[R-sig-ME] Continuous vs. categorical correlated group effects
Ben Bolker
bbolker at gmail.com
Thu Jan 4 02:12:27 CET 2018
That's fine.
Note that linearly scaling your predictor variable (e.g. subtracting
the mean and scaling by the standard deviation, which is what scale()
does) changes only the parameterization and not the underlying
definition of the model (e.g. the likelihood and any inferences drawn
the model will be the same). In contrast, log-transforming the
predictor changes the meaning of the model -- it might be a more
sensible model, but it will be different from the original model.
cheers
Ben Bolker
On 18-01-03 08:07 PM, Drager, Andrea Pilar wrote:
> 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
>
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