DHARMa: fittedModel not in class of supported models for a glmmTMB model

[Franelizabethgalvez]

Hi, I was trying to correct a warning message from DHARMa: Warning message:
In simulateResiduals(fittedModel = m2) :DHARMa: fittedModel not in class of supported models. Absolutely no guarantee that this will work!

For this model:
m1<-glmmTMB(NPQ ~ Temp*LUZ*Tiempo + (1|ID) + (1|Intensidad), data=dp, family=beta_family(link="logit"), ziformula= ~(1|Intensidad))

Despite installing the current (development) version from this repository. (devtools::install_github(repo = "florianhartig/DHARMa", subdir = "DHARMa", dependencies = T) the warning keeps appearing and the plot does not appear, but says: Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' is a list, but does not have components 'x' and 'y'

I have checked other models that used to work fine with DHARMa and now I get the same warning and error,

Unfortunately my other computer was stolen and I don't know what version of DHARMa I had, but with this version installed it should work.

This code: devtools::install_github(repo = "florianhartig/DHARMa", subdir = "DHARMa", dependencies = T, build_vignettes = T) gives me this error:Error: Vignette re-building failed. Execution halted Error: Failed to install 'DHARMa' from GitHub: Failed to install 'glmmTMB' from GitHub: ! System command 'R' failed ding ‘troubleshooting.rmd’ only runs the installation omitting: build_vignettes = T , maybe that's the problem!!

My R version: R 4.4.0 GUI 1.80 Big Sur ARM build in macOS 13.0

If anyone has any recommendations or help, I would appreciate it.

THANKS!! ;)

[florianhartig]

I'm a bit surprised by the warning - could you paste a reproducible example? See also https://github.com/florianhartig/DHARMa/wiki/Asking-for-help

EDIT: what I mean is can you dput your data or produce a reduced example?

[Franelizabethgalvez]

Hi, thanks for the reply! ;) (I managed to solve the problem, but I still leave the example for anyone who has the same problem in the future)

Sorry for not providing a reproducible example, but here it is:

For this data: dp<-read.delim("UV_T_NPQ_0_24h.txt", header=T, stringsAsFactors=T)

dp$Temp<-factor(dp$Temp)
dp$Tiempo<-factor(dp$Tiempo)
dp$LUZ<-factor(dp$LUZ)
dp$ID<-factor(dp$ID)

Fit this model: m1<-glmmTMB(NPQ ~ Temp*LUZ*Tiempo + (1|ID) + (1|Intensidad), data=dp, family=beta_family(link="logit"), ziformula= ~(1|Intensidad))

When checking residuals :
library("DHARMa")
m2simuOutput <- simulateResiduals(fittedModel = m1)
this warning appeared: Warning message: In simulateResiduals(fittedModel = m2) :DHARMa: fittedModel not in class of supported models. Absolutely no guarantee that this will work!
when running the plot :
plot(m2simuOutput)
this error appeared: Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' is a list, but does not have components 'x' and 'y'

Try to install the current (development) version: devtools::install_github(repo = "florianhartig/DHARMa", subdir = "DHARMa", dependencies = T, build_vignettes = T) but I got error with build_vignettes so I removed it. Then I restarted R and then removed stringsAsFactors=T when I loaded my data and finally DHARMa ran without warnings or errors.

UV_T_NPQ_0_24h.txt

Thanks for your quick response ;)

Session info:
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Ventura 13.0

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Santiago
tzcode source: internal

attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base

other attached packages:
[1] DHARMa_0.4.6 ggeffects_1.5.2 forecast_8.22.0 lsmeans_2.30-0
[5] emmeans_1.10.1 multcompView_0.1-10 multcomp_1.4-25 TH.data_1.1-2
[9] survival_3.5-8 mvtnorm_1.2-4 betareg_3.1-4 doBy_4.6.21
[13] see_0.8.4.2 report_0.5.8.3 parameters_0.21.7.2 modelbased_0.8.7.1
[17] effectsize_0.8.8.1 datawizard_0.10.0.6 correlation_0.8.4.3 bayestestR_0.13.2.2
[21] easystats_0.7.1.3 insight_0.19.11.4 performance_0.11.0.9 sjlabelled_1.2.0
[25] sjmisc_2.8.10 sjPlot_2.8.16 lubridate_1.9.3 forcats_1.0.0
[29] stringr_1.5.1 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[33] tibble_3.2.1 tidyverse_2.0.0 reshape_0.8.9 bbmle_1.0.25.1
[37] knitr_1.47 TMB_1.9.11 glmmTMB_1.1.9-9000 lme4_1.1-35.3
[41] Matrix_1.7-0 car_3.1-2 carData_3.0-5 MASS_7.3-60.2
[45] magrittr_2.0.3 ggpubr_0.6.0 ggplot2_3.5.1 plyr_1.8.9
[49] dplyr_1.1.4 devtools_2.4.5 usethis_2.2.3 remotes_2.5.0
[53] pkgbuild_1.4.4

loaded via a namespace (and not attached):
[1] rstudioapi_0.16.0 estimability_1.5.1 modeltools_0.2-23 nloptr_2.0.3
[5] fs_1.6.4 vctrs_0.6.5 memoise_2.0.1 minqa_1.2.7
[9] rstatix_0.7.2 htmltools_0.5.8.1 curl_5.2.1 broom_1.0.6
[13] Formula_1.2-5 TTR_0.24.4 htmlwidgets_1.6.4 sandwich_3.1-0
[17] zoo_1.8-12 cachem_1.1.0 commonmark_1.9.1 mime_0.12
[21] lifecycle_1.0.4 pkgconfig_2.0.3 gap_1.5-3 R6_2.5.1
[25] fastmap_1.2.0 rbibutils_2.2.16 shiny_1.8.1.1 digest_0.6.35
[29] numDeriv_2016.8-1.1 colorspace_2.1-0 pkgload_1.3.4 fansi_1.0.6
[33] timechange_0.3.0 abind_1.4-5 mgcv_1.9-1 compiler_4.4.0
[37] microbenchmark_1.4.10 withr_3.0.0 tseries_0.10-56 backports_1.5.0
[41] ggsignif_0.6.4 sjstats_0.19.0 sessioninfo_1.2.2 tools_4.4.0
[45] lmtest_0.9-40 quantmod_0.4.26 httpuv_1.6.15 nnet_7.3-19
[49] quadprog_1.5-8 glue_1.7.0 nlme_3.1-164 promises_1.3.0
[53] grid_4.4.0 generics_0.1.3 gtable_0.3.5 tzdb_0.4.0
[57] hms_1.1.3 xml2_1.3.6 Deriv_4.1.3 utf8_1.2.4
[61] flexmix_2.3-19 pillar_1.9.0 later_1.3.2 splines_4.4.0
[65] lattice_0.22-6 tidyselect_1.2.1 miniUI_0.1.1.1 reformulas_0.2.0
[69] urca_1.3-3 xfun_0.44 timeDate_4032.109 stringi_1.8.4
[73] boot_1.3-30 codetools_0.2-20 cli_3.6.2 xtable_1.8-4
[77] Rdpack_2.6 munsell_0.5.1 modelr_0.1.11 Rcpp_1.0.12
[81] coda_0.19-4.1 parallel_4.4.0 bdsmatrix_1.3-7 ellipsis_0.3.2
[85] fracdiff_1.5-3 gap.datasets_0.0.6 profvis_0.3.8 urlchecker_1.0.1
[89] xts_0.13.2 scales_1.3.0 rlang_1.1.3 cowplot_1.1.3

[florianhartig]

Hi @Franelizabethgalvez,

glad that this is solved. It sounds still a bit odd to me because glmmTMB is supported and should't be flagged, and also the stringAsFactors = T shouldn't really change anything if your data was coded correct in the first place.

If I were you, I would definitely investigate what changed in your model between the two ways to provide the data, and please feel free to add here any insight you have.

Best
F