predRisk {cvmaPLFAM}

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.