David et al
Thanks, I should have made the post more complete. I routinely use apply
functions, but often avoid mapply() as I find it so non-intuitive. In this
instance, I think the situation demands I change that position, though.
Reproducible code for the current implementation of the function is
B <- c(0,1)
sem1 = runif(10, 1, 2)
x <- rnorm(10)
X <- cbind(1, x)
eta <- numeric(10)
for(j in 1:nrow(X)){
fun <- function(u) 1/ (1 + exp(- (B[1] + B[2] * (x[j] + u)))) *
dnorm(u, 0, sem1[j])
eta[j] <- integrate(fun, -Inf, Inf)$value
}
I can't get my head around how mapply() would work here. It accepts as its
first argument a function. But, in my case I have two functions: the user
defined integrand, fun(), an then of course calling the R function integrate().
I was thinking maybe along these lines, but this is obviously wrong.
mapply(integrate(function(u) 1/ (1 + exp(- (B[1] + B[2] * (x + u)))) * dnorm(u,
0, sem1), -Inf, Inf)$value, MoreArgs = list(B, x, sem1))
> -----Original Message-----
> From: David Winsemius [mailto:dwinsem...@comcast.net]
> Sent: Thursday, December 06, 2012 1:59 PM
> To: Doran, Harold
> Cc: r-help@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
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
> David Winsemius, MD
> Alameda, CA, USA
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