classFA {FAiR} | R Documentation |
These classes encapsulate estimates from factor analysis
models. The "FA.general"
and "FA.2ndorder"
classes extend
class "FA"
and have additional slots to contain estimates from a
second-order model with one general second-order factor and multiple
second-order factors respectively
Objects can be created by calls of the form new("FA", ...)
. However,
this use of new("FA", ...)
is not recommended. Both
Factanal
and Rotate
call new("FA", ...)
internally to create the appropriate object for analysis with the help
of the formal methods defined for create_FAobject
.
loadings
:"array"
. This array
has as many rows as there are outcome variables, as many columns
as there are factors, and five shelves. Each shelf is a matrix
and contains the primary pattern ('PP'), primary structure ('PS'),
reference pattern ('RP'), reference structure ('RS'), and factor
contribution ('FC') matrices respectively, which can be extracted via
coef()
using the appropriate two-letter string for the
matrix
argument that defaults to 'PP'.correlations
:"array"
. This array
has as many rows and columns as there are factors and three shelves.
Each shelf is a correlation matrix and contains the correlations
among the primary factors ('PF'), among reference factors ('RF'),
and between each primary factor and the corresponding reference
factor ('PR'), which is a diagonal matrix. There is currently no
special accessor function for these three matrices.trans_mats
:"array"
. This array
has as many rows and columns as there are factors and two shelves.
Both shelves are the identity matrix if semi-exploratory or
confirmatory factor analysis was used in the call to
Factanal
and are the identity matrix prior to the
call to Rotate
in exploratory factor analysis.
Rotate
fills these two shelves with the transformation
matrix for the primary factors ('primary') and the transformation
matrix for the reference factors ('reference'). Both matrices have
unit-length columns and the rotated primary pattern matrix is equal
to the unrotated primary pattern matrix times the inverse of the
transpose of the transformation matrix for the primary factors.uniquenesses
:"numeric"
. This
numeric vector has as many elements as there are outcome variables
and contains the unique variances (also called specific variances)
of the factor analysis model.restrictions
:"restrictions"
. This
slot contains information on the restrictions that were placed on
the factor analysis model while it was estimated.vcov
:"matrix"
. This matrix is
a variance-covariance matrix and thus has as many rows and columns
as there are parameters to be estimated. Note that it is calibrated
to the sample covariance matrix, rather than the sample correlation
matrix.zstats
:"list"
. A list of matrices
containing the z-statistic for each estimated parameter under a null
hypothesis that the parameter is zero. scores
:"matrix"
. If, in the
call to Factanal
the user specifies that factor
scores should be calculated, this matrix contains the factor scores
and has as many rows as there are complete observations and as
many columns as there are factors. If factor scores were not requested,
then this matrix has one row, one column, and a NA_real_ in its
only cell.manifest
:"list"
. This list
contains information on the left-hand side of the factor analysis
model. It has an element for the sample covariance matrix, the
sample correlation matrix, the number of observations in the sample,
and possibly a matrix of standardized scores on the outcome variables. rgenoud
:"list"
. This is a list of
lists each containing what is returned by genoud
,
which is called internally by Factanal
and
Rotate
.model
:"character"
. This
character string indicates whether a SEFA, EFA, or CFA model was
estimated.method
:"character"
. This
character string indicates whether maximum-likelihood or weighted
least squares was used to estimate the factor analysis model. call
:"language"
. This slot
contains the call to Factanal
.seeds
:"matrix"
. This matrix
has two columns and either one or two rows. The first row contains
the unif.seed
used by genoud
and the
int.seed
in the call to Factanal
. If
Rotate
is used to transform the factors after they
have been extracted via exploratory factor analysis, this matrix
has a second row containing the unif.seed
and
int.seed
used in the call to Rotate
.signature(object = "FA")
: Bayesian Information Criterion signature(object = "FA")
: Extract coefficients.
This method has two additional arguments, matrix
and level
.
matrix
should be one of "PP"
(default) for the primary
pattern matrix, "PS"
for the primary structure matrix,
"RP"
for the reference pattern matrix, "RS"
for the
reference structure matrix, or "FC"
for the factor contribution
matrix. level
should be 1
(default) to extract
from level one of the model or 2
to extract from level two of
the model (if the model has two levels).signature(object = "FA")
: Extract confidence intervals.
Does not work yet.signature(object = "FA")
: Extract log-likelihood,
requires that the model be estimated by maximum likelihood.signature(x = "FA", y = "ANY")
: Plots from nFactors signature(fitted = "FA")
: Plot profile likelihoods
for all fixed and zero coefficients in the primary pattern matrix. This
method has two additional arguments, param
, which is currently
ignored and delta
which indicates how far on either side of the
constrained optimum to plot.signature(object = "FA")
: This method displays some
information about the restrictions that were imposed on the model and
prints a variety of model fit statistics (see note below). signature(object = "FA")
: Gathers the coefficients,
intercorrelations, and z-statistics, typically for showing signature(object = "FA")
: Extracts the
variance-covariance matrix
The model comparison statistics printed by the show
method are
largely resused from the summary.sem
function in sem. Most of
these statistics are defined in Bollen (1989). The exception is the stochastic
information complexity (SIC), which is defined in Preacher, Cai, and MacCallum
(2007).
Ben Goodrich http://wiki.r-project.org/rwiki/doku.php?id=packages:cran:fair
Bollen, K. A. (1989) Structural Equations With Latent Variables. Wiley.
Preacher, K.J., Cai, L., and MacCallum, R.C. ``Alternatives to traditional model comparison strategies for covariance structure models.'' in Modeling Contextual Effects in Longitudinal Studies, eds. Little, T.D., Bovaird, J.A., and Card, N.A. Psychology Press.
BIC
, coef
,
profile
, confint
logLik
, plot
, show
,
summary
, vcov
, and S3methodsFA.
showClass("FA")