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study24_broad_esm.R
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study24_broad_esm.R
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# Create a function that is able to parse a matrix containing alot of data
# -----------------------------------------------------------------------------
# Set up directories
git.dir <- "E:/Hughes/Git"
reponame <- "splines" #"optinterval"
# Load packages
library(GA)
# library(splines)
#library(ggplot2)
#theme_bw2 <- theme_set(theme_bw(base_size = 14))
#theme_update(plot.title = element_text(hjust = 0.5))
# Source scripts to set up environment
set.seed(256256)
niter <- 1000
sdev <- 4
source(paste(git.dir, reponame, "fn_diag/fix_functions.R", sep = "/"))
source(paste(git.dir, reponame, "fn_diag/study_rdata_resamp.R", sep = "/"))
# -----------------------------------------------------------------------------
# Set basic parameters
data.names <- paste0("d", as.vector(outer(1:3, c("b", "a"), paste0)))[-1]
par.names <- paste(data.names, "p", sep = ".")
fn.names <- paste("pred", data.names, sep = ".")
t1.names <- paste(data.names, "t", sep = ".")
study.fn <- function(data, par, fn, nobs, t1, tlast = 24, logauc = F) {
niter <- dim(data)[2]
absorp <- ifelse((dim(par)[1] %% 2) != 0, T, F)
if (absorp) data[1, ] <- 0
all.sumexp <- apply(data, 2, function(x) {
optim.sumexp.new(
data.frame(time = t1, conc = x), oral = absorp
)
})
print("sumexp done")
res.sumexp <- lapply(all.sumexp, best.sumexp.aic)
fit.par <- lapply(res.sumexp, function(x) x$sumexp)
true.tlast <- apply(par, 2, function(x) {
list(seq(0, pred.tlast.lam(x), length.out = length(t1)))
})
obs.tlast.mat <- apply(data, 2, function(x) {
out <- try(seq(0, obs.tlast.lam(data.frame(t1, x)), length.out = length(t1)))
if (class(out) == "try-error") browser()
out
})
obs.tlast <- split(t(obs.tlast.mat), seq(NROW(t(obs.tlast.mat))))
print("tlast done")
res.interv <- mapply(fit.par, obs.tlast, SIMPLIFY = F, FUN = function(x, t) {
optim.interv.dtmax(x, t)
})
res.times <- sapply(res.interv, FUN = function(x) {
x$times
})
print("intervals done")
auc24 <- data.frame(
true = apply(par, 2, function(x) integrate(fn, 0, 24, p = x)$value),
basic = apply(par, 2, function(x) auc.interv(t1, x, fn)),
optint = mapply(data.frame(par), data.frame(res.times),
FUN = function(x, y) auc.interv(y, x, fn)
)
)
auctlast <- data.frame(
true = apply(par, 2, function(x) integrate(fn, 0, 168, p = x)$value),
basic = apply(par, 2, function(x) auc.interv(t1, x, fn)),
optint = mapply(data.frame(par), data.frame(res.times),
FUN = function(x, y) auc.interv(y, x, fn)
)
)
aucinf <- try(data.frame(
true = apply(par, 2, function(x) {
auc <- integrate(fn, 0, 168, p = x)$value
inf <- fn(168, x)/abs(max(x[ceiling(length(x)/2)]))
auc + inf
}),
basic = apply(par, 2, function(x) {
auc <- auc.interv(t1, x, fn)
inf <- auc.interv.lam(x, t1)
auc + inf
}),
optint = mapply(data.frame(par), data.frame(res.times), FUN = function(x, t) {
auc <- auc.interv(t, x, fn)
inf <- auc.interv.lam(x, t)
auc + inf
})
))
if (class(aucinf) == "try-error") browser()
test <- try(apply(par, 2, function(x) pred.sumexp(x, tmax.sumexp(x))))
if (class(test) == "try-error") browser()
cmax <- data.frame(
true = apply(par, 2, function(x) pred.sumexp(x, tmax.sumexp(x))),
basic = apply(par, 2, function(x) max(pred.sumexp(x, t1))),
optint = mapply(data.frame(par), data.frame(res.times),
FUN = function(x, t) max(pred.sumexp(x, t))
)
)
tmax <- data.frame(
true = apply(par, 2, tmax.sumexp),
basic = mapply(data.frame(par), cmax$basic,
FUN = function(x, cmax) t1[which(pred.sumexp(x, t1) == cmax)][1]
),
optint = mapply(data.frame(par), cmax$optint, data.frame(res.times),
FUN = function(x, cmax, t) t[which(pred.sumexp(x, t) == cmax)][1]
)
)
tlast <- data.frame(
true = sapply(true.tlast, function(x) tail(unlist(x), 1)),
obs = sapply(obs.tlast, function(x) tail(unlist(x), 1))
)
return(list(par = par, fit.par = fit.par, tlast = tlast, sumexp = res.sumexp,
tbas = t1, optint = res.times, interv.optint = res.interv, auc24 = auc24,
auctlast = auctlast, aucinf = aucinf, cmax = cmax, tmax = tmax
))
}
fin.res <- list(NULL)
for (i in 1:5) {
fin.res[[i]] <- list(
data = data.names[i],
result = study.fn(get(data.names[i]),
par = get(par.names[i]), fn = get(fn.names[i]),
t1 = get(t1.names[i]), nobs = 9
) # study.fn
) # list
print(paste0(i, "done"))
} # for loop
# Runs
# ARabcd
# a - max number of exponentials (2, 3)
# b - include sigma in mle (0 - off, 1 - on)
# c - ofv comparitive criterion (1 - lrt, 2 - aic, 3 - bic)
# d - error model (1 - loprop, 2- hiprop, 3 - loboth, 4- hiboth)
setwd("E:/Hughes/Git/splines/fn_diag")
saveRDS(fin.res[[1]]$result, "d2b-broad-AR3024.rds")
saveRDS(fin.res[[2]]$result, "d3b-broad-AR3024.rds")
saveRDS(fin.res[[3]]$result, "d1a-broad-AR3024.rds")
saveRDS(fin.res[[4]]$result, "d2a-broad-AR3024.rds")
saveRDS(fin.res[[5]]$result, "d3a-broad-AR3024.rds")