On 6/20/2011 12:23 PM, ivan wrote:
Hi,
I have two datasets, x and y. Simplified x and y denote:
X
Y
A B C A B C . . . . . . . . . . . . . . . . . .
I want to implement all possible models such as lm(X$A~Y$A), lm(X$B~Y$B),
lm(X$C~Y$C)... I have tried the following:
fun<- function(x,y){
for(i in 1:length(colnames(x))){
for(j in 1:length(colnames(y))){
if(colnames(x)[i]==colnames(y)[j]){
models=list(lm(ts(x[i])~ts(y[j])))
}
else{}
}
}
return(models)
}
The problem is that this returns only one of the three models, namely the
last one. What am I doing wrong? Thank you very much in advance.
You are overwriting the variable "models" each iteration through the
loop. When you return it, it only has the last model. The most direct
fix is to initialize it at the start of the function (models <- list()),
and append to it in the loop (models <- append(models, etc...))
The longer answer is that this is not the best (most R-ish) way to
approach the problem. Consider a different algorithm: gather a list of
the column names that are common (and for which there should be a model
each) and then iterate over that collection creating a model for each
one and returning that in a list.
library("plyr")
fun<- function(x,y){
cols <- intersect(colnames(x), colnames(y))
names(cols) <- cols
llply(cols, function(col) {lm(ts(x[col])~ts(y[col]))})
}
# make fake data.
# NOTE: you should have included something like this in your question.
# I made some because I'm in something like a good mood, but that
# isn't something you should count on.
X <- data.frame(matrix(rnorm(100), ncol=10))
names(X) <- LETTERS[1:10]
Y <- data.frame(matrix(rnorm(100), ncol=10))
names(Y) <- LETTERS[1:10]
fun(X,Y)
Regards
[[alternative HTML version deleted]]
Yeah, don't do that either. The posting guide is your friend (or will
at least help you get answers).
--
Brian S. Diggs, PhD
Senior Research Associate, Department of Surgery
Oregon Health & Science University
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