library(PopED)
ff.emax.hill <- function(model_switch,xt,parameters,poped.db){
with(as.list(parameters),{
y=xt
DOSE = xt
y=BASE + EMAX*DOSE^(GAMMA)/(ED50^(GAMMA) + DOSE^(GAMMA))
return(list( y= y,poped.db=poped.db))
})
}
## -- parameter definition function
sfg.emax.hill <- function(x,a,bpop,b,bocc){
parameters=c( EMAX=bpop[1]*exp(b[1]),
ED50=bpop[2]*exp(b[2]),
GAMMA=bpop[3],
BASE=bpop[4]+b[3])
return( parameters )
}
poped.db <- create.poped.database(ff_fun=ff.emax.hill,
fError_fun=feps.add.prop,
fg_fun=sfg.emax.hill,
groupsize=100,
m=1,
bpop=c(EMAX=100,ED50=20,GAMMA=4.5,BASE=1),
d=c(EMAX=0.0625,ED50=0.0625,BASE=0.0625),
sigma=diag(c(0.01,.1)),
xt=seq(0,50,length.out=8),
minxt=0,
maxxt=50,
ourzero=0)
library(ggplot2)
plot1 <- plot_model_prediction(poped.db,IPRED=T,DV=T)
plot1 + xlab("Dose")
## evaluate initial design
evaluate_design(poped.db)
# Optimization of doses
output <- poped_optim(poped.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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