compPcaParams: Computes the center vector, eigenvalues and loading matrix...

In FastHCS: Robust Algorithm for Principal Component Analysis

Description

This function is used in FastHCS to compute the parameter estimates of the PCA models used at different steps of the algorithm. It is an internal function not intended to be called by the user.

Usage

  compPcaParams(x,fitd,q=NULL,z0=NULL,seed=1)

Arguments

x	A data matrix x.
fitd	The (internal) result of a call to FastHCS.
q	Desired rank of the SVD decomposition.
z0	Optional. Result of a call to FHCSkernelEVD.
seed	Seed used to initialize the RNG. Defaults to 1.

Value

A list with the following components:

center	The multivariate mean of the observations with indexes in best.
loadings	The (rank q) loadings matrix of the observations with indexes in best.
eigenvalues	The eigenvalues of the observations with indexes in best multiplied by a consistency factor.
scores	The value of the projected on the space of the principal components data (the centred data multiplied by the loadings matrix) is returned. Hence, cov(scores) is the diagonal matrix diag(eigenvalues).

Author(s)

Kaveh Vakili, Eric Schmitt

FastHCS documentation built on July 8, 2020, 7:14 p.m.