mca

0th

Percentile

Multiple Correspondence Analysis

Computes a multiple correspondence analysis of a set of factors.

Keywords
multivariate, category
Usage
mca(df, nf = 2, abbrev = FALSE)
Arguments
df

A data frame containing only factors

nf

The number of dimensions for the MCA. Rarely 3 might be useful.

abbrev

Should the vertex names be abbreviated? By default these are of the form ‘factor.level’ but if abbrev = TRUE they are just ‘level’ which will suffice if the factors have distinct levels.

Value

An object of class "mca", with components

rs

The coordinates of the rows, in nf dimensions.

cs

The coordinates of the column vertices, one for each level of each factor.

fs

Weights for each row, used to interpolate additional factors in predict.mca.

p

The number of factors

d

The singular values for the nf dimensions.

call

The matched call.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

predict.mca, plot.mca, corresp

Aliases
  • mca
  • print.mca
Examples
# NOT RUN {
farms.mca <- mca(farms, abbrev=TRUE)
farms.mca
plot(farms.mca)
# }
Documentation reproduced from package MASS, version 7.3-51.4, License: GPL-2 | GPL-3

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