McFadden's pseudo R-squared
McFadden's pseudo R-squared is a likelihood-based goodness-of-fit measure comparing a fitted discrete-choice model with an intercept-only null model.
Statistics Slam Dunk
McFadden's pseudo-R2 is a goodness-of-fit metric for generalized linear models based on the log-likelihoods of a fitted model and a null model.
- How it is used
- Evaluates logistic-regression goodness of fit when ordinary R² is unavailable.
- How deep it goes
- Defines the log-likelihood comparison, gives interpretation thresholds, and demonstrates calculation without deriving the formula.
- Examples
- Uses NBA playoff qualification models based on points allowed and points scored, returning pseudo-R² values of 0.31 and 0.06.
- What you can do after
- Apply it via pscl::pR2() to compare logistic-regression fit and identify the stronger predictor model.
Read how Statistics Slam Dunk explains McFadden's pseudo R-squared →
Statistics Every Programmer Needs
McFadden's pseudo R-squared is a likelihood-based measure of how much a fitted discrete-choice model improves over an intercept-only null model.
- How it is used
- Evaluates logistic-regression goodness of fit and compares predictive value against an intercept-only null model.
- How deep it goes
- Defines the likelihood ratio, identifies fitted-model attributes, and explains comparison limits without deriving likelihood estimation.
- Examples
- Uses statsmodels regression tables and fitted-model log-likelihood attributes to evaluate binary-outcome models.
- What you can do after
- Evaluate logistic-regression fit and compare models using their improvement over a shared null-model likelihood.
Read how Statistics Every Programmer Needs explains McFadden's pseudo R-squared →
Related concepts
- log probabilityuses · A log probability is the logarithm, usually the natural logarithm, of a probability assigned to an event, outcome, token, or sequence.
- pseudo r-squaredkind of · Pseudo R-squared is a family of fit measures for logistic regression that quantify model fit using likelihood-based comparisons rather than explained variance.
- goodness-of-fit measuremeasures · A goodness-of-fit measure quantifies how closely a statistical model or representation agrees with observed data or specified relationships.
- sigmoidmeasures · A sigmoid is a smooth, bounded, S-shaped function that maps real-valued inputs between two limiting values, commonly zero and one.
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