includeCluster)
parNames = c("Intercept", "Urban", "Cluster Var", "BYM2 Phi", "BYM2 Tot. Var", "BYM2 Spatial Var", "BYM2 iid Var", "Cluster SD", "BYM2 Tot. SD", "BYM2 Spatial SD", "BYM2 iid SD")
else
parNames = c("Intercept", "Urban", "BYM2 Phi", "BYM2 Tot. Var", "BYM2 Spatial Var", "BYM2 iid Var", "BYM2 Tot. SD", "BYM2 Spatial SD", "BYM2 iid SD")
includeI = c(1, rep(2, includeUrban), 3:length(parNames))
parNames = parNames[includeI]
# Go through education data set
sampCountyDat = matrix(NA, nrow=47, ncol=Nsim)
sampClusterDatUrban = matrix(NA, nrow=47, ncol=Nsim)
sampClusterDatRural = matrix(NA, nrow=47, ncol=Nsim)
sampPixelDatUrban = matrix(NA, nrow=47, ncol=Nsim)
sampPixelDatRural = matrix(NA, nrow=47, ncol=Nsim)
# for the final parameters to store, 2 fixed effects, 2 + includeCluster estimated
# hyperparameters, and 2 hyperparameters we will get via transformation
sampCountyDatPar = numeric(length(parNames))
sampCountyDatSD = numeric(length(parNames))
sampCountyDat10 = numeric(length(parNames))
sampCountyDat50 = numeric(length(parNames))
sampCountyDat90 = numeric(length(parNames))
names(sampCountyDatPar) = parNames
names(sampCountyDatSD) = parNames
names(sampCountyDat10) = parNames
names(sampCountyDat50) = parNames
names(sampCountyDat90) = parNames
# Extract data
dat$admin1 = factor(dat$admin1)
# INLA data