[R-sig-eco] pollination experiment with missing value

[ONKELINX, Thierry]

Dear Humberto,

I suppose you are interessed in the significance of the treatment
factor. You can test that effect by comparing models with and without
the term. You can get the multiple comparisons with the multcomp
package. Here is an example using the Pastes dataset.

library(lme4)
library(multcomp)
data(Pastes)
Model <- lmer(strength ~ cask + (1|batch), data = Pastes)
Model2 <- lmer(strength ~ (1|batch), data = Pastes)
anova(Model, Model2)
glht(Model, linfct = mcp(cask = "Tukey"))
confint(glht(Model, linfct = mcp(cask = "Tukey")))

PS You have a lot of treatments to test with few replicates. Testing
less treatment with more replicates would be a better design. I'm
affraid that the power of your current design will be rather low.

HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium

tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-sig-ecology-bounces at r-project.org
[mailto:r-sig-ecology-bounces at r-project.org] Namens Humberto Dutra
Verzonden: dinsdag 8 december 2009 0:25
Aan: r-sig-ecology at r-project.org
Onderwerp: [R-sig-eco] pollination experiment with missing value

Dear subscribers
I performed a  quite simple pollination experiment: 
12 plants
12 pollination treatments per plant,
plant 1 had treatments a, b, c,
d... etc plant 2 had treatments a, b, c, d... 
The idea was to run a simple anova using plants as blocks to see the
effects of treatments on fruit production, but I lost some a few
treatments in some plants, thus I have to deal with missing values First
I tried the obvious, arc transformed fruits 
> fruset.aov<-aov(arcfruit~treatment+Error(Plant),
na.action=na.omit, fruset)

but I get the wrong DF because of the missing values, and I cannot
perform the multiple comparisons test. Is there a better way to deal
with this unbalanced design?

I did a little bit more research and  I decided to run a GLM for mixed
effects using a binomial distribution
>y<-cbind(fruits,nofruits)
> model<-glmer(y ~ treatment+ (1|Plant), binomial,data=fruset)

but them I cannot get the anova table 
> anova(model,test="F")
Error in anova(model, test = "F") : 
  single argument anova for GLMMs not yet implemented

Some people suggested using Anova function on the package car, but I
don't see how can I get it to work with a mixed effects model like this.


Any suggestions are appreciated. Are there other straight forward ways
to analyze such data, given the missing values, and multiple comparisons
follow up?
Thank you

Humberto





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