Re: [R] Pairwise n for large correlation tables?
On Tue, 8 Aug 2006, [EMAIL PROTECTED] wrote:
Try this:
# mat is test matrix
mat - matrix(1:25, 5)
mat[2,2] - mat[3,4] - NA
crossprod(!is.na(mat))
Exactly what I was looking for! Thanks.
--Adam
On 8/7/06, Adam D. I. Kramer [EMAIL PROTECTED] wrote:
Hello,
I'm using a very large data set (n 100,000 for 7 columns), for which I'm
pretty happy dealing with pairwise-deleted correlations to populate my
correlation table. E.g.,
a - cor(cbind(col1, col2, col3),use=pairwise.complete.obs)
...however, I am interested in the number of cases used to compute each
cell of the correlation table. I am unable to find such a function via
google searches, so I wrote one of my own. This turns out to be highly
inefficient (e.g., it takes much, MUCH longer than the correlations do). Any
hints, regarding other functions to use or ways to maket his speedier, would
be much appreciated!
pairwise.n - function(df=stop(Must provide data frame!)) {
if (!is.data.frame(df)) {
df - as.data.frame(df)
}
colNum - ncol(df)
result -
matrix(data=NA,nrow=colNum,ncol=ncolNum,dimnames=list(colnames(df),colnames(df)))
for(i in 1:colNum) {
for (j in i:colNum) {
result[i,j] - length(df[!is.na(df[i])!is.na(df[j])])/colNum
}
}
result
}
--
Adam D. I. Kramer
University of Oregon
__
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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] Pairwise n for large correlation tables?
Hello,
I'm using a very large data set (n 100,000 for 7 columns), for which I'm
pretty happy dealing with pairwise-deleted correlations to populate my
correlation table. E.g.,
a - cor(cbind(col1, col2, col3),use=pairwise.complete.obs)
...however, I am interested in the number of cases used to compute each
cell of the correlation table. I am unable to find such a function via
google searches, so I wrote one of my own. This turns out to be highly
inefficient (e.g., it takes much, MUCH longer than the correlations do). Any
hints, regarding other functions to use or ways to maket his speedier, would
be much appreciated!
pairwise.n - function(df=stop(Must provide data frame!)) {
if (!is.data.frame(df)) {
df - as.data.frame(df)
}
colNum - ncol(df)
result -
matrix(data=NA,nrow=colNum,ncol=ncolNum,dimnames=list(colnames(df),colnames(df)))
for(i in 1:colNum) {
for (j in i:colNum) {
result[i,j] - length(df[!is.na(df[i])!is.na(df[j])])/colNum
}
}
result
}
--
Adam D. I. Kramer
University of Oregon
__
R-help@stat.math.ethz.ch 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.
Re: [R] Pairwise n for large correlation tables?
Try this:
# mat is test matrix
mat - matrix(1:25, 5)
mat[2,2] - mat[3,4] - NA
crossprod(!is.na(mat))
On 8/7/06, Adam D. I. Kramer [EMAIL PROTECTED] wrote:
Hello,
I'm using a very large data set (n 100,000 for 7 columns), for which I'm
pretty happy dealing with pairwise-deleted correlations to populate my
correlation table. E.g.,
a - cor(cbind(col1, col2, col3),use=pairwise.complete.obs)
...however, I am interested in the number of cases used to compute each
cell of the correlation table. I am unable to find such a function via
google searches, so I wrote one of my own. This turns out to be highly
inefficient (e.g., it takes much, MUCH longer than the correlations do). Any
hints, regarding other functions to use or ways to maket his speedier, would
be much appreciated!
pairwise.n - function(df=stop(Must provide data frame!)) {
if (!is.data.frame(df)) {
df - as.data.frame(df)
}
colNum - ncol(df)
result -
matrix(data=NA,nrow=colNum,ncol=ncolNum,dimnames=list(colnames(df),colnames(df)))
for(i in 1:colNum) {
for (j in i:colNum) {
result[i,j] - length(df[!is.na(df[i])!is.na(df[j])])/colNum
}
}
result
}
--
Adam D. I. Kramer
University of Oregon
__
R-help@stat.math.ethz.ch 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.
__
R-help@stat.math.ethz.ch 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.
Re: [R] Pairwise n for large correlation tables?
Hi,
You can use complete.cases
It should run faster than the code you suggested.
See following example:
x - matrix(runif(30),10,3)
# introduce missing values
x[sample(1:10,3),1] - NA
x[sample(1:10,3),2] - NA
x[sample(1:10,3),3] - NA
cor(x,use=pairwise.complete.obs)
n - ncol(x)
n.na - matrix(0, n, n)
for (i in seq(1, n)) {
n.na[i,i] - sum( complete.cases(x[, i]) )
for (j in seq(i+1, length=n-i)) {
ok - sum( complete.cases(x[, i], x[, j]) )
n.na[i,j] - n.na[j,i] - ok
}
}
HTH
-Christos
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Adam D. I. Kramer
Sent: Monday, August 07, 2006 10:04 PM
To: r-help@stat.math.ethz.ch
Subject: [R] Pairwise n for large correlation tables?
Hello,
I'm using a very large data set (n 100,000 for 7 columns), for which I'm
pretty happy dealing with pairwise-deleted correlations to populate my
correlation table. E.g.,
a - cor(cbind(col1, col2, col3),use=pairwise.complete.obs)
...however, I am interested in the number of cases used to compute each cell
of the correlation table. I am unable to find such a function via google
searches, so I wrote one of my own. This turns out to be highly inefficient
(e.g., it takes much, MUCH longer than the correlations do). Any hints,
regarding other functions to use or ways to maket his speedier, would be
much appreciated!
pairwise.n - function(df=stop(Must provide data frame!)) {
if (!is.data.frame(df)) {
df - as.data.frame(df)
}
colNum - ncol(df)
result -
matrix(data=NA,nrow=colNum,ncol=ncolNum,dimnames=list(colnames(df),colnames(
df)))
for(i in 1:colNum) {
for (j in i:colNum) {
result[i,j] - length(df[!is.na(df[i])!is.na(df[j])])/colNum
}
}
result
}
--
Adam D. I. Kramer
University of Oregon
__
R-help@stat.math.ethz.ch 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.
__
R-help@stat.math.ethz.ch 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.
