principalComponents {nFactors}R Documentation

Principal Component Analysis

Description

The principalComponents function return a principal component analysis. Other R functions give the same results, but principalComponents is mainly customed for the other factor analysis functions available in the nfactors package. To retain only a small number of components the componentAxis function has to be used.

Usage

 principalComponents(R)
 

Arguments

R numeric: correlation or covariance matrix

Value

values numeric: variance of each component
varExplained numeric: variance explained by each component
varExplained numeric: cumulative variance explained by each component
loadings numeric: loadings of each variable on each component

Author(s)

Gilles Raiche, Universite du Quebec a Montreal raiche.gilles@uqam.ca, http://www.er.uqam.ca/nobel/r17165/

See Also

componentAxis, iterativePrincipalAxis, rRecovery

Examples

# .......................................................
# Exemple from Kim and Mueller (1978, p. 10)
# Population: upper diagonal
# Simulated sample: lower diagnonal
 R <- matrix(c( 1.000, .6008, .4984, .1920, .1959, .3466,
                .5600, 1.000, .4749, .2196, .1912, .2979,
                .4800, .4200, 1.000, .2079, .2010, .2445,
                .2240, .1960, .1680, 1.000, .4334, .3197,
                .1920, .1680, .1440, .4200, 1.000, .4207,
                .1600, .1400, .1200, .3500, .3000, 1.000),
                nrow=6, byrow=TRUE)

# Factor analysis: Principal components -
# Kim et Mueller (1978, p. 21)
# Replace upper diagonal by lower diagonal
 RU <- diagReplace(R, upper=TRUE)
 principalComponents(RU)

# Replace lower diagonal by upper diagonal
 RL <- diagReplace(R, upper=FALSE)
 principalComponents(RL)
# .......................................................
 

[Package nFactors version 2.2 Index]