[R-sig-ME] Convergence warning message

Wed Mar 16 21:12:56 CET 2016

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 op 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 op 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 op 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 op 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 op 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
>
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>
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