##### Asked by: Judi Hoareau

asked in category: General Last Updated: 19th February, 2020# What is pseudo R square?

**Pseudo**R2 is a measure of how well variables of the model explain some phenomenon. If we catch with our variables more than 0,5 we can form our expectation for the model, but there are other unexplained issues and then try to find other factors that can explain and test our thesis.

Consequently, what is a good pseudo R Squared?

A rule of thumb that I found to be quite helpful is that a McFadden's **pseudo R**-**squared** ranging from 0.2 to 0.4 indicates very **good** model fit. As such, the model mentioned above with a McFadden's **pseudo R**-**squared** of 0.192 is likely not a terrible model, at least by this metric, but it isn't particularly strong either.

Furthermore, what is nagelkerke r2? **Nagelkerke's** R ^{2} ^{2} is an adjusted version of the Cox & Snell R-square that adjusts the scale of the statistic to cover the full range from 0 to 1. McFadden's R ^{2} ^{3} is another version, based on the log-likelihood kernels for the intercept-only model and the full estimated model.

In respect to this, how do you interpret pseudo R Squared?

A **pseudo R**-**squared** only has meaning when compared to another **pseudo R**-**squared** of the same type, on the same data, predicting the same outcome. In this situation, the higher **pseudo R**-**squared** indicates which model better predicts the outcome.

What is a strong R Squared?

**R**-**squared** should accurately reflect the percentage of the dependent variable variation that the linear model explains. Your **R**^{2} should not be any higher or lower than this value. Any study that attempts to predict human behavior will tend to have **R**-**squared** values less than 50%.