Dear Friends,

I have been trying to learn how to use the derivative free optimization algorithms implemented in the package RGENOUD by Mebane and Sekhon. However, it does not seem to work for reasons best described as my total ignorance. If anybody has experience using this package, it would be really helpful if you can point out where I'm making a mistake. 

Thanks in advance

Anup

Sample code attached

library(rgenoud)

nobs <- 5000
t.beta <- c(0,1,-1)
X <- as.matrix(cbind(rep(1, nobs), runif(nobs), runif(nobs))) # Creating the design matrix
prodterm <- (X%*%t.beta)+rnorm(nrow(X))
Y <- as.matrix(ifelse(prodterm<0, 0, 1))

# Defining the likelihood function

log.like <- function(beta, Y, X)
{
term1 <- pnorm(X%*%beta)
term2 <- 1-term1
loglik <- (sum(Y*log(term1))+sum((1-Y)*log(term2))) # Likelihood function to be maximized
}

stval <- c(0,0,0)
opt.output <- optim(stval,log.like,Y=Y[,1], X=X[,1:3],
hessian=T, method="BFGS", control=c(fnscale=-1,trace=1))
opt.output

### Now using GENOUD gives me errors

genoud.output <- genoud(log.like,beta=stval,X=X[,1:3], Y=Y[,1], nvars=3, pop.size=3000, max=TRUE)


       
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