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

[Ben Bolker]

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
>