[R] R computing speed

[jim holtman]

I would suggest that you use Rprof to get a profile of the code to see
where time is being spent.  You did not provide commented, minimal,
self-contained, reproducible code, so it is hard to tell from just
looking at the code to determine what is happening.  Rprof should
provide an idea of where to look in your code for optimization.  You
might consider colMeans instead of the "apply", but I am not sure if
this will make a significant change in the execution time.

On Dec 11, 2007 6:55 AM, Carlo Fezzi <C.fezzi at uea.ac.uk> wrote:
> Dear helpers,
>
> I am using R version 2.5.1 to estimate a multinomial logit model using my
> own maximum likelihood function (I work with share data and the default
> function of R cannot deal with that).
>
> However, the computer (I have an Athlon XP 3200+ with 512 GB ram) takes
> quite a while to estimate the model.
>
> With 3 categories, 5 explanatory variables and roughly 5000 observations it
> takes 2-3 min. For 10 categories and 10 explanatory variables (still 5000
> obs) more than 1 hour.
>
> Is there any way I can speed up this process? (Modifying the code or
> modifying some R options maybe?)
>
> I would be really grateful if anybody could help me with this issue, I
> attach my code below.
>
> Many thanks,
>
> Carlo
>
> ***************************************
> Carlo Fezzi
>
> Centre for Social and Economic Research
> on the Global Environment (CSERGE),
> School of Environmental Sciences,
> University of East Anglia,
> Norwich, NR4 7TJ
> United Kingdom.
>
> ***************************************
>
>
>
> # MULTILOGIT
>
> # This function computes the estimates of a multinomial logit model
>
> # inputs:       a matrix vector of 1 and 0 (y) or of shares
> #               a matrix of regressors (x) - MUST HAVE COLUMN NAMES! -
> #               names of the variables, default = colnames(x)
> #               optimization methods, default = 'BFGS'
> #               base category, default = 1
> #               restrictions, default = NULL
> #               weights, default all equal to 1
>
>
> # outputs:      an object of class "multilogit.c"
>
> # McFadden D. (1974) "Conditional logit analysis of qualitative choice
> behavior", in Zarembka P. (ed.), Frontiers in Econometrics, Academic Press.
>
>
> multilogit.c <- function(y, xi, xi.names = colnames(xi), c.base=1,
> rest=NULL, w = rep(1,nrow(y)), method='BFGS')
> {
>
>        n.obs <- sum(w)
>        xi<-cbind(1,xi)
>        colnames(xi)[1]<-"Intercept"
>
>        nx<-ncol(xi)
>        ny<-ncol(y)
>
>        beta<-numeric(nx*ny)
>
>        negll<- function(beta,y,xi)
>        {
>                beta[rest]<-0
>                beta[(((c.base-1)*nx)+1):(c.base*nx)]<-0
>                lli <- y  * (xi%*%matrix(beta,nx,ny) - log ( apply(exp(
> xi%*%matrix(beta,nx,ny)) ,1,sum ) )     )
>                lli<-lli*w
>                -sum(lli)
>        }
>
>        pi<- apply((y*w),2,mean)/mean(w)
>
>        ll0 <- (t(pi)%*%log(pi))*sum(w)
>
>        result<-c(      optim(par = rep(0,nx*(ny)), fn = negll, y=y, xi=xi,
> hessian=T, method=method),
>                        list(varnames=xi.names, rest=rest, nx=nx, ny=ny,
> npar=nx*(ny-1)-length(rest), ll0=ll0,   pi=pi, xi=xi,
> n.obs=n.obs,c.base=c.base,w=w))
>
>        result$par <- result$par[-(((c.base-1)*nx)+1):-(c.base*nx)]
>        result$hessian <-
> result$hessian[-(((c.base-1)*nx)+1):-(c.base*nx),-(((c.base-1)*nx)+1):-(c.ba
> se*nx)]
>
>        class(result)<-"multilogit.c"
>        return(result)
> }
>
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>



-- 
Jim Holtman
Cincinnati, OH
+1 513 646 9390

What is the problem you are trying to solve?