# predict.extlasso: Prediction of coefficients of a penalized linear regression... In extlasso: Maximum penalized likelihood estimation with extended lasso penalty

## Description

The function computes estimated coefficients value at a given lambda or L1 norm or fraction of norm using a ‘extlasso’ object obtained using ‘extlasso’ function.

## Usage

 ```1 2``` ```## S3 method for class 'extlasso' predict(object,mode=c("fraction","norm","lambda"),at=0,...) ```

## Arguments

 `object` A ‘extlasso’ object obtained using ‘extlasso’ function. `mode` If mode="lambda", prediction is made for a given lambda, if mode="norm", prediction is made for a given L1 norm and if mode="fraction", prediction is made for a fraction of norm value. Default is mode="lambda" `at` A value at which prediction is to be made. Default is at = 0. `...` Not used. Other arguments to predict.

## Value

A vector of estimated coefficients of length p or p+1 at the given value of lambda or L1 norm or fraction of norm, depending on intercept=TRUE or FALSE in ‘extlasso’ object. Here p is number of predictor variables.

## Author(s)

B N Mandal and Jun Ma

## References

Mandal, B.N. and Jun Ma, (2014). A Jacobi-Armijo Algorithm for LASSO and its Extensions.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```x=matrix(rnorm(100*30),100,30) y=sample(c(0,1),100,replace=TRUE) g1=extlasso(x,y,family="binomial") predict(g1,mode="lambda",at=0.1) predict(g1,mode="L1norm",at=1) predict(g1,mode="fraction",at=0.5) x=matrix(rnorm(100*30),100,30) y=rnorm(100) g1=extlasso(x,y,family="normal") predict(g1,mode="lambda",at=0.09) predict(g1,mode="L1norm",at=0.6) predict(g1,mode="fraction",at=0.8) ```

extlasso documentation built on May 2, 2019, 11:39 a.m.