[R-sig-ME] Convergence warning message
Christopher David Desjardins
cddesjardins at gmail.com
Wed Mar 16 20:44:04 CET 2016
Thanks, Jacquelyn and Ben. Jacquelyn, did you mean to attach some code or
just reference the site that Ben did? I had seen Ben's comments on
StackOverflow about potential false convergence messages, so I'll dig a bit
deeper. I just wanted to make sure it wasn't something obvious that I had
overlooked first.
>From what I've read online, glmmPQL is inappropriate with Bernoulli trials.
Is that correct?
Chris
On Wed, Mar 16, 2016 at 2:35 PM, Ben Bolker <bbolker at gmail.com> wrote:
>
> Good question.
>
> I'm afraid that for data sets ~ 100,000 observations or bigger, our
> convergence calculations aren't terribly reliable -- see e.g. the third set
> of figures under https://rpubs.com/bbolker/lme4_convergence ... I would
> follow Jackie's advice ...
>
>
> On 16-03-16 02:24 PM, Jackie Wood wrote:
>
>> Hi Chris,
>>
>> Try checking ?convergence....coincidentally, I was having a similar
>> problem
>> just yesterday. There are some step by step
>> instructions for trouble shooting/double checking convergence warnings.
>> For
>> example, a bit of example code is provided to run your model using a
>> number
>> of different optimizers. If all optimizers yield similar values, it's
>> possible that you could be getting false convergence warnings. I'm not
>> sure
>> if that's the case with your data, but it might be a place to start!
>>
>> Jacquelyn
>>
>> On Wed, Mar 16, 2016 at 1:56 PM, Christopher David Desjardins <
>> cddesjardins at gmail.com> wrote:
>>
>> I am trying to fit a mixed effects binomial model.
>>>
>>> The data consists of
>>> - A dependent variable consisting of Bernoulli trials (outcome)
>>> - A time variable (time), which has been mean centered
>>> - An id variable (id)
>>> - A categorical covariate (cat_cov)
>>> - A blocking variable (block) which id is nested in. I realize in the
>>> model
>>> below that it should be (1 | id/block) but I am just trying to
>>> troubleshoot
>>> my problem at the moment.
>>>
>>> When I run the following:
>>>
>>> example_data <- read.csv("https://cddesja.github.io/example_data.csv",
>>> header = T)
>>> example_data$cat_cov <- as.factor(example_data$cat_cov)
>>> example_data$id <- as.factor(example_data$id)
>>> example_data$block <- as.factor(example_data$block)
>>> main_effects <- glmer(outcome ~ 1 + cat_cov + time + I(time^2) + (1 |
>>> id),
>>> data = example_data, family = "binomial")
>>>
>>> That last line of code gives a warning message:
>>>
>>> main_effects <- glmer(outcome ~ 1 + cat_cov + time + I(time^2) + (1 |
>>>>
>>> id), data = example_data, family = "binomial")
>>> Warning messages:
>>> 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>>> :
>>> Model failed to converge with max|grad| = 4.36001 (tol = 0.001,
>>> component
>>> 1)
>>> 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>>> :
>>> Model is nearly unidentifiable: very large eigenvalue
>>> - Rescale variables?
>>>
>>> I am not exactly sure how to proceed. I know the issue is with cat_cov,
>>> though it's unclear to me why. If I swap out in a different categorical
>>> covariate in the model, not included in that data set, I don't get this
>>> message. I am not running into complete separation with cat_cov. So,
>>> I'm a
>>> little perplexed.
>>>
>>> Any advice on what I should do or something I could look at it would be
>>> very helpful.
>>>
>>> Thanks,
>>> Chris
>>> --
>>> https://cddesja.github.io/
>>>
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>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>>
>>
>>
>>
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