Compute the Ledoit and Wolf shrinkage estimator of
the covariance matrix (Ledoit and Wolf 2004)
,
which can be used for the initial inverse covariance matrix
in ggmncv.
ledoit_wolf(Y, ...)
| Y | A data matrix (or data.frame) of dimensions n by p. |
|---|---|
| ... | Currently ignored. |
Inverse correlation matrix.
Ledoit O, Wolf M (2004). “A well-conditioned estimator for large-dimensional covariance matrices.” Journal of Multivariate Analysis, 88(2), 365--411.
# ptsd Y <- ptsd[,1:5] # shrinkage ledoit_wolf(Y) #> B1 B2 B3 B4 B5 #> B1 1.7481888 -0.40369737 -0.1292942 -0.59450245 -0.2768127 #> B2 -0.4036974 1.91707343 -0.9664283 0.03540245 -0.2046325 #> B3 -0.1292942 -0.96642834 2.1197014 -0.43273664 -0.3563303 #> B4 -0.5945024 0.03540245 -0.4327366 1.92211201 -0.6171229 #> B5 -0.2768127 -0.20463250 -0.3563303 -0.61712287 1.7916529 # non-reg solve(cor(Y)) #> B1 B2 B3 B4 B5 #> B1 1.8038784 -0.43079211 -0.1181438 -0.63367318 -0.2829828 #> B2 -0.4307921 2.00902137 -1.0592620 0.06464077 -0.2066964 #> B3 -0.1181438 -1.05926201 2.2398875 -0.46685141 -0.3751841 #> B4 -0.6336732 0.06464077 -0.4668514 2.00050585 -0.6560128 #> B5 -0.2829828 -0.20669638 -0.3751841 -0.65601277 1.8525881