CV_lbd: Cross validation to select lambda
In bingx1990/LOVE: Overlapping clustering based on latent factor models
Description
Usage
Arguments
Value
Description
Cross-validation to select λ for estimating the precision
matrix of Z. Split the data into two parts. Estimating Cov(Z) on two datasets.
Then, for each value in lbdGrids, calculate Omega on the first dataset
and calculate the loss on the second dataset. Choose the value which minimizes
<Cov(Z), Ω> - log(det(Ω)).
Usage
1
CV_lbd(X, lbdGrids, AI, pureVec, diagonal)
Arguments
X
A n by p data matrix.
lbdGrids
A vector of numerical constants.
AI
A p by K matrix.
pureVec
The estimated set of pure variables.
diagonal
Logical. If TRUE, the covariance matrix of Z is
diagonal; else FALSE.
Value
The selected λ.
bingx1990/LOVE documentation built on Jan. 23, 2022, 1:30 a.m.
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Description Usage Arguments Value
Description
Cross-validation to select λ for estimating the precision
matrix of Z. Split the data into two parts. Estimating Cov(Z) on two datasets.
Then, for each value in lbdGrids, calculate Omega on the first dataset
and calculate the loss on the second dataset. Choose the value which minimizes
<Cov(Z), Ω> - log(det(Ω)).
Usage
1 | CV_lbd(X, lbdGrids, AI, pureVec, diagonal)
|
Arguments
X |
A n by p data matrix. |
lbdGrids |
A vector of numerical constants. |
AI |
A p by K matrix. |
pureVec |
The estimated set of pure variables. |
diagonal |
Logical. If TRUE, the covariance matrix of Z is diagonal; else FALSE. |
Value
The selected λ.
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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