BPCA_initmodel {pcaMethods} | R Documentation

## Initialize BPCA model

### 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

[Package pcaMethods version 1.79.1 Index]