acp: Principal component analysis

In amap: Another Multidimensional Analysis Package

Principal component analysis

Description

Principal component analysis

Usage

acp(x,center=TRUE,reduce=TRUE,wI=rep(1,nrow(x)),wV=rep(1,ncol(x)))
pca(x,center=TRUE,reduce=TRUE,wI=rep(1,nrow(x)),wV=rep(1,ncol(x)))
## S3 method for class 'acp'
print(x, ...) 

Arguments

x	Matrix / data frame
center	a logical value indicating whether we center data
reduce	a logical value indicating whether we "reduce" data i.e. divide each column by standard deviation
wI, wV	weigth vector for individuals / variables
...	arguments to be passed to or from other methods.

Details

This function offer a variant of princomp and prcomp functions, with a slightly different graphic representation (see plot.acp).

Value

An object of class acp The object is a list with components:

sdev	the standard deviations of the principal components.
loadings	the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). This is of class "loadings": see loadings for its print method.
scores	if scores = TRUE, the scores of the supplied data on the principal components.
eig	Eigen values

Author(s)

Antoine Lucas

See Also

plot.acp,acpgen, princomp

Examples

data(lubisch)
lubisch <- lubisch[,-c(1,8)]
p <- acp(lubisch)
plot(p)

amap documentation built on Oct. 30, 2024, 9:09 a.m.