I still cannot solve the problem: 'invalid function value in 'nlm'
optimizer'
I want to get the MLE for theta[1] and theta[2], my code is below:
x <- c(2,5,3,7,3,2,4)
delta <- c(1, 0, 1, 1, 1, 0, 1)
# -log likelihood
#alpha<-theta[1]
#lamda<-theta[2]
ln<-function(theta,x1,x2 )
{
-sum(delta)*log(theta[1]*theta[2])-sum(delta)*(theta[1]-1)*log(x[delta==1])+theta[2]*sum(x^theta[1])
}
#MLE
nlm(ln,theta<-c(1,1),x1=x, x2=delta, hessian=TRUE)
On Tue, Apr 28, 2015 at 6:40 AM, Duncan Murdoch <murdoch.dun...@gmail.com>
wrote:
> On 28/04/2015 2:43 AM, Hanze Zhang wrote:
> > Hi, R users,
> >
> >
> > I am using nlm function to get the MLE of parameter alpha and lambda
> from a
> > parametric survival model (Weibull distribution). However, this message
> > always came out: ' invalid function value in 'nlm' optimizer'. Could
> anyone
> > help me? Code is
> >
> > project<-read.table(file="C://data.txt", header=T, as.is=T)
> > names(project)
> > attach(project)
> >
> > x<-time
> > delta<-ind
> >
> >
> > # -log likelihood
> > #alpha<-theta[1]
> > #lambda<-theta[2]
> > ln<-function(theta)
> > {
> >
> >
> -sum(delta)*log(theta[1]*theta[2])-sum(delta)*(theta[1]-1)*log(x[delta==1])+theta[2]*sum(x^theta[1])
> > }
> >
> > #MLE
> > nlm(ln,theta<-c(1,1),hessian=TRUE)
>
> You are taking logs of parameters. Probably the optimizer is setting
> the parameters to negative values, and so the log returns NaN.
>
> You can avoid this by testing your parameters on input, and always
> returning a valid number. There are lots of ways to do this: One
> strategy is to return +Inf for invalid values; another is to move the
> parameter to the nearest boundary, and apply a penalty according to how
> far you moved it. Or just take the absolute value of the parameter. Or
> reparametrize so that illegal values aren't possible.
>
> Duncan Murdoch
>
>
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