predRisk: Output the prediction risks of each method for partial linear...

In cvmaPLFAM: Cross-Validation Model Averaging for Partial Linear Functional Additive Models

Output the prediction risks of each method for partial linear functional additive models (PLFAMs)

Description

Calculate the estimated weights for averaging across all candidate models and the corresponding mean squared prediction error risk. The methods include AIC, BIC, SAIC, SBIC, and CVMA for PLFAMs.

Usage

predRisk(M, nump, numq, a2, a3, nfolds, XX.train, Y.train, XX.pred, Y.pred)

Arguments

M	The number of candidate models.
nump	The number of scalar predictors in candidate models.
numq	The number of funtional principal components (FPCs) in candidate models.
a2	The number of FPCs in each candidate model. See modelspec.
a3	The index for each component in each candidate model. See modelspec.
nfolds	The number of folds used in cross-validation.
XX.train	The training data of predictors processed.
Y.train	The training data of response variable.
XX.pred	The test data of predictors processed.
Y.pred	The test data of response variable.

Value

A list of

aic	Mean squared error risk in training data set, produced by AIC model selection method.
bic	Mean squared error risk in training data set, produced by BIC model selection method.
saic	Mean squared error risk in training data set, produced by SAIC model averaging method.
sbic	Mean squared error risk in training data set, produced by SBIC model averaging method.
cv	Mean squared error risk in training data set, produced by CVMA method.
ws	A list of weights estimator for five methods.
predaic	Mean squared prediction error risk in test data set, produced by AIC model selection method.
predbic	Mean squared prediction error risk in test data set, produced by BIC model selection method.
predsaic	Mean squared prediction error risk in test data set, produced by SAIC model averaging method.
predsbic	Mean squared prediction error risk in test data set, produced by SBIC model averaging method.
predcv	Mean squared prediction error risk in test data set, produced by CVMA method.

cvmaPLFAM documentation built on July 9, 2023, 6:30 p.m.