# loo_R2.brmsfit: Compute a LOO-adjusted R-squared for regression models In brms: Bayesian Regression Models using 'Stan'

## Description

Compute a LOO-adjusted R-squared for regression models

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```## S3 method for class 'brmsfit' loo_R2( object, resp = NULL, summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ... ) ```

## Arguments

 `object` An object of class `brmsfit`. `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 `TRUE`. `robust` If `FALSE` (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If `TRUE`, the median and the median absolute deviation (MAD) are applied instead. Only used if `summary` is `TRUE`. `probs` The percentiles to be computed by the `quantile` function. Only used if `summary` is `TRUE`. `...` Further arguments passed to `posterior_epred` and `log_lik`, which are used in the computation of the R-squared values.

## 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 LOO-adjusted R-squared values. If `summary = FALSE`, the posterior draws of the LOO-adjusted R-squared values are returned in an S x M matrix (S is the number of draws).

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```## Not run: fit <- brm(mpg ~ wt + cyl, data = mtcars) summary(fit) loo_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)) loo_R2(fit, newdata = nd) ## End(Not run) ```

brms documentation built on Aug. 23, 2021, 5:08 p.m.