MCA: Multiple Correspondence Analysis (MCA)

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Multiple Correspondence Analysis (MCA)

Description

Performs Multiple Correspondence Analysis (MCA) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables. Performs also Specific Multiple Correspondence Analysis with supplementary categories and supplementary categorical variables. Missing values are treated as an additional level, categories which are rare can be ventilated.

Usage

MCA(
  d,
  ncp = mca.ncp(d, ind.sup, quanti.sup, quali.sup),
  ind.sup = NULL,
  quanti.sup = NULL,
  quali.sup = NULL,
  row.w = NULL
)

Arguments

d	A data frame or a table with n rows and p columns, i.e. a contingency table.
ncp	The number of dimensions kept in the results. All of them, by default: one per active variable, and never more than n - 1. A variable with J_q modalities contributes J_q - 1 of them – its indicator columns sum to one, so one of them is redundant – which makes J - Q for Q active variables holding J modalities between them, and never more than n - 1. The modalities are counted as observed among the active individuals: a level nobody takes carries nothing.
ind.sup	A vector indicating the indexes of the supplementary individuals.
quanti.sup	A vector indicating the indexes of the quantitative supplementary variables.
quali.sup	A vector indicating the indexes of the categorical supplementary variables.
row.w	An optional row weights (by default, a vector of 1 for uniform row weights); the weights are given only for the active individuals.

Value

The MCA on the dataset.

See Also

MCA, CA, PCA, plot.factorial, factorial-class

Examples

data (tea, package = "FactoMineR")
MCA (tea, quanti.sup = 19, quali.sup = 20:36)

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.