mxRObjective {OpenMx}

DEPRECATED: Create MxRObjective Object

Description

WARNING: Objective functions have been deprecated as of OpenMx 2.0.

Please use mxFitFunctionR() instead. As a temporary workaround, mxRObjective returns a list containing a NULL MxExpectation object and an MxFitFunctionR object.

All occurrences of

mxRObjective(fitfun, ...)

Should be changed to

mxFitFunctionR(fitfun, ...)

Arguments

objfun

A function that accepts two arguments.

...

The initial state information to the objective function.

Details

NOTE: THIS DESCRIPTION IS DEPRECATED. Please change to using mxExpectationNormal and mxFitFunctionML as shown in the example below.

The fitfun argument must be a function that accepts two arguments. The first argument is the mxModel that should be evaluated, and the second argument is some persistent state information that can be stored between one iteration of optimization to the next iteration. It is valid for the function to simply ignore the second argument.

The function must return either a single numeric value, or a list of exactly two elements. If the function returns a list, the first argument must be a single numeric value and the second element will be the new persistent state information to be passed into this function at the next iteration. The single numeric value will be used by the optimizer to perform optimization.

The initial default value for the persistent state information is NA.

Throwing an exception (via stop) from inside fitfun may result in unpredictable behavior. You may want to wrap your code in tryCatch while experimenting.

Value

Returns a list containing a NULL mxExpectation object and an MxFitFunctionR object.

References

The OpenMx User's guide can be found at https://openmx.ssri.psu.edu/documentation/.

Examples

# Create and fit a model using mxFitFunctionR

library(OpenMx)

A <- mxMatrix(nrow = 2, ncol = 2, values = c(1:4), free = TRUE, name = 'A')
squared <- function(x) { x ^ 2 }

# Define the objective function in R

objFunction <- function(model, state) {
    values <- model$A$values 
    return(squared(values[1,1] - 4) + squared(values[1,2] - 3) +
        squared(values[2,1] - 2) + squared(values[2,2] - 1))
}

# Define the expectation function

fitFunction <- mxFitFunctionR(objFunction)

# Define the model

tmpModel <- mxModel(model="exampleModel", A, fitFunction)

# Fit the model and print a summary

tmpModelOut <- mxRun(tmpModel)
summary(tmpModelOut)