OpenMx (version 2.2.2)

mxComputeNumericDeriv: Numerically estimate Hessian using Richardson extrapolation

Description

For N free parameters, Richardson extrapolation requires (iterations * (N^2 + N)) function evaluations.

Usage

mxComputeNumericDeriv(freeSet = NA_character_, ...,
  fitfunction = "fitfunction", parallel = TRUE, stepSize = 1e-04,
  iterations = 4L, verbose = 0L, knownHessian = NULL)

Arguments

freeSet

names of matrices containing free variables

...

Not used. Forces remaining arguments to be specified by name.

fitfunction

name of the fitfunction (defaults to 'fitfunction')

parallel

whether to evaluate the fitfunction in parallel (defaults to TRUE)

stepSize

starting set size (defaults to 0.0001)

iterations

number of Richardson extrapolation iterations (defaults to 4L)

verbose

Level of debugging output.

knownHessian

an optional matrix of known Hessian entries

Details

The implementation is closely based on the numDeriv R package.

Examples

library(OpenMx)
data(demoOneFactor)
factorModel <- mxModel(name ="One Factor",
	mxMatrix(type = "Full", nrow = 5, ncol = 1, free = FALSE, values = .2, name = "A"),
	mxMatrix(type = "Symm", nrow = 1, ncol = 1, free = FALSE, values = 1 , name = "L"),
	mxMatrix(type = "Diag", nrow = 5, ncol = 5, free = TRUE , values = 1 , name = "U"),
	mxAlgebra(A %*% L %*% t(A) + U, name = "R"),
	mxExpectationNormal(covariance = "R", dimnames = names(demoOneFactor)),
	mxFitFunctionML(),
	mxData(cov(demoOneFactor), type = "cov", numObs = 500),
	mxComputeSequence(
		list(mxComputeNumericDeriv(), mxComputeReportDeriv())
	)
)
factorModelFit <- mxRun(factorModel)
factorModelFit$output$hessian