Description Usage Arguments Details Value Author(s) References Examples

View source: R/APFAfunctions.R

The function returns the difference in AIC or BIC associated with merging a node pair in an APFA igraph object.

1 |

`G` |
APFA igraph object |

`nodeset` |
vector of length two, contain the names of the nodes to be merged. |

`crit` |
Information criterion, 'AIC' or 'BIC' or a positive numerical value for the tuning parameter. |

`NS` |
Node symbol array |

The node symbol array corresponding to G may be supplied to increase speed

`dIC`

is The penalized likelihood criterion, IC(A) = -2(A) + alpha*dim(A), where
dim(A) is the number of free parameters under A, and 'alpha' is a tuning parameter.
For the AIC, alpha=2 and for the BIC, alpha= log(N). BIC penalises the parameters more heavily and
so selects simpler models.

The difference in IC is d(IC) = IC(A_0) - IC(A) = G^2 - alpha*df\ where A_0 is the APFA obtained after merging the two nodes in A, G^2 is the deviance statistic and d.f. is the associated degrees of freedom.

A numerical vector of length three containing d(IC), G^2 and the degrees of freedom.

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 C.S.D.A.

1 2 3 4 5 6 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.