factorScorePfa {robustfa}

Factor Analysis by Principal Factor Analysis (PFA)

Description

Perform principal factor factor analysis on a covariance matrix or data matrix.

Usage

factorScorePfa(x, factors = 2, covmat = NULL, cor = FALSE, 
rotation = c("varimax", "none"), 
scoresMethod = c("none", "regression", "Bartlett"))

Arguments

x

A numeric matrix or an object that can be coerced to a numeric matrix.

factors

The number of factors to be fitted.

covmat

A covariance matrix, or a covariance list as returned by cov.wt. Of course, correlation matrices are covariance matrices.

cor

A logical value indicating whether the calculation should use the covariance matrix (cor = FALSE) or the correlation matrix (cor = TRUE).

rotation

character. "none" or "varimax": it will be called with first argument the loadings matrix, and should return a list with component loadings giving the rotated loadings, or just the rotated loadings.

scoresMethod

Type of scores to produce, if any. The default is "none", "regression" gives Thompson's scores, "Bartlett" gives Bartlett's weighted least-squares scores.

Details

Other feasible usages are:

factorScorePfa(factors, covmat)

factorScorePfa(x, factors, rotation, scoresMethod)

If x is missing, then the following components of the result will be NULL: scores, ScoringCoef, meanF, corF, and n.obs.

Value

An object of class "factorScorePfa" with components:

call

The matched call.

loadings

A matrix of loadings, one column for each factor. This is of class "loadings" if rotation = "varimax": see loadings for its print method; It is a plain matrix if rotation = "none".

communality

The common variance.

uniquenesses

The uniquenesses/specific variance computed.

covariance

The robust/classical covariance matrix.

correlation

The robust/classical correlation matrix.

usedMatrix

The used matrix (running matrix). It may be the covariance or correlation matrix according to the value of cor.

reducedCorrelation

The last reduced correlation matrix.

factors

The argument factors.

method

The method: always "pfa".

scores

If requested, a matrix of scores. NULL if x is missing.

scoringCoef

The scoring coefficients. NULL if x is missing.

meanF

The sample mean of the scores. NULL if x is missing.

corF

The sample correlation matrix of the scores. NULL if x is missing.

scoresMethod

The argument scoresMethod.

n.obs

The number of observations if available. NULL if x is missing.

center

The center of the data.

eigenvalues

The eigenvalues of the usedMatrix.

Author(s)

Ying-Ying Zhang (Robert) [e-mail]

References

Zhang, Y. Y. (2013), An Object Oriented Solution for Robust Factor Analysis.

See Also

factorScorePca, factanal

Examples

data(stock611)
R611 = cor(stock611[,3:12]); R611

## covmat is a matrix
fsPfa1 = factorScorePfa(factors = 3, covmat = R611); fsPfa1

## covmat is a list
covx = rrcov::Cov(stock611[,3:12])
covmat = list(cov = rrcov::getCov(covx), center = rrcov::getCenter(covx), n.obs = [e-mail])
fsPfa2 = factorScorePfa(factors = 3, cor = TRUE, covmat = covmat); fsPfa2

## fsPfa3 contains scores etc.
fsPfa3 = factorScorePfa(x = stock611[,3:12], factors = 2, 
cor = TRUE, rotation = "varimax", scoresMethod = "regression"); fsPfa3