mdspca: Graphical representation of a correlation matrix using a...

In psy: Various Procedures Used in Psychometrics

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

psy documentation built on April 22, 2022, 1:08 a.m.