Harold -
The first way that comes to mind is
doit = function(i,j)exp(sum(log((exp(tmp[i,]*(qq$nodes[j]-b))) /
(factorial(tmp[i,]) * exp(exp(qq$nodes[j]-b)))))) *
((1/(s*sqrt(2*pi))) * exp(-((qq$nodes[j]-0)^2/(2*s^2))))/
dnorm(qq$nodes[j]) * qq$weights[j]
t(outer(1:3,1:2,Vectorize(doit)))
but you said you wanted to use apply. That leads to
doit1 = function(tmpi,nod,wt)
exp(sum(log((exp(tmpi*(nod-b))) / (factorial(tmpi) *
exp(exp(nod-b)))))) * ((1/(s*sqrt(2*pi))) *
exp(-((nod-0)^2/(2*s^2))))/dnorm(nod) * qq$weights[j]
apply(tmp,1,function(x)mapply(function(n,w)doit1(x,n,w),qq$nodes,qq$weights))
Both seem to agree with your rr1.
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Mon, 4 Oct 2010, Doran, Harold wrote:
Sorry about that; s <- 1
-----Original Message-----
From: Gabriela Cendoya [mailto:gabrielacendoya.rl...@gmail.com]
Sent: Monday, October 04, 2010 1:44 PM
To: Doran, Harold
Cc: R-help
Subject: Re: [R] Help with apply
You are missing "s" in your definitions so I can't reproduce your code.
tmp <- data.frame(var1 = sample(c(0:10), 3, replace = TRUE), var2 =
sample(c(0:10), 3, replace = TRUE), var3 = sample(c(0:10), 3, replace = TRUE))
str(tmp)
'data.frame': 3 obs. of 3 variables:
$ var1: int 9 3 9
$ var2: int 4 6 2
$ var3: int 2 9 3
#I can run the following double loop and yield what I want in the end (rr1) as:
library(statmod)
Q <- 2
b <- runif(3)
qq <- gauss.quad.prob(Q, dist = 'normal', mu = 0, sigma=1)
rr1 <- matrix(0, nrow = Q, ncol = nrow(tmp))
L <- nrow(tmp)
for(j in 1:Q){
+ for(i in 1:L){
+ rr1[j,i] <- exp(sum(log((exp(tmp[i,]*(qq$nodes[j]-b))) /
(factorial(tmp[i,]) *
+ exp(exp(qq$nodes[j]-b)))))) * ((1/(s*sqrt(2*pi))) *
exp(-((qq$nodes[j]-0)^2/(2*s^2))))/dnorm(qq$nodes[j]) * qq$weights[j]
+ }
+ }
Error: objeto 's' no encontrado
rr1
[,1] [,2] [,3]
[1,] 0 0 0
[2,] 0 0 0
Gabriela
2010/10/4, Doran, Harold <hdo...@air.org>:
Suppose I have the following data:
tmp <- data.frame(var1 = sample(c(0:10), 3, replace = TRUE), var2 =
sample(c(0:10), 3, replace = TRUE), var3 = sample(c(0:10), 3, replace =
TRUE))
I can run the following double loop and yield what I want in the end (rr1)
as:
library(statmod)
Q <- 2
b <- runif(3)
qq <- gauss.quad.prob(Q, dist = 'normal', mu = 0, sigma=1)
rr1 <- matrix(0, nrow = Q, ncol = nrow(tmp))
L <- nrow(tmp)
for(j in 1:Q){
for(i in 1:L){
rr1[j,i] <-
exp(sum(log((exp(tmp[i,]*(qq$nodes[j]-b))) / (factorial(tmp[i,]) *
exp(exp(qq$nodes[j]-b)))))) *
((1/(s*sqrt(2*pi))) * exp(-((qq$nodes[j]-0)^2/(2*s^2))))/dnorm(qq$nodes[j])
* qq$weights[j]
}
}
rr1
But, I think this is so inefficient for large Q and nrow(tmp). The function
I am looping over is:
fn <- function(x, nodes, weights, s, b) {
exp(sum(log((exp(x*(nodes-b))) / (factorial(x) *
exp(exp(nodes-b)))))) *
((1/(s*sqrt(2*pi))) *
exp(-((nodes-0)^2/(2*s^2))))/dnorm(nodes) * weights
}
I've tried using apply in a few ways, but I can't replicate rr1 from the
double loop. I can go through each value of Q one step at a time and get
matching values in the rr1 table, but this would still require a loop and
that I think can be avoided.
apply(tmp, 1, fn, nodes = qq$nodes[1], weights = qq$weights[1], s = 1, b =
b)
Does anyone see an efficient way to compute rr1 without a loop?
Harold
sessionInfo()
R version 2.10.1 (2009-12-14)
i386-pc-mingw32
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United
States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United
States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] minqa_1.1.9 Rcpp_0.8.6 MiscPsycho_1.6 statmod_1.4.6
lattice_0.17-26 gdata_2.8.0
loaded via a namespace (and not attached):
[1] grid_2.10.1 gtools_2.6.2 tools_2.10.1
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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--
_________________________
Lic. María Gabriela Cendoya
Magíster en Biometría
Profesor Adjunto
Facultad de Ciencias Agrarias
UNMdP - Argentina
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
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.
