bp.vcov: Variance-covariance matrices for back-projection coefficients

In bbolker/cpcbp: common principal components/back-projection analysis

Description

Calculates variance-covariance matrices for back-projection coefficients for each variable for a CPC/Flury back-projection analysis

Usage

bp.vcov(x, f, cpcmat, eigvar)

Arguments

x	a numeric matrix (or data frame) with all numeric values, or (if f is missing) a list of a data matrix and a grouping variable
f	a factor describing the group structure of the data
cpcmat	CPC matrix (calculated if not specified)
eigvar	variances of eigenvector elements (calculated if not specified)

Value

An n by n by n array (where n is the number of variables/columns of x), where [i,j,k] gives the jk element of the covariance matrix for the ith variable

Author(s)

Ben Bolker

Examples

bp.vcov(simdata())

bbolker/cpcbp documentation built on May 11, 2019, 9:28 p.m.