factorScorePca: Factor Analysis by Principal Component Analysis (PCA)
In robustfa: Object Oriented Solution for Robust Factor Analysis

factorScorePca

R Documentation

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.

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.

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 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.

Author(s)

Ying-Ying Zhang (Robert) robertzhangyying@qq.com

References

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

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 <- rrcov::Cov(stock611[,3:12])
covmat <- list(cov=rrcov::getCov(covx), center=rrcov::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

robustfa documentation built on April 16, 2023, 5:18 p.m.