The definition of the argument to change permutations in the presentations and in the books is done as in anova.cca(... , step = 1000).
step is not an argument included in or used by anova.cca(). Somehow, anova.cca() does not throw errors if one uses gibberish arguments, but the argument to change the permutations is permutations, as in anova.cca(... , permutations = how(nperms = 999).
How how() works here is less trivial than just changing the number attributed to nperms and needs to be reviewed in this workshop. See this document for help.
Moreover, in certain slides, the number of permutations used is 1,000 instead of 999. There is no explanation provided for this choice.
The p-value for permutations in the models there is calculated as p = (N + 1) / (n + 1), where n is the number of permutations requested, and N is the number of values of the test statistics (any type) that have been as extreme as the test statistic for the observed data.
That addition of +1 is what explains why we often see 999 or 199 permutations instead of 1000 or 200.
The reason that a division of 90/(99 +1) is often preferred over a 90/(100 + 1) is that it makes p-values "pretty" or easily interpretable in terms of the number of test statistics from permuted values that came as extreme as the observed test statistic.
The definition of the argument to change permutations in the presentations and in the books is done as in
anova.cca(... , step = 1000).stepis not an argument included in or used byanova.cca(). Somehow,anova.cca()does not throw errors if one uses gibberish arguments, but the argument to change the permutations ispermutations, as inanova.cca(... , permutations = how(nperms = 999).How
how()works here is less trivial than just changing the number attributed tonpermsand needs to be reviewed in this workshop. See this document for help.Moreover, in certain slides, the number of permutations used is 1,000 instead of 999. There is no explanation provided for this choice.
The p-value for permutations in the models there is calculated as p = (N + 1) / (n + 1), where n is the number of permutations requested, and N is the number of values of the test statistics (any type) that have been as extreme as the test statistic for the observed data.
That addition of +1 is what explains why we often see 999 or 199 permutations instead of 1000 or 200.
The reason that a division of 90/(99 +1) is often preferred over a 90/(100 + 1) is that it makes p-values "pretty" or easily interpretable in terms of the number of test statistics from permuted values that came as extreme as the observed test statistic.