On 27/08/15 11:57, Mehdi Abedi wrote:
Dear Thierry,
Yes i am using (success, failure) but in this case i need to change all
data frame. I was thinking to use codes which is not necessary to create
new column when you have a ll of species. Because we know
success(germinated seeds) and we know failure (Total seeds -
success(germinated seeds)).
Yes i used codes with ANOVA but there is no P- value for study.
model2<- glmer(cbind(germinated, Nongerminated) ~ temp *light +
(1|Replication ), data=growthdata,
+ family=binomial)
anova(model2)
Analysis of Variance Table
Df Sum Sq Mean Sq F value
temp 2 30.600 15.300 15.300
light 1 46.231 46.231 46.231
temp:light 2 22.877 11.439 11.439
p-values are difficult. See here:
<http://glmm.wikidot.com/faq>
Better to stick to reporting your effect sizes: your analysis of
deviance only tells you if you have enough data to see a difference, not
how big the differences are.
Also, if Replication is 1:nrow(growthdata), you could use a simple GLM
and estimate your over-dispersion term (the residual deviance divided by
the residual sum of squares should be OK). You can use this to correct
the standard errors with summary(glm.obj, dispersion=overdisp).
Bob
Warm regards,
Mehdi
On Thu, Aug 27, 2015 at 1:56 PM, Thierry Onkelinx <thierry.onkel...@inbo.be>
wrote:
Dear Mehdi,
Assuming that you want to model the probability of germination, yes.
Note that cbind(seed, 100) is WRONG syntax.
CORRECT syntax: cbind(n_success, n_failure)
Have you tried anova(your.model)?
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
2015-08-27 11:19 GMT+02:00 Mehdi Abedi <abedim...@gmail.com>:
Dear Thierry and Mariano,
Could we apply these glmer for seed germination in petridishes which the
total number of seeds is defined as well? like cbind(seeds,100).
In addition what is the simple way to get ANOVA liked tables (i think
with
Chisquare would be better test than F value) for these test with having
P-
value as well?
Warm regards,
Mehdi
On Thu, Aug 27, 2015 at 12:20 PM, Thierry Onkelinx
<thierry.onkel...@inbo.be> wrote:
Dear Mariano,
The binomial distribution (not error family) assumes that you have a
number of successes and failures. If the potential number of seeds is
fixed by the morphology of the plant, then a binomial distribution is
reasonable. If the potential number of seeds is dictated by
morphology, then I'd rather see it as counts and use a Poisson or
negative binomial.
The correct syntax in the binomial case is cbind(success, failure). Or
in your case cbind(seeds, 4 - seeds).
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
2015-08-26 20:32 GMT+02:00 Mariano Devoto <mdev...@agro.uba.ar>:
Dear all. I am analysing data from a field experiment on a crop
pollination. I want to test if there are differences in the number of
seeds
per fruit between three treatments. The experimental design consists
of
four separate sites where small subplots (ca. 5 plants each) received
one
of the treatments. In each site, 8 subplots were allocated to
treatment
A,
8 to treatment B and 4 to treatment C. When fruits were ripe I
collected
all plants from each subplot and counted stems, fruits per stem and
seeds
per fruit. I think a GLMM is the best way to go as I expect random
effects
related to field and subplot identity, and my response variable
(number
of
seeds) is clearly non-normal. My main concern is the choice of the
error
family. As I’m counting seeds I first though of a Poisson model, but
then
realized that seed numbers only range from 0 to 4. I am now
considering
using a binomial model such as this:
glmer(cbind(seeds,4) ~ treatment + (1|site) + (1|subplot),
data=seed.data,
family=binomial)
Does this make sense?
I would welcome any advice before hitting “SEND” in Tinn-R :-).
--
*Mariano Devoto*
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--
Mehdi Abedi
Department of Range Management
Faculty of Natural Resources & Marine Sciences
Tarbiat Modares University (TMU)
46417-76489, Noor
Mazandaran, IRAN
mehdi.ab...@modares.ac.ir
Homepage
Tel: +98-122-6253101
Fax: +98-122-6253499
--
Bob O'Hara
Biodiversity and Climate Research Centre
Senckenberganlage 25
D-60325 Frankfurt am Main,
Germany
Tel: +49 69 7542 1863
Mobile: +49 1515 888 5440
WWW: http://www.bik-f.de/root/index.php?page_id=219
Blog: http://blogs.nature.com/boboh
Journal of Negative Results - EEB: www.jnr-eeb.org
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