R: Numerically estimate Hessian using Richardson extrapolation

mxComputeNumericDeriv {OpenMx} | R Documentation

## Numerically estimate Hessian using Richardson extrapolation

### Description

For N free parameters, Richardson extrapolation requires (iterations * (N^2 + N)) function evaluations. The implementation is closely based on the numDeriv R package.

### Usage

    mxComputeNumericDeriv(
      freeSet = NA_character_,
      ...,
      fitfunction = "fitfunction",
      parallel = TRUE,
      stepSize = imxAutoOptionValue("Gradient step size"),
      iterations = 4L,
      verbose = 0L,
      knownHessian = NULL,
      checkGradient = TRUE,
      hessian = TRUE,
      analytic = TRUE
    )

### 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` | integer. Level of run-time diagnostic output. Set to zero to disable
`knownHessian` | an optional matrix of known Hessian entries
`checkGradient` | whether to check the first order convergence criterion (gradient is near zero)
`hessian` | whether to estimate the Hessian. If FALSE then only the gradient is estimated.
`analytic` | Use the analytic Hessian, if available.

### Details

In addition to an estimate of the Hessian, forward, central, and backward estimates of the gradient are made available in this compute plan's output slot.

When `checkGradient=TRUE`, the central difference estimate of the gradient is used to determine whether the first order convergence criterion is met. In addition, the forward and backward difference estimates of the gradient are compared for symmetry. When sufficient asymmetry is detected, the standard error is flagged. In the case, profile likelihood confidence intervals should be used for inference instead of standard errors (see `mxComputeConfidenceInterval`).

If provided, the square matrix `knownHessian` should have dimnames set to the names of some subset of the free parameters. Entries of the matrix set to NA will be estimated numerically while entries containing finite values will be copied to the Hessian result.

### 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

[Package OpenMx version 2.20.6 Index]