coef.cv.ncpen {ncpen} | R Documentation |
cv.ncpen
.The function returns the optimal vector of coefficients.
## S3 method for class 'cv.ncpen' coef(object, type = c("rmse", "like"), ...)
object |
(cv.ncpen object) fitted |
type |
(character) a cross-validated error type which is either |
... |
other S3 parameters. Not used.
Each error type is defined in |
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. |
Dongshin Kim, Sunghoon Kwon, Sangin Lee
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
### 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)