Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/APFAfunctions.R

Uses the edge probabilities from `G`

to calculate the log likelihood of the model.

1 | ```
LogLike.APFA(G, dat, complete.cases=TRUE)
``` |

`G` |
a fitted APFA |

`dat` |
a data frame that contains the same variables that G is based on. |

`complete.cases` |
a Boolean that determines whether incomplete cases are included in the calculations (see Details). |

An observation in the data may not be in the sample space of the APFA, i.e. there may not a root-to-sink path in the APFA generating
the observation. However, there will be a partial path, that is, generating the initial part of the observation.
If `complete.cases`

is true, such observations are excluded from the calculations, otherwise contributions from the partial path are included.

See the reference below for the per-symbol log-likelihood.

Returns the log-likelihood and the per-symbol log-likelihood.

Smitha Ankinakatte and David Edwards

Thollard, F.; Dupont, P. & de la Higuera, C. Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality 17th International Conference on Machine Learning., 2000, 975-982;

Ankinakatte, S. and Edwards, D. Modelling discrete longitudinal data using acyclic probabilistic finite automata. Submitted to Computational Statistica and Data Analysis.

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