Posterior interval plots for key parameters. Uses bayesplot::mcmc_intervals.

Usage

# S3 method for tridim_transformation
plot(x, convert_euclidean = FALSE, ...)

Arguments

x: A tridim_transformation object
convert_euclidean: Whether to convert matrix coefficients to scale(phi) and rotation(theta). Defaults to FALSE.
...: Extra parameters to be passed to bayesplot::mcmc_intervals()

Value

A ggplot object produced by bayesplot::mcmc_intervals()

Examples

euc2 <- fit_transformation(depV1+depV2~indepV1+indepV2,
                           data = 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: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.106 seconds (Warm-up)
#> Chain 1:                0.113 seconds (Sampling)
#> Chain 1:                0.219 seconds (Total)
#> Chain 1: 
plot(euc2)


# same but for converted coefficients
plot(euc2, convert_euclidean=TRUE)