| r2 {lvmisc} | R Documentation |
Compute R squared
Description
Returns the R squared values according to the model class.
Usage
r2(model)
## Default S3 method:
r2(model)
## S3 method for class 'lm'
r2(model)
## S3 method for class 'lmerMod'
r2(model)
Arguments
model |
An object containing a model. |
Details
R squared computations.
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".
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.
Conditional R squared
R_{cond}^{2} = \frac{var(f) + var(r)}{var(f) + var(r) + var(\epsilon)}
References
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. doi:10.1111/j.2041-210x.2012.00261.x.
Examples
m1 <- lm(Sepal.Length ~ Species, data = iris)
r2(m1)
if (require(lme4, quietly = TRUE)) {
m2 <- lmer(
Sepal.Length ~ Sepal.Width + Petal.Length + (1 | Species), data = iris
)
r2(m2)
}