predict.ncpen {ncpen} | R Documentation |
ncpen
objectThe function provides various types of predictions from a fitted ncpen
object:
response, regression, probability, root mean squared error (RMSE), negative log-likelihood (LIKE).
## S3 method for class 'ncpen' predict(object, type = c("y", "reg", "prob", "rmse", "like"), new.y.vec = NULL, new.x.mat = NULL, prob.cut = 0.5, ...)
object |
(ncpen object) fitted |
type |
(character) type of prediction.
|
new.y.vec |
(numeric vector). vector of new response at which predictions are to be made. |
new.x.mat |
(numeric matrix). matrix of new design at which predictions are to be made. |
prob.cut |
(numeric) threshold value of probability for |
... |
other S3 parameters. Not used. |
prediction values depending on type
for all lambda values.
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.
### 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 fit = ncpen(y.vec=y.vec[1:190],x.mat=x.mat[1:190,]) predict(fit,"y",new.x.mat=x.mat[190:200,]) ### logistic regression with classo penalty sam = sam.gen.ncpen(n=200,p=20,q=5,k=3,cf.min=0.5,cf.max=1,corr=0.5,family="binomial") x.mat = sam$x.mat; y.vec = sam$y.vec fit = ncpen(y.vec=y.vec[1:190],x.mat=x.mat[1:190,],family="binomial",penalty="classo") predict(fit,"y",new.x.mat=x.mat[190:200,]) predict(fit,"y",new.x.mat=x.mat[190:200,],prob.cut=0.3) predict(fit,"reg",new.x.mat=x.mat[190:200,]) predict(fit,"prob",new.x.mat=x.mat[190:200,]) ### 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 fit = ncpen(y.vec=y.vec[1:190],x.mat=x.mat[1:190,],family="multinomial",penalty="classo") predict(fit,"y",new.x.mat=x.mat[190:200,]) predict(fit,"reg",new.x.mat=x.mat[190:200,]) predict(fit,"prob",new.x.mat=x.mat[190:200,])