fast_dca {svs}

Discriminant Correspondence Analysis

Description

A fast procedure for computing discriminant correspondence analysis.

Usage

fast_dca(dat, clusters1 = NULL, clusters2 = NULL, members = FALSE)

Arguments

dat

Input data: can be a table or a data frame (but the data frame must have only two columns).

clusters1

A clustering of the first set of levels: either a list or the output of kmeans.

clusters2

A clustering of the second set of levels: either a list or the output of kmeans.

members

Logical indicating whether the (supplementary) coordinates for the individual levels should also be computed.

Value

A list with components:

val

The eigenvalues or principal inertias, indicating how much each latent axis explains.

cen1

The coordinates of the cluster centers for the first set of levels.

cen2

The coordinates of the cluster centers for the second set of levels.

mem1

If members = TRUE: The coordinates of the first set of individual levels.

mem2

If members = TRUE: The coordinates of the second set of individual levels.

References

Abdi, H. (2007) Discriminant correspondence analysis. In: N. Salkind (ed.) Encyclopedia of measurement and statistics. Thousand Oaks: SAGE.

Examples

SndT_Fra <- read.table(system.file("extdata", "SndT_Fra.txt", package = "svs"),
   header = TRUE, sep = "\t", quote = "\"", encoding = "UTF-8",
   stringsAsFactors = FALSE)
sca_SndT_Fra <- fast_sca(SndT_Fra)
kcl_SndT_Fra <- kmeans(sca_SndT_Fra$pos1, centers = 7)
dca_SndT_Fra <- fast_dca(SndT_Fra, clusters1 = kcl_SndT_Fra)
dca_SndT_Fra