plam.fit {cvmaPLFAM} | R Documentation |
Fitting partial linear functional additive model
Description
Calculate the prediction values and prediction errors across all candidate models.
Usage
plam.fit(
M,
nump,
numq,
a3,
X.train,
ZZ.train,
y.train,
X.pred,
ZZ.pred,
y.pred,
nbasis,
tt
)
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. |
a3 |
The index for each component in each candidate model. See |
X.train |
The training data of scalar predictors. |
ZZ.train |
The training data of the functional predictor. |
y.train |
The training data of response variable. |
X.pred |
The test data of scalar predictors. |
ZZ.pred |
The test data of the functional predictor. |
y.pred |
The test data of response variable. |
nbasis |
The number of basis functions used for spline approximation. |
tt |
The vector of recording/measurement points for the functional predictor. |
Value
A list
of
muhat.train |
A |
ehat.train |
A |
muhat.pred |
A |
prederr |
A |
edf |
A |