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Question for ANOVA Post-hoc test
Posts: 2
02 April 2023, 8:50 PM
In this week's lecture, we used the `pairwise.t.test()` function to conduct one step Bonferroni t-test. The `p.adj` parameter in this function is set to `none` in the lecture material. But after reading the function documentation, it seems that `none` does not adjust p-values for multiple comparisons. So should I change the method to `bonferroni`?
Posts: 128
07 April 2023, 1:11 PM
Either is fine. When you use p.adj = "none", you compare the p values with alpha/m (0.0167 in this example):
> pairwise.t.test(dtf2$wg, dtf2$diet, pool.sd = FALSE,var.equal = TRUE, p.adj = "none") Pairwise comparisons using t tests with non-pooled SD data: dtf2$wg and dtf2$diet diet1 diet2 diet2 0.0018 - diet3 0.0010 0.2879 P value adjustment method: none
When you use p.adj = "bonferroni", you compare the p values with alpha (0.05 in this example):
> pairwise.t.test(dtf2$wg, dtf2$diet, pool.sd = FALSE,var.equal = TRUE, p.adj = "bonferroni") Pairwise comparisons using t tests with non-pooled SD data: dtf2$wg and dtf2$diet diet1 diet2 diet2 0.0055 - diet3 0.0030 0.8636 P value adjustment method: bonferroni
The p-values in the latter is just 3 times the former ones.
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