# Make plots of predictions comparing BaTFLED to mean for each sample
# Usage1: head_to_head_mean.R <run_prefix1> <run_prefix2>
# Usage2: head_to_head.R <run_prefix> mean
args <- commandArgs(TRUE)
run1 <- args[1]
run2 <- args[2]
# Determine the number of runs with this prefix
n.files <- length(list.files(path = dirname(run1),
pattern = paste0(basename(run1), '.[0-9]+.out')))
if(run2=='mean') {
# Compare to predicting the mean
for(fld in 1:nfiles) {
load(paste0(run1, '.', (fld-1), '/image.Rdata'))
if(nrow(test.m1.mat)) {
}
}
}
load(sum_file)
prefix.split <- strsplit(run_prefix, split='/')
run_prefix <- prefix.split[[1]][length(prefix.split[[1]])]
for(resp in dimnames(results$summaries)[[2]])
for(type in dimnames(results$summaries)[[1]])
for(type.mean in dimnames(results$mean)[[2]])
if(resp %in% dimnames(results$mean)[[1]]) {
pred <- results$summaries[type, resp,]
mean <- results$mean[resp, type.mean,]
# print(pred)
# print(mean)
if(sum(is.na(pred))==0 & sum(is.na(mean))==0) {
png(paste0(run_prefix, '_', type.mean, '_', resp, '.png'))
plot(mean, pred, xlim=range(pred,mean), ylim=range(pred,mean),
main=paste(type, resp), pch=19, cex = 2)
abline(0,1, col='blue')
# Write percentages on plots
upper <- sum(pred > mean)/length(pred)
lower <- sum(mean > pred)/length(pred)
mtext(sprintf('%.2f%%', upper*100),side=3,line=-1.5,
at=par("usr")[1]+0.5*diff(par("usr")[1:2]), cex=1.2)
mtext(sprintf('%.2f%%', lower*100),side=1,line=-1.5,
at=par("usr")[1]+.5*diff(par("usr")[1:2]), cex=1.2)
dev.off()
} else {
# Reset these if only one was all NAs
pred <- c(NA)
mean <- c(NA)
}
}
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