StatDA (version 1.7.11)

pfa: 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, ...)

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

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

Author

Peter Filzmoser <> 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)))