I’m a bit puzzled about how to interpret check_outliers() and could use some guidance.

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 resulting plots.

For context: I simulated a dataset for my website to represent looking time in some participants. When I fit the model and run check_model(), the diagnostics don’t look too bad. However, the Influential observations plot from check_outliers() is a bit unclear to me.

Specifically:

• No outliers are flagged by the function.

• But many points appear outside the dotted reference lines, which I understood as a visual guide for potential outliers.

I hope this isn’t too basic a question — could you help me understand how to interpret this, or point me in the right direction?

library(lmerTest)
library(tidyverse)
library(easystats)
#> # Attaching packages: easystats 0.7.5
#> ✔ bayestestR  0.17.0     ✔ correlation 0.8.8   
#> ✔ datawizard  1.2.0      ✔ effectsize  1.0.1   
#> ✔ insight     1.4.2      ✔ modelbased  0.13.0  
#> ✔ performance 0.15.1     ✔ parameters  0.28.1  
#> ✔ report      0.6.1      ✔ see         0.11.0.7

df = read_csv("https://raw.githubusercontent.com/DevStart-Hub/DevStart/refs/heads/dev/resources/Stats/Dataset.csv")

df$Id = factor(df$Id)
df$Event = factor(df$Event)
df$SES = factor(df$SES)
df$TrialN = standardize(df$TrialN)

mod <- lmer(LookingTime ~ Event * TrialN + (1 + TrialN | Id), data = df)

check_model(mod)

check_outliers(mod)
#> OK: No outliers detected.
#> - Based on the following method and threshold: cook (0.7).
#> - For variable: (Whole model)

plot(check_outliers(mod))

^{Created on 2025-09-04 with reprex v2.1.1}

Originally posted by @TommasoGhilardi in https://github.com/easystats/performance/discussions/856

Start by reading the check_outliers() entry point and the Influential observations plot shown in the report, then compare its Cook threshold output with the dotted reference lines. Check how check_model() and check_outliers() describe their diagnostics. Done means adding documentation that clearly explains what each visual guide and the “No outliers detected” result represents.