[R-sig-ME] glmmPQL crashes on inclusion of corSpatial object

Thierry Onkelinx thierry.onkelinx at inbo.be
Mon Jul 25 22:56:53 CEST 2016


Dear Patrick,

I've put a quick and dirty example of your model fit with INLA on
http://rpubs.com/INBOstats/spde

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

2016-07-25 15:38 GMT+02:00 Patrick Johann Schratz <patrick.schratz op gmail.com
>:

> Dear Thierry,
>
> thanks for your answer.
>
> I also recognized that the spatial correlation structure is causing the
> problems as the random effects seem to work without any problems when
> omitting the spatial correlations part.
> However, trying the formula with a random effect of 2 levels
> (=evaluation) instead of 4(=date) works:
>
> /fit <- glmmPQL(fo, random = ~1 | evaluation, data = d, //
> //                correlation = correl, family = binomial)/
>
> Which gives me troubles tracking down the problem. glmmPQL should be
> able to handle 4 level random effects in combination with spatial
> correlation structures?
>
> Setting "fixed = TRUE" in the corSpatial setup still causes the fitting
> of glmmPQL() to break in my case (with "date" as random effect). Did you
> also change other values of the corSpatial setup?
>
> Thanks for your link to the R-INLA package. Looks very promising.
> However it will take me quite some time to adapt my code to R-INLA´s
> syntax and I doubt I have the time right now.
>
> Best regards,
> Patrick
>
>
>
> Am 25.07.16 um 13:36 schrieb Thierry Onkelinx:
> > Dear Patrick,
> >
> > It seems like the correlation structure makes the model unstable. I
> > get convergence when setting fixed = TRUE, but the estimate are very
> > unstable.
> >
> > I'd suggest to have a look at the INLA package which allows to fit
> > spatially correlated random intercepts. It's described in "Spatial and
> > Spatio-Temporal Bayesian models with R-INLA" (Blangiardo & Cameletti,
> > 2015)
> >
> > 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
> >
> > 2016-07-25 12:38 GMT+02:00 Patrick Johann Schratz
> > <patrick.schratz op gmail.com <mailto:patrick.schratz op gmail.com>>:
> >
> >     MacbookPro:
> >
> >     > sessionInfo()
> >     R version 3.3.1 (2016-06-21)
> >     Platform: x86_64-apple-darwin15.5.0 (64-bit)
> >     Running under: OS X 10.11.6 (El Capitan)
> >
> >     locale:
> >     [1] de_DE.UTF-8/de_DE.UTF-8/de_DE.UTF-8/C/de_DE.UTF-8/de_DE.UTF-8
> >
> >     attached base packages:
> >     [1] stats     graphics  grDevices utils     datasets methods base
> >
> >     other attached packages:
> >     [1] lme4_1.1-12  Matrix_1.2-6 tibble_1.1   nlme_3.1-128
> >     MASS_7.3-45 gstat_1.1-3  sp_1.2-3
> >
> >     loaded via a namespace (and not attached):
> >      [1] Rcpp_0.12.6      lattice_0.20-33  intervals_0.15.1 FNN_1.1
> >           spacetime_1.1-5  zoo_1.7-13  assertthat_0.1 grid_3.3.1
> >      pacman_0.4.1  minqa_1.2.4      nloptr_1.0.4
> >     [12] xts_0.9-7        splines_3.3.1    tools_3.3.1
> >
> >
> >
> >     Am 25.07.16 um 12:34 schrieb Phillip Alday:
> >
> >         Hi Patrick,
> >
> >         can you send us your sessionInfo()? Knowing the R version is
> >         important, but a few other details matter for debugging this
> >         type of problem!
> >
> >         Best,
> >         Phillip
> >
> >
> >             On 25 Jul 2016, at 19:55, Patrick Johann Schratz
> >             <patrick.schratz op gmail.com
> >             <mailto:patrick.schratz op gmail.com>> wrote:
> >
> >             Link to data:
> >             <https://www.dropbox.com/s/yi3vf0bmqvydr8h/data.Rd?dl=0>
> >             (1170 obs, 9 variables, .Rd file) [plain link in case sth
> >             goes wrong
> >             with the hyperlink:
> >             https://www.dropbox.com/s/yi3vf0bmqvydr8h/data.Rd?dl=0]
> >
> >             Simply read it in using `readRDS(file)`.
> >
> >             I´m trying to setup a GLMM using the `glmmPQL` function
> >             from the `MASS`
> >             package including a random effects part and accounting for
> >             spatial
> >             autocorrelation. However, R (Version: 3.3.1) crashes upon
> >             execution.
> >
> >                  library(nlme)
> >
> >                  # setup model formula
> >                  fo <- hail ~ prec_nov_apr + t_min_nov_apr +
> >             srad_nov_apr + age
> >
> >                  # setup corSpatial object
> >                  correl = corSpatial(value = c(10000, 0.1), form = ~ry
> >             + rx, nugget
> >             = TRUE,
> >                                      fixed = FALSE, type = "exponential")
> >                  correl = Initialize(correl, data = d)
> >
> >                  # fit model
> >                  fit5 <- MASS::glmmPQL(fo, random = ~1 | date, data = d,
> >                                  correlation = correl, family = binomial)
> >
> >             What I tried so far:
> >
> >             - reduce number of observation
> >             - play with `corSpatial` parameters (range and nugget)
> >             - reduce number of fixed predictors
> >             - execute code on Windows, Linux (Debian) and Mac R
> >             installations
> >
> >
> >             While I get no error message on my local pc (RStudio just
> >             crashes),
> >             running the script on a server returns the following error
> >             message:
> >
> >             `R: malloc.c:3540: _int_malloc: Assertion (fwd->size &
> >             0x4) == 0'
> >             failed. Aborted`
> >
> >             Debugging leads me to a "glibc" c++ library problem. I
> >             also run valgrind
> >             on it. If you need the output, just ask!
> >
> >             Help ist highly appreciated!
> >             Cheers, Patrick
> >
> >                     [[alternative HTML version deleted]]
> >
> >             _______________________________________________
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> >
> >
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> >
> >
>
>
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>
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