# pairwiseCI.package: Wrapper functions for two-sample confidence intervals and... In pairwiseCI: Confidence Intervals for Two Sample Comparisons

## Description

A collection of wrapper functions for simple evaluation of factorial trials. The function pairwiseCI allows to calculate 2-sample confidence intervals for all-pairs and many-to-one comparisons between levels of a factor. Intervals are NOT adjusted for multiple hypothesis testing per default. The function pairwiseTest allows to calculate p-values of two-sample tests for all-pairs and many-to-one comparisons between levels of a factor. P-values are NOT adjusted for multiple hypothesis testing per default. Both function allow splitting of the data according to additional factors. Intervals can be plotted, summary.pairwiseTest allows to use the p-value adjustments as implemented in p.adjust(stats). Different test and interval methods (parametric, nonparametric, bootstrap for robust estimators of location, binomial proportions) are implemented in a unified user level function.

## Author(s)

Frank Schaarschmidt and Daniel Gerhard, for the Institute of Biostatistics, Leibniz Universitaet Hannover Maintainer: Frank Schaarschmidt <[email protected]>

`pairwise.t.test(stats)` `pairwise.prop.test(stats)` `p.adjust(stats)`
 ``` 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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72``` ```# some examples: # In many cases the shown examples might not make sense, # but display how the functions can be used. data(Oats) Oats # # all pairwise comparisons, # separately for each level of nitro: apc <- pairwiseCI(yield ~ Variety, data=Oats, by="nitro", method="Param.diff") apc # Intervals can be plotted: plot(apc) # See ?pairwiseCI or ?pairwiseCImethodsCont # for further options for intervals of 2 samples # of continuous data. # Or a test apcTest <- pairwiseTest(yield ~ Variety, data=Oats, by="nitro", method="t.test") # with holm-adjusted p-values: summary(apcTest, p.adjust.method="holm") # # If only comparisons to one control would be of interest: # many to one comparisons, with variety Marvellous as control, # for each level of nitro separately: m21 <- pairwiseCI(yield ~ Variety, data=Oats, by="nitro", method="Param.diff", control="Marvellous") ############################################## # # Proportions: another structure of the data is needed. # Currently the data.frame data must contain two columns, # specifying the number of successes and failures on each # unit. # The rooting example: # Calculate confidence intervals for the # difference of proportions between the 3 doses of IBA, # separately for 4 combinations of "Age" and "Position". # Note: we pool over Rep in that way. Whether this makes # sense or not, is decision of the user. data(rooting) rooting # Confidence intervals for the risk difference aprootsRD<-pairwiseCI(cbind(root, noroot) ~ IBA, data=rooting, by=c("Age", "Position"), method="Prop.diff") # See ?pairwiseCIProp for further options to compare proportions # Or a test: aprootsTest<-pairwiseTest(cbind(root, noroot) ~ IBA, data=rooting, by=c("Age", "Position"), method="Prop.test") aprootsTest summary(aprootsTest, p.adjust.method="holm") ```