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%
% Copyright 2007-2021 by the individuals mentioned in the source code history
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% Unless required by applicable law or agreed to in writing, software
% distributed under the License is distributed on an "AS IS" BASIS,
% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
% See the License for the specific language governing permissions and
% limitations under the License.
\name{mxDataWLS}
\alias{mxDataWLS}
\alias{MxDataLegacyWLS-class}
\title{Create legacy MxData Object for Least Squares (WLS, DWLS, ULS) Analyses}
\description{
This function creates a new \link{MxData} object of type
\dQuote{ULS} (unweighted least squares), \dQuote{WLS} (weighted least squares)
or \dQuote{DWLS} (diagonally-weighted least squares). The appropriate
fit function to include with these models is \code{\link{mxFitFunctionWLS}}
\emph{note}: This function continues to work, but is deprecated. Use \link{mxData} and \link{mxFitFunctionWLS} instead.
}
\usage{
mxDataWLS(data, type = "WLS", useMinusTwo = TRUE, returnInverted = TRUE,
fullWeight = TRUE, suppressWarnings = TRUE, allContinuousMethod =
c("cumulants", "marginals"), silent=!interactive())
}
\arguments{
\item{data}{A matrix or data.frame which provides raw data to be used for WLS.}
\item{type}{A character string 'WLS' (default), 'DWLS', or 'ULS' for
weighted, diagonally weighted, or unweighted least squares, respectively}
\item{useMinusTwo}{Logical indicating whether to use -2LL (default) or -LL.}
\item{returnInverted}{Logical indicating whether to return the information matrix (default) or the covariance matrix.}
\item{fullWeight}{Logical determining if the full weight matrix is
returned (default). Needed for standard error and quasi-chi-squared
calculation.}
\item{suppressWarnings}{Logical that determines whether to suppress
diagnostic warnings. These warnings are likely only helpful to developers.}
\item{allContinuousMethod}{A character string 'cumulants' (default) or
'marginals'. See mxFitFunctionWLS.}
\item{silent}{Whether to report progress}
}
\details{
The mxDataWLS function creates an \link{MxData} object, which can be used in
\link{MxModel} objects. This function takes raw data and returns an \code{MxData} object to be used in a model to fit with weighted least squares.
\emph{note}: This function continues to work, but is deprecated. Use \link{mxData} and \link{mxFitFunctionWLS} instead.
}
\value{
Returns a new \link{MxData} object.
}
\references{
The OpenMx User's guide can be found at \url{https://openmx.ssri.psu.edu/documentation/}.
Browne, M. W. (1984). Asymptotically Distribution-Free Methods for the Analysis of Covariance Structures. \emph{British Journal of Mathematical and Statistical Psychology}, \strong{37}, 62-83.
}
\seealso{
\link{mxFitFunctionWLS}. \link{MxData} for the S4 class created by mxData. \link{matrix} and \link{data.frame} for objects which may be entered as arguments in the \sQuote{observed} slot. More information about the OpenMx package may be found \link[=OpenMx]{here}.
}
\examples{
# Create and fit a model using mxMatrix, mxAlgebra, mxExpectationNormal, and mxFitFunctionWLS
library(OpenMx)
# Simulate some data
x=rnorm(1000, mean=0, sd=1)
y= 0.5*x + rnorm(1000, mean=0, sd=1)
tmpFrame <- data.frame(x, y)
tmpNames <- names(tmpFrame)
wdata <- mxDataWLS(tmpFrame)
# Define the matrices
S <- mxMatrix(type = "Full", nrow = 2, ncol = 2, values=c(1,0,0,1),
free=c(TRUE,FALSE,FALSE,TRUE), labels=c("Vx", NA, NA, "Vy"), name = "S")
A <- mxMatrix(type = "Full", nrow = 2, ncol = 2, values=c(0,1,0,0),
free=c(FALSE,TRUE,FALSE,FALSE), labels=c(NA, "b", NA, NA), name = "A")
I <- mxMatrix(type="Iden", nrow=2, ncol=2, name="I")
# Define the expectation
expCov <- mxAlgebra(solve(I-A) \%*\% S \%*\% t(solve(I-A)), name="expCov")
expFunction <- mxExpectationNormal(covariance="expCov", dimnames=tmpNames)
# Choose a fit function
fitFunction <- mxFitFunctionWLS()
# Define the model
tmpModel <- mxModel(model="exampleModel", S, A, I, expCov, expFunction, fitFunction,
wdata)
# Fit the model and print a summary
tmpModelOut <- mxRun(tmpModel)
summary(tmpModelOut)
}
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