fold.cv.ncpen {ncpen}

fold.cv.ncpen: extracts fold ids for cv.ncpen.

Description

The function returns fold configuration of the samples for CV.

Usage

fold.cv.ncpen(c.vec, n.fold = 10, family = c("gaussian", "binomial",
  "multinomial", "cox", "poisson"))

Arguments

c.vec

(numeric vector) vector for construction of CV ids: censoring indicator for cox and response vector for the others.

n.fold

(numeric) number of folds for CV.

family

(character) regression model. Supported models are gaussian, binomial, poisson, multinomial, and cox. Default is gaussian.

Value

fold ids of the samples.

idx

fold ids.

n.fold

the number of folds.

family

the model.

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.

See Also

cv.ncpen, plot.cv.ncpen , gic.ncpen

Examples

### linear regression with scad penalty
sam =  sam.gen.ncpen(n=200,p=20,q=5,cf.min=0.5,cf.max=1,corr=0.5)
x.mat = sam$x.mat; y.vec = sam$y.vec
fold.id = fold.cv.ncpen(c.vec=y.vec,n.fold=10)
### logistic regression with classo penalty
sam =  sam.gen.ncpen(n=200,p=20,q=5,cf.min=0.5,cf.max=1,corr=0.5,family="binomial")
x.mat = sam$x.mat; y.vec = sam$y.vec
fold.id = fold.cv.ncpen(c.vec=y.vec,n.fold=10,family="binomial")
### poison regression with mlog penalty
sam =  sam.gen.ncpen(n=200,p=20,q=5,cf.min=0.5,cf.max=1,corr=0.5,family="poisson")
x.mat = sam$x.mat; y.vec = sam$y.vec
fold.id = fold.cv.ncpen(c.vec=y.vec,n.fold=10,family="poisson")
### multinomial regression with sridge penalty
sam =  sam.gen.ncpen(n=200,p=20,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
fold.id = fold.cv.ncpen(c.vec=y.vec,n.fold=10,family="multinomial")
### cox regression with mcp penalty
sam =  sam.gen.ncpen(n=200,p=20,q=5,r=0.2,cf.min=0.5,cf.max=1,corr=0.5,family="cox")
x.mat = sam$x.mat; y.vec = sam$y.vec
fold.id = fold.cv.ncpen(c.vec=x.mat[,21],n.fold=10,family="cox")