Comments (4)
@IndrajeetPatil We could add a ci
argument or similar to performance::check_model()
, save as attribute, and then in see we would set SE = FALSE
in geom_smooth()
.
from performance.
That would be good.
I wonder if we should also try to detect if number of discrete fitted values is small/all the predictors are categorical and then omit the linearity plot and change the homogeneity plot to be something better for categorical regression ?
from performance.
Upon further testing, it's not always just the SE that causes issues with visualization. Sometimes the LOESS curve bends in odd ways between discrete values that produces the same effect as seen above. So in addition to SE=F
, maybe also method="lm"
? I know a linear fit isn't ideal when evaluating these things, but it's better than nothing when the y-axis goes haywire. For example, see the check_model()
outlier plot of a 2x2 factorial anova below (this can of course happen with any of the plots with a LOESS curve).
library(tidyverse)
library(easystats)
#> Warning: package 'easystats' was built under R version 4.2.3
#> # Attaching packages: easystats 0.7.0
#> ✔ bayestestR 0.13.2 ✔ correlation 0.8.4
#> ✔ datawizard 0.9.1 ✔ effectsize 0.8.6
#> ✔ insight 0.19.8 ✔ modelbased 0.8.7
#> ✔ performance 0.10.9 ✔ parameters 0.21.5
#> ✔ report 0.5.8 ✔ see 0.8.2
df<-read_csv("https://raw.githubusercontent.com/jbohenek/biol_5130/main/opsin.csv")
#> Rows: 33 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (2): population, water
#> dbl (1): sws1
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
fit<-lm(sws1 ~ water*population, data=df)
check_model(fit)
Created on 2024-02-17 with reprex v2.1.0
from performance.
We should probably just have alternative visualizations for categorical models. The current plots really only work for models with continuous predictors so that there are numerous fitted values on the x axis
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
- cannot apply check_model title with patchwork::plot_annotation HOT 4
- 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 2
- Error in `check_model(<glmer>)` HOT 3
- Problems using `r2_nakagawa()` HOT 3
- 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.