Vectorizing integrate()

v <- 1:5
w <- rep(1, 5)
fun <- function(x, m, n) n*exp(-m*x)
eta <- sapply(1:5, function(i) integrate(fun, 0, Inf, m=v[i],

eta

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
project.org] On Behalf Of David Winsemius
Sent: Thursday, December 06, 2012 12:59 PM
To: Doran, Harold
Cc: r-help at r-project.org
Subject: Re: [R] Vectorizing integrate()


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

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David Winsemius, MD
Alameda, CA, USA