[R-sig-ME] glmmADMB

[Thierry Onkelinx]

Dear Silva,

it's not a good idea to repeat the rows. It's better to transform PE into 4
TRUE/FALSE variables and add those variables to the model. This allows you
to keep the original number of rows. Your model reduces to a plain glm

PA ~ AL + PE1 + PE2 + PE3 + PE4

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

2015-04-30 17:08 GMT+02:00 Silvia Rodríguez Fernández <sileiris op gmail.com>:

> Thierry, thanks a lot for your quick response.
>
>
> We sampled each site (small water points) only once and we noted the
> different PE (human disturbance types). However for our analyses we have
> done as if each site was sampled more than once. That is we repeated each
> row as many times as different perturbance types were recorded in each
> site.
>
>
>   Site
>
> PA
>
> AL
>
> PE
>
> 1
>
> 0
>
> 38
>
> 1
>
>  1
>
> 0
>
> 38
>
> 3
>
> 2
>
> 0
>
> 138
>
> 1
>
> 3
>
> 0
>
> 382
>
> 1
>
> 3
>
> 0
>
> 382
>
> 3
>
> 4
>
> 0
>
> 382
>
> 1
>
> 4
>
> 0
>
> 382
>
> 3
>
>
> Our final aim is to obtain probabilities of presence/absence of each
> amphibian species in a site in relation to the different types of
> disturbance and altitude, using the "invlogit" function. I think the
> "cbind" function is not useful in this case because we are not modelling
> proportions.
>
>
> What do you think?
>
>
> Best regards,
>
>
> Silvia
>
> 2015-04-30 15:25 GMT+02:00 Thierry Onkelinx <thierry.onkelinx op inbo.be>:
>
>> Dear Silvia,
>>
>> I presume that the values of AL and PE are constant within the site. Did
>> you sample different locations within each site simultaneous ? Or did you
>> sample the same location at each site but at different dates?
>> In case of different locations per site you can simplify your model to.
>> glm(cbind(n.present, n.absent) ~ AL + PE, family = binomial) With n.present
>> the number of present locations per site.
>>
>> Best regards,
>>
>> Thierry
>>
>> 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
>>
>> 2015-04-30 14:50 GMT+02:00 Silvia Rodríguez Fernández <sileiris op gmail.com
>> >:
>>
>>> Dear list members,
>>>
>>> I´m a PhD student in trouble. I´m running a mix effects model with a
>>> dependent variable (PA: presence/absence, 0/1), one fixed explanatory and
>>> continuous variable (AL: altitude), one fixed factor (PE: initially 16
>>> levels, but reduced to 4 to reduce complexity) and one random term (2421
>>> sites). Basically, the structure of a logistic regression but with a
>>> random
>>> term to prevent temporal pseudoreplication.
>>>
>>> > model1<-glmmadmb(PA~PE+AL+(1|site), family="binomial")
>>>
>>> My data are quite unbalanced becouse I´ve many more zeros than ones. I´ve
>>> tried making a random selection of absences but I get similar problems
>>> than
>>> when using the whole dataset.
>>>
>>> I´m getting an output of results in R, but also getting a warning of lack
>>> of convergence, such as:
>>>
>>> Convergence failed:log-likelihood of gradient= -0.0195034
>>>
>>>
>>> Can I trust my results in spite of the warning?
>>>
>>> What other alternatives do you suggest?
>>>
>>>
>>> I´ve tried with the classical lmer and glmer, and I also get convergence
>>> problems as expected.
>>>
>>> I´ve also tried with the MCMCglmm package, but I´ve problems with the
>>> specification of the priors.
>>>
>>>
>>> Any help is welcomed.
>>>
>>>
>>> Silvia
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models op r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>>
>

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