Hi,
I came across a paper by Cole et al (2009; doi: 10.1093/ijc/dyp269) in which they provided R code for a MLE appoach. Does anyone have a suggestion on how to write this in Stata code?
Kjell Vegard
I came across a paper by Cole et al (2009; doi: 10.1093/ijc/dyp269) in which they provided R code for a MLE appoach. Does anyone have a suggestion on how to write this in Stata code?
Code:
int.p1<-function(xx) {
1/(1+exp(-b0-b1*log(xx)))*dlnorm(xx, c0, c1)
}
int.p0<-function(xx) {
1/(1+exp(b0+b1*log(xx)))*dlnorm(xx, c0, c1)
}
logitln <- function(para){
b0 <<- para[1]
b1 <<- para[2]
c0 <<- para[3]
c1 <<- para[4]
px <- 1/(1+exp(-b0-b1*log(x)))
logL.1<-sum((1-delta)*(y*log(px)+(1-y)*log
(1-px)+log(dlnorm(x,c0,c1))))
logL.2<-sum(delta*y*log(integrate(int.p1,
lower=0,upper=LD)$value)+delta*(1-y)*
log(integrate(int.p0,lower=0,upper=LD)$value))
-(logL.1+logL.2)
}
fit <- optim(par=c(-1, 1, 0, 1), fn=logitln,
hessian = T)
rbind(est=fit$par, se=sqrt(diag(solve
(fit$hessian))))