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cv.nclreg_fit: Internal function of cross-validation for nclreg

Description

Internal function to conduct k-fold cross-validation for nclreg, produces a plot, and returns cross-validated loss values for lambda

Usage

cv.nclreg_fit(x, y, weights, offset, lambda=NULL, balance=TRUE, 
              rfamily=c("clossR", "closs", "gloss", "qloss"), s=1.5, 
              nfolds=10, foldid, type = c("loss", "error"), 
              plot.it=TRUE, se=TRUE, n.cores=2, trace=FALSE, 
              parallel=FALSE, ...)

Value

an object of class "cv.nclreg" is returned, which is a list with the ingredients of the cross-validation fit.

fit

a fitted nclreg object for the full data.

residmat

matrix of loss values or errors with row values for lambda and column values for kth cross-validation

cv

The mean cross-validated loss values or errors - a vector of length length(lambda).

cv.error

estimate of standard error of cv.

foldid

an optional vector of values between 1 and nfold identifying what fold each observation is in.

lambda

a vector of lambda values

lambda.which

index of lambda that gives minimum cv value.

lambda.optim

value of lambda that gives minimum cv value.

Arguments

x

x matrix as in nclreg.

y

response y as in nclreg.

weights

Observation weights; defaults to 1 per observation

offset

this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. Currently only one offset term can be included in the formula.

lambda

Optional user-supplied lambda sequence; default is NULL, and nclreg chooses its own sequence

balance

for rfamily="closs", "gloss", "qloss" only

rfamily

response variable distribution and nonconvex loss function

s

nonconvex loss tuning parameter for robust regression and classification.

nfolds

number of folds >=3, default is 10

foldid

an optional vector of values between 1 and nfold identifying what fold each observation is in. If supplied, nfold can be missing and will be ignored.

type

cross-validation criteria. For type="loss", loss function values and type="error" is misclassification error.

plot.it

a logical value, to plot the estimated loss values if TRUE.

se

a logical value, to plot with standard errors.

n.cores

The number of CPU cores to use. The cross-validation loop will attempt to send different CV folds off to different cores.

trace

a logical value, print progress of cross validation or not

parallel

a logical value, parallel computing or not

...

Other arguments that can be passed to nclreg.

Author

Zhu Wang <zwang145@uthsc.edu>

Details

The function runs nclreg nfolds+1 times; the first to compute the lambda sequence, and then to compute the fit with each of the folds omitted. The error or the loss value is accumulated, and the average value and standard deviation over the folds is computed. Note that cv.nclreg can be used to search for values for alpha: it is required to call cv.nclreg with a fixed vector foldid for different values of alpha.

References

Zhu Wang (2021), MM for Penalized Estimation, TEST, tools:::Rd_expr_doi("10.1007/s11749-021-00770-2")

See Also

nclreg and plot, predict, and coef methods for "cv.nclreg" object.