CA {biplotEZ}

Correspondence Analysis (CA) method

Description

This function produces a list of elements to be used for CA biplot construction by approximation of the Pearson residuals.

Usage

CA(bp, dim.biplot = c(2,1,3), e.vects = 1:ncol(bp$X), variant = "Princ", 
lambda.scal = FALSE)

Arguments

bp

object of class biplot obtained from preceding function biplot(center = FALSE). In order to maintain the frequency table, the input should not be centered or scaled. For CA, bp should be a contingency table.

dim.biplot

dimension of the biplot. Only values 1, 2 and 3 are accepted, with default 2.

e.vects

which eigenvectors (canonical variates) to extract, with default 1:dim.biplot.

variant

which correspondence analysis variant, with default "Princ", presents a biplot with rows in principal coordinates and columns in standard coordinates. variant = "Stand", presents a biplot with rows in standard coordinates and columns in principal coordinates. variant = "symmetric", presents a symmetric biplot with row and column standard coordinates scaled equally by the singular values.

lambda.scal

logical value to request lambda-scaling, default is FALSE.

Value

A list with the following components is available:

Z

Combined data frame of the row and column coordinates.

r

Numer of levels in the row factor.

c

Numer of levels in the column factor.

rowcoor

Row coordinates based on the selected variant.

colcoor

Column coordinates based on the selected variant.

P

Correspondence Matrix

Smat

Standardised Pearson residuals.

SVD

Singular value decomposition solution: d, u, v.

qual

Quality of the approximation.

lambda.val

The computed lambda value if lambda-scaling is requested.

Examples

# Creating a CA biplot with rows in principal coordinates:
biplot(HairEyeColor[,,2], center = FALSE) |> CA() |> plot()
# Creating a CA biplot with rows in standard coordinates:
biplot(HairEyeColor[,,2], center = FALSE) |> CA(variant = "Stand") |> 
samples(col=c("magenta","purple"), pch=c(15,17), label.col="black") |> plot()
# Creating a CA biplot with rows and columns scaled equally:
biplot(HairEyeColor[,,2], center = FALSE) |> CA(variant = "Symmetric") |> 
samples(col=c("magenta","purple"), pch=c(15,17), label.col="black") |> plot()