Comments (19)
bwiernik
commented on June 9, 2024
Can you give a reproducible example?
from performance.
scrryl
commented on June 9, 2024
# Set seed for reproducibility
set.seed(250419)
# Generate random x values
x <- rnorm(n = 500,
mean = 5,
sd = 2)
# Generate y values y = 5x + e
y <- 5*x + rnorm(n = 500,
mean = 5,
sd = 2)
# Generate z as offset
z <- runif(500, min = 0, max = 6719)
mock_data <- data.frame(x, y, z) |>
dplyr::mutate(y = round(y), z = round(z)) |> # both should be whole numbers since they're counts
dplyr::filter(!x < 0, !y < 0)
# Run model
model1 <- stats::glm(y ~ x + offset(log(z)),family = "quasipoisson", data = mock_data)
performance::check_model(model1)
from performance.
bwiernik
commented on June 9, 2024
That code produces a qq plot for me. What are you seeing?
from performance.
scrryl
commented on June 9, 2024
I see this:
from performance.
bwiernik
commented on June 9, 2024
Is anyone able to reproduce @strengejacke @IndrajeetPatil @mattansb @DominiqueMakowski @rempsyc
from performance.
bwiernik
commented on June 9, 2024
@scrryl Are you getting any errors or warnings? What happens if you make the plot window/pane larger?
from performance.
scrryl
commented on June 9, 2024
nope! no errors or warnings
when I make pane larger:
from performance.
bwiernik
commented on June 9, 2024
> sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.4.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] Matrix_1.6-1.1 gtable_0.3.4 jsonlite_1.8.8 dplyr_1.1.4 compiler_4.3.2
[6] tidyselect_1.2.1 Rcpp_1.0.12 VGAM_1.1-9 see_0.8.3.5 textshaping_0.3.6
[11] systemfonts_1.0.5 splines_4.3.2 scales_1.3.0 marginaleffects_0.14.0 readxl_1.4.3
[16] lattice_0.21-9 ggplot2_3.5.0 R6_2.5.1 labeling_0.4.3 patchwork_1.2.0
[21] generics_0.1.3 ggrepel_0.9.3 tibble_3.2.1 insight_0.19.10 munsell_0.5.0
[26] shadowtext_0.1.2 pillar_1.9.0 rlang_1.1.3 easystats_0.7.1.1 utf8_1.2.4
[31] performance_0.11.0.3 cli_3.6.2 mgcv_1.9-0 withr_3.0.0 magrittr_2.0.3
[36] grid_4.3.2 rstudioapi_0.15.0 nlme_3.1-163 lifecycle_1.0.4 vctrs_0.6.5
[41] glue_1.7.0 data.table_1.14.10 farver_2.1.1 cellranger_1.1.0 sessioninfo_1.2.2
[46] ragg_1.2.5 stats4_4.3.2 fansi_1.0.6 colorspace_2.1-0 purrr_1.0.2
[51] tools_4.3.2 pkgconfig_2.0.3
from performance.
scrryl
commented on June 9, 2024
R version 4.1.3 (2022-03-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 13.6.3
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gt_0.8.0 modelsummary_1.3.0 corrplot_0.92 Hmisc_4.7-0
[5] Formula_1.2-4 survival_3.2-13 lattice_0.20-45 scales_1.3.0
[9] lme4_1.1-29 Matrix_1.4-0 jtools_2.2.0 forcats_0.5.1
[13] stringr_1.5.1 dplyr_1.1.2 purrr_1.0.1 readr_2.1.2
[17] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] readxl_1.4.0 backports_1.4.1 systemfonts_1.0.4 sp_1.4-7
[5] splines_4.1.3 crosstalk_1.2.0 listenv_0.8.0 leaflet_2.1.1
[9] digest_0.6.29 htmltools_0.5.2 fansi_1.0.3 DHARMa_0.4.6
[13] magrittr_2.0.3 checkmate_2.1.0 googlesheets4_1.0.0 cluster_2.1.2
[17] see_0.8.0 tzdb_0.3.0 globals_0.15.0 modelr_0.1.8
[21] svglite_2.1.0 jpeg_0.1-9 colorspace_2.1-0 ggrepel_0.9.1
[25] rvest_1.0.4 haven_2.5.0.9000 xfun_0.30 leafem_0.2.0
[29] crayon_1.5.1 jsonlite_1.8.0 glue_1.6.2 kableExtra_1.3.4.9000
[33] gtable_0.3.0 gargle_1.2.0 webshot_0.5.5 car_3.1-0
[37] abind_1.4-5 DBI_1.1.2 rstatix_0.7.0 Rcpp_1.0.8.3
[41] performance_0.11.0 viridisLite_0.4.2 htmlTable_2.4.0 units_0.8-0
[45] foreign_0.8-82 proxy_0.4-26 stats4_4.1.3 datawizard_0.10.0
[49] htmlwidgets_1.5.4 httr_1.4.7 RColorBrewer_1.1-3 ellipsis_0.3.2
[53] pkgconfig_2.0.3 farver_2.1.1 nnet_7.3-17 dbplyr_2.2.0
[57] utf8_1.2.2 tidyselect_1.2.0 labeling_0.4.3 rlang_1.1.1
[61] munsell_0.5.0 cellranger_1.1.0 tools_4.1.3 cli_3.6.1
[65] generics_0.1.2 broom_0.8.0 evaluate_0.23 fastmap_1.1.1
[69] yaml_2.3.5 tables_0.9.10 knitr_1.39 fs_1.5.2
[73] pander_0.6.5 satellite_1.0.4 future_1.26.1 nlme_3.1-155
[77] xml2_1.3.3 compiler_4.1.3 rstudioapi_0.15.0 png_0.1-7
[81] e1071_1.7-9 ggsignif_0.6.4 reprex_2.0.1 stringi_1.7.6
[85] classInt_0.4-3 nloptr_2.0.1 ggsci_2.9 vctrs_0.6.2
[89] pillar_1.9.0 lifecycle_1.0.4 furrr_0.3.1 insight_0.19.10
[93] data.table_1.14.2 cowplot_1.1.1 raster_3.5-15 mapview_2.11.0
[97] patchwork_1.1.1 R6_2.5.1 latticeExtra_0.6-29 KernSmooth_2.23-20
[101] gridExtra_2.3 parallelly_1.32.0 codetools_0.2-18 boot_1.3-28
[105] MASS_7.3-55 gtools_3.9.2.2 assertthat_0.2.1 withr_2.5.0
[109] broom.mixed_0.2.9.4 mgcv_1.8-39 parallel_4.1.3 hms_1.1.1
[113] terra_1.5-21 grid_4.1.3 rpart_4.1.16 class_7.3-20
[117] minqa_1.2.4 rmarkdown_2.14 carData_3.0-5 googledrive_2.0.0
[121] ggpubr_0.4.0 sf_1.0-7 lubridate_1.8.0 base64enc_0.1-3
from performance.
bwiernik
commented on June 9, 2024
Just for clarity, can you run your code above from a fresh R session and post the session info? Please run the code from your post above (as I edited it so it would run).
from performance.
mattansb
commented on June 9, 2024
I'm also getting only 3 plots:
# Set seed for reproducibility
set.seed(250419)
# Generate random x values
x <- rnorm(n = 500,
mean = 5,
sd = 2)
# Generate y values y = 5x + e
y <- 5*x + rnorm(n = 500,
mean = 5,
sd = 2)
# Generate z as offset
z <- runif(500, min = 0, max = 6719)
mock_data <- data.frame(x, y, z) |>
dplyr::mutate(y = round(y), z = round(z)) |> # both should be whole numbers since they're counts
dplyr::filter(!x < 0, !y < 0)
# Run model
model1 <- stats::glm(y ~ x + offset(log(z)),family = "quasipoisson", data = mock_data)
performance::check_model(model1)
Created on 2024-04-03 with reprex v2.1.0
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.3.2 (2023-10-31 ucrt)
#> os Windows 11 x64 (build 22631)
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_Israel.utf8
#> ctype English_Israel.utf8
#> tz Asia/Jerusalem
#> date 2024-04-03
#> pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> boot 1.3-28.1 2022-11-22 [2] CRAN (R 4.3.2)
#> cli 3.6.2 2023-12-11 [1] CRAN (R 4.3.2)
#> colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.3.1)
#> curl 5.2.1 2024-03-01 [1] CRAN (R 4.3.3)
#> datawizard 0.10.0 2024-03-26 [1] CRAN (R 4.3.3)
#> DHARMa 0.4.6 2022-09-08 [1] CRAN (R 4.3.2)
#> digest 0.6.35 2024-03-11 [1] CRAN (R 4.3.3)
#> dplyr 1.1.4 2023-11-17 [1] CRAN (R 4.3.2)
#> evaluate 0.23 2023-11-01 [1] CRAN (R 4.3.2)
#> fansi 1.0.6 2023-12-08 [1] CRAN (R 4.3.2)
#> farver 2.1.1 2022-07-06 [1] CRAN (R 4.3.1)
#> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.1)
#> fs 1.6.3 2023-07-20 [1] CRAN (R 4.3.1)
#> generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.1)
#> ggplot2 3.5.0 2024-02-23 [1] CRAN (R 4.3.1)
#> ggrepel 0.9.5 2024-01-10 [1] CRAN (R 4.3.2)
#> glue 1.7.0 2024-01-09 [1] CRAN (R 4.3.2)
#> gtable 0.3.4 2023-08-21 [1] CRAN (R 4.3.1)
#> highr 0.10 2022-12-22 [1] CRAN (R 4.3.1)
#> htmltools 0.5.8 2024-03-25 [1] CRAN (R 4.3.3)
#> insight 0.19.10 2024-03-22 [1] CRAN (R 4.3.3)
#> knitr 1.45 2023-10-30 [1] CRAN (R 4.3.2)
#> labeling 0.4.3 2023-08-29 [1] CRAN (R 4.3.1)
#> lattice 0.21-9 2023-10-01 [2] CRAN (R 4.3.2)
#> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.2)
#> lme4 1.1-35.1 2023-11-05 [1] CRAN (R 4.3.2)
#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.1)
#> MASS 7.3-60.0.1 2024-01-13 [1] CRAN (R 4.3.2)
#> Matrix 1.6-5 2024-01-11 [1] CRAN (R 4.3.2)
#> mgcv 1.9-0 2023-07-11 [2] CRAN (R 4.3.2)
#> minqa 1.2.6 2023-09-11 [1] CRAN (R 4.3.1)
#> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.3.1)
#> nlme 3.1-163 2023-08-09 [2] CRAN (R 4.3.2)
#> nloptr 2.0.3 2022-05-26 [1] CRAN (R 4.3.1)
#> patchwork 1.2.0 2024-01-08 [1] CRAN (R 4.3.2)
#> performance 0.11.0 2024-03-22 [1] CRAN (R 4.3.3)
#> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.1)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.1)
#> purrr 1.0.2 2023-08-10 [1] CRAN (R 4.3.1)
#> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.3.1)
#> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.3.0)
#> R.oo 1.26.0 2024-01-24 [1] CRAN (R 4.3.2)
#> R.utils 2.12.3 2023-11-18 [1] CRAN (R 4.3.2)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.1)
#> Rcpp 1.0.12 2024-01-09 [1] CRAN (R 4.3.2)
#> reprex 2.1.0 2024-01-11 [1] CRAN (R 4.3.2)
#> rlang 1.1.3 2024-01-10 [1] CRAN (R 4.3.2)
#> rmarkdown 2.26 2024-03-05 [1] CRAN (R 4.3.3)
#> rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.3.3)
#> scales 1.3.0 2023-11-28 [1] CRAN (R 4.3.2)
#> see 0.8.2 2024-02-14 [1] CRAN (R 4.3.2)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.1)
#> styler 1.10.2 2023-08-29 [1] CRAN (R 4.3.1)
#> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.3.1)
#> tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.3.3)
#> utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.1)
#> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.2)
#> withr 3.0.0 2024-01-16 [1] CRAN (R 4.3.2)
#> xfun 0.43 2024-03-25 [1] CRAN (R 4.3.3)
#> xml2 1.3.6 2023-12-04 [1] CRAN (R 4.3.2)
#> yaml 2.3.8 2023-12-11 [1] CRAN (R 4.3.2)
#>
#> [1] C:/Users/user/AppData/Local/R/win-library/4.3
#> [2] C:/Program Files/R/R-4.3.2/library
#>
#> ──────────────────────────────────────────────────────────────────────────────from performance.
mattansb
commented on June 9, 2024
Could be related to this message? (But the plot still works)
(c_norm <- performance::check_normality(model1))
#> There's no formal statistical test for normality for generalized linear model.
#> Instead, please use `simulate_residuals()` and `check_residuals()` to check for uniformity of residuals.
plot(c_norm)
from performance.
bwiernik
commented on June 9, 2024
Honestly that seems reasonable --- quasipoisson residuals shouldn't be normal. Do you know where the different behavior is coming from @strengejacke ?
from performance.
strengejacke
commented on June 9, 2024
quasipoisson is not supported by DHARMa, that's why it fails. You have to explicitly set residual_type = "normal", until we fixed this:
set.seed(250419)
# Generate random x values
x <- rnorm(n = 500,
mean = 5,
sd = 2)
# Generate y values y = 5x + e
y <- 5*x + rnorm(n = 500,
mean = 5,
sd = 2)
# Generate z as offset
z <- runif(500, min = 0, max = 6719)
mock_data <- data.frame(x, y, z) |>
dplyr::mutate(y = round(y), z = round(z)) |> # both should be whole numbers since they're counts
dplyr::filter(!x < 0, !y < 0)
# Run model
model1 <- stats::glm(y ~ x + offset(log(z)),family = "quasipoisson", data = mock_data)
performance::check_model(model1, residual_type = "normal")
Created on 2024-04-03 with reprex v2.1.0
from performance.
strengejacke
commented on June 9, 2024
This is the error:
set.seed(250419)
# Generate random x values
x <- rnorm(
n = 500,
mean = 5,
sd = 2
)
# Generate y values y = 5x + e
y <- 5 * x + rnorm(
n = 500,
mean = 5,
sd = 2
)
# Generate z as offset
z <- runif(500, min = 0, max = 6719)
mock_data <- data.frame(x, y, z) |>
# both should be whole numbers since they're counts
datawizard::data_modify(y = round(y), z = round(z)) |>
datawizard::data_filter(!x < 0, !y < 0)
# Run model
model1 <- glm(y ~ x + offset(log(z)), family = "quasipoisson", data = mock_data)
DHARMa::simulateResiduals(model1)
#> Error in simulate.lm(object, nsim = nsim, ...): family 'quasipoisson' not implementedCreated on 2024-04-03 with reprex v2.1.0
However, at least in simulateResiduals(), there's a check for that family:
if (is.null(integerResponse)) {
if (family$family %in% c("binomial", "poisson", "quasibinomial",
"quasipoisson", "Negative Binom", "nbinom2", "nbinom1",
"genpois", "compois", "truncated_poisson", "truncated_nbinom2",
"truncated_nbinom1", "betabinomial", "Poisson", "Tpoisson",
"COMPoisson", "negbin", "Tnegbin") | grepl("Negative Binomial",
family$family))
integerResponse = TRUE
else integerResponse = FALSE
}So the package stops at a later point. @florianhartig is it correct that quasi-families are not yet supported, or is this not intended? I could open an issue at the DHARMa repo.
from performance.
strengejacke
commented on June 9, 2024
Ah, I see. It's is stats::simulate() where the error comes from.
from performance.
scrryl
commented on June 9, 2024
quasipoisson is not supported by DHARMa, that's why it fails. You have to explicitly set
residual_type = "normal", until we fixed this:set.seed(250419) # Generate random x values x <- rnorm(n = 500, mean = 5, sd = 2) # Generate y values y = 5x + e y <- 5*x + rnorm(n = 500, mean = 5, sd = 2) # Generate z as offset z <- runif(500, min = 0, max = 6719) mock_data <- data.frame(x, y, z) |> dplyr::mutate(y = round(y), z = round(z)) |> # both should be whole numbers since they're counts dplyr::filter(!x < 0, !y < 0) # Run model model1 <- stats::glm(y ~ x + offset(log(z)),family = "quasipoisson", data = mock_data) performance::check_model(model1, residual_type = "normal")
Created on 2024-04-03 with reprex v2.1.0
residual_type = "normal" was the fix. I'll keep a lookout for updates.
Thank you all so much!
from performance.
bwiernik
commented on June 9, 2024
Note that quasipoisson model residuals should not be normally distributed, so this plot isn't really meaningful
from performance.
strengejacke
commented on June 9, 2024
residual_type = "normal" was the fix. I'll keep a lookout for updates.
Make sure you have the latest easystats package installed:
install.packages("easystats")then run:
easystats::install_latest()and you should be fine.
from performance.
Related Issues (20)
- incorrect warning with old `ggplot2`/failure to load `see` HOT 2
- check_model "Error in match.arg" HOT 5
- Error in performance::check_distribution(): in call bw.SJ() HOT 2
- Revising `check_model()` HOT 1
- check_model failing on logistic regression HOT 2
- r2_nakagawa and glmmTMB with beta_family HOT 2
- Outlier detection in Linear mixed models failed? HOT 5
- cannot apply check_model title with patchwork::plot_annotation HOT 4
- check_model error suggestions are not complete HOT 4
- Error and Incomplete Output Using performance::check_collinearity with Cox Models HOT 1
- Normality of Residuals of check_model is abnormal. HOT 2
- Revise compare_models() for Bayesian models HOT 5
- R-squared for glmmTMB (binomial) HOT 9
- check_model() bugged for lmer models *only* when run as part of an RMD chunk HOT 3
- check_predictions() fails when outcome is log-transformed and named like a valid function HOT 1
- Error in `check_model(<glmer>)` HOT 3
- Problems using r2_nakagawa()
- check_model fails if dependent variable is labelled HOT 5
- Remove unnecessary `tryCatch()` statements targeting `insight::download_model()` HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from performance.