On this Picostat.com statistics page, you will find information about the tinting data set which pertains to Car Window Tinting Experiment Data. The tinting data set is found in the DAAG R package. You can load the tinting data set in R by issuing the following command at the console data("tinting"). This will load the data into a variable called tinting. If R says the tinting data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the tinting R data set. The size of this file is about 9,010 bytes.

Car Window Tinting Experiment Data

Description

These data are from an experiment that aimed to model the effects of the tinting of car windows on visual performance. The authors were mainly interested in effects on side window vision, and hence in visual recognition tasks that would be performed when looking through side windows.

Usage

tinting

Format

This data frame contains the following columns:

case

observation number

id

subject identifier code (1-26)

age

age (in years)

sex

a factor with levels f female, m male

tint

an ordered factor with levels representing degree of tinting: no < lo < hi

target

a factor with levels locon: low contrast, hicon: high contrast

it

the inspection time, the time required to perform a simple discrimination task (in milliseconds)

csoa

critical stimulus onset asynchrony, the time to recognize an alphanumeric target (in milliseconds)

agegp

a factor with levels younger, 21-27, older, 70-78

Details

Visual light transmittance (VLT) levels were 100% (tint=none), 81.3% (tint=lo), and 35.1% (tint=hi). Based on these and other data, Burns et al. argue that road safety may be compromised if the front side windows of cars are tinted to 35

Source

Burns, N.R., Nettlebeck, T., White, M. and Willson, J., 1999. Effects of car window tinting on visual performance: a comparison of younger and older drivers. Ergonomics 42: 428-443.

Examples

levels(tinting$agegp) <- capstring(levels(tinting$agegp))
xyplot(csoa ~ it | sex * agegp, data=tinting) # Simple use of xyplot()
pause()xyplot(csoa ~ it|sex*agegp, data=tinting, panel=panel.superpose, groups=target)
pause()xyplot(csoa ~ it|sex*agegp, data=tinting, panel=panel.superpose, col=1:2,
  groups=target, key=list(x=0.14, y=0.84, points=list(pch=rep(1,2),
  col=1:2), text=list(levels(tinting$target), col=1:2), border=TRUE))
pause()xyplot(csoa ~ it|sex*agegp, data=tinting, panel=panel.superpose,
  groups=tint, type=c("p","smooth"), span=0.8, col=1:3,
  key=list(x=0.14, y=0.84, points=list(pch=rep(1,2), col=1:3),
  text=list(levels(tinting$tint), col=1:3), border=TRUE))

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Dataset imported from https://www.r-project.org.