dataFigure2 | R Documentation |
The data, taken from \insertCitec17superb7, is an example where the "stand-alone" 95\% confidence interval of the means returns a result in contradiction with the result of a statistical test. The paradoxical result is resolved by using adjusted confidence intervals, here the correlation- and different-adjusted confidence interval.
data(dataFigure2)
An object of class data.frame.
doi: 10.5709/acp-0214-z
library(ggplot2) library(gridExtra) data(dataFigure2) options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages ## realize the plot with unadjusted (left) and ajusted (right) 95% confidence intervals plt2a <- superbPlot(dataFigure2, WSFactors = "Moment(2)", adjustments=list(purpose = "difference"), variables = c("pre","post"), 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) plt2b <- superbPlot(dataFigure2, WSFactors = "Moment(2)", adjustments=list(purpose = "difference", decorrelation = "CA"), variables = c("pre","post"), plotStyle="bar" ) + xlab("Group") + ylab("Score") + labs(title="Correlation and difference-adjusted\n95% CI") + coord_cartesian( ylim = c(85,115) ) + geom_hline(yintercept = 100, colour = "black", linewidth = 0.5, linetype=2) plt2 <- grid.arrange(plt2a,plt2b,ncol=2) ## realise the correct t-test to see the discrepancy t.test(dataFigure2$pre, dataFigure2$post, paired=TRUE)
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