Performs pairwise t-tests for an analysis of variance, making corrections for multiple comparisons.
posthocPairwiseT(x, ...)An aov object
Arguments to be passed to pairwise.t.test
As per pairwise.t.test
The intention behind this function is to allow users to use simple
tools for multiple corrections (e.g., Bonferroni, Holm) as post hoc
corrections in an ANOVA context, using the fitted model object (i.e., an
aov object) as the input. The reason for including this function is
that Tukey / Scheffe methods for constructing simultaneous confidence
intervals (as per TukeyHSD) are not often discussed in the
context of an introductory class, and the more powerful tools provided by
the multcomp package are not appropriate for students just beginning
to learn statistics.
This function is currently just a wrapper function for
pairwise.t.test, and it only works for one-way ANOVA, but
this may change in future versions.
# create the data set to analyse:
dataset <- data.frame(
outcome = c( 1,2,3, 2,3,4, 5,6,7 ),
group = factor(c( "a","a","a", "b","b","b","c","c","c"))
)
# run the ANOVA and print out the ANOVA table:
anova1 <- aov( outcome ~ group, data = dataset )
summary(anova1)
#> Df Sum Sq Mean Sq F value Pr(>F)
#> group 2 26 13 13 0.00659 **
#> Residuals 6 6 1
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Currently, the following two commands are equivalent:
posthocPairwiseT( anova1 )
#>
#> Pairwise comparisons using t tests with pooled SD
#>
#> data: outcome and group
#>
#> a b
#> b 0.2666 -
#> c 0.0081 0.0208
#>
#> P value adjustment method: holm
pairwise.t.test( dataset$outcome, dataset$group )
#>
#> Pairwise comparisons using t tests with pooled SD
#>
#> data: dataset$outcome and dataset$group
#>
#> a b
#> b 0.2666 -
#> c 0.0081 0.0208
#>
#> P value adjustment method: holm