Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1 2 3 4 5 6 7 8 | ```
FaCov(x, ...)
## S3 method for class 'formula'
FaCov(formula, data = NULL, factors = 2, cor = FALSE, method = "mle",
scoresMethod = "none", ...)
## Default S3 method:
FaCov(x, factors = 2, cor = FALSE, cov.control = CovControlMcd(),
method = c("mle", "pca", "pfa"),
scoresMethod = c("none", "regression", "Bartlett"), ...)
``` |

`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 |

`factors` |
The number of factors to be fitted. |

`cor` |
A logical value indicating whether the calculation should use the covariance matrix ( |

`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 |

`cov.control` |
Specifies which covariance estimator to use by providing a |

`FaCov`

, serving as a constructor for objects of class `FaCov-class`

is a generic function with "formula" and "default" methods.

An S4 object of class `FaCov-class`

which is a subclass of the virtual class `Fa-class`

.

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

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

`FaClassic-class`

, `FaCov-class`

, `FaRobust-class`

, `Fa-class`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
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
``` |

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