Description Usage Arguments Details Value
factor_mle
implements regularized maximum likelihood
estimation on a data matrix where the mean is low rank (with the
rank known) and the columns are heteroscedastic but independent.
1 2 | factor_mle(Y, k, itermax = 100, tol = 10^-6, print_diff = FALSE,
sig_reg = 0.01)
|
Y |
A matrix. This is an |
k |
A numeric. The rank of the mean matrix. |
itermax |
An integer. The maximum number of block-coordinate ascent iterations to perform when calculating the MLE. |
tol |
A numeric. When the difference from one of the ratio of
two successive log-likelihoods is less than |
print_diff |
A logical. Should we print to the screen the updates? |
sig_reg |
The regularization parameter. Never set to 0. |
This function calculates the regularized MLE under a normal model with a low-rank mean, a diagonal column covariance matrix, and an identity row covariance matrix. The regularization is on the covariance matrix.
The unregularized version of this factor analysis can be found in
Sun et al
(2012). However, the likelihood is unbounded and there are many
"MLE's" that have that unbounded likelihood. This apparently was
unnoticed in
Sun et al
(2012). sig_reg
should never be set to 0.
A
The low rank mean estimate.
sig_diag
A vector of the variance estimates of the
columns.
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