[R] Vectorizing integrate()

[arun]

Hi,
Using David's function:
fun <- function(u, m, s) 1/ (1 + exp(- (B[1] + B[2] *
      (m + u)))) * dnorm(u, 0, s)

 res<-mapply(function(i) integrate(fun,-Inf,Inf,m=x[i],s=sem1[i])$value,1:nrow(X))
res
# [1] 0.5212356 0.6214989 0.5306124 0.5789414 0.3429795 0.6972879 0.5952949
 #[8] 0.7531899 0.4740685 0.7576587
identical(res,eta)
#[1] TRUE
A.K.

----- Original Message -----
From: "Doran, Harold" <HDoran at air.org>
To: David Winsemius <dwinsemius at comcast.net>
Cc: "r-help at r-project.org" <r-help at r-project.org>
Sent: Friday, December 7, 2012 10:14 AM
Subject: Re: [R] Vectorizing integrate()

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:dwinsemius at comcast.net]
> Sent: Thursday, December 06, 2012 1:59 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
> >
> >     [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help at 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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