R: Compute a LOO-adjusted R-squared for regression models

loo_R2.brmsfit {brms} | R Documentation

## Compute a LOO-adjusted R-squared for regression models

### Description

Compute a LOO-adjusted R-squared for regression models

### Usage

    ## 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

    ## 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)

[Package brms version 2.17.0 Index]