# Details: Model goodness of fit in GAMLj

keywords jamovi, GLM, Mixed, effect size indices, R-squared, loglikelihood ratio test

3.0.0

# Introduction

Here you can find information regarding the R-squared computed for different models and their associated inferential tests.

# GLM (linear model)

The R-squared produced by GAMLj is
the standard R-squared produced by virtually any statistical software .
It is computed as \(SS_{model}\)
divided by \(SS_{total}\). In GAMLj, it is extracted from the R model object
by `performance::r2(model)`

(Lüdecke
(2020)). The adjusted R squared is
computed as

\[R_{adj}^2={{SS_{model}-SS_{res} \cdot ({df_{model}/df_{res}) \cdot }}\over{ SS_{model}+SS_{res}(df_{res}+1)/df_{res}}}\]

The F-tests associated to the R-squares tests the null-hypothesis that the model predictors explain no variance, which is equivalent to compare the model at hand with an intercept-only model.

# GzLM (Generalized linear Models)

For the generalized linear model, the R-squared

# Additional references

*"Extracting, Computing and Exploring the Parameters of Statistical Models Using r"*. https://cran.r-project.org/package=parameters.

## Comments?

Got comments, issues or spotted a bug? Please open an issue on GAMLj at github or send me an email