Performs Principal Component Analysis (PCA) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables. Missing values are replaced by the column mean.
PCA(
d,
scale.unit = TRUE,
ncp = ncol(d) - length(quanti.sup) - length(quali.sup),
ind.sup = NULL,
quanti.sup = NULL,
quali.sup = NULL,
row.w = NULL,
col.w = NULL
)The PCA on the dataset.
A data frame with n rows (individuals) and p columns (numeric variables).
A boolean, if TRUE (value set by default) then data are scaled to unit variance.
The number of dimensions kept in the results (by default 5).
A vector indicating the indexes of the supplementary individuals.
A vector indicating the indexes of the quantitative supplementary variables.
A vector indicating the indexes of the categorical supplementary variables.
An optional row weights (by default, a vector of 1 for uniform row weights); the weights are given only for the active individuals.
An optional column weights (by default, uniform column weights); the weights are given only for the active variables.
PCA, CA, MCA, plot.factorial, keiser, factorial-class
require (datasets)
data (iris)
PCA (iris, quali.sup = 5)
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