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getting covariance ignoring NaN missing values

3 messages · arun, Sachinthaka Abeywardana

arun
# 
Hi,
If you look at ?cov(),
there are options for 'use':
set.seed(15)
a=array(rnorm(9),dim=c(3,3))
?a[3,2]<- NaN

?cov(a,use="complete.obs")
#?????????? [,1]??????? [,2]?????? [,3]
#[1,]? 1.2360602 -0.32167789? 0.8395953
#[2,] -0.3216779? 0.08371491 -0.2185001
#[3,]? 0.8395953 -0.21850006? 0.5702960
?cov(a,use="na.or.complete")
#?????????? [,1]??????? [,2]?????? [,3]
#[1,]? 1.2360602 -0.32167789? 0.8395953
#[2,] -0.3216779? 0.08371491 -0.2185001
#[3,]? 0.8395953 -0.21850006? 0.5702960
?cov(a,use="pairwise.complete.obs")
#?????????? [,1]??????? [,2]?????? [,3]
#[1,]? 1.2570603 -0.32167789? 0.7377472
#[2,] -0.3216779? 0.08371491 -0.2185001
#[3,]? 0.7377472 -0.21850006? 0.4433438
A.K.




----- Original Message -----
From: Sachinthaka Abeywardana <sachin.abeywardana at gmail.com>
To: "r-help at r-project.org" <r-help at r-project.org>
Cc: 
Sent: Thursday, March 7, 2013 10:36 PM
Subject: [R] getting covariance ignoring NaN missing values

Hi all,

I have a matrix that has many NaN values. As soon as one of the columns has
a missing (NaN) value the covariance estimation gets thrown off.

Is there a robust way to do this?

Thanks,
Sachin

a=array(rnorm(9),dim=c(3,3))> a? ? ? ? ? ? [,1]? ? ?  [,2]? ? ? [,3]
[1,] -0.79418236? 0.7813952? 0.855881
[2,] -1.65347906 -1.9462446 -0.376325
[3,] -0.03144987? 0.6756862 -1.879801> a[3,2]=NANError: object 'NAN'
not found> a[3,2]=NaN> a? ? ? ? ? ? [,1]? ? ?  [,2]? ? ? [,3]
[1,] -0.79418236? 0.7813952? 0.855881
[2,] -1.65347906 -1.9462446 -0.376325
[3,] -0.03144987? ? ? ? NaN -1.879801> cov(a)? ? ? ? ?  [,1] [,2]? ? ?  [,3]
[1,]? 0.6585217?  NA -0.5777408
[2,]? ? ? ?  NA?  NA? ? ? ?  NA
[3,] -0.5777408?  NA? 1.8771214

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