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 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.