[R] Efficient way to code using optim()

[Giovanni Petris]

Unless this is a homework problem, you would be much better off using
glm(). 

Giovanni

> Date: Fri, 30 Oct 2009 12:23:45 -0700
> From: parkbomee <bbom419 at hotmail.com>
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> Hi all,
>  
> I am trying to estimate a simple logit model.
> By using MLE, I am maximizing the log likelihood, with optim().
> The thing is, each observation has different set of choice options, so I need a loop inside the objective function,
> which I think slows down the optimization process.
>  
> The data is constructed so that each row represent the characteristics for one alternative,
> and CS is a variable that represents choice situations. (say, 1 ~ Number of observations)
> cum_count is the ¡°cumulative¡± count of each choice situations, i.e. number of available alternatives in each CS.
> So I am maximizing the sum of [exp(U(chosen)) / sum(exp(U(all alternatives)))]
>  
> When I have 6,7 predictors, the running time is about 10 minutes, and it slows down exponentially as I have more predictors. (More theta¡¯s to estimate)
> I want to know if there is a way I can improve the running time.
> Below is my code..
>  
> simple_logit = function(theta){
>                 realized_prob = rep(0, max(data$CS))
>                 theta_multiple = as.matrix(data[,4:35]) %*% as.matrix(theta)
>                 realized_prob[1] = exp(theta_multiple[1]) / sum(exp(theta_multiple[1:cum_count[1]]))
>                 for (i in 2:length(realized_prob)){
>                                 realized_prob[i] = exp(theta_multiple[cum_count[(i-1)]+1]) / sum(exp(theta_multiple[((cum_count[(i-1)]+1):cum_count[i])]))
>                                 }
>                 -sum(log(realized_prob))
> }
>  
> initial = rep(0,32)
> out33 = optim(initial, simple_logit, method="BFGS", hessian=TRUE)
>  
>  
>  
> Many thanks in advance!!! 		 	   		  
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