pfa {StatDA}R Documentation

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 <P.Filzmoser@tuwien.ac.at> http://www.statistik.tuwien.ac.at/public/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=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)))


[Package StatDA version 1.1 Index]