bayes_R2.brmsfit {brms}  R Documentation 
Compute a Bayesian version of Rsquared for regression models
Description
Compute a Bayesian version of Rsquared for regression models
Usage
## S3 method for class 'brmsfit'
bayes_R2(
object,
resp = NULL,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
...
)
Arguments
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

Details
For an introduction to the approach, see Gelman et al. (2018) and https://github.com/jgabry/bayes_R2/.
Value
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 Rsquared values.
If summary = FALSE
, the posterior draws of the Bayesian
Rsquared values are returned in an S x M matrix (S is the number of draws).
References
Andrew Gelman, Ben Goodrich, Jonah Gabry & Aki Vehtari. (2018).
Rsquared 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)
Examples
## 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)