### This script plot X-Y protein production rates with labeled outliers.
rm(list = ls())
suppressMessages(library(cubfits, quietly = TRUE))
source("00-set_env.r")
source(paste(prefix$code.plot.ps, "u0-get_case_main.r", sep = ""))
### Pre processed phi.Obs.
fn.in <- paste(prefix$data, "pre_process.rda", sep = "")
load(fn.in)
for(i.case in case.names){
### Subset of mcmc output.
fn.in <- paste(prefix$subset, i.case, "_PM.rda", sep = "")
if(!file.exists(fn.in)){
cat("File not found: ", fn.in, "\n", sep = "")
next
}
load(fn.in)
fn.in <- paste(prefix$subset, i.case, "_PM_scaling.rda", sep = "")
if(!file.exists(fn.in)){
cat("File not found: ", fn.in, "\n", sep = "")
next
}
load(fn.in)
### Plot posterior mean.
fn.out <- paste(prefix$plot.ps.single,
"prxy_wci_", i.case, ".pdf", sep = "")
pdf(fn.out, width = 5, height = 5)
### x-axis: predicted, y-axis: observed.
plotprxy(phi.Obs, phi.PM,
y.ci = phi.CI,
xlab = "Observed Production Rate (log10)",
ylab = "Predicted Production Rate (log10)",
weights = 1 / phi.STD.log10,
main = paste(i.case, " posterior mean", sep = ""))
mtext(paste(workflow.name, ", ", get.case.main(i.case, model), sep = ""),
line = 3, cex = 0.6)
mtext(date(), line = 2.5, cex = 0.4)
dev.off()
### Plot posterior median.
fn.out <- paste(prefix$plot.ps.single,
"prxy_wci_med_", i.case, ".pdf", sep = "")
pdf(fn.out, width = 5, height = 5)
### x-axis: predicted, y-axis: observed.
plotprxy(phi.Obs, phi.MED,
y.ci = phi.CI,
xlab = "Observed Production Rate (log10)",
ylab = "Predicted Production Rate (log10)",
weights = 1 / phi.STD.log10,
main = paste(i.case, " posterior median", sep = ""))
mtext(paste(workflow.name, ", ", get.case.main(i.case, model), sep = ""),
line = 3, cex = 0.6)
mtext(date(), line = 2.5, cex = 0.4)
dev.off()
### Plot posterior log10 mean.
fn.out <- paste(prefix$plot.ps.single,
"prxy_wci_log10_", i.case, ".pdf", sep = "")
pdf(fn.out, width = 5, height = 5)
### x-axis: predicted, y-axis: observed.
plotprxy(phi.Obs, 10^(phi.PM.log10),
xlab = "Observed Production Rate (log10)",
ylab = "Predicted Production Rate (log10)",
y.ci = 10^(phi.CI.log10),
weights = 1 / phi.STD.log10,
main = paste(i.case, " posterior log10 mean", sep = ""))
mtext(paste(workflow.name, ", ", get.case.main(i.case, model), sep = ""),
line = 3, cex = 0.6)
mtext(date(), line = 2.5, cex = 0.4)
dev.off()
}
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