principalComponents | R Documentation |
The principalComponents
function returns a principal component
analysis. Other R functions give the same results, but
principalComponents
is customized mainly for the other factor
analysis functions available in the nfactors package. In order to
retain only a small number of components the componentAxis
function
has to be used.
principalComponents(R)
R |
numeric: correlation or covariance matrix |
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 |
Gilles Raiche
Centre sur les Applications des Modeles de
Reponses aux Items (CAMRI)
Universite du Quebec a Montreal
raiche.gilles@uqam.ca
Joliffe, I. T. (2002). Principal components analysis (2th Edition). New York, NJ: Springer-Verlag.
Kim, J.-O. and Mueller, C. W. (1978). Introduction to factor analysis. What it is and how to do it. Beverly Hills, CA: Sage.
Kim, J.-O. and Mueller, C. W. (1987). Factor analysis. Statistical methods and practical issues. Beverly Hills, CA: Sage.
componentAxis
, iterativePrincipalAxis
,
rRecovery
# ....................................................... # Example 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 component - # Kim et Mueller (1978, p. 21) # Replace upper diagonal with lower diagonal RU <- diagReplace(R, upper=TRUE) principalComponents(RU) # Replace lower diagonal with upper diagonal RL <- diagReplace(R, upper=FALSE) principalComponents(RL) # .......................................................
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