Calculate the estimated weights for averaging across all candidate models and the corresponding mean squared prediction error risk.
cvpredRisk(
M,
nump,
numq,
a2,
a3,
nfolds,
X.train,
ZZ.train,
Y.train,
X.pred,
ZZ.pred,
Y.pred,
nbasis,
tt
)A list of
Mean squared error risk in training data set, produced by CVMA method.
A vector of weights estimator.
Mean squared prediction error risk in test data set, produced by CVMA method.
The number of candidate models.
The number of scalar predictors in candidate models.
The number of funtional principal components (FPCs) in candidate models.
The number of FPCs in each candidate model. See modelspec.
The index for each component in each candidate model. See modelspec.
The number of folds used in cross-validation.
The training data of scalar predictors.
The training data of the functional predictor.
The training data of response variable.
The test data of scalar predictors.
The test data of the functional predictor.
The test data of response variable.
The number of basis functions used for spline approximation.
The vector of recording/measurement points for the functional predictor.