# pfa: Principal Factor Analysis In StatDA: Statistical Analysis for Environmental Data

## Description

Computes the principal factor analysis of the input data.

## Usage

 1 2 3 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://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

 1 2 3 4 5 6 7 8 data(moss) var=c("Ni","Cu","Mg","Rb","Mn") x=log10(,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 March 13, 2020, 2:42 a.m.