envfit permutation significance
Thanks very much Gavin. I guess to distill your comments you would agree with my last thought...the envfit vector placement has meaning, it just can't be predicted from the RDA with great confidence. On Mon, Nov 10, 2014 at 11:50 PM, Gavin Simpson-2 [via r-sig-ecology] <
ml-node+s471788n7579207h25 at n2.nabble.com> wrote:
Well, just because the vector(s) in question don't hit some a priori specified statistical significance doesn't mean the vector doesn't exist; we can still plot it. As for interpretation, the length of the vector is not unusual when considered against a null distribution of r values generated by permutation. This doesn't mean the vector is random, just that it is no more "important" than a vector in ordination space where there was no relationship between the axis scores and the variable for which the vector is being tested. You need to ask yourself whether the permutation test is valid - are you allowed to permute the things you permuted? - and whether you have sufficient power to detect an "effect", which usually boils down to having enough unique permutations to get a "significant" p value. If you want to restrict the plot to only those vectors that are "significant", then look at the `p.max` argument in `?plot.envfit HTH Gavin On 10 November 2014 22:08, jsgro <[hidden email] <http://user/SendEmail.jtp?type=node&node=7579207&i=0>> wrote:
I have fitted a vector (a variable) using envfit to an RDA of sites, species, and environmental variables. The envfit permutation (999 permutations)
was
not significant, but envfit placed the vector on the RDA plot. How
exactly
do I interpret this plot? Is the placement of the vector meaningless
(just
random) because the permutation result was not significant? or does the vector placement have meaning, but it just cannot be predicted from the
RDA
with confidence? -- View this message in context:
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