Comments (4)
plot()
returns a list of ggplot-objects, not an object where you can add layers from patchwork. However, is you use see::plots()
on a list of ggplot-objects, that will return a patchwork-object:
library(easystats)
library(patchwork)
m <- lm(mpg ~ factor(cyl) + disp + hp, data = mtcars)
pp <- check_model(m)
x <- plot(pp)
plots(x, n_columns = 2) + plot_annotation(title = 'mtcars model')
Created on 2024-04-17 with reprex v2.1.0
from performance.
@strengejacke i had thought plot() returned the patchwork object. Did we change that at some point? I don't seem much value in retuning a list vs the final thing that is shown by default--should we change it?
from performance.
Maybe we can just add an else
here?
https://github.com/easystats/see/blob/7af4340caa87077c50d4baa83ef35f6ff712f7d4/R/plot.check_model.R#L276
from performance.
Yes I am fairly sure that plot()
returned the patchwork object at some point, and now returns a list
.
from performance.
Related Issues (20)
- Revising `check_model()` HOT 1
- check_model failing on logistic regression HOT 2
- Check_model in version 0.11.0 no longer produces qq plot residuals HOT 19
- r2_nakagawa and glmmTMB with beta_family HOT 4
- Outlier detection in Linear mixed models failed? HOT 5
- check_model error suggestions are not complete HOT 5
- 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()` HOT 1
- check_model fails if dependent variable is labelled HOT 5
- Remove unnecessary `tryCatch()` statements targeting `insight::download_model()` HOT 2
- check_collinearity() does not work with orthogonal polynomials HOT 10
- Should check_overdispersion give warning when applied to quasipoisson? HOT 1
- Check for influential observations of GLM w/o numeric variables
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from performance.