sharpener: Sharpener data

In GLMsData: Generalized Linear Model Data Sets

Sharpener data

Description

The sharpener data

Usage

data(sharpener)

Format

A data frame with 15 observations on the following 11 variables.

Y

the measured response; a numeric vector

X1

a measured predictor; a numeric vector

X2

a measured predictor; a numeric vector

X3

a measured predictor; a numeric vector

X4

a measured predictor; a numeric vector

X5

a measured predictor; a numeric vector

X6

a measured predictor; a numeric vector

X7

a measured predictor; a numeric vector

X8

a measured predictor; a numeric vector

X9

a measured predictor; a numeric vector

X10

a measured predictor; a numeric vector

Details

The data come from a study about making a point.

Examples

	### The data are actually random numbers, generated in R as follows:
	nxvars <- 10   # The number of explanatory variables
	nobs <- 15	   # The number of observations
	set.seed(5000) # To ensure reproducibility
	
	# Ensure the response is normally distributed
	y  <- round( rnorm( nobs,0,1), 2) + 10	
	
	# The explanatory variables
	rd <- runif( nxvars*nobs, 0, 1)
	rd <- round( matrix( rd, ncol=nxvars), 2)
	
	# Convert to a dataframe
	rdf <- data.frame( Y=y )
	
	for (i in (1:nxvars)){
		code <- paste( "rdf$X",i," <- rd[,",i,"]", sep="")
		eval( parse(text=code))
	}
	
	head( rdf )
	data(sharpener)
	head( sharpener )

GLMsData documentation built on Aug. 22, 2022, 9:10 a.m.