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study09_broad_new.R
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study09_broad_new.R
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# Create a function that is able to parse a matrix containing alot of data
# -----------------------------------------------------------------------------
# Set up directories
if (!exists("git.dir")) {
rm(list = ls(all = T))
wd <- c("C:/Users/Jim Hughes/Documents", "C:/Users/hugjh001/Documents",
"C:/Users/hugjh001/Desktop", "C:/windows/system32")
graphics.off()
if (getwd() == wd[1]) {
gir.dir <- paste0(getwd(), "/GitRepos")
reponame <- "optinterval"
} else if (getwd() == wd[2]) {
git.dir <- getwd()
reponame <- "optinterval"
} else if (getwd() == wd[3] | getwd() == wd[4]) {
git.dir <- "E:/Hughes/Git"
reponame <- "splines"
}
rm("wd")
}
# Load packages
library(GA)
#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
source(paste(git.dir, reponame, "study_functions.R", sep = "/"))
source(paste(git.dir, reponame, "study_rdata.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 = ".")
t2.names <- paste(data.names, "t", sep = ".")
study.fn <- function(data, par, fn, nobs, t2, sdev = 0.05, tlast = 24, logauc = F) {
niter <- dim(data)[2]
absorp <- ifelse((dim(par)[1] %% 2) != 0, T, F)
t1 <- seq(0, tlast, length.out = nobs)
err <- matrix(
1 + rnorm(n = length(t1)*niter, mean = 0, sd = sdev),
nrow = length(t1), ncol = niter
)
subd <- data[which(time.samp %in% t1),]*err
fit.par <- apply(subd, 2, function(x) {
chisq.sumexp(optim.sumexp(
data.frame(time = t1, conc = x), oral = absorp
))$par
})
t2 <- sapply(fit.par, simplify = "array",
FUN = function(x) c(0, round(optim.interv(t1, x)$par, 1), tlast)
)
t3.tmax <- sapply(fit.par, tmax.sumexp)
t3 <- mapply(fit.par, t3.tmax,
FUN = function(x, y) {
z <- c(0, round(optim.interv(t1, x, tmax = y)$par, 1), y, tlast)
return(z[order(z)])
})
auc <- data.frame(
true = apply(par, 2, function(x) integrate(fn, 0, tlast, p = x)$value),
basic = apply(par, 2, function(x) auc.interv(t1, x, fn)),
optint = mapply(data.frame(par), data.frame(t2),
FUN = function(x, y) auc.interv(y, x, fn)
),
optintwCmax = mapply(data.frame(par), data.frame(t3),
FUN = function(x, y) auc.interv(y, x, fn)
)
)
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(t2),
FUN = function(x, y) max(pred.sumexp(x, y))
),
optintwCmax = mapply(data.frame(par), data.frame(t3),
FUN = function(x, y) max(pred.sumexp(x, y))
)
)
tmax <- data.frame(
true = apply(par, 2, tmax.sumexp),
basic = mapply(data.frame(par), cmax$basic,
FUN = function(x, y) t1[which(pred.sumexp(x, t1) == y)][1]
),
optint = mapply(data.frame(par), cmax$optint, data.frame(t2),
FUN = function(x, y, z) z[which(pred.sumexp(x, z) == y)][1]
),
optintwCmax = mapply(data.frame(par), cmax$optintwCmax, data.frame(t3),
FUN = function(x, y, z) z[which(pred.sumexp(x, z) == y)][1]
)
)
return(list(auc = auc, cmax = cmax, tmax = tmax))
}
# -----------------------------------------------------------------------------
optim.sumexp <- function(data, oral = F, nexp = 3) {
x <- data[which(data[, 2] > 0 & data[, 1] != 0), 1]
y <- data[which(data[, 2] > 0 & data[, 1] != 0), 2]
opt.par <- list(NULL)
opt.val <- list(NULL)
opt.gra <- list(NULL)
opt.con <- list(NULL)
opt.mes <- list(NULL)
lm.sub <- which(y == max(y))[1]:length(y)
lmres <- unname(lm(log(y[lm.sub]) ~ x[lm.sub])$coefficients)
for (i in 1:nexp) {
if (i == 1 & !oral) {
optres <- list(
par = c(lmres[2], lmres[1]),
value = mle.sumexp(unname(c(lmres[2], lmres[1])), x, y, 0.01),
counts = NULL, convergence = 0, message = NULL
)
} else {
j <- 1
best <- Inf
repeat {
gares <- ga("real-valued",
mle.sumexp, x = x, y = y, ga = T, sigma = 0.01,
min = c(rep(lmres[2]*50, i + oral), rep(lmres[1]-2, i)),
max = c(rep(lmres[2]/50, i + oral), rep(lmres[1]+2, i)),
selection = gareal_lrSelection,
crossover = gareal_spCrossover,
mutation = gareal_raMutation,
maxiter = 50,
popSize = 250,
monitor = F
)
optres <- optim(
gares@solution[1, ],
mle.sumexp,
method = "BFGS",
x = x, y = y, sigma = 0.01
)
if (optres$value < best) {
best <- optres$value
j <- 1
} else {
j <- j + 1
if (j == 7) break
}
}
}
opt.par[[i]] <- optres$par
opt.val[[i]] <- optres$value
opt.gra[[i]] <- optres$counts
opt.con[[i]] <- optres$convergence
opt.mes[[i]] <- ifelse(is.null(optres$message),
"NULL", optres$message)
}
res <- list(par = opt.par, value = opt.val, counts = opt.gra,
convergence = opt.con, message = opt.mes)
res
}
fin.res <- list(NULL)
for (i in 3) {
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]),
t2 = get(t2.names[i]), nobs = 9
)
)
}