Now that’s brilliant! And to get a vector of counts I could extend it to
rr <- crossprod(!is.na(tmp))
rr[lower.tri(rr),]
Thanks, Bill!
From: William Dunlap
Sent: Tuesday, January 21, 2020 3:00 PM
To: Doran, Harold
Cc: r-help@r-project.org
Subject: Re: [R] N Sizes between Pairs of Columns using cor(, , , use =
'pairwise')
External email alert: Be wary of links & attachments.
crossprod(!is.na<https://nam01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fis.na%2F=02%7C01%7CHDoran%40air.org%7C8fd9799ec01c4a7550e008d79eac8b57%7C9ea45dbc7b724abfa77cc770a0a8b962%7C0%7C0%7C637152336221357438=k2OoWHLJts1VNYOBVSn%2Fwv85LLjdpAcH93o7k6HWnJ0%3D=0>(tmp))
Bill Dunlap
TIBCO Software
wdunlap
tibco.com<https://nam01.safelinks.protection.outlook.com/?url=http%3A%2F%2Ftibco.com%2F=02%7C01%7CHDoran%40air.org%7C8fd9799ec01c4a7550e008d79eac8b57%7C9ea45dbc7b724abfa77cc770a0a8b962%7C0%7C0%7C637152336221367435=aonp2ClFToaETLQMSZ7ie629zzGBDxtOWRf3XjHMGMQ%3D=0>
On Tue, Jan 21, 2020 at 11:56 AM Doran, Harold
mailto:hdo...@air.org>> wrote:
I'm trying to find an efficient way to find the N size on correlations produced
when using the pairwise option in cor().
Here is a sample to illustrate:
### Create a sample data frame
tmp <- data.frame(v1 = rnorm(10), v2 = rnorm(10), v3 = rnorm(10), v4 =
rnorm(10))
### Create some random missingness
for(i in 1:4) tmp[sample(1:10, 2, replace = FALSE), i] <- NA
### Correlate
cor(tmp, use = 'pairwise')
Now, a REALLY bad idea would be this (but conceptually it illustrates what I
want)
### Identify all column pairs
pairs <- combn(4,2)
### Now, write code to loop over each pair of columns and identify where both
rows are TRUE
!is.na<https://nam01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fis.na%2F=02%7C01%7CHDoran%40air.org%7C8fd9799ec01c4a7550e008d79eac8b57%7C9ea45dbc7b724abfa77cc770a0a8b962%7C0%7C0%7C637152336221377429=LwSEn2KSTjjhT8BhnEvNwSKgt%2BFCj8WIgc8%2FOEsWXJY%3D=0>(tmp[,
pairs[,1]])
Of course doing this when the number of pairwise combinations is silly. So,
hmmm, I don't see as a by-product of the cor() function N sizes, and certainly
looping over pairs of columns would be doable, but not efficient, but any
suggestions on this?
Thanks,
Harold
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