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The data_plot() function usually stores information (such as title, axes labels, etc.) as attributes, while add_plot_attributes() adds this information to the plot.

Usage

add_plot_attributes(x)

Arguments

x

An object.

Examples

# \donttest{
library(rstanarm)
library(bayestestR)
library(see)
library(ggplot2)

model <- suppressWarnings(stan_glm(
  Sepal.Length ~ Petal.Width + Species + Sepal.Width,
  data = iris,
  chains = 2, iter = 200
))
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.26 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 15
#> Chain 1:            adapt_window = 75
#> Chain 1:            term_buffer = 10
#> Chain 1: 
#> Chain 1: Iteration:   1 / 200 [  0%]  (Warmup)
#> Chain 1: Iteration:  20 / 200 [ 10%]  (Warmup)
#> Chain 1: Iteration:  40 / 200 [ 20%]  (Warmup)
#> Chain 1: Iteration:  60 / 200 [ 30%]  (Warmup)
#> Chain 1: Iteration:  80 / 200 [ 40%]  (Warmup)
#> Chain 1: Iteration: 100 / 200 [ 50%]  (Warmup)
#> Chain 1: Iteration: 101 / 200 [ 50%]  (Sampling)
#> Chain 1: Iteration: 120 / 200 [ 60%]  (Sampling)
#> Chain 1: Iteration: 140 / 200 [ 70%]  (Sampling)
#> Chain 1: Iteration: 160 / 200 [ 80%]  (Sampling)
#> Chain 1: Iteration: 180 / 200 [ 90%]  (Sampling)
#> Chain 1: Iteration: 200 / 200 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 1:                0.008 seconds (Sampling)
#> Chain 1:                0.013 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 9e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: WARNING: There aren't enough warmup iterations to fit the
#> Chain 2:          three stages of adaptation as currently configured.
#> Chain 2:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 2:          the given number of warmup iterations:
#> Chain 2:            init_buffer = 15
#> Chain 2:            adapt_window = 75
#> Chain 2:            term_buffer = 10
#> Chain 2: 
#> Chain 2: Iteration:   1 / 200 [  0%]  (Warmup)
#> Chain 2: Iteration:  20 / 200 [ 10%]  (Warmup)
#> Chain 2: Iteration:  40 / 200 [ 20%]  (Warmup)
#> Chain 2: Iteration:  60 / 200 [ 30%]  (Warmup)
#> Chain 2: Iteration:  80 / 200 [ 40%]  (Warmup)
#> Chain 2: Iteration: 100 / 200 [ 50%]  (Warmup)
#> Chain 2: Iteration: 101 / 200 [ 50%]  (Sampling)
#> Chain 2: Iteration: 120 / 200 [ 60%]  (Sampling)
#> Chain 2: Iteration: 140 / 200 [ 70%]  (Sampling)
#> Chain 2: Iteration: 160 / 200 [ 80%]  (Sampling)
#> Chain 2: Iteration: 180 / 200 [ 90%]  (Sampling)
#> Chain 2: Iteration: 200 / 200 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.007 seconds (Warm-up)
#> Chain 2:                0.008 seconds (Sampling)
#> Chain 2:                0.015 seconds (Total)
#> Chain 2: 

result <- hdi(model, ci = c(0.5, 0.75, 0.9, 0.95))
data <- data_plot(result, data = model)

p <- ggplot(
  data,
  aes(x = x, y = y, height = height, group = y, fill = fill)
) +
  ggridges::geom_ridgeline_gradient()

p

p + add_plot_attributes(data)

# }