[R-sig-eco] best choice of GLMM for seed set data
Bob O'Hara
bohara at senckenberg.de
Thu Aug 27 16:29:49 CEST 2015
On 27/08/15 15:33, Mehdi Abedi wrote:
> Thanks Bob for reply.
> I have problem that when you have results of effect size i am not sure
> to how get only main effect without their levels data. I previously
> asked this simple question but i couldn't get clear answer. The
> question is simple: how we can have only temp, light and their
> interactions(similar like output of anova() without their levels.
You can't: that's what the effect sizes are! For more on how to
interpret them, see any good stats text, or this paper:
<http://dx.doi.org/10.5735/086.046.0205>
Bob
> *Call:*
> glm(formula = cbind(A..hierochuntica, A..hierochunticano) ~ temp *
> light, family = binomial)
>
> Deviance Residuals:
> Min 1Q Median 3Q Max
> -4.4473 -1.1993 0.9904 2.0101 3.6663
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) 2.66616 0.14341 18.591 < 2e-16 ***
> temp20/30 0.08538 0.20671 0.413 0.679596
> temp25/35 -0.67373 0.18001 -3.743 0.000182 ***
> lightlight 0.51189 0.23048 2.221 0.026350 *
> temp20/30:lightlight 0.62839 0.37291 1.685 0.091965 .
> temp25/35:lightlight 2.09080 0.43729 4.781 1.74e-06 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> (Dispersion parameter for binomial family taken to be 1)
>
> Null deviance: 334.88 on 47 degrees of freedom
> Residual deviance: 209.30 on 42 degrees of freedom
> AIC: 331.56
>
> Number of Fisher Scoring iterations: 6
>
> this is by *anova(model,test="Chi")*
> Analysis of Deviance Table
>
> Model: binomial, link: logit
>
> Response: cbind(A..hierochuntica, A..hierochunticano)
>
> Terms added sequentially (first to last)
>
>
> Df Deviance Resid. Df Resid. Dev Pr(>Chi)
> NULL 47 334.88
> temp 2 10.144 45 324.73 0.006271 **
> light 1 86.860 44 237.87 < 2.2e-16 ***
> temp:light 2 28.569 42 209.30 6.255e-07 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
> *anova(model, test="F")*
> Analysis of Deviance Table
>
> Model: binomial, link: logit
>
> Response: cbind(A..hierochuntica, A..hierochunticano)
>
> Terms added sequentially (first to last)
>
>
> Df Deviance Resid. Df Resid. Dev F Pr(>F)
> NULL 47 334.88
> temp 2 10.144 45 324.73 5.0718 0.006271 **
> light 1 86.860 44 237.87 86.8597 < 2.2e-16 ***
> temp:light 2 28.569 42 209.30 14.2847 6.255e-07 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> Warning message:
> In anova.glm(model, test = "F") :
> using F test with a 'binomial' family is inappropriate
>
> Warm regards,
> Mehdi
>
>
> On Thu, Aug 27, 2015 at 2:38 PM, Bob O'Hara <bohara at senckenberg.de
> <mailto:bohara at senckenberg.de>> wrote:
>
> 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.onkelinx at inbo.be <mailto:thierry.onkelinx at 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
> <abedimail at gmail.com <mailto:abedimail at 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.onkelinx at inbo.be
> <mailto:thierry.onkelinx at 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
> <mdevoto at agro.uba.ar <mailto:mdevoto at 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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>
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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.abedi at modares.ac.ir
> <mailto:mehdi.abedi at 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
> <http://www.jnr-eeb.org>
>
>
> _______________________________________________
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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.abedi at modares.ac.ir <mailto:Mehdi.abedi at modares.ac.ir>/
>
> /Homepage
> <http://www.modares.ac.ir/en/Schools/nat/Academic_Staff/%7Emehdi.abedi>/
>
> /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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