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Last data update: 2014.03.03

R: get coefficients or make coefficient predictions from an...

coef.HDtweedie

R Documentation

get coefficients or make coefficient predictions from an "HDtweedie" object.

Description

Computes the coefficients at the requested values for lambda from a fitted HDtweedie object.

Usage

## S3 method for class 'HDtweedie'
coef(object, s = NULL, ...)

Arguments

`object`	fitted `HDtweedie` model object.
`s`	value(s) of the penalty parameter `lambda` at which predictions are required. Default is the entire sequence used to create the model.
`...`	not used. Other arguments to predict.

Details

s is the new vector at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the coef function will use linear interpolation to make predictions. The new values are interpolated using a fraction of coefficients from both left and right lambda indices.

Value

The coefficients at the requested values for lambda.

Author(s)

Wei Qian, Yi Yang and Hui Zou
Maintainer: Wei Qian <weiqian@stat.umn.edu>

References

Qian, W., Yang, Y., Yang, Y. and Zou, H. (2013), “Tweedie's Compound Poisson Model With Grouped Elastic Net,” submitted to Journal of Computational and Graphical Statistics.

Examples

# load HDtweedie library
library(HDtweedie)

# load data set
data(auto)

# fit the lasso
m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5)

# the coefficients at lambda = 0.01
coef(m0,s=0.01)

# define group index
group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21)

# fit grouped lasso
m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5)

# the coefficients at lambda = 0.01 and 0.04
coef(m1,s=c(0.01,0.04))

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(HDtweedie)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HDtweedie/coef.HDtweedie.Rd_%03d_medium.png", width=480, height=480)
> ### Name: coef.HDtweedie
> ### Title: get coefficients or make coefficient predictions from an
> ###   "HDtweedie" object.
> ### Aliases: coef.HDtweedie
> ### Keywords: models regression
> 
> ### ** Examples
> 
> # load HDtweedie library
> library(HDtweedie)
> 
> # load data set
> data(auto)
> 
> # fit the lasso
> m0 <- HDtweedie(x=auto$x,y=auto$y,p=1.5)
> 
> # the coefficients at lambda = 0.01
> coef(m0,s=0.01)
                       1
(Intercept) -0.055919922
CAR_TYPE_2   0.000000000
CAR_TYPE_3   0.000000000
CAR_TYPE_4   0.000000000
CAR_TYPE_5   0.000000000
CAR_TYPE_6   0.000000000
JOBCLASS_3   0.000000000
JOBCLASS_4   0.000000000
JOBCLASS_5   0.000000000
JOBCLASS_6   0.000000000
JOBCLASS_7   0.000000000
JOBCLASS_8   0.000000000
JOBCLASS_9   0.000000000
MAX_EDUC_2   0.000000000
MAX_EDUC_3   0.000000000
MAX_EDUC_4   0.000000000
MAX_EDUC_5   0.000000000
KIDSDRIV     0.000000000
KIDSDRIV2    0.000000000
KIDSDRIV3    0.000000000
TRAVTIME     0.000000000
TRAVTIME2   -0.022868351
TRAVTIME3    0.000000000
BLUEBOOK    -0.058530567
BLUEBOOK2   -0.022798853
BLUEBOOK3    0.000000000
NPOLICY      0.000000000
NPOLICY2     0.000000000
NPOLICY3    -0.001885975
MVR_PTS      0.290668624
MVR_PTS2     0.000000000
MVR_PTS3    -0.003345931
AGE          0.015615244
AGE2         0.000000000
AGE3         0.000000000
HOMEKIDS     0.000000000
HOMEKIDS2    0.000000000
HOMEKIDS3    0.000000000
YOJ          0.000000000
YOJ2         0.000000000
YOJ3         0.000000000
INCOME       0.000000000
INCOME2      0.000000000
INCOME3      0.006162290
HOME_VAL    -0.038507332
HOME_VAL2    0.000000000
HOME_VAL3    0.000000000
SAMEHOME    -0.002501553
SAMEHOME2    0.000000000
SAMEHOME3    0.000000000
CAR_USE      0.000000000
RED_CAR      0.000000000
REVOLKED     1.313816847
GENDER      -0.009220928
MARRIED      0.000000000
PARENT1      0.000000000
AREA         0.633743866
> 
> # define group index
> group1 <- c(rep(1,5),rep(2,7),rep(3,4),rep(4:14,each=3),15:21)
> 
> # fit grouped lasso
> m1 <- HDtweedie(x=auto$x,y=auto$y,group=group1,p=1.5)
> 
> # the coefficients at lambda = 0.01 and 0.04
> coef(m1,s=c(0.01,0.04))
                        1             2
(Intercept) -0.0139344872  2.762691e-02
CAR_TYPE_2   0.0000000000  0.000000e+00
CAR_TYPE_3   0.0000000000  0.000000e+00
CAR_TYPE_4   0.0000000000  0.000000e+00
CAR_TYPE_5   0.0000000000  0.000000e+00
CAR_TYPE_6   0.0000000000  0.000000e+00
JOBCLASS_3   0.0000000000  0.000000e+00
JOBCLASS_4   0.0000000000  0.000000e+00
JOBCLASS_5   0.0000000000  0.000000e+00
JOBCLASS_6   0.0000000000  0.000000e+00
JOBCLASS_7   0.0000000000  0.000000e+00
JOBCLASS_8   0.0000000000  0.000000e+00
JOBCLASS_9   0.0000000000  0.000000e+00
MAX_EDUC_2   0.0000000000  0.000000e+00
MAX_EDUC_3   0.0000000000  0.000000e+00
MAX_EDUC_4   0.0000000000  0.000000e+00
MAX_EDUC_5   0.0000000000  0.000000e+00
KIDSDRIV     0.0000000000  0.000000e+00
KIDSDRIV2    0.0000000000  0.000000e+00
KIDSDRIV3    0.0000000000  0.000000e+00
TRAVTIME     0.0115355246  3.165135e-03
TRAVTIME2   -0.0197503791 -4.967120e-03
TRAVTIME3   -0.0087967500 -5.057641e-03
BLUEBOOK    -0.0769476264 -5.050464e-02
BLUEBOOK2   -0.0775000064 -4.497963e-02
BLUEBOOK3   -0.0143151087 -7.967835e-03
NPOLICY      0.0003483026  2.071023e-04
NPOLICY2     0.0006801757  3.157850e-04
NPOLICY3    -0.0018126902 -1.777935e-03
MVR_PTS      0.0775199095  7.451852e-02
MVR_PTS2     0.1028374463  1.013421e-01
MVR_PTS3    -0.0096263869 -9.301774e-03
AGE          0.0116242657  3.623374e-03
AGE2        -0.0037004413 -6.317712e-04
AGE3         0.0068829188  2.980813e-03
HOMEKIDS    -0.0001501087 -8.438859e-05
HOMEKIDS2   -0.0002194621 -1.499156e-04
HOMEKIDS3   -0.0002037064 -1.748394e-04
YOJ          0.0000000000  0.000000e+00
YOJ2         0.0000000000  0.000000e+00
YOJ3         0.0000000000  0.000000e+00
INCOME      -0.0001461974 -1.332826e-05
INCOME2     -0.0019271557 -1.315205e-03
INCOME3      0.0058768768  3.242854e-03
HOME_VAL    -0.0473411490 -2.811231e-02
HOME_VAL2   -0.0153580926 -5.406091e-03
HOME_VAL3    0.0121535289  3.741908e-03
SAMEHOME    -0.0042132405  0.000000e+00
SAMEHOME2   -0.0037517973 -3.480539e-04
SAMEHOME3   -0.0020591768  0.000000e+00
CAR_USE      0.0813016134  3.096446e-02
RED_CAR      0.0000000000  0.000000e+00
REVOLKED     1.3779096826  1.350769e+00
GENDER      -0.0732833392 -2.111859e-02
MARRIED     -0.0128853050  0.000000e+00
PARENT1      0.0000000000  0.000000e+00
AREA         0.7529625516  6.932212e-01
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>