performance
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easystats
:muscle: Models' quality and performance metrics (R2, ICC, LOO, AIC, BF, ...)
FIXES: #878 Reproduces the error when check_dag encounters multiple colliders in adjustment nodes, causing: 'length = 2' in coercion to 'logical(1)' The test currently fails as expected and will pass...
Thanks for the package! `check_dag` reports a somewhat cryptic error with some DAGs. Reproducible example below (a bit weird DAG, I know...) Thanks! ``` r library(performance) check_dag( y ~ x...
Automatically updated the `DESCRIPTION` file using `usethis::use_latest_dependencies()`.
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The check_model() is very useful function for my work. However, it has recently failed to work recently (only blank plots are displayed). I'm sure that both the 'performance' package and...
- Tests document expected behavior of theme argument - Tests currently FAIL due to bugs in see package - Covers: standard themes, custom themes, string themes, attribute storage - Bug...
Fixes #873 - The linearity and variance plots were hard to read when models had only categorical predictors - the confidence ribbons were huge and covered up the actual data...
For models with a categorical predictor (3 levels here): ```r star dplyr::mutate(dplyr::across(dplyr::starts_with("star"), forcats::as_factor)) star_maths_lm
Taking a simple case from the documentation: https://easystats.github.io/performance/reference/check_model.html ```r library(ggplot2) library(performance) m
I've been having a problem with using `check_overdispersion()` on a binomial glm. The dataset is quite small and overdispersion is expected (due to the impact of weather etc), but `check_overdispersion()`...
I’m a bit puzzled about how to interpret check_outliers() and could use some guidance. I’m running some models and using `check_outliers()`, and I’m a bit unsure how to interpret the...
