[R-sig-ME] Continuous vs. categorical correlated group effects

[Drager, Andrea Pilar]

Dear Ben,

Thank you very much! I have only 23 species, and yes, you are right  
that the glmer model did run before but with lots of warnings:
Warning messages:
1: Some predictor variables are on very different scales: consider rescaling
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
   Model is nearly unidentifiable: very large eigenvalue
  - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
  - Rescale variables?

I log-transformed my abundance variable and now both the glmer and  
MCMCglmm models run fine, with no warnings.






Quoting Ben Bolker <bbolker at gmail.com>:

> The first thing I would try is rescaling your abundance value.  The
> second is to tell us *exactly* what error messages
> you get when you run glmer.  Also, how many species do you have?
>
> ===
> fake_data <- data.frame(
>    species_id = rep(outer(LETTERS,LETTERS,paste,sep="/"),40),
>    stringsAsFactors=FALSE)
> nn <- nrow(fake_data)
> set.seed(101)
> fake_data$resp <- rbinom(nn,prob=0.06,size=1)
> fake_data$abund <- rlnorm(nn,meanlog=log(2500),
>                           sdlog=0.75)
>
> library(lme4)
> g1 <- glmer(resp ~ abund +(1|species_id),data=fake_data,
>       family=binomial(link='logit'))
>
> ## produces a fit, but lots of warnings.
>
> fake_data$sc_abund <- scale(fake_data$abund)
>
> update(g1, . ~ . - abund + sc_abund)
>
> ## The glm works on the first 1000 rows, but is very slow for the
> whole data set (I may have invented too many species)
>
>
> On Tue, Jan 2, 2018 at 5:21 PM, Drager, Andrea Pilar
> <andrea.p.drager at rice.edu> wrote:
>>
>> summary(flor_data)
>>
>>  species_id         binary_individual_response
>>
>>  Length:29609       Min.   :0.00000
>>  Class :character   1st Qu.:0.00000
>>  Mode  :character   Median :0.00000
>>                     Mean   :0.06018
>>                     3rd Qu.:0.00000
>>                     Max.   :1.00000
>>
>>   species_abund
>>   Min.   :  11.23
>>   1st Qu.:1996.23
>>   Median :2548.23
>>   Mean   :3438.20
>>   3rd Qu.:5310.23
>>   Max.   :6116.23
>>
>>
>> The following is also the case:
>>
>> Won't run-->glmer(binary_indivdual_response ~ species_abund
>> +(1|species_id),family=binomial(link='logit')
>>
>> Runs-->glm(binary_individual_response ~ species_abund + species_id,
>> family=binomial(link='logit')
>>
>>
>>
>> Quoting Ben Bolker <bbolker at gmail.com>:
>>
>>> Can you show us the summary() of your data?
>>>   Is it possible you have complete separation in your continuous
>>> predictor?
>>>
>>> On 18-01-02 02:38 PM, Drager, Andrea Pilar wrote:
>>>>
>>>>
>>>> Hi All,
>>>>
>>>> I am having trouble running a Bayesian mixed model in MCMCglmm where I
>>>> have individual-level data for my response variable, and species-level
>>>> data as the random effect (such as "species"), plus any other
>>>> species-level continuous variable, such as abundance, in the model. But
>>>> if the the other species-level variable is categorical--whether because
>>>> I make it a random effect or because it is in fact categorical--the
>>>> model runs! Could someone please explain the stats behind this?
>>>>
>>>>
>>>> prior = list(R = list(V = 1, nu = 0, fix = 1),  G = list(G1=list(V =
>>>> 1,nu = 0.002)))
>>>>
>>>> Won't run-->MCMCglmm(binary_individual_repsonse ~
>>>> species_abund_continuous,
>>>>                      random = ~ species_id_categorical, family =
>>>> "categorical")
>>>>
>>>>             Error : Mixed model equations singular: use a (stronger)
>>>> prior
>>>>
>>>>
>>>> Runs-->MCMCglmm(binary_individual_response ~ 1,
>>>>                 random = ~ species_abund_categorical +
>>>> species_id_categorical, family = "categorical")
>>>>
>>>> Runs-->MCMCglmm(binary_individual_response  ~ species_id_categorical,
>>>>                 random = ~ species_abund_categorical, family=
>>>> "categorical")
>>>>
>>>>
>>>> Thanks in advance!
>>>> Andrea Pilar Drager
>>>> PhD. student
>>>> Ecology and Evolutionary Biology, Rice University
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>>
>> Andrea Pilar Drager
>> PhD. student
>> Ecology and Evolutionary Biology, Rice University
>>


Andrea Pilar Drager
PhD. student
Ecology and Evolutionary Biology, Rice University