GLMM for Combined experiments and overdispersed data

wrote:

Dear Peter,

Both models will yield identical results in case tree_id uses unique codes
over the blocks.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

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

2017-04-24 15:55 GMT+02:00 Peter Claussen <dakotajudo at mac.com>:

Juan,

I would model this as

m3 = glmer(resp ~ trt * farm +  (1| bk/tree), family = binomial, data=df)
or
m3 = glmer(resp ~ trt * farm +  (1| bk) +  (1| tree_id), family =
binomial, data=df)
(I can?t say off the top of my head if what the difference would be if
you?re dealing with over-dispersion).

1. I?m assuming that block is a somewhat uniform grouping of trees, so
that including block in the model gives you an estimate of spatial
variability in the response, and if that is important relative to
tree-to-tree variation.

2. You will most certainly want to include trt*farm to test for
treatment-by-environment interaction. If interaction is not significant,
you may choose to exclude interaction from the model. If there is
interaction, then you will want to examine each farm to determine if
cross-over interaction present.

If your experiment is to determine the ?best? fungicide spraying system,
and cross-over interaction is present, then you have no ?best? system. You
might have cross-over arising because, say, system 1 ranks ?best? on farm
1, but system 2 ranks ?best? on farm 2.

There is extensive literature on the topic, mostly from the plant
breeding genotype-by-environment interaction side. Some of the associated
statistics implemented in the agricolae package, i.e. AMMI.

Peter

On Apr 24, 2017, at 6:56 AM, Juan Pablo Edwards Molina <

edwardsmolina at gmail.com> wrote:

I?m sorry... I?m new in the list, and when I figured out that the

question

would suit best in the mixed model list I had already post it in general
R-help. I don?t know if there?s a way to "cancel a question"... I will

take

care of it from now on.

Dear Thierry, thanks for your answer.
Yes, I am not interested in the effect of a specific farm, they simply
represent the total of farms from the region where I want to suggest the
best treatments.

I Followed your suggestions, but still have a couple of doubts,

1- May "farm" be include as a simple fixed effect or interacting with

the

treatment?

m3 = glmer(resp ~ trt * farm + (1|tree_id), family = binomial, data=df)
m4 = glmer(resp ~ trt + farm + (1|tree_id), family = binomial, data=df)

?2 - ?
In case of significant
?[ trt * farm ], should I report the results for each farm??

Thanks again Thierry,

Juan Edwards


*Juan*

On Mon, Apr 24, 2017 at 4:29 AM, Thierry Onkelinx <

thierry.onkelinx at inbo.be>

wrote:

Dear Juan,

Use unique id's for random effects variables.

??
So each bk should only be

present in one farm. And each tree_id should be present in only one

bk. In

case each block has different treatments then each tree_id should be

unique

to one combination of bk and trt.

Farm has too few levels to be a random effects. So either model is as a
fixed effect or drop it. In case you drop it, the information will be
picked up by bk. Note that trt + (1|farm) is less complex than trt *

farm.

Assuming that you are not interested in the effect of a specific farm,

you

could use sum, polynomial or helmert contrasts for the farms. Unlike

the

default treatment contrast, these type of contrasts sum to zero. Thus

the

effect of trt will be that for the average farm instead of the

reference

farm.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature

and

Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

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

2017-04-21 22:32 GMT+02:00 Juan Pablo Edwards Molina <
edwardsmolina at gmail.com>:

I am analyzing data from 3 field experiments (farms=3) for a citrus

flower

disease: response variable is binomial because the flower can only be
diseased or healthy.

I have particular interest in comparing 5 fungicide spraying systems
(trt=5).

Each farm had 4 blocks (bk=4) including 2 trees as subsamples

(tree=2) in

which I assessed 100 flowers each one. This is a quick look of the

data:

farm      trt      bk    tree   dis   tot     <fctr>   <fctr>  <fctr>
<fctr> <int> <int>
iaras      cal      1      1     0    100
iaras      cal      1      2     1    100
iaras      cal      2      1     1    100
iaras      cal      2      2     3    100
iaras      cal      3      1     0    100
iaras      cal      3      2     5    100...

The model I considered was:

resp <- with(df, cbind(dis, tot-dis))

m1 = glmer(resp ~ trt + (1|farm/bk) , family = binomial, data=df)

I tested the overdispersion with the overdisp_fun() from GLMM page
<http://glmm.wikidot.com/faq>

       chisq         ratio             p          logp
4.191645e+02  3.742540e+00  4.804126e-37 -8.362617e+01

As ratio (residual dev/residual df) > 1, and the p-value < 0.05, I
considered to add the observation level random effect (link
<http://r.789695.n4.nabble.com/Question-on-overdispersion-td

3049898.html

)

to deal with the overdispersion.

farm      trt      bk    tree   dis   tot tree_id    <fctr>   <fctr>
<fctr> <fctr> <int> <int> <fctr>
iaras      cal      1      1     0    100    1
iaras      cal      1      2     1    100    2
iaras      cal      2      1     1    100    3...

so now was added a random effect for each row (tree_id) to the model,

but

I
am not sure of how to include it. This is my approach:

m2 = glmer(resp ~ trt + (1|farm/bk) + (1|tree_id), family = binomial,
data=df)

I also wonder if farm should be a fixed effect, since it has only 3
levels...

m3 = glmer(resp ~ trt * farm + (1|farm:bk) + (1|tree_id), family =
binomial, data=df)

I really appreciate your suggestions about my model specifications...



*Juan? Edwards- - - - - - - - - - - - - - - - - - - - - - - -# PhD

student

- ESALQ-USP/Brazil*

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