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

	[[alternative HTML version deleted]]

