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All occurrences of
mxFIMLObjective(covariance, means, dimnames = NA, thresholds = NA, vector = FALSE, threshnames = dimnames)
Should be changed to
mxExpectationNormal(covariance, means, dimnames = NA, thresholds = NA, threshnames = dimnames) mxFitFunctionML(vector = FALSE)
NOTE: THIS DESCRIPTION IS DEPRECATED. Please change to using mxExpectationNormal and mxFitFunctionML as shown in the example below.
Objective functions were functions for which free parameter values are chosen such that the value of the objective function is minimized. The mxFIMLObjective function used full-information maximum likelihood to provide maximum likelihood estimates of free parameters in the algebra defined by the 'covariance' and 'means' arguments. The 'covariance' argument takes an MxAlgebra object, which defines the expected covariance of an associated MxData object. The 'means' argument takes an MxAlgebra object, which defines the expected means of an associated MxData object. The 'dimnames' arguments takes an optional character vector. If this argument is not a single NA, then this vector is used to assign the dimnames of the means vector as well as the row and columns dimnames of the covariance matrix.
The 'vector' argument is either TRUE or FALSE, and determines whether the objective function returns a column vector of the likelihoods, or a single -2*(log likelihood) value.
thresholds: The name of the thresholds matrix. When needed (for modelling ordinal data), this matrix should be created using mxMatrix(). The thresholds matrix must have as many columns as there are ordinal variables in the model, and number of rows equal to one fewer than the maximum number of levels found in the ordinal variables. The starting values of this matrix must also be set to reasonable values. Fill each column with a set of ordered start thresholds, one for each level of this column's factor levels minus 1. These thresholds may be free if you wish them to be estimated, or fixed. The unused rows in each column, if any, can be set to any value including NA.
threshnames: A character vector consisting of the variables in the thresholds matrix, i.e., the names of ordinal variables in a model. This is necessary for OpenMx to map the thresholds matrix columns onto the variables in your data. If you set the dimnames of the columns in the thresholds matrix then threshnames is not needed.
Usage Notes: dimnames must be supplied where the matrices referenced by the covariance and means algebras are not themselves labeled. Failure to do so leads to an error noting that the covariance or means matrix associated with the FIML objective does not contain dimnames.
mxFIMLObjective evaluates with respect to an MxData object. The MxData object need not be referenced in the mxFIMLObjective function, but must be included in the MxModel object. mxFIMLObjective requires that the 'type' argument in the associated MxData object be equal to 'raw'. Missing values are permitted in the associated MxData object.
To evaluate, place MxFIMLObjective objects, the mxData object for which the expected covariance approximates, referenced MxAlgebra and MxMatrix objects, and optional MxBounds and MxConstraint objects in an MxModel object. This model may then be evaluated using the mxRun function.
The results of the optimization can be reported using the summary function, or accessed directly in the 'output' slot of the resulting model (i.e., modelName$output). Components of the output may be referenced using the Extract functionality.
# Create and fit a model using mxMatrix, mxAlgebra, mxExpectationNormal, and mxFitFunctionML
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)
# Define the matrices
M <- mxMatrix(type = "Full", nrow = 1, ncol = 2, values=c(0,0),
free=c(TRUE,TRUE), labels=c("Mx", "My"), name = "M")
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", means="M", dimnames=tmpNames)
# Choose a fit function
fitFunction <- mxFitFunctionML()
# Define the model
tmpModel <- mxModel(model="exampleModel", M, S, A, I, expCov, expFunction, fitFunction,
mxData(observed=tmpFrame, type="raw"))
# Fit the model and print a summary
tmpModelOut <- mxRun(tmpModel)
summary(tmpModelOut)
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