OpenMx: mxComputeOnce – R documentation

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mxComputeOnce

Compute something once

Description

Some models are optimized for a sparse Hessian. Therefore, it can be much more efficient to compute the inverse Hessian in comparison to computing the Hessian and then inverting it.

Usage

mxComputeOnce(
  from,
  what = NULL,
  how = NULL,
  ...,
  freeSet = NA_character_,
  verbose = 0L,
  .is.bestfit = FALSE
)

Arguments

`from`	the object to perform the computation (a vector of expectation or fit function names)
`what`	what to compute
`how`	to compute it (optional)
`...`	Not used. Forces remaining arguments to be specified by name.
`freeSet`	names of matrices containing free variables
`verbose`	integer. Level of run-time diagnostic output. Set to zero to disable
`.is.bestfit`	do not use; for backward compatibility

Details

The information matrix is only valid when parameters are at the maximum likelihood estimate. The information matrix is returned in model$output$hessian. You cannot request both the information matrix and the Hessian. The information matrix is invariant to the sign of the log likelihood scale whereas the Hessian is not. Use the how parameter to specify which approximation to use (one of "default", "hessian", "sandwich", "bread", and "meat").

Examples

data(demoOneFactor)
factorModel <- mxModel(name ="One Factor",
  mxMatrix(type="Full", nrow=5, ncol=1, free=TRUE, values=0.2, 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(expression=A %*% L %*% t(A) + U, name="R"),
    mxFitFunctionML(),mxExpectationNormal(covariance="R", dimnames=names(demoOneFactor)),
    mxData(observed=cov(demoOneFactor), type="cov", numObs=500),
    mxComputeOnce('fitfunction', 'fit'))
factorModelFit <- mxRun(factorModel)
factorModelFit$output$fit  # 972.15

OpenMx

Extended Structural Equation Modelling

v2.19.5

Apache License (== 2.0)

Authors

Steven M. Boker [aut], Michael C. Neale [aut], Hermine H. Maes [aut], Michael J. Wilde [ctb], Michael Spiegel [aut], Timothy R. Brick [aut], Ryne Estabrook [aut], Timothy C. Bates [aut], Paras Mehta [ctb], Timo von Oertzen [ctb], Ross J. Gore [aut], Michael D. Hunter [aut], Daniel C. Hackett [ctb], Julian Karch [ctb], Andreas M. Brandmaier [ctb], Joshua N. Pritikin [aut, cre], Mahsa Zahery [aut], Robert M. Kirkpatrick [aut], Yang Wang [ctb], Ben Goodrich [ctb], Charles Driver [ctb], Massachusetts Institute of Technology [cph], S. G. Johnson [cph], Association for Computing Machinery [cph], Dieter Kraft [cph], Stefan Wilhelm [cph], Sarah Medland [cph], Carl F. Falk [cph], Matt Keller [cph], Manjunath B G [cph], The Regents of the University of California [cph], Lester Ingber [cph], Wong Shao Voon [cph], Juan Palacios [cph], Jiang Yang [cph], Gael Guennebaud [cph], Jitse Niesen [cph]

Initial release

2021-03-26