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
}
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
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