sharpener {GLMsData} | R Documentation |
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 )
[Package GLMsData version 1.4 Index]