The p-value calculated by adonis is sometimes wrong.
For example, the following code
library("vegan")
options(digits = 12)
g = as.factor(c("Control","Control","Group1", "Group2", "Group2"))
d = as.dist(matrix(c(0,3,7,2,1, 3,0,5,4,1, 7,5,0,2,6, 4,2,4,0,2, 1,1,6,2,0), ncol=5))
pp = adonis(d ~ g)
ppreturns
Call:
adonis(formula = d ~ g)
Permutation: free
Number of permutations: 120
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
g 2 23.3 11.65 3.584615385 0.7818791946 0.20833
Residuals 2 6.5 3.25 0.2181208054
Total 4 29.8 1.0000000000
The p-value is wrong (only 24 permutations with high f instead of 39). The correct p-value can be computed from the definition to be
(rowSums(t(pp$f.perms >= pp$aov.tab$F.Model[1]))+1) / (length(pp$f.perms) + 1)
which correctly returns
It looks like the bug is due to rounding. When comparing f.perms and F.mod, the value of f.perms is rounded but the value of F.mod is not (although it is rounded afterwards). This makes the >= comparison fail in this example because the value of f.perms is rounded down.
The p-value calculated by adonis is sometimes wrong.
For example, the following code
returns
The p-value is wrong (only 24 permutations with high f instead of 39). The correct p-value can be computed from the definition to be
which correctly returns
It looks like the bug is due to rounding. When comparing f.perms and F.mod, the value of f.perms is rounded but the value of F.mod is not (although it is rounded afterwards). This makes the >= comparison fail in this example because the value of f.perms is rounded down.