dataFigure3 {superb}

Data for Figure 3

Description

The data, inspired from (Cousineau and Laurencelle 2016), is an example where the "stand-alone" 95\ a result in contradiction with the result of a statistical test. The paradoxical result is resolved by using adjusted confidence intervals, here the cluster- and different-adjusted confidence interval.

Usage

data(dataFigure3)

Format

An object of class data.frame.

Source

doi:10.5709/acp-0214-z

References

Cousineau D, Laurencelle L (2016). “A Correction Factor for the Impact of Cluster Randomized Sampling and Its Applications.” Psychological Methods, 21, 121 – 135. doi:10.1037/met0000055.

Examples

library(ggplot2)
library(gridExtra)
data(dataFigure3)

options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages

## realize the plot with unadjusted (left) and ajusted (right) 95% confidence intervals
plt3a <- superbPlot(dataFigure3, BSFactors = "grp", 
    adjustments=list(purpose = "difference", samplingDesign = "SRS"), 
    variables = c("VD"), plotStyle="bar" ) + 
  xlab("Group") + ylab("Score") + labs(title="Difference-adjusted 95% CI\n") +
  coord_cartesian( ylim = c(85,115) ) +
  geom_hline(yintercept = 100, colour = "black", linewidth = 0.5, linetype=2)
plt3b <- superbPlot(dataFigure3, BSFactors = "grp", 
    adjustments=list(purpose = "difference", samplingDesign = "CRS"), 
    variables = c("VD"), plotStyle="bar", clusterColumn = "cluster" ) + 
  xlab("Group") + ylab("Score") + labs(title="Cluster and difference-adjusted\n95% CI") +
  coord_cartesian( ylim = c(85,115) ) + 
  geom_hline(yintercept = 100, colour = "black", linewidth = 0.5, linetype=2)
plt3  <- grid.arrange(plt3a,plt3b,ncol=2)

## realise the correct t-test to see the discrepancy
res   <- t.test(dataFigure3$VD[dataFigure3$grp==1], 
               dataFigure3$VD[dataFigure3$grp==2],
               var.equal=TRUE)
micc  <- mean(c(0.491334683772226, 0.20385744842838)) # mean ICC given by superbPlot
lam   <- CousineauLaurencelleLambda(c(micc, 5,5,5,5,5,5))
tcorr <- res$statistic / lam
pcorr <- 1-pt(tcorr,4)
# let's see the t value and its p value:
c(tcorr, pcorr)