TTT method for lambda_z estimation [🇷 for BE/BA]
❝ Yes, we've been planning to do that just following what you've proposed here.
❝ Thank you so much for always being so helpful.
Hello,
if I've got some time, I'm happy to help you.
As for the selection of number of points used, I wrote a quick function, which gives you an overview:
f <- function(dat){
m <- which.max(dat$conc)
f2 <- function(m) return(cbind((nrow(dat)-m+1),abs(extractAIC(lm(log(conc)~time,dat[m:nrow(dat),])))[2],summary(lm(log(conc)~time,dat[m:nrow(dat),]))$adj.r.squared))
overview <- as.data.frame(do.call(rbind,lapply((m+1):(nrow(dat)-2),f2)))
names(overview) <- c("n","AIC","adjR2")
n_ARS=0
r.adj=0
for (i in (nrow(dat)-2):(which.max(dat$conc)+1)) {
if (r.adj - summary(lm(log(conc)~time,dat[i:nrow(dat),]))$adj.r.squared <(0.0001)) {
n_ARS = nrow(dat)-i+1
r.adj = summary(lm(log(conc)~time,dat[i:nrow(dat),]))$adj.r.squared
}
}
n_TTT_ARS=0
r.adj2=0
for (i in (nrow(dat)-2):(min(seq_along(dat$time)[dat$time>=dat$time[which.max(dat$conc)]*2]))) {
if (r.adj2 - summary(lm(log(conc)~time,dat[i:nrow(dat),]))$adj.r.squared <(0.0001)) {
n_TTT_ARS = nrow(dat)-i+1
r.adj2 = summary(lm(log(conc)~time,dat[i:nrow(dat),]))$adj.r.squared
}
}
require(PK)
l <- lee(conc=dat$conc, time=dat$time)
n_lee <- sum(dat$time> l$chgpt)
n_TTT <- sum(dat$time> (dat$time[which.max(dat$conc)]*2))
n_AIC <- overview$n[which.min(abs(overview$AIC))]
plot(l,log="y")
print(overview)
cat("\n")
return(data.frame(TTT=n_TTT, AIC=n_AIC, ARS=n_ARS,TTT_ARS=n_TTT_ARS,lee=n_lee))
}
The code makes an comparison between TTT, AIC criterium, ARS, the combination of TTT and ARS and the lee (although sometimes lee doesn't work according to the lt parameter set).
You can adjust the lee parameters by typing it into the function eg.
f(dat,lt=TRUE, method="ols")
Maybe it's helpfull for you
Ace
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Complete thread:
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