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.Filzmose@tuwien.ac.at>

### References

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

### 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 1.0 Index]