Thank you.
But I'd prefer using a written function which allows me more flexible model
specification.
Later on, I could have random parameters.
So I want to know if there is any more efficient way so that I can speed it up.
> Date: Fri, 30 Oct 2009 16:10:29 -0600
> To: bbom...@hotmail.com
> CC: r-help@r-project.org
> Subject: Re: [R] Efficient way to code using optim()
> From: gpet...@uark.edu
>
>
> 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 <bbom...@hotmail.com>
> > Sender: r-help-boun...@r-project.org
> > Importance: Normal
> > Precedence: list
> >
> >
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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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> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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> >
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> >
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