Compute a covariance matrix with data with NA values
My data contains some missing values. I want to compute a covariance matrix for three of the variables I have. The location of missing values differ across different variables, and I want to use all the non-missing data to construct the covariance matrix. I want the covariance function to handle missing values by pairwise deletion; all available observations should be used to calculate each pairwise correlation without regard to whether variables outside that pair
cov() function does not seem to handle NA values, if I am not mistaken.
I can technically write up the function from scratch to do this. But this seems like a basic problem, so I was guessing there might be some library already written that handle this. Has anyone faced this problem?
Any help will be greatly appreciated. Thanks!
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