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

In gdm: Generalized Dissimilarity Modeling

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)

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.

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)).

gdm documentation built on Dec. 1, 2022, 5:06 p.m.