>>>>> "JK" == John Kane <jrkrid...@yahoo.ca>
>>>>> on Mon, 24 Aug 2009 10:01:56 -0700 (PDT) writes:
JK> I may be misunderstanding the question but would cor(d1,
JK> use='complete.obs') or some other variant of "use" help?
Yes! I've been a bit appalled to read that nobody else gave
this (in my view *the correct* !) answer.
Leaving away NA's via na.omit() or similar is more general, but
for cor() definitely less flexible!
Martin Maechler, ETH Zurich
JK> --- On Mon, 8/24/09, Christian Meesters
JK> <meest...@imbie.uni-bonn.de> wrote:
>> From: Christian Meesters <meest...@imbie.uni-bonn.de>
>> Subject: [R] robust method to obtain a correlation coeff?
>> To: "r-help@r-project.org Help" <r-help@r-project.org>
>> Received: Monday, August 24, 2009, 10:47 AM Hi,
>>
>> Being a R-newbie I am wondering how to calculate a
>> correlation coefficient (preferably with an associated
>> p-value) for data like:
>>
>> > d[,1] [1] 25.5 25.3 25.1 NA 23.3 21.5 23.8 23.2 24.2
>> 22.7 27.6 24.2 ... > d[,2] [1] 0.0 11.1 0.0 NA 0.0
>> 10.1 10.6 9.5 0.0 57.9 0.0 0.0 ...
>>
>> Apparently corr(d) from the boot-library fails with NAs
>> in the data, also cor.test cannot cope with a different
>> number of NAs. Is there a solution to this problem
>> (calculating a correlation coefficient and ignoring
>> different number of NAs), e.g. Pearson's corr coeff?
>>
>> If so, please point me to the relevant piece of
>> documentation.
>>
>> TIA Christian
>>
>> ______________________________________________
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>>
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JK> commented, minimal, self-contained, reproducible code.
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