# r2: Compute R squared In lvmisc: Veras Miscellaneous

## Description

Returns the R squared values according to the model class.

## Usage

  1 2 3 4 5 6 7 8 9 10 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(ε)}

Where var(\hat{y}) is the variance explained by the model and var(ε) is the residual variance.

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(ε)}

Where var(f) is the variance of the fixed effects, var(r) is the variance of the random effects and var(ε) is the residual variance.

## Conditional R squared

R_{cond}^{2} = \frac{var(f) + var(r)}{var(f) + var(r) + var(ε)}

## 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

 1 2 3 4 5 6 7 8 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) } 

lvmisc documentation built on April 5, 2021, 5:06 p.m.