EstScore: Estimate compontent scores for each subject using the result...

In cbird: Clustering of Multivariate Binary Data with Dimension Reduction via L1-Regularized Likelihood Maximization

Description

This function estimates components scores for each subject using the result of CLUSBIRD.

Usage

EstScore(X, A, mu, N.ite=10000, N.random=1, show.random.ite=FALSE,
oblique=FALSE, mc.cores=1)

Arguments

X	Binary data matrix (N * D).
A	Loading matrix (D * L) estimated by cbird.
mu	A D-length mean vector estimated by cbird.
N.ite	The number of maximum of iterations for the EM algorithm.
N.random	The number of random sets of parameters for initial random starts.
show.random.ite	If "TRUE", the number of each iteration is shown on the R console.
oblique	If "TRUE", the oblique component scores F are estimated. The default is "FALSE".
mc.cores	If "parallel" package has been installed, "EstScore" adopts a multithread process for multiple initial random starts. If "mc.cores"=1, "parallel" package is not needed, and a single core process is conducted.

Value

F	An estimated component score matrix (N * D) containing scores for subjects.
n.ite	The number of iteration needed for convergence.
loss	The value of loss function used in ALS algorithm

Author(s)

Michio Yamamoto
[e-mail]

References

Yamamoto, M. and Hayashi, K. (2015). Clustering of multivariate binary data with dimension reduction via L1-regularized maximization. Pattern Recognition, 48, 3959-3968.

Examples

##See the example of the function "cbird".

cbird documentation built on May 2, 2019, 2:42 a.m.