factorScorePca: Factor Analysis by Principal Component Analysis (PCA)

Description

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

Usage

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

Arguments

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.

Value

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: NULL. The reduced correlation matrix, reducedCorrelation is calculated in factorScorePfa.R.
factors: The argument factors.
method: The method: always "pca".
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.

Details

Other feasible usages are:

factorScorePca(factors, covmat)

factorScorePca(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.

References

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

Examples

Run this code


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

## covmat is a matrix
fsPca1=factorScorePca(factors = 3, covmat = R611); fsPca1

## covmat is a list
covx <- Cov(stock611[,3:12])
covmat <- list(cov=getCov(covx), center=getCenter(covx), n.obs=covx@n.obs)
fsPca2=factorScorePca(factors = 3, covmat = covmat); fsPca2

## fsPca3 contains scores etc.
fsPca3=factorScorePca(x = stock611[,3:12], factors = 2, cor = TRUE, rotation = "varimax", 
scoresMethod = "regression"); fsPca3

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