[R-sig-ME] Convergence warning message

Ben Bolker bbolker at gmail.com
Wed Mar 16 21:34:46 CET 2016 

https://github.com/lme4/lme4/blob/master/man/convergence.Rd

On Wed, Mar 16, 2016 at 4:12 PM, Thierry Onkelinx
<thierry.onkelinx at inbo.be> wrote:
> Dear Jackie,
>
> 127.0.01 points to localhost, which will work only on your computer.
>
> 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
>
>
> 2016-03-16 20:56 GMT+01:00 Jackie Wood <jackiewood7 at gmail.com>:
>> Hi Chris,
>>
>> You can find the example code I was talking about here if you haven't
>> tracked it down already:
>>
>> http://127.0.0.1:29918/library/lme4/html/convergence.html
>>
>> Jackie
>>
>>
>>
>> On Wed, Mar 16, 2016 at 3:44 PM, Christopher David Desjardins <
>> cddesjardins at gmail.com> wrote:
>>
>>> 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/
>>> >>>
>>> >>>          [[alternative HTML version deleted]]
>>> >>>
>>> >>> _______________________________________________
>>> >>> R-sig-mixed-models at r-project.org mailing list
>>> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>> >>>
>>> >>>
>>> >>
>>> >>
>>> >>
>>> > _______________________________________________
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>>> >
>>>
>>>
>>>
>>> --
>>> https://cddesja.github.io/
>>>
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>>>
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>>>
>>
>>
>>
>> --
>> Jacquelyn L.A. Wood, PhD.
>> 224 Montrose Avenue
>> Toronto, ON
>> M6G 3G7
>> Phone: (514) 293-7255
>>
>>         [[alternative HTML version deleted]]
>>
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