Multiple Correspondence Analysis

Computes a multiple correspondence analysis of a set of factors.

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

Examples

farms.mca <- mca(farms, abbrev=TRUE)
farms.mca
#> Call:
#> mca(df = farms, abbrev = TRUE)
#> 
#> Multiple correspondence analysis of 20 cases of 4 factors
#> 
#> Correlations 0.806 0.745  cumulative % explained 26.87 51.71 
plot(farms.mca)