gdm.crossvalidation: Cross-Validation Assessment of a Fitted GDM

View source: R/gdm.crossvalidation.R

gdm.crossvalidationR Documentation

Cross-Validation Assessment of a Fitted GDM

Description

Undertake a cross-validation assessment of a GDM fit using all the predictors included in the formated GDM input table (spTable). The cross-validation is run using a specified proportion (train.proportion) of the randomly selected sites included in spTable to train the model, with the remaining sites being used to test the performance of the model predictions. The test is repeated a specified number of times (n.crossvalid.tests), with a unique random sample taken each time. Outputs are a number of cross-validation test metrics.

Usage

gdm.crossvalidation(spTable, train.proportion=0.9, n.crossvalid.tests=1,
geo=FALSE, splines=NULL, knots=NULL)

Arguments

spTable

(dataframe) A dataframe holding the GDM input table for model fitting.

train.proportion

(float) The proportion of sites in 'spTable' to use in training the GDM, with the remaining proportion used to test the model. (default = 0.9)

n.crossvalid.tests

(integer) The number of cross-validation sets to use in testing the GDM. (default = 1)

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 April 5, 2022, 9:06 a.m.