S3methods: S3 methods for "FA" objects

In FAiR: Factor Analysis in R

Description

These methods are technically S4 but are the result of making well-known S3 methods into S4 generic functions and defining methods for objects of FA-class and / or restrictions-class. In any event, they provide somewhat standard post-estimation functions for factor analysis models.

Usage

## S4 method for signature 'FA'
deviance(object)
## S4 method for signature 'FA'
df.residual(object)
## S4 method for signature 'restrictions'
df.residual(object)
## S4 method for signature 'FA'
fitted(object, reduced = TRUE, standardized = TRUE)
## S4 method for signature 'restrictions'
fitted(object, reduced = TRUE, standardized = TRUE)
## S4 method for signature 'FA'
influence(model)
## S4 method for signature 'FA'
model.matrix(object, standardized = TRUE)
## S4 method for signature 'FA'
pairs(x, ...)
## S4 method for signature 'FA'
residuals(object, standardized = TRUE)
## S4 method for signature 'FA'
rstandard(model)
## S4 method for signature 'FA'
simulate(object, nsim = 1, seed = NULL, standardized = TRUE, ...)
## S4 method for signature 'FA'
weights(object)

Arguments

object	An object of FA-class or restrictions-class, as appropriate
model	An object of FA-class
x	An object of FA-class
reduced	Logical indicating whether communalities should be on the diagonal of the fitted matrix.
standardized	Logical indicating whether the matrix should be calibrated for standardized variables.
nsim	number of simulations
seed	seed to use for random number generation; if NULL the current seed is used
...	additional argument(s) for methods

Details

The code for each of these methods is quite short. There are some other method definitions for objects that inherit from restrictions-class and FA-class but they differ only in implementation and not in their nature or their options.

Value

deviance	returns the value of the discrepancy function
df.residual	returns the degrees of freedom
fitted	returns the model's estimate of the covariance or correlation matrix among manifest variables in common factor space
influence	returns a square matrix that is equal to residuals() * weights()
model.matrix	returns the sample covariance or correlation matrix among outcomes
pairs	returns nothing but plots the estimated reference structure correlations in a nice form
residuals	returns a square matrix that contains the difference between model.matrix() and fitted() and has uniquenesses along the diagonal (based on correlations by default)
rstandard	covariance residuals standardized by the standard deviations of the manifest variables
weights	returns a square matrix with the weights used in the discrepancy function. For Yates' weighted least squares estimator these weights are as defined in equation 188. For maximum likelihood estimation, these weights are proportional to the reciprocal of the crossproduct of the uniquenesses and are only approximately equal to the implied weights that would be used if minimizing the weighted sum of squared residuals. For ease of interpretation they are rescaled so that the mean weight is 1.0.

Author(s)

Ben Goodrich

References

Yates, A. (1987) Multivariate Exploratory Data Analysis: A Perspective on Exploratory Factor Analysis. State University of New York Press.

See Also

loadings, cormat, and uniquenesses

Examples

  ## See the example for Factanal()

FAiR documentation built on May 29, 2017, 6:08 p.m.