predict.logit: Prediction for an object with S3 class `"logit"`

Description

Predicting responses of the given design data.

Usage

## S3 method for class 'logit':
predict(object, newdata, lambda.idx = c(1:3), p.pred.idx = c(1:5), ...)

Arguments

object

An object with S3 class "logit"

newdata

An optional data frame in which to look for variables with which to predict. If omitted, the traning data of the are used.

lambda.idx

The indices of the regularizaiton parameters in the solution path to be displayed. The default values are c(1:3).

p.pred.idx

The indices of the predicted response vectors in the solution path to be displayed. The default values are c(1:5).

...

Arguments to be passed to methods.

Value

p.predThe predicted response vectors based on the estimated models.

Details

predict.logit produces predicted values of the responses of the newdata from the estimated beta values in the object, i.e. $$\hat{p} = \frac{e^{\hat{\beta}_0 + X_{new} \hat{\beta}}}{1+e^{\hat{\beta}_0 + X_{new} \hat{\beta}}}.$$

Examples

Run this code

## generate data
set.seed(123)
n = 100
d = 200
d1 = 10
rho0 = 0.3
lambda = c(3:1)*sqrt(log(d)/n)
Sigma = matrix(0,nrow=d,ncol=d)
Sigma[1:d1,1:d1] = rho0
diag(Sigma) = 1
mu = rep(0,d)
X = mvrnorm(n=2*n,mu=mu,Sigma=Sigma)
X.fit = X[1:n,]
X.pred = X[(n+1):(2*n),]
beta = c(rep(sqrt(1/3),3),rep(0,d-3))
Y.fit = exp(X.fit%*%beta)/(1+exp(X.fit%*%beta))

## Regression with "l1".
out=picasso(X=X.fit,Y=Y.fit,lambda=lambda,family="binomial")

## Display results
p=predict(out,X.pred)