library(PopED)
library(babelmixr2)
f <- function() {
ini({
tEmax <- 100
tED50 <- 20
tGamma <- 4.5
tBase <- 1
eta.emax ~ 0.0625
eta.ed50 ~ 0.0625
eta.base ~ 0.0625
prop.sd <- sqrt(0.01)
add.sd <- sqrt(0.1)
})
model({
EMAX <- tEmax*exp(eta.emax)
ED50 <- tED50*exp(eta.ed50)
GAMMA <- tGamma
BASE <- tBase+eta.base
y <- time
DOSE <- time
y <- BASE + EMAX*DOSE^(GAMMA)/(ED50^(GAMMA) + DOSE^(GAMMA))
y ~ add(add.sd) + prop(prop.sd)
})
}
e <- et(seq(0,50,length.out=8))
babel.db <- nlmixr2(f, e, "poped",
popedControl(groupsize=100,
minxt=0,
maxxt=50,
ourzero=0))
library(ggplot2)
plot1 <- plot_model_prediction(babel.db,IPRED=T,DV=T)
plot1 + xlab("Dose")
## evaluate initial design
## $rse
## EMAX ED50 GAMMA BASE d_EMAX d_ED50 d_BASE SIGMA[1,1]
## 2.588804 2.554668 1.112444 3.922208 14.825482 14.375072 33.257104 7.072694
## SIGMA[2,2]
## 18.487445
evaluate_design(babel.db)
# Optimization of doses
output <- poped_optim(babel.db, opt_xt = T, parallel = T)
summary(output)
get_rse(output$FIM,output$poped.db)
plot_model_prediction(output$poped.db) + xlab("Dose")
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