Compute a Bayesian version of R-squared for regression models
Compute a Bayesian version of R-squared for regression models
## S3 method for class 'brmsfit' bayes_R2( object, resp = NULL, summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ... )
object |
An object of class |
resp |
Optional names of response variables. If specified, predictions are performed only for the specified response variables. |
summary |
Should summary statistics be returned
instead of the raw values? Default is |
robust |
If |
probs |
The percentiles to be computed by the |
... |
Further arguments passed to
|
For an introduction to the approach, see Gelman et al. (2018) and https://github.com/jgabry/bayes_R2/.
If summary = TRUE, an M x C matrix is returned
(M = number of response variables and c = length(probs) + 2)
containing summary statistics of the Bayesian R-squared values.
If summary = FALSE, the posterior samples of the Bayesian
R-squared values are returned in an S x M matrix (S is the number of samples).
Andrew Gelman, Ben Goodrich, Jonah Gabry & Aki Vehtari. (2018).
R-squared for Bayesian regression models, The American Statistician.
10.1080/00031305.2018.1549100 (Preprint available at
https://stat.columbia.edu/~gelman/research/published/bayes_R2_v3.pdf)
## Not run: fit <- brm(mpg ~ wt + cyl, data = mtcars) summary(fit) bayes_R2(fit) # compute R2 with new data nd <- data.frame(mpg = c(10, 20, 30), wt = c(4, 3, 2), cyl = c(8, 6, 4)) bayes_R2(fit, newdata = nd) ## End(Not run)
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