[R-sig-eco] Adonis and Random Effects

[Erin Nuccio]

Thanks Steve and Jari for your responses.  I really do appreciate it -- trying to analyze this data has been a challenge! 

Based on both your responses, it seems like I won't be able to get adonis to do the appropriate tests for the Grassland effect (since I do not know how to design custom permutation matrices, as Jari mentioned).  Steve you're correct that I'm not using Euclidean distances (I'm using Unifrac distances).

Since I'm mainly interested in the amount of variance explained by each variable, would it be worthwhile to explore using varpart? I can convert Grassland into a distance matrix (pairwise distances between sites), and I would guess that I would have to turn Treatment into a dummy variable.  I also have additional environmental data that I ignored with my attempt at adonis.

Many thanks for your input,
Erin




On Feb 4, 2013, at 6:22 AM, Steve Brewer <jbrewer at olemiss.edu> wrote:

> Erin,
> 
> There have been a lot of similar queries (e.g., repeated measures, nested
> permanova). Jari can correct me if I am wrong, but as far as I know, no
> one has developed a way to define multiple error terms in adonis.
> 
> 
> You can use adonis, however, to get the split-plot effects. If you want to
> make a grassland a random effect, use the following statement
> 
> adonis(formula = community_distance_matrix ~ Treatment + Grassland +
> GrasslandPlot, strata = GrasslandPlot)
> 
> 
> The treatment effect will be correct because the residual error term
> (which is equivalent to treatment x GrasslandPlot interaction nested
> within Grassland) is the correct error term. The Grassland effect,
> however, will not be tested correctly because it is using the residual
> error term when it should be using GrasslandPLot as the error term. You
> can determine what the F stat for Grassland should be, however, using the
> Ms Grassland and MS GrasslandPlot from the anova table to construct the F
> test. You just won't get a p-value for the test.
> 
> If you want to treat Grassland as a fixed effect, the model is similar but
> defines the interaction
> 
> adonis(formula = community_distance_matrix ~ Treatment*Grassland +
> GrasslandPlot, strata = GrasslandPlot)
> 
> 
> In this case, the treatment x grassland interaction will be tested
> correctly, as will the treatment effect, but not the Grassland effect.
> 
> Unfortunately, you cannot just take averages of abundances across the
> treatment and control in each plot and then do a separate analysis of
> Grassland and GrasslandPLot (unless you're using Euclidean distances). I
> suspect you're not using Euclidean distances.
> 
> Hope this helps some.
> 
> Good luck,
> Steve
> 
> 
> J. Stephen Brewer 
> Professor 
> Department of Biology
> PO Box 1848
> University of Mississippi
> University, Mississippi 38677-1848
> Brewer web page - http://home.olemiss.edu/~jbrewer/
> FAX - 662-915-5144
> Phone - 662-915-1077
> 
> 
> 
> 
> On 2/4/13 1:14 AM, "Erin Nuccio" <enuccio at gmail.com> wrote:
> 
>> Hello List,
>> 
>> Is adonis capable of modeling random effects?  I'm analyzing the impact
>> of a treatment on the microbial community in a split-plot design (2
>> treatments per plot, 4 plots per grassland, 3 grasslands total). I would
>> like to quantify how much of the variance is due to the Treatment versus
>> the Grassland.  It seems like Grassland should be a random effect, since
>> there are thousands of grasslands, and I'm only looking at 3.
>> 
>> I have tried to use the notation that works with lme4, and it's not
>> working for me (see below for formula and error messages).  If adonis
>> can't do random effects, are there any alternatives?  Or, considering my
>> goal, are there any other programs I should look into?  Any suggestions
>> would be highly appreciated!
>> 
>> Thanks for your help,
>> Erin
>> 
>> 
>> 
>> Here's what I think I should run:
>> adonis(formula = community_distance_matrix ~ Treatment + (1|Grassland) +
>> (1|GrasslandPlot), strata = GrasslandPlot)
>> 
>> Here are my factors:
>> 'data.frame':    24 obs. of  4 variables:
>> $ Treatment    : Factor w/ 2 levels "T1","T2": 1 1 1 1 1 2 2 2 1 1 ...
>> $ Grassland    : Factor w/ 3 levels "G1","G2","G3": 3 3 1 1 1 2 2 1 2 2
>> ...
>> $ Plot         : Factor w/ 4 levels "P1","P2","P3","P4": 1 2 2 3 4 1 3 2
>> 1 2 ...
>> $ GrasslandPlot: Factor w/ 12 levels "G1:P1","G1:P2","G1:P3"..: 9 10 2 3
>> 4 5 7 2 5 6 ...
>> 
>> And here's the error message:
>> Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
>> contrasts can be applied only to factors with 2 or more levels
>> In addition: Warning messages:
>> 1: In Ops.factor(1, Grassland) : | not meaningful for factors
>> 2: In Ops.factor(1, GrasslandPlot) : | not meaningful for factors
>> 
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