glmnet-package            package:glmnet            R Documentation

_E_l_a_s_t_i_c _n_e_t _m_o_d_e_l _p_a_t_h_s _f_o_r _s_o_m_e _g_e_n_e_r_a_l_i_z_e_d _l_i_n_e_a_r _m_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     This package fits lasso and elastic-net model paths for
     regression, logistic and multinomial regression using coordinate
     descent. The algorithm is extremely fast, and exploits sparsity in
     the input x matrix where it exists. A variety of predictions can
     be made from the fitted models.

_D_e_t_a_i_l_s:


       Package:  glmnet
       Type:     Package
       Version:  1.0
       Date:     2008-05-14
       License:  What license is it under?

     Very simple to use. Accepts 'x,y' data for regression models, and
     produces the regularization path over a grid of values for the
     tuning parameter 'lambda'. Only 5 functions: 'glmnet'
      'predict.glmnet'
      'plot.glmnet'
      'print.glmnet'
      'coef.glmnet'

_A_u_t_h_o_r(_s):

     Jerome Friedman, Trevor Hastie and Rob Tibshirani
      Maintainer: Trevor Hastie  <hastie@stanford.edu>

_R_e_f_e_r_e_n_c_e_s:

     Friedman, J., Hastie, T. and Tibshirani, R. (2008) _Regularization
     Paths for Generalized Linear Models via Coordinate Descent_

_E_x_a_m_p_l_e_s:

     x=matrix(rnorm(100*20),100,20)
     y=rnorm(100)
     g2=sample(1:2,100,replace=TRUE)
     g4=sample(1:4,100,replace=TRUE)
     fit1=glmnet(x,y)
     predict(fit1,newx=x[1:5,],s=c(0.01,0.005))
     predict(fit1,type="coef")
     plot(fit1,xvar="lambda")
     fit2=glmnet(x,g2,family="binomial")
     predict(fit2,type="response",newx=x[2:5,])
     predict(fit2,type="nonzero")
     fit3=glmnet(x,g4,family="multinomial")
     predict(fit3,newx=x[1:3,],type="response",s=0.01)

