predict.cosso {cosso}R Documentation

Make predictions or extract coefficients from a cosso model

Description

Make prediction for future observations or extract the model parameters at a particular smoothing parameter.

Usage

 
## S3 method for class 'cosso'
predict(object,xnew,M,type=c("fit","coefficients","nonzero"),eps=1e-7,...) 

Arguments

object

a cosso object.

xnew

matrix of new values for x at which predictions are to be made. Object must be a matrix and have the same dimension as the training design points. Continuous variable will also have to be scaled to [0,1] interval.

M

a smoothing parameter value. M should be taken between 0 and p. If not provided, a cross-validation procedure will be carried out to select an appropriate value.

type

if type="fit" (default), fitted values will be returned. If type="coefficients", model coefficients will be returned. Abbreviations are allowed.

eps

an effective zero, default is 1e-7

...

additional arguments for predict function.

Value

The object returned depends on type.

When type="fit", predicted

eta

function value will be given at the new design points xnew.

When type="coefficients", three sets of coefficients will be returned.

Intercept

the estimated intercept. If family="Cox", the intercept is zero.

coefs

the estimated coefficients for kernel representers.

theta

the estimated scale parameters for each functional component.

When type="nonzero", a list of the indices of the nonconstant functional components will be returned.

Author(s)

Hao Helen Zhang and Chen-Yen Lin

See Also

plot.cosso

Examples

 
## Gaussian
set.seed(20130310)
x=cbind(rbinom(200,1,.7),matrix(runif(200*7,0,1),nc=7))
y=x[,1]+sin(2*pi*x[,2])+5*(x[,4]-0.4)^2+rnorm(200,0,1)
G.Obj=cosso(x,y,family="Gaussian")
predict.cosso(G.Obj,M=2,type="nonzero")
predict.cosso(G.Obj,xnew=x[1:3,],M=2,type="fit")
## Clean up
rm(list=ls())

## Not run: 
## Binomial
set.seed(20130310)
x=cbind(rbinom(200,1,.7),matrix(runif(200*9,0,1),nc=9))
trueProb=1/(1+exp(-x[,1]-sin(2*pi*x[,2])-5*(x[,4]-0.4)^2))
y=rbinom(200,1,trueProb)

B.Obj=cosso(x,y,family="Bin") 
f.hat=predict.cosso(B.Obj,xnew=x,M=2,type="fit")
prob.hat=1/(1+exp(-f.hat))
## Clean up
rm(list=ls())

## End(Not run)

[Package cosso version 2.1-1 Index]