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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