[R-sig-ME] Low rank fixed effect design matrix LMER package
surajkeshri at gmail.com
Thu Feb 11 02:39:08 CET 2016
I'm trying to implement some pieces of LMER algorithm in Python. I'm facing
low rank fixed effect design matrix issue. One way to take care of low rank
is to detect linearly dependent columns using ideas from SVD/QR
decomposition and remove it. My design matrix is very big and sparse.
Therefore, instead of doing SVD of the design matrix (X), I do SVD of X^T *
X. However, one has to decide a threshold for the singular values. When I
look at the singular values of X^T*X, the range is very large (1e+12 to
1e-4). In this situation, how does one decide the threshold so that X^T*X
is invertible (after removing linearly dependent columns)? How does LMER
package solve this problem?
Thank you so much for any help!!
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