mdspca {psy}R Documentation

Graphical representation of a correlation matrix using a Principal Component Analysis

Description

Similar to many routines, the interest is in the possible representation of both variables and subjects (and by the way categorical variables) with active and supplementary points. Missing data are omitted.

Usage

mdspca(datafile, supvar="no", supsubj="no", namesupvar=colnames(supvar,do.NULL=FALSE), namesupsubj=colnames(supsubj, do.NULL=FALSE), dimx=1, dimy=2, cx=0.75)

Arguments

datafile name of datafile
supvar matrix corresponding to supplementary variables (if any), supvar="no" by default
supsubj matrix corresponding to supplementary subjects (if any), supsubj="no" by default
namesupvar names of the points corresponding to the supplementary variables
namesupsubj names of the points corresponding to the supplementary subjects
dimx rank of the component displayed on the x axis (1 by default)
dimy rank of the component displayed on the y axis (2 by default)
cx size of the lettering (0.75 by default, 1 for bigger letters, 0.5 for smaller)

Value

A diagram (two diagrams if supplementary subjects are used)

Author(s)

Bruno Falissard

Examples

data(sleep)

mdspca(sleep[,c(2:5,7:11)])
## three consistent groups of variables, paradoxical sleep (in other words: dream)
## is negatively correlated with danger

mdspca(sleep[,c(2:5,7:11)],supvar=sleep[,6],namesupvar="Total.sleep",supsubj=sleep[,1],namesupsubj="",cx=0.5)
## Total.sleep is here a supplementary variable since it is deduced
## from Paradoxical.sleep and Slow.wave.sleep
## The variable Species is displayed in the subject plane,
## Rabbit and Cow have a high level of danger

[Package psy version 0.7 Index]