Description Usage Arguments Value Note References See Also Examples

Computes maximum-likelihood estimates for the parameters of the general location model from an incomplete mixed dataset.

1 2 |

`s` |
summary list of an incomplete data matrix |

`margins` |
vector describing the sufficient configurations or margins in the
desired loglinear model. The variables are ordered in the original
order of the columns of |

`design` |
design matrix specifying the relationship of the continuous
variables to the categorical ones. The dimension is |

`start` |
optional starting value of the parameter. This is a list such as one
created by this function or by |

`prior` |
Optional vector or array of hyperparameter(s) for a Dirichlet prior
distribution. By default, uses a uniform prior on the cell
probabilities. ECM finds the posterior mode, which under
a uniform prior is the same as a maximum-likelihood estimate. If
structural zeros appear in the table, hyperparameters for those cells
should be set to |

`maxits` |
maximum number of iterations performed. The algorithm will stop if the parameter still has not converged after this many iterations. |

`showits` |
if |

`eps` |
optional convergence criterion. The algorithm stops when the maximum relative difference in every parameter from one iteration to the next is less than or equal to this value. |

a list representing the maximum-likelihood estimates (or posterior
mode) of the normal parameters. This list contains cell probabilities,
cell means, and covariances. The parameter can be transformed back to
the original scale and put into a more understandable format by the
function `getparam.mix`

.

If zero cell counts occur in the complete-data table, the maximum likelihood estimate may not be unique, and the algorithm may converge to different stationary values depending on the starting value. Also, if zero cell counts occur in the complete-data table, the ML estimate may lie on the boundary of the parameter space.

Schafer, J. L. (1996) *Analysis of Incomplete Multivariate Data.*
Chapman \& Hall, Chapter 9.

`prelim.mix`

, `em.mix`

,
`getparam.mix`

,
`loglik.mix`

.

1 2 3 4 5 6 | ```
data(stlouis)
s <- prelim.mix(stlouis,3) # preliminary manipulations
margins <- c(1,2,3) # saturated loglinear model
design <- diag(rep(1,12)) # identity matrix, D=no of cells
thetahat <- ecm.mix(s,margins,design) # should be same as em.mix(s)
loglik.mix(s,thetahat) # loglikelihood at thetahat
``` |

```
Steps of ECM:
1...2...3...4...5...6...7...8...9...10...11...12...13...14...15...16...17...18...19...20...21...22...23...24...25...26...27...28...29...30...31...32...33...34...35...36...37...38...39...40...41...42...43...44...45...46...47...48...49...50...51...52...53...54...55...56...57...58...59...60...61...62...63...64...65...66...67...68...69...70...71...72...73...74...75...76...77...78...79...80...81...82...83...84...85...86...87...88...89...90...91...92...93...94...95...96...97...98...99...100...101...102...103...104...105...106...107...108...109...110...111...112...113...114...115...116...117...118...119...120...121...122...123...124...125...126...127...128...129...130...131...132...133...134...135...136...137...138...139...140...141...142...143...144...145...146...147...148...149...150...151...152...153...154...155...156...157...158...159...160...161...162...163...164...165...166...167...168...169...170...171...172...173...174...175...176...177...178...179...180...181...
[1] -110.3436
```

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