# ReactTime: Reaction Time Data In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

## Description

Data from Maxwell and Delaney (1990, p. 497) representing the reaction times of 10 subjects in some task where visual stimuli are tilted at 0, 4, and 8 degrees; with noise absent or present. Each subject responded to 3 tilt x 2 noise = 6 conditions. The data thus comprise a repeated measure design with two within-S factors.

## Usage

 `1` ```data(ReactTime) ```

## Format

A data frame with 10 observations giving the reaction time for the 6 conditions.

`deg0NA`

a numeric vector

`deg4NA`

a numeric vector

`deg8NA`

a numeric vector

`deg0NP`

a numeric vector

`deg4NP`

a numeric vector

`deg8NP`

a numeric vector

## Source

Baron, J. and Li, Y. (2003). Notes on the use of R for psychology experiments and questionnaires, https://cran.r-project.org/doc/contrib/Baron-rpsych.pdf

## References

Michael Friendly (2010). HE Plots for Repeated Measures Designs. Journal of Statistical Software, 37(4), 1-40. doi: 10.18637/jss.v037.i04.

Maxwell, S. E. & Delaney, H. D. (1990). Designing Experiments and Analyzing Data: A model comparison perspective. Pacific Grove, CA: Brooks/Cole.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29``` ```data(ReactTime) (RT.mod <- lm(as.matrix(ReactTime)~1)) # within-S factors within <- expand.grid(tilt=ordered(c(0,4,8)), noise=c("NA", "NP")) Anova(RT.mod, idata=within, idesign=~tilt * noise) heplot(RT.mod, idata=within, idesign=~tilt * noise, iterm="tilt") # plotting means and std errors directly levels <- expand.grid(Tilt=c(0,4,8), noise=c("NA", "NP")) (means.df <- data.frame(levels, mean=colMeans(ReactTime), se=sqrt(diag(var(ReactTime)))/9)) with(means.df, { plot(Tilt, mean, type="n", main="Reaction Time data", xlab="Tilt", ylab="Reaction time") colors <- rep(c("red", "blue"), each=3) pts <- rep(c(15, 16), each=3) lines(Tilt[1:3], mean[1:3], col="red", lwd=2) lines(Tilt[4:6], mean[4:6], col="blue", lwd=2) points(Tilt, mean, pch=pts, col=colors, cex=1.2) arrows(Tilt, mean-se, Tilt, mean+se, angle=90, code=3, col=colors, len=.05, lwd=2) # labels at last point, in lieu of legend text(Tilt, mean-10, labels="NA", col="red", pos=1) text(Tilt, mean-10, labels="NP", col="blue", pos=1) } ) ```

### Example output  ```Loading required package: car

Call:
lm(formula = as.matrix(ReactTime) ~ 1)

Coefficients:
deg0NA  deg4NA  deg8NA  deg0NP  deg4NP  deg8NP
(Intercept)  462     510     528     492     660     762

Note: model has only an intercept; equivalent type-III tests substituted.

Type III Repeated Measures MANOVA Tests: Pillai test statistic
Df test stat approx F num Df den Df    Pr(>F)
(Intercept)  1   0.98518   598.45      1      9 1.527e-09 ***
tilt         1   0.88760    31.59      2      8 0.0001596 ***
noise        1   0.78955    33.77      1      9 0.0002560 ***
tilt:noise   1   0.91822    44.91      2      8 4.472e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Note: model has only an intercept; equivalent type-III tests substituted.
Tilt noise mean        se
deg0NA    0    NA  462  6.324555
deg4NA    4    NA  510  9.558139
deg8NA    8    NA  528  8.777075
deg0NP    0    NP  492  9.838197
deg4NP    4    NP  660 12.171612
deg8NP    8    NP  762 12.976712
```

heplots documentation built on Oct. 7, 2021, 1:07 a.m.