S3methodsFAiR {FAiR} | R Documentation |
These S3 methods for objects of class "FA" or that inherit from class "FA" provide fairly standard post-estimation functions for factor analysis models.
## S3 method for class 'FA': deviance(object, ...) ## S3 method for class 'FA.general': deviance(object, ...) ## S3 method for class 'FA.2ndorder': deviance(object, ...) ## S3 method for class 'FA': df.residual(object, ...) ## S3 method for class 'FA.general': df.residual(object, ...) ## S3 method for class 'FA.2ndorder': df.residual(object, ...) ## S3 method for class 'FA': fitted(object, ...) ## S3 method for class 'FA.general': fitted(object, ...) ## S3 method for class 'FA.2ndorder': fitted(object, ...) ## S3 method for class 'FA': influence(model, ...) ## S3 method for class 'FA.general': influence(model, ...) ## S3 method for class 'FA.2ndorder': influence(model, ...) ## S3 method for class 'FA': model.matrix(object, ...) ## S3 method for class 'FA.general': model.matrix(object, ...) ## S3 method for class 'FA.2ndorder': model.matrix(object, ...) ## S3 method for class 'FA': pairs(x, ...) ## S3 method for class 'FA.general': pairs(x, ...) ## S3 method for class 'FA.2ndorder': pairs(x, ...) ## S3 method for class 'FA': predict(object, ...) ## S3 method for class 'FA.general': predict(object, ...) ## S3 method for class 'FA.2ndorder': predict(object, ...) ## S3 method for class 'FA': residuals(object, ...) ## S3 method for class 'FA.general': residuals(object, ...) ## S3 method for class 'FA.2ndorder': residuals(object, ...) ## S3 method for class 'FA': rstandard(model, ...) ## S3 method for class 'FA.general': rstandard(model, ...) ## S3 method for class 'FA.2ndorder': rstandard(model, ...) ## S3 method for class 'FA': weights(object, ...) ## S3 method for class 'FA.general': weights(object, ...) ## S3 method for class 'FA.2ndorder': weights(object, ...)
object |
An object of class 'FA' or that inherits from 'FA' |
model |
An object of class 'FA' or that inherits from 'FA' |
x |
An object of class 'FA' or that inherits from 'FA' |
... |
additional argument(s) for methods |
The code for all of these methods is quite short. Feel free to
simply call body()
on the method.FA
to see what
the function does. There is no difference in functionality for
the methods that inherit from class 'FA' relative to those that
are defined for class 'FA'.
deviance |
returns a scalar indicating the discrepancy from a perfect fitting model. |
df.residual |
returns an integer indicating the difference between the number of nonredundant elements in the sample correlation matrix and the number of estimated parameters. |
fitted |
returns a square matrix of estimated correlations in the common factor space with communalities along the diagonal. |
influence |
returns a square matrix that is equal to
residuals() * weights() . |
model.matrix |
returns the sample correlation matrix among outcomes. |
pairs |
returns nothing but plots the estimated reference structure correlations; to repeat: these are the reference structure correlations, rather than the primary pattern coefficients. |
predict |
returns a matrix with the predicted values of the
outcomes, which is defined as the product of the factor score
matrix and the primary pattern coefficient matrix. Thus, an error
is returned if factor scores were not calculated by
Factanal . |
residuals |
returns a square matrix that contains the
difference between model.matrix() and fitted() and thus
has uniquenesses along the diagonal. |
rstandard |
returns residuals rescaled into a correlation
matrix and thus has ones along the diagonal. |
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. |
Ben Goodrich http://wiki.r-project.org/rwiki/doku.php?id=packages:cran:fair
Yates, A. (1987) Multivariate Exploratory Data Analysis: A Perspective on Exploratory Factor Analysis. State University of New York Press.
confint
, deviance
,
df.residual
, fitted
, influence
,
model.matrix
, pairs
, predict
,
residuals
, rstandard
, FA-class.
## See the example for Factanal()