predict.ncpen: predict.ncpen: make predictions from an `ncpen` object

Description

The function provides various types of predictions from a fitted ncpen object: response, regression, probability, root mean squared error (RMSE), negative log-likelihood (LIKE).

Usage

# S3 method for ncpen
predict(object, type = c("y", "reg", "prob", "rmse",
  "like"), new.y.vec = NULL, new.x.mat = NULL, prob.cut = 0.5, ...)

Arguments

object

(ncpen object) fitted ncpen object.

type

(character) type of prediction. y returns new responses from new.x.mat. reg returns new linear predictors from new.x.mat. prob returns new class probabilities from new.x.mat for binomial and multinomial. rmse returns RMSE from new.y.vec and new.x.mat. prob returns LIKE from new.y.vec and new.x.mat.

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 binomial.

...

other S3 parameters. Not used.

Value

prediction values depending on type for all lambda values.

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.

Examples

Run this code

# NOT RUN {
### 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,])
# }