superb: Summary statistics plotted with correct error bars

The library superb offers two main functions, superbPlot() and GRD(). The purpose of superbPlot() is to provide a plot with summary statistics and correct error bars. With simple adjustments, the error bar are adjusted to the design (within or between), to the purpose (single or pair-wise differences), to the sampling method (simple randomized samples or cluster randomized samples) and to the population size (infinite or of a specific size).

GRD() can easily generate random data from any design (within or between) using any population distribution with any parameters, and with various effect sizes. GRD is useful to test statistical procedures such as aov() or plotting procedures such as superbPlot().

Installation

install.packages("superb")
library(superb)

Examples

This is a simple example illustrating the ToothGrowth of rats as a function of the dose of vitamin and the form of the vitamin (pills or juice)

superbPlot(ToothGrowth, 
    BSFactor = c("dose","supp"), 
    variables = "len" )

This explicitely indicates to display the median instead of the default mean statistics

superbPlot(ToothGrowth, 
    BSFactor = c("dose","supp"), 
    variables = "len",
    statistic = "median")

This example generates scores for 3000 simulated participants in a 3 x 2 design with repeated-measures on days. The factor day is belived to improve the scores (reducing it)

testdata <- GRD(
    RenameDV   = "score", 
    SubjectsPerGroup = 1000, 
    BSFactors  = "Difficulty(3)", 
    WSFactors  = "Day(2)",
    Population = list(mean = 75,stddev = 12,rho = 0.5),
    Effects    = list("Day" = slope(-3) )
)
head(testdata)

superbPlot(testdata, 
    BSFactor  = "Difficulty", 
    WSFactor  = "Day(2)",
    variables = c("score.1","score.2"),
    plotStyle = "line"
)

For more

Consult the documentation, of the vignettes.

A general introduction to the superb framework is under consideration at Advances in Methods and Practices in Psychological Sciences.