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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.
% Author: Michael D. Hunter
% Revision History
% Fri Oct 8 15:56:13 EDT 2010 - Created file
% Modified from mxFIMLObjective.Rd
\name{mxFitFunctionRow}
\alias{mxFitFunctionRow}
\alias{MxFitFunctionRow-class}
\alias{print,MxFitFunctionRow-method}
\alias{show,MxFitFunctionRow-method}
\title{Create an MxFitFunctionRow Object}
\description{
mxFitFunctionRow returns an MxFitFunctionRow object.
}
\usage{
mxFitFunctionRow(rowAlgebra, reduceAlgebra, dimnames,
rowResults = "rowResults", filteredDataRow = "filteredDataRow",
existenceVector = "existenceVector", units="-2lnL")
}
\arguments{
\item{rowAlgebra}{A character string indicating the name of the algebra to be evaluated row-wise.}
\item{reduceAlgebra}{A character string indicating the name of the algebra that collapses the row results into a single number which is then optimized.}
\item{dimnames}{A character vector of names corresponding to columns be extracted from the data set.}
\item{rowResults}{The name of the auto-generated "rowResults" matrix. See details.}
\item{filteredDataRow}{The name of the auto-generated "filteredDataRow" matrix. See details.}
\item{existenceVector}{The name of the auto-generated "existenceVector" matrix. See details.}
\item{units}{(optional) The units of the fit statistic.}
}
\details{
Fit functions are functions for which free parameter values are optimized such that the value of a cost function is minimized. The mxFitFunctionRow function evaluates a user-defined \link{MxAlgebra} object called the \sQuote{rowAlgebra} in a row-wise fashion. It then stores results of the row-wise evaluation in another \link{MxAlgebra} object called the \sQuote{rowResults}. Finally, the mxFitFunctionRow function collapses the row results into a single number which is then used for optimization. The \link{MxAlgebra} object named by the \sQuote{reduceAlgebra} collapses the row results into a single number.
The \sQuote{filteredDataRow} is populated in a row-by-row fashion with all the non-missing data from the current row. You cannot assume that the length of the filteredDataRow matrix remains constant (unless you have no missing data). The \sQuote{existenceVector} is populated in a row-by-row fashion with a value of 1.0 in column j if a non-missing value is present in the data set in column j, and a value of 0.0 otherwise. Use the functions \link{omxSelectRows}, \link{omxSelectCols}, and \link{omxSelectRowsAndCols} to shrink other matrices so that their dimensions will be conformable to the size of \sQuote{filteredDataRow}.
}
\value{
Returns a new MxFitFunctionRow object. Only one MxFitFunction object should be included in each model. There is no need for an MxExpectation object when using mxFitFunctionRow.
}
\seealso{
Other fit functions:
\code{\link{mxFitFunctionMultigroup}}, \code{\link{mxFitFunctionML}},
\code{\link{mxFitFunctionWLS}}, \code{\link{mxFitFunctionAlgebra}},
\code{\link{mxFitFunctionGREML}}, \code{\link{mxFitFunctionR}}
More information about the OpenMx package may be found \link[=OpenMx]{here}.
}
\references{
The OpenMx User's guide can be found at \url{https://openmx.ssri.psu.edu/documentation/}.
}
\examples{
# Model that adds two data columns row-wise, then sums that column
# Notice no optimization is performed here.
library(OpenMx)
xdat <- data.frame(a=rnorm(10), b=1:10) # Make data set
amod <- mxModel(model="example1",
mxData(observed=xdat, type='raw'),
mxAlgebra(sum(filteredDataRow), name = 'rowAlgebra'),
mxAlgebra(sum(rowResults), name = 'reduceAlgebra'),
mxFitFunctionRow(
rowAlgebra='rowAlgebra',
reduceAlgebra='reduceAlgebra',
dimnames=c('a','b'))
)
amodOut <- mxRun(amod)
mxEval(rowResults, model=amodOut)
mxEval(reduceAlgebra, model=amodOut)
# Model that find the parameter that minimizes the sum of the
# squared difference between the parameter and a data row.
bmod <- mxModel(model="example2",
mxData(observed=xdat, type='raw'),
mxMatrix(values=.75, ncol=1, nrow=1, free=TRUE, name='B'),
mxAlgebra((filteredDataRow - B) ^ 2, name='rowAlgebra'),
mxAlgebra(sum(rowResults), name='reduceAlgebra'),
mxFitFunctionRow(
rowAlgebra='rowAlgebra',
reduceAlgebra='reduceAlgebra',
dimnames=c('a'))
)
bmodOut <- mxRun(bmod)
mxEval(B, model=bmodOut)
mxEval(reduceAlgebra, model=bmodOut)
mxEval(rowResults, model=bmodOut)
}
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