What is pseudo R2 in logistic regression?
Mia Smith
Published Feb 26, 2026
What is pseudo R2 in logistic regression?
LL-based pseudo-R2 measures draw comparisons between the LL of the estimated model and the LL of the null model. The null model contains no parameters but the intercept. Pseudo-R2s can then be interpreted as a measure of improvement over the null model in terms of LL and thus give an indication of goodness of fit.
Can you use R2 for logistic regression?
R squared is a useful metric for multiple linear regression, but does not have the same meaning in logistic regression. Instead, the primary use for these pseudo R squared values is for comparing multiple models fit to the same dataset.
What is McFadden’s pseudo R2?
McFadden’s pseudo-R squared denotes the corresponding value but for the null model – the model with only an intercept and no covariates. To try and understand whether this definition makes sense, suppose first that the covariates in our current model in fact give no predictive information about the outcome.
How is pseudo R Squared calculated?
Technically, R2 cannot be computed the same way in logistic regression as it is in OLS regression. The pseudo-R2, in logistic regression, is defined as 1−L1L0, where L0 represents the log likelihood for the “constant-only” model and L1 is the log likelihood for the full model with constant and predictors.
How do you calculate pseudo R Squared?
R2 = 1 – [Σi(yi-πˆi)2]/[Σi(yi-ȳ)2], where πˆi are the model’s predicted values. McFadden’s Pseudo R-Squared. R2 = 1 – [ln LL(Mˆfull)]/[ln LL(Mˆintercept)]. This approach is one minus the ratio of two log likelihoods.
What is LLR p value?
The p value is listed as LLR p-value (bottom of the top right area), and it’s the certainty we can have in our results. You can think of it as the percent chance that the regression can create a meaningful representation of us completing a scarf.
What is a good pseudo R squared value?
A rule of thumb that I found to be quite helpful is that a McFadden’s pseudo R2 ranging from 0.2 to 0.4 indicates very good model fit.
