Logistic Regression with Plain-English Interpretation

Usage

logistic_interpret(formula, data, conf.level = 0.95, context = NULL)

Arguments

formula: A formula of the form outcome ~ predictor1 + predictor2 + ...
data: A data frame containing the variables
conf.level: Confidence level. Default 0.95.
context: Optional description of the study design or sampling method, echoed back in the printed report. Default NULL.

Value

An object of class statease_logistic containing logistic regression results and interpretation. Use print() to display the formatted report.

Examples

df <- data.frame(
  passed      = c(1,1,0,1,0,1,1,0,1,1,0,0,1,1,0),
  study_hours = c(9,8,3,7,2,9,8,3,7,6,2,1,8,7,3),
  attendance  = c(90,85,50,80,45,95,88,55,78,70,40,35,92,83,52)
)
result <- logistic_interpret(passed ~ study_hours + attendance, data = df)
#> Warning: Sample size is small (n < 20). Interpret results with caution.
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Waiting for profiling to be done...
print(result)
#> 
#> -- statease Logistic Regression Report --------------------------
#>   Outcome      : passed
#>   Predictors   : study_hours, attendance
#>   N            : 15
#> -----------------------------------------------------------------
#>   Overall Model Fit:
#>   Chi-square   : 20.190  (df = 2)  p = 0.0000
#>   Nagelkerke R2: 1.0000 (large effect)
#>   The overall model is statistically significant (p = 0.0000 < alpha 0.05).
#> -----------------------------------------------------------------
#>   Individual Predictors:
#> 
#>   study_hours
#>     Coefficient  : 30.250  (SE = 140113.493)
#>     z-statistic  : 0.000
#>     p-value      : 0.9998  [not significant]
#>     Odds Ratio   : 13716359765703.211
#>     95% CI (OR) : [0.000, Inf]
#>     Interpretation: each unit increase in study_hours increases the odds by 1371635976570221.0%.
#> 
#>   attendance
#>     Coefficient  : -2.253  (SE = 18616.367)
#>     z-statistic  : -0.000
#>     p-value      : 0.9999  [not significant]
#>     Odds Ratio   : 0.105
#>     95% CI (OR) : [0.000, Inf]
#>     Interpretation: each unit increase in attendance decreases the odds by 89.5%.
#> -----------------------------------------------------------------
#>   Assumption Checks:
#>     Multicollinearity (VIF) : WARNING  (max VIF = 42.1, threshold = 5)
#>     Complete separation     : WARNING  (possible separation detected; coefficient estimates may be unreliable)
#>     Linearity of the logit  : NOTE     (not automatically tested; consider a diagnostic such as the Box-Tidwell test or component-plus-residual plots for continuous predictors)
#> 
#>   NOTE: Assumption checks are diagnostic tools and may be
#>   influenced by sample size and other characteristics of the
#>   data. Passing a check does not prove that an assumption is
#>   satisfied, and a warning does not automatically invalidate
#>   the analysis. Interpret these results alongside your
#>   knowledge of the data.
#> -----------------------------------------------------------------
#>   Interpretation:
#>   The model is statistically significant (p = 0.0000 < alpha 0.05).
#>   Nagelkerke R2 = 1.0000 suggests a large amount of
#>   variance in passed is explained by the predictors.
#>   Non-significant predictors: study_hours, attendance
#> -----------------------------------------------------------------
#>