DAAG (version 1.22)

tinting: 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

Examples

# NOT RUN {
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))
# }