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new cv package: cross-validation of regression models
2023 Nov 03
0
new cv package: cross-validation of regression models
...pports parallel computations. - mse() (mean-squared error) and BayesRule() are cross-validation criteria ("cost functions"), suitable for use with cv(). - cvSelect() cross-validates a selection procedure for a regression model. cvSelect() also supports parallel computations. - selectStepAIC() is a model-selection procedure, suitable for use with cvSelect(), based on the stepAIC() function in the MASS package. - selectTrans() is a procedure for selecting predictor and response transformations in regression, also suitable for use with cvSelect(), based on the powerTransform() fu...
new cv package: cross-validation of regression models
2023 Nov 03
0
new cv package: cross-validation of regression models
...pports parallel computations. - mse() (mean-squared error) and BayesRule() are cross-validation criteria ("cost functions"), suitable for use with cv(). - cvSelect() cross-validates a selection procedure for a regression model. cvSelect() also supports parallel computations. - selectStepAIC() is a model-selection procedure, suitable for use with cvSelect(), based on the stepAIC() function in the MASS package. - selectTrans() is a procedure for selecting predictor and response transformations in regression, also suitable for use with cvSelect(), based on the powerTransform() fu...