── Assumption checks ─────────────────────────────────────────────────────────── ── Summary ── ✔ The outcome variable is binary ✔ Predictor variables are not highly correlated with each other ✔ The outcome is not separated by predictors ✔ The sample size is large enough ✔ Continuous variables either have a linear relationship with the log-odds of the outcome or are absent ✔ No observations unduly influence model estimates Your model was checked for logistic regression assumptions in the following areas: Binary outcome: The outcome variable was checked for containing precisely two levels. Multicollinearity: The `vif()` function from the car package was used to check for highly correlated predictor variables. Separation: The `detectseparation()` function from the detectseparation package was used to check for complete or quasi-complete separation in the data. Sample size: A rule of thumb was applied, requiring at least 10 events per predictor variable and at least 10 events per level of categorical variables to ensure sufficient data for reliable estimates. Linearity: A likelihood ratio test was conducted to assess improvements in model fit compared to a model using Box-Tidwell power transformations on continuous predictors. Any observed improvement likely indicates non-linear relationships between the continuous predictors and the log-odds of the outcome. Influential observations: A test to identify observations that could disproportionately influence model statistics was applied. The test simultaneously examined three metrics: Cook's distance (measuring overall observation impact), leverage (quantifying an observation's distance from the data centre), and standardised residuals (indicating how unusual an observation is relative to the model). To minimise false positive, an observation was flagged only if it met at least two of these diagnostic criteria. ✔ These tests found no issues with your model.