thanks, appreciate it


On Fri, Mar 8, 2013 at 2:49 PM, arun <smartpink111@yahoo.com> wrote:

>
>
> 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@gmail.com>
> To: "r-help@r-project.org" <r-help@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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>
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>
>

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