gdm.single.crossvalidation: Single GDM Cross-Validation Test, Internal Function

Description

Undertake a cross-validation assessment of a GDM, using a single training and testing dataset.

Usage

gdm.single.crossvalidation(spTable_train, spTable_test, geo=FALSE,
splines=NULL, knots=NULL)

Value

List, providing cross-validation statistics. These are metrics that describe how well the model fit using the sitepair training table predicts the dissimilarities in the sitepair testing table. Metrics provided include: 'Deviance.Explained' (the deviance explained for the training data); 'Test.Deviance.Explained' (the deviance explained for the test data); 'Mean.Error'; 'Mean.Absolute.Error'; 'Root.Mean.Squre.Error'; 'Obs.Pred.Correlation' (Pearson's correlation coefficient between observed and predicted values); 'Equalised.RMSE' (the average root mean square error across bands of observed dissimilarities (0.05 dissimialrity units)); 'Error.by.Observed.Value' (the average root mean square error and number of observations within bands of observed dissimilarities (0.05 dissimialrity units)).

Arguments

spTable_train: (dataframe) A dataframe holding the GDM input table for model fitting.
spTable_test: (dataframe) A dataframe holding the GDM input table for model testing, having identical column names to 'spTable_train' but using different site-pairs.
geo: (boolean) Geographic distance to be used in model fitting (default = FALSE).
splines: (vector) An optional vector of the number of I-spline basis functions to be used for each predictor in fitting the model.
knots: (vector) An optional vector of knots in units of the predictor variables to be used in the fitting process.