car: The car data

In nnlasso: Non-Negative Lasso and Elastic Net Penalized Generalized Linear Models

Description

Car data has 105 observations and 12 variables. All variables except the 12th are standardized such that mean of each of them is 0 and standard deviation is 1. First 10 variables are various characteristics of the cars. The 11th variable y is the price. The 12th variable is a binary variable.

Usage

data("car")

Format

A data frame with 105 observations on the following 12 variables.

Weight

weights of the cars

Length

overall length

Wheel.base

length of wheelbase

Width

width of car

Frt.Leg.Room

maximum front leg room

Front.Hd

distance between the car's head-liner and the head of a 5 ft. 9 in. front seat passenger

Turning

the radius of the turning circle

Disp

engine displacement

HP

net horsepower

Tank

fuel refill capacity

y

price

y1

High or low price

Details

The data is created from car90 data of rpart package with selected 11 variables. The selected variables are Weight,Length,Wheel.base,Width,Frt.Leg.Room,Front.Hd,Turning,Disp,HP,Tank,Price. All these variables are standardized such that each of them has mean 0 and standard deviation 1. Price variable has been renamed as y. The variable y1 is a dichotomous variable created from that the data such that if price >=25000, then y1=1 else y1=0. Only complete cases are considered, so the data has 105 observations in place of 111 observations in car90 data set.

Source

Terry Therneau, Beth Atkinson and Brian Ripley (2014). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-8. http://CRAN.R-project.org/package=rpart

Examples

data(car)

Example output

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