Description Usage Arguments Details Value Examples
Does k
fold crossvalidation for rgam
.
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x 
Input matrix, of dimension 
y 
Response 
lambda 
A usersupplied 
family 
Response type. Either 
offset 
Offset vector as in 
init_nz 
A vector specifying which features we must include when computing the nonlinear features. Default is to construct nonlinear features for all given features. 
gamma 
Scale factor for nonlinear features (vs. original features),
to be between 0 and 1. Default is 0.8 if 
nfolds 
Number of folds for CV (default is 10). Although 
foldid 
An optional vector of values between 1 and 
keep 
If 
parallel 
If TRUE, use parallel foreach to fit each fold. Must
register parallel before hand, such as doMC or others. Note that this also
passes 
verbose 
Print information as model is being fit? Default is

... 
Other arguments that can be passed to 
The function runs rgam
nfolds+1 times; the first to get the lambda
sequence, and then the remainder to compute the fit with each of the folds
omitted. The error is accumulated, and the average error and standard
deviation over the folds is computed.
Note that cv.rgam
only does crossvalidation for lambda but not for
the degrees of freedom hyperparameter.
An object of class "cv.rgam"
.
glmfit 
A fitted 
lambda 
The values of 
nzero_feat 
The number of nonzero features for the model 
nzero_lin 
The number of nonzero linear components for the model

nzero_nonlin 
The number of nonzero nonlinear components for the
model 
fit.preval 
If 
cvm 
The mean crossvalidated error: a vector of length

cvse 
Estimate of standard error of 
cvlo 
Lower curve = 
cvup 
Upper curve = 
lambda.min 
The value of 
lambda.1se 
The largest value of 
foldid 
If 
name 
Name of error measurement used for CV. 
call 
The call that produced this object. 
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