ncl_fit: Internal function to fit a nonconvex loss based robust linear...

In mpath: Regularized Linear Models

Internal function to fit a nonconvex loss based robust linear model

Description

Fit a linear model via penalized nonconvex loss function.

Usage

ncl_fit(x,y, weights, offset=NULL, 
        rfamily=c("clossR", "closs", "gloss", "qloss"), 
        s=NULL, fk=NULL, iter=10, reltol=1e-5, trace=FALSE)

Arguments

x	input matrix, of dimension nobs x nvars; each row is an observation vector.
y	response variable. Quantitative for rfamily="clossR" and -1/1 for classifications.
weights	observation weights. Can be total counts if responses are proportion matrices. Default is 1 for each 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.
rfamily	Response type and relevant loss functions (see above)
s	nonconvex loss tuning parameter for robust regression and classification.
fk	predicted values at an iteration in the MM algorithm
iter	number of iteration in the MM algorithm
reltol	convergency criteria
trace	If TRUE, fitting progress is reported

Details

The robust linear model is fit by majorization-minimization along with least squares. Note that the objective function is

weights*loss

.

Value

An object with S3 class "ncl" for the various types of models.

call	the call that produced the model fit
fitted.values	predicted values
h	pseudo response values in the MM algorithm

Author(s)

Zhu Wang <[e-mail]>

References

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

See Also

ncl

mpath documentation built on Jan. 7, 2023, 1:17 a.m.