R help - Sep 2010 - Help on glm and optim

Dear all,

I'm trying to use the "optim" function to replicate the results 
from the "glm" using an example from the help page of "glm",
but I could not get the "optim" function to work. Would you please
point out where I did wrong? Thanks a lot.

The following is the code:

# Step 1: fit the glm
clotting <- data.frame(
    u = c(5,10,15,20,30,40,60,80,100),
    lot1 = c(118,58,42,35,27,25,21,19,18),
    lot2 = c(69,35,26,21,18,16,13,12,12))
fit1 <- glm(lot1 ~ log(u), data=clotting, family=Gamma)

# Step 2: use optim
# define loglikelihood function to be maximized over
# theta is a vector of three parameters: intercept, cofficient for log(u) and
dispersion parameter
loglik <- function(theta,data){
        E <- 1/(theta[1]+theta[2]*log(data$u))
        V <- theta[3]*E^2
        loglik <-
sum(dgamma(data$lot1,shape=1/theta[3],rate=1/(E*theta[3]),log=T))
        return(loglik)
}

# use the glm result as initial values
theta <- c(as.vector(coef(fit1)),0.002446059)
fit2 <- optim(theta, loglik,  clotting, gr = NULL, hessian = TRUE,
        control = list(fnscale = -1))

# Then I got the following error message:
Error in optim(theta, loglik, clotting, gr = NULL, hessian = TRUE, control =
list(fnscale = -1)) :
  non-finite finite-difference value [3]


Wayne (Yanwei) Zhang
Statistical Research
CNA
Phone: 312-822-6296
Email: Yanwei.Zhang@cna.com<mailto:Yanwei.Zhang@cna.com>




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On Thu, 2 Sep 2010, Zhang,Yanwei wrote:
> Dear all,
>
> I'm trying to use the "optim" function to replicate the
results  from the "glm" using an example from the help page of
"glm", but I could not get the "optim" function to work.
Would you please point out where I did wrong? Thanks a lot.
>
> The following is the code:
>
> # Step 1: fit the glm
> clotting <- data.frame(
>    u = c(5,10,15,20,30,40,60,80,100),
>    lot1 = c(118,58,42,35,27,25,21,19,18),
>    lot2 = c(69,35,26,21,18,16,13,12,12))
> fit1 <- glm(lot1 ~ log(u), data=clotting, family=Gamma)
>
> # Step 2: use optim
> # define loglikelihood function to be maximized over
> # theta is a vector of three parameters: intercept, cofficient for log(u)
and dispersion parameter
> loglik <- function(theta,data){
>        E <- 1/(theta[1]+theta[2]*log(data$u))
>        V <- theta[3]*E^2
>        loglik <-
sum(dgamma(data$lot1,shape=1/theta[3],rate=1/(E*theta[3]),log=T))
>        return(loglik)
> }
>
> # use the glm result as initial values
> theta <- c(as.vector(coef(fit1)),0.002446059)
> fit2 <- optim(theta, loglik,  clotting, gr = NULL, hessian = TRUE,
>        control = list(fnscale = -1))
>
> # Then I got the following error message:
> Error in optim(theta, loglik, clotting, gr = NULL, hessian = TRUE, control
= list(fnscale = -1)) :
>  non-finite finite-difference value [3]
>
If you use trace(loglik, tracer=quote(print(theta))) to trace the inputs to
loglik() you will find that it is being called with negative values of theta[3]
to get finite differences.   One fix is to reparametrize and use the log scale
rather than the scale as a parameter.

    -thomas


Thomas Lumley
Professor of Biostatistics
University of Washington, Seattle