# coef.cv.ncpen: coef.cv.ncpen: extracts the optimal coefficients from... In ncpen: Unified Algorithm for Non-convex Penalized Estimation for Generalized Linear Models

## Description

The function returns the optimal vector of coefficients.

## Usage

 ```1 2``` ```## S3 method for class 'cv.ncpen' coef(object, type = c("rmse", "like"), ...) ```

## Arguments

 `object` (cv.ncpen object) fitted `cv.ncpen` object. `type` (character) a cross-validated error type which is either `rmse` or `like`. `...` other S3 parameters. Not used. Each error type is defined in `cv.ncpen`.

## Value

the optimal coefficients vector selected by cross-validation.

 `type` error type. `lambda` the optimal lambda selected by CV. `beta` the optimal coefficients selected by CV.

## Author(s)

Dongshin Kim, Sunghoon Kwon, Sangin Lee

## References

Lee, S., Kwon, S. and Kim, Y. (2016). A modified local quadratic approximation algorithm for penalized optimization problems. Computational Statistics and Data Analysis, 94, 275-286.

`cv.ncpen`, `plot.cv.ncpen` , `gic.ncpen`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```### linear regression with scad penalty sam = sam.gen.ncpen(n=200,p=10,q=5,cf.min=0.5,cf.max=1,corr=0.5) x.mat = sam\$x.mat; y.vec = sam\$y.vec fit = cv.ncpen(y.vec=y.vec,x.mat=x.mat,n.lambda=10) coef(fit) ### logistic regression with classo penalty sam = sam.gen.ncpen(n=200,p=10,q=5,cf.min=0.5,cf.max=1,corr=0.5,family="binomial") x.mat = sam\$x.mat; y.vec = sam\$y.vec fit = cv.ncpen(y.vec=y.vec,x.mat=x.mat,n.lambda=10,family="binomial",penalty="classo") coef(fit) ### multinomial regression with sridge penalty sam = sam.gen.ncpen(n=200,p=10,q=5,k=3,cf.min=0.5,cf.max=1,corr=0.5,family="multinomial") x.mat = sam\$x.mat; y.vec = sam\$y.vec fit = cv.ncpen(y.vec=y.vec,x.mat=x.mat,n.lambda=10,family="multinomial",penalty="sridge") coef(fit) ```