dataFigure3 | R Documentation |
The data, inspired from \insertCitecl16superb, 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.
data(dataFigure3)
An object of class data.frame.
doi: 10.5709/acp-0214-z
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)
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