update_A: Update low rank mean in regularized maximum likelihood factor...
In dcgerard/succotashr: Surrogate and Confounder Correction Occuring Together with Adaptive Shrinkage
Description
Usage
Arguments
Details
Value
Description
This is the update of the low rank mean in the regularized factor
analysis. The low rank mean is not regularized, so this is the
exact same update as in
Sun et al
(2012).
Usage
1
update_A(Y, sig_diag, k)
Arguments
Y
A matrix. This is the n by p data matrix.
sig_diag
A vector of length p. These are the
estimates of the variances of the columns of Y.
k
An integer. The rank of the mean matrix.
Details
The update is just a truncated SVD of a scaled data matrix, where
the scaling depends on the current estimates of the variances of
the columns.
Value
A_new The update of the mean matrix.
dcgerard/succotashr documentation built on May 15, 2019, 1:25 a.m.
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Description Usage Arguments Details Value
Description
This is the update of the low rank mean in the regularized factor analysis. The low rank mean is not regularized, so this is the exact same update as in Sun et al (2012).
Usage
1 | update_A(Y, sig_diag, k)
|
Arguments
Y |
A matrix. This is the |
sig_diag |
A vector of length |
k |
An integer. The rank of the mean matrix. |
Details
The update is just a truncated SVD of a scaled data matrix, where the scaling depends on the current estimates of the variances of the columns.
Value
A_new The update of the mean matrix.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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