robustfa (version 1.0-4)

FaCov: Robust Factor Analysis

Description

Robust factor analysis are obtained by replacing the classical covariance matrix by a robust covariance estimator. This can be one of the available estimators in rrcov, i.e., MCD, OGK, M, S, SDE, or MVE estimator.

Usage

FaCov(x, ...)
## S3 method for class 'formula':
FaCov(formula, data = NULL, factors = 2, cor = FALSE, method = "mle", 
scoresMethod = "none", \dots)
## S3 method for class 'default':
FaCov(x, factors = 2, cor = FALSE, cov.control = CovControlMcd(), 
method = c("mle", "pca", "pfa"), 
scoresMethod = c("none", "regression", "Bartlett"), ...)

Arguments

x

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

...

Arguments passed to or from other methods.

formula

A formula with no response variable, referring only to numeric variables.

data

An optional data frame (or similar: see model.frame) containing the variables in the formula.

factors

The number of factors to be fitted.

cor

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

method

The method of factor analysis, one of "mle" (the default), "pca", and "pfa".

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.

cov.control

Specifies which covariance estimator to use by providing a CovControl-class object. The default is CovControlMcd-class which will indire

Value

• An S4 object of class FaCov-class which is a subclass of the virtual class Fa-class.

Details

FaCov, serving as a constructor for objects of class FaCov-class is a generic function with "formula" and "default" methods.

References

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

See Also

FaClassic-class, FaCov-class, FaRobust-class, Fa-class

Examples

data("hbk")
hbk.x = hbk[,1:3] 

##
## faCovPcaRegMcd is obtained from FaCov.default
##
faCovPcaRegMcd = FaCov(x = hbk.x, factors = 2, method = "pca",
scoresMethod = "regression", cov.control = CovControlMcd()); faCovPcaRegMcd

##
## In fact, it is equivalent to use FaCov.formula
## faCovForPcaRegMcd = faCovPcaRegMcd
##
faCovForPcaRegMcd = FaCov(~., data = as.data.frame(hbk.x), 
factors = 2, method = "pca", scoresMethod = "regression", 
cov.control = CovControlMcd()); faCovForPcaRegMcd