# Re: [R] Pairwise n for large correlation tables?

From: Christos Hatzis <christos_at_nuverabio.com>
Date: Tue 08 Aug 2006 - 12:44:03 EST

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

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
}
```--
University of Oregon

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