mcdonald: Pollution dataset from McDonald and Schwing (1973)

In bestglm: Best Subset GLM and Regression Utilities

Description

Regression data used to illustrate ridge regression

Usage

data("mcdonald")

Format

A data frame with 60 observations on the following 16 variables.

PREC

Average annual precipitation in inches

JANT

Average January temperature in degrees F

JULT

Same for July

OVR65

Percent of 1960 SMSA population aged 65 or older

POPN

Average household size

EDUC

Median school years completed by those over 22

HOUS

Percent of housing units which are sound & with all facilities

DENS

Population per sq. mile in urbanized areas, 1960

NONW

Percent non-white population in urbanized areas, 1960

WWDRK

Percent employed in white collar occupations

POOR

Percent of families with income < $3000

HC

Relative hydrocarbon pollution potential

NOX

Same for nitric oxides

SOx

Same for sulphur dioxide

HUMID

Annual average percent relative humidity at 1pm

MORT

Total age-adjusted mortality rate per 100,000

Details

Ridge regression example

Source

Gary C. McDonald and Richard C. Schwing (1973), Instabilities of Regression Estimates Relating Air Pollution to Mortality, Technometrics 15/3, 463-481.

Examples

data(mcdonald)
vifx(mcdonald[, -ncol(mcdonald)])

Example output

Loading required package: leaps
      PREC       JANT       JULT      OVR65       POPN       EDUC       HOUS 
  4.113888   6.143551   3.967774   7.470045   4.307618   4.860538   3.994781 
      DENS       NONW      WWDRK       POOR         HC        NOX        SOx 
  1.658281   6.779599   2.841582   8.717068  98.639935 104.982405   4.228929 
     HUMID 
  1.907092 

bestglm documentation built on March 26, 2020, 7:25 p.m.