FitRwithPCAandWALS: Calculate a low-rank approximation to the correlation matrix...

In Correlplot: A Collection of Functions for Graphing Correlation Matrices

Calculate a low-rank approximation to the correlation matrix with four methods

Description

Function FitRwithPCAandWALS uses principal component analysis (PCA) and weighted alternating least squares (WALS) to calculate different low-rank approximations to the correlation matrix.

Usage

FitRwithPCAandWALS(R, nd = 2, itmaxout = 10000, itmaxin = 10000, eps = 1e-08)

Arguments

R	The correlation matrix
nd	The dimensionality of the low-rank solution (2 by default)
itmaxout	Maximum number of iterations for the outer loop of the algorithm
itmaxin	Maximum number of iterations for the inner loop of the algorithm
eps	Numerical criterion for convergence of the outer loop

Details

Four methods are run succesively: standard PCA; PCA with an additive adjustment; WALS avoiding the fit of the diagonal; WALS avoiding the fit of the diagonal and with an additive adjustment.

Value

A list object with fields:

Rhat.pca	Low-rank approximation obtained by PCA
Rhat.pca.adj	Low-rank approximation obtained by PCA with adjustment
Rhat.wals	Low-rank approximation obtained by WALS without fitting the diagonal
Rhat.wals.adj	Low-rank approximation obtained by WALS without fitting the diagonal and with adjustment

Author(s)

Jan Graffelman ([e-mail])

References

Graffelman, J. and De Leeuw, J. (2023) Improved approximation and visualization of the correlation matrix. The American Statistician pp. 1–20. Available online as latest article \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00031305.2023.2186952")}

See Also

wAddPCA

Examples

data(HeartAttack)
X <- HeartAttack[,1:7]
X[,7] <- log(X[,7])
colnames(X)[7] <- "logPR"
R <- cor(X)
## Not run: 
out <- FitRwithPCAandWALS(R)

## End(Not run)

Correlplot documentation built on June 22, 2024, 11:34 a.m.