[R-sig-eco] Nested Permanova with repeated measures

[Paul Moquin]

Hi Stephen and all Permanoverts!

Thank you for posting your thoughts about repeated measure Permanova. I am
following your methods (From Feb 25 2013- posted below) where you use
adonis for the time and time by treatment interaction and , in a seperate
step, the BiodiversityR for the main effect. When I run nested.npmanova the
p value I get for my treatment is 1.0 leading me to believe there is
something wrong- there is of course the possibility that there really is
nothing there but when I look at the plots there seems to be some kind of
treatment effect at least for 5 of the 10 species....so if not significant
at least not p=1! I will provide a glimpse into the design and data I have
in the hopes of finding my issue.

Melting permafrost which slumps into Arctic lakes has been associated with
phisio-chemical and biological changes in these lakes. To replicate these
slumps, I ran an experiment in an unslumped Arctic lake where I
dosed mesocosms with melted permafrost soil. The experiment had three
replicates of 3 treatment levels plus a control for a total of 12
mesocosms. The experiment was set up in a randomized block design (ie 3
blocks with 4 mesocosms each- one of each treatment). I have zooplankton
community data for 11 time steps (weeks).

For a number of other univariate parameters I have used a mixed effects
model with mesocosm within block as my random terms and treatment and time
and their interaction as my main effects. (though I am not really
interested in the effect of time)

But in multivariate space I think the best I can do is:
to test time and its interaction with treatment I have

*adonis(community~week*treatment, method="bray", perm=999,strata =
enviro$mesocosm, data=enviro) *
and to test the main effect:


*library(BiodiversityR)*
*nested.npmanova(community ~treatment + mesocosm, method="bray", data
= enviro) *

>
>

Does this seem right? I acknowledge that I loose the ability to use "block"
here but not much I can do right?


I get this as the result of the nested model in BiodiversityR:

*Total sum of squares for non-parametric manova: 26.8071635047485 *

*Nested anova for mc nested within t.level *

*                  Df       SumsofSquares       F               N.Perm
 Pr(>F)   *
*treatment    3        1.4607                     0.1727      100
 1.000000   *
*mesocosm  1        2.8188                     15.8910    100
 0.009901 ***
*Residuals 127       22.5277                   0.1774                   *
*---*
*Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1*
*> *

Thank you very much

Paul Moquin



[R-sig-eco] Permanova with nested data*Steve Brewer* jbrewer at olemiss.edu
<r-sig-ecology%40r-project.org?Subject=Re%3A%20%5BR-sig-eco%5D%20Permanova%20with%20nested%20data&In-Reply-To=%3CCD50D4BB.1E27D%25jbrewer%40olemiss.edu%3E>
*Mon Feb 25 16:13:18 CET 2013*


   - Previous message: [R-sig-eco] lme4 for pseudoreplication avoidance
   <https://stat.ethz.ch/pipermail/r-sig-ecology/2013-February/003594.html>
   - Next message: [R-sig-eco] Validation of linear regression
   <https://stat.ethz.ch/pipermail/r-sig-ecology/2013-February/003597.html>
   - *Messages sorted by:* [ date ]
   <https://stat.ethz.ch/pipermail/r-sig-ecology/2013-February/date.html#3595>
    [ thread ]
   <https://stat.ethz.ch/pipermail/r-sig-ecology/2013-February/thread.html#3595>
    [ subject ]
   <https://stat.ethz.ch/pipermail/r-sig-ecology/2013-February/subject.html#3595>
    [ author ]
   <https://stat.ethz.ch/pipermail/r-sig-ecology/2013-February/author.html#3595>

------------------------------

Beth and others,

Given several recent queries regarding how to analyze repeated-measures
and split-plot perManova using adonis, I thought I would pass along what I
think is a reasonable solution.

I just saw the recent exchange over the use of BiodiversityR to do nested
perMANOVA. I was unaware of this function until today.

With that in mind, it is possible to do a simple repeated-measures
permanova using two different analyses, one for the within-subjects
effects and one for the between-subjects effects. The same approach
applies to a simple split-plot analysis.

For the within-subjects (or sub-plot) effects, you use adonis and the
strata function. The model formula could look something like:

Assume species responses are in "Speciesdata" and the treatment, time, and
plot effects are in "envfactors"

Adonis(Speciesdata ~ betweensubtrtmt * time + plot, data = envfactors,
strata = plot)

Where plot is nested within the betweensubtrtmnt

Strata restricts the permutation, and the residual error term will give
you the correct test for the time effect and the betweensubtrtmnt * time
interaction, but the test for the betweensubtrmnt main effect will be
wrong because plot, and not the residual error term, is the correct error
term for testing it.

To get a test for the betweensubtrtmnt main effect, load the BiodiversityR
package (I use the 1.6 version, but see the recent discussion about this)
and use the nested.npmanova function.

nested.npmanova(speciesdata ~ betweensubtrtmnt + plot, data = envfactors)

In this case, the betweensubtrtmnt is tested with plot; plot is tested
with the residual error term but that latter test is not correct in this
instance and is usually not of interest anyway.

Note that the default distance is euclidean; you'll to use "method" to
specify a different distance, e.g.,

nested.npmanova(speciesdata ~ betweensubtrtmnt + plot, data = envfactors;
method ="bray")


The same principles apply to a simple split-plot design, except that the
whole-plot treatment is treated like a between-subjects treatment and the
sub-plot treatment is treated like the time effect.

Hope this is of some help.

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

	[[alternative HTML version deleted]]