Computes R-squared using Bayesian R-squared approach. For detail refer to: Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari (2018). R-squared for Bayesian regression models. The American Statistician, doi:10.1080/00031305.2018.1549100.

Usage

# S3 method for tridim_transformation
R2(object, summary = TRUE, probs = c(0.055, 0.945), ...)

Arguments

object: An object of class tridim_transformation
summary: Whether summary statistics should be returned instead of raw sample values. Defaults to TRUE
probs: The percentiles used to compute summary, defaults to 89% credible interval.
...: Unused.

Value

vector of values or a data.frame with summary

Examples

euc2 <- fit_transformation(depV1+depV2~indepV1+indepV2,
  NakayaData, transformation = 'euclidean')
#> 
#> SAMPLING FOR MODEL 'tridim_transformation' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.095 seconds (Warm-up)
#> Chain 1:                0.093 seconds (Sampling)
#> Chain 1:                0.188 seconds (Total)
#> Chain 1: 
R2(euc2)
#>          R2   R2_5.5   R2_94.5
#> 1 0.9386744 0.925752 0.9456517