mxComputeEM {OpenMx} | R Documentation |

The EM algorithm constitutes the following steps: Start with an initial parameter vector. Predict the missing data to form a completed data model. Optimize the completed data model to obtain a new parameter vector. Repeat these steps until convergence criteria are met.

mxComputeEM(expectation, predict, mstep, observedFit = "fitfunction", ..., maxIter = 500L, tolerance = 1e-09, verbose = 0L, freeSet = NA_character_, accel = "varadhan2008", information = NA_character_, infoArgs = list())

`expectation` |
a vector of expectation names |

`predict` |
what to predict from the observed data (available options depend on the expectation) |

`mstep` |
a compute plan to optimize the completed data model |

`observedFit` |
the name of the observed data fit function (defaults to "fitfunction") |

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

`maxIter` |
maximum number of iterations |

`tolerance` |
optimization is considered converged when the maximum relative change in fit is less than tolerance |

`verbose` |
level of diagnostic output |

`freeSet` |
names of matrices containing free variables |

`accel` |
name of acceleration method ("varadhan2008" or "ramsay1975") |

`information` |
name of information matrix approximation method |

`infoArgs` |
arguments to control the information matrix method |

This compute plan does not work with any and all expectations. It requires a special kind of expectation that can predict its missing data to create a completed data model.

The EM algorithm does not produce a parameter covariance matrix for standard errors. The Oakes (1999) direct method and S-EM, an implementation of Meng & Rubin (1991), are included.

Ramsay (1975) was recommended in Bock, Gibbons, & Muraki (1988).

Bock, R. D., Gibbons, R., & Muraki, E. (1988). Full-information
item factor analysis. *Applied Psychological Measurement,
6*(4), 431-444.

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from
incomplete data via the EM algorithm. *Journal of the Royal Statistical Society.
Series B (Methodological)*, 1-38.

Meng, X.-L. & Rubin, D. B. (1991). Using EM to obtain asymptotic variance-covariance
matrices: The SEM algorithm. *Journal of the American Statistical Association,
86* (416), 899-909.

Oakes, D. (1999). Direct calculation of the information matrix via
the EM algorithm. *Journal of the Royal Statistical Society:
Series B (Statistical Methodology), 61*(2), 479-482.

Ramsay, J. O. (1975). Solving implicit equations in psychometric data analysis.
*Psychometrika, 40* (3), 337-360.

Varadhan, R. & Roland, C. (2008). Simple and globally convergent
methods for accelerating the convergence of any EM
algorithm. *Scandinavian Journal of Statistics, 35*, 335-353.

[Package *OpenMx* version 2.6.8 Index]