Returns the R squared values according to the model class.
Usage
r2(model)
# S3 method for default
r2(model)
# S3 method for lm
r2(model)
# S3 method for lmerMod
r2(model)
Value
If the model is a linear model, it returns a data.frame
with the R squared and adjusted R squared values. If the model is a
linear mixed model it return a data.frame with the marginal and
conditional R squared values as described by Nakagawa and Schielzeth
(2013). See the formulas for the computations in "Details".
Arguments
model
An object containing a model.
R squared
$$R^2 = \frac{var(\hat{y})}{var(\epsilon)}$$
Where \(var(\hat{y})\) is the variance explained by the model and
\(var(\epsilon)\) is the residual variance.
Adjusted R squared
$$R_{adj}^{2} = 1 - (1 - R^2)\frac{n - 1}{n - p - 1}$$
Where \(n\) is the number of data points and \(p\) is the number of
predictors in the model.
Marginal R squared
$$R_{marg}^{2} = \frac{var(f)}{var(f) + var(r) + var(\epsilon)}$$
Where \(var(f)\) is the variance of the fixed effects, \(var(r)\) is
the variance of the random effects and \(var(\epsilon)\) is the
residual variance.
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method
for obtaining R2 from generalized linear mixed-effects models. Methods
in Ecology and Evolution, 4(2), 133–142.
tools:::Rd_expr_doi("10.1111/j.2041-210x.2012.00261.x").