Hi Steve, You mentioned that nested.npmanova won't test GrasslandPlot correctly for a split-plot design. However, does adonis test GrasslandPlot correctly, since it's using the split-plot error term to test all effects?
Here are the formulas again..... adonis(community_dist ~ Grassland*Treatment + GrasslandPlot, strata = GrasslandPlot) nested.npmanova(community_dist ~ Grassland + GrasslandPlot) Thank you, Erin On Mar 10, 2013, at 8:17 AM, JOHN S BREWER wrote: > Erin, > > Please check the February 25 post I made called "Permanova with nested data." > It explains how to test whole plot and split plot effects correctly in > adonis. But to answer your question, even if you treat Grassland as a > fixed-plot effect (which seems perfectly reasonable), Grassland is a > whole-plot effect. Using the model formula given and strata, adonis uses the > split-plot error term (i.e., the residual error term) to test all effects. > That's wrong because Grassland needs to be tested with the whole-plot error > term. In the post I referred to, I describe how you can do a separate test > for the whole plot using the BiodiversityR package and the nested.npmanova > function. In this case, you would only include Grassland and GrasslandPlot as > terms in the model. It's just doing a two-way nested manova. The whole-plot > effect of Grassland will be tested correctly using the GrasslandPlot term. > GrasslandPlot will be tested with the residual error term, which will be > wrong, but you can ! ignore that. I've tried it with my own data and it works. One cautionary note. See the posts by Jari Oksanen and others about the versions of BiodiversityR and R used. > > Hope this helps > > Steve > ________________________________________ > From: Erin Nuccio [enuc...@gmail.com] > Sent: Saturday, March 09, 2013 9:09 PM > To: JOHN S BREWER > Cc: r-sig-ecology@r-project.org > Subject: Re: [R-sig-eco] Adonis and Random Effects > > Hi Steve and R list, > > I was hoping you could clarify something you mentioned in previous post. > > A quick recap... I have a split-plot design where I determined the microbial > communities at 3 grasslands (see post script for design). I am trying > quantify the how much of my community can be explained by Treatment or > Grassland effect. After talking with a statistician, it seems like treating > Grassland as a Fixed effect would be reasonable (because I have such a small > number of grasslands). > > You mentioned that if I treat Grassland as a Fixed effect, and use the > following formula, the Grassland effect would not be tested correctly: > > adonis(formula = community_distance_matrix ~ Treatment*Grassland + > GrasslandPlot, strata = GrasslandPlot) > > Why is this? Is there any way to remedy this? > > Thanks for your feedback, > Erin > > > Experimental design: > 4 split plots * 2 Treatments * 3 Grasslands = 24 observations > Treatment: 2 levels (each within 1 split plot) > Grassland: 3 levels > GrasslandPlot: 12 levels (4 split plots nested in 3 Grasslands) > > > > > > On Feb 4, 2013, at 6:22 AM, Steve Brewer 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" <enuc...@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. >>> >>> Thanks for your help, >>> Erin >>> >>> >>> 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 >>> >>> _______________________________________________ >>> R-sig-ecology mailing list >>> R-sig-ecology@r-project.org >>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> >> > _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
