BPCA_initmodel: Initialize BPCA model

In pcaMethods: A collection of PCA methods

Description

Model initialization for Bayesian PCA. This function is NOT inteded to be run separately!

Usage

BPCA_initmodel(y, components)

Arguments

y	numeric matrix containing missing values. Missing values are denoted as 'NA'
components	Number of components used for estimation

Details

The function calculates the initial Eigenvectors by use of SVD from the complete rows. The data structure M is created and initial values are assigned.

Value

List containing

rows	Row number of input matrix
cols	Column number of input matrix
comps	Number of components to use
yest	(working variable) current estimate of complete data
row_miss	(Array) Indizes of rows containing missing values
row_nomiss	(Array) Indices of complete rows (such with no missing values)
nans	Matrix of same size as input data. TRUE if input == NA, false otherwise
mean	Column wise data mean
PA	(d x k) Estimated principal axes (eigenvectors, loadings) The matrix ROWS are the vectors
tau	Estimated precision of the residual error
scores	Estimated scores

Further elements are: galpha0, balpha0, alpha, gmu0, btau0, gtau0, SigW. These are working variables or constants.

Author(s)

Wolfram Stacklies

pcaMethods documentation built on Nov. 8, 2020, 6:19 p.m.