pfa: Principal Factor Analysis

In StatDA: Statistical Analysis for Environmental Data

Principal Factor Analysis

Description

Computes the principal factor analysis of the input data.

Usage

pfa(x, factors, data = NULL, covmat = NULL, n.obs = NA, subset, na.action,
start = NULL, scores = c("none", "regression", "Bartlett"),
rotation = "varimax", maxiter = 5, control = NULL, ...)

Arguments

x	(robustly) scaled input data
factors	number of factors
data	default value is NULL
covmat	(robustly) computed covariance or correlation matrix
n.obs	number of observations
subset	if a subset is used
start	starting values
scores	which method should be used to calculate the scores
rotation	if a rotation should be made
maxiter	maximum number of iterations
control	default value is NULL
na.action	what to do with NA values
...	arguments for creating a list

Value

loadings	A matrix of loadings, one column for each factor. The factors are ordered in decreasing order of sums of squares of loadings.
uniquness	uniquness
correlation	correlation matrix
criteria	The results of the optimization: the value of the negativ log-likelihood and information of the iterations used.
factors	the factors
dof	degrees of freedom
method	"principal"
n.obs	number of observations if available, or NA
call	The matched call.
STATISTIC, PVAL	The significance-test statistic and p-value, if can be computed

Author(s)

Peter Filzmoser <[e-mail]> http://cstat.tuwien.ac.at/filz/

References

C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley and Sons, Chichester, 2008.

Examples

data(moss)
var=c("Ni","Cu","Mg","Rb","Mn")
x=log10(moss[,var])

x.mcd=robustbase::covMcd(x,cor=TRUE)
x.rsc=scale(x,x.mcd$cent,sqrt(diag(x.mcd$cov)))
pfa(x.rsc,factors=2,covmat=x.mcd,scores="regression",rotation="varimax",
    maxit=0,start=rep(0,ncol(x.rsc)))

StatDA documentation built on June 7, 2023, 6:26 p.m.