Description Usage Arguments Details Value Author(s) References Examples
View source: R/APFAfunctions.R
The function measures the prediction efficiency of the model using K-fold cross-validation.
1 | cross.validate(Data, K = 10, crit = NULL, beagle = TRUE, dir='')
|
Data |
A data frame |
K |
Number of cross validations |
crit |
The model selection criterion, either AIC or BIC, for penalised likelihood method. |
beagle |
If |
dir |
specifying the path for 'beagle.jar' directory. |
The cross validation for a given data frame is done as follows,\
1. The data is divided in to K
subsets of equal sizes.\
2. At each cross validation step in k=1:K
, $k^th$ subset is taken as the test data
and the rest as training data.\
3. APFA model is fitted to the training data using a model selection method (AIC, BIC or Beagle),
then using the edge probabilities of the fitted model, the loglikelihood and the per-symbol loglikelihood
are calculated for the test data set.\
4. The function returns the mean of the log-likelihood from K-cross validation and pzero. \
Returns per symbol loglikelihood of the K-cross validation.
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\
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