| 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.