On Dec 6, 2012, at 10:10 AM, Doran, Harold wrote:
> I have written a program to solve a particular logistic regression problem
> using IRLS. In one step, I need to integrate something out of the linear
> predictor. The way I'm doing it now is within a loop and it is as you would
> expect slow to process, especially inside an iterative algorithm.
>
> I'm hoping there is a way this can be vectorized, but I have not found it so
> far. The portion of code I'd like to vectorize is this
>
> for(j in 1:nrow(X)){
> fun <- function(u) 1/ (1 + exp(- (B[1] + B[2] * (x[j] + u)))) * dnorm(u, 0,
> sd[j])
> eta[j] <- integrate(fun, -Inf, Inf)$value
> }
>
The Vectorize function is just a wrapper to mapply. If yoou are able to get
that code to execute properly for your un-posted test cases, then why not use
mapply?
> Here X is an n x p model matrix for the fixed effects, B is a vector with the
> estimates of the fixed effects at iteration t, x is a predictor variable in
> the jth row of X, and sd is a variable corresponding to x[j].
>
> Is there a way this can be done without looping over the rows of X?
>
> Thanks,
> Harold
>
> [[alternative HTML version deleted]]
>
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David Winsemius, MD
Alameda, CA, USA
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