robustfa (version 1.0-4)

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

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.

Value

• An object of class "factorScorePca" 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 covariance matrix.
• correlation
  The correlation matrix.
• usedMatrix
  The used matrix (running matrix).
• 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 S.

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.

See Also

factorScorePfa, factanal

Examples

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=[e-mail])
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