predict.nnlasso: Prediction of coefficients of a penalized linear regression... In nnlasso: Non-Negative Lasso and Elastic Net Penalized Generalized Linear Models

Description

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

Usage

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

Arguments

 `object` A ‘nnlasso’ object obtained using ‘nnlasso’ 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 ‘nnlasso’ object. Here p is number of predictor variables.

Author(s)

Baidya Nath Mandal and Jun Ma

References

Mandal, B.N. and Ma, J. (2016). L1 regularized multiplicative iterative path algorithm for non-negative generalized linear models.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```data(car) attach(car) x=as.matrix(car[,1:10]) g1=nnlasso(x,y1,family="binomial") predict(g1,mode="lambda",at=0.1) predict(g1,mode="L1norm",at=1) predict(g1,mode="fraction",at=0.5) g1=nnlasso(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) detach(car) ```

nnlasso documentation built on May 2, 2019, 8:19 a.m.