How to turn off CIs for check_model? about performance HOT 4 OPEN

@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.