Generates a data frame of bins representing the kernel density (or
histogram) of a vector, suitable for use in generating predictive
distributions for visualization. These functions were originally
designed for use with the now-deprecated predict_curve(), and
may be deprecated in the future.
density_bins(x, n = 101, ...) histogram_bins(x, n = 30, breaks = n, ...)
Arguments
| x | A numeric vector |
|---|---|
| n | Number of bins |
| ... | |
| breaks | Used to set bins for |
Value
A data frame representing bins and their densities with the following columns:
Bin midpoint
Lower endpoint of each bin
Upper endpoint of each bin
Density estimate of the bin
Details
These functions are simple wrappers to density() and
hist() that compute density estimates and return their results
in a consistent format: a data frame of bins suitable for use with
the now-deprecated predict_curve().
density_bins computes a kernel density estimate using
density().
histogram_bins computes a density histogram using hist().
See also
See add_predicted_draws() and stat_lineribbon() for a better approach. These
functions may be deprecated in the future.
Examples
#> #>#> #> #>library(tidyr)#> #>#> #> #>if ( require("rstanarm", quietly = TRUE) && require("modelr", quietly = TRUE) ) { theme_set(theme_light()) m_mpg = stan_glm(mpg ~ hp * cyl, data = mtcars) step = 1 mtcars %>% group_by(cyl) %>% data_grid(hp = seq_range(hp, by = step)) %>% add_predicted_draws(m_mpg) %>% summarise(densities = list(density_bins(.prediction))) %>% unnest(densities) %>% ggplot() + geom_rect(aes( xmin = hp - step/2, ymin = lower, ymax = upper, xmax = hp + step/2, fill = ordered(cyl), alpha = density )) + geom_point(aes(x = hp, y = mpg, fill = ordered(cyl)), shape = 21, data = mtcars) + scale_alpha_continuous(range = c(0, 1)) + scale_fill_brewer(palette = "Set2") }#> #> SAMPLING FOR MODEL 'continuous' 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) #> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup) #> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 1: #> Chain 1: Elapsed Time: 0.268 seconds (Warm-up) #> Chain 1: 0.291 seconds (Sampling) #> Chain 1: 0.559 seconds (Total) #> Chain 1: #> #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2). #> Chain 2: #> Chain 2: Gradient evaluation took 0 seconds #> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. #> Chain 2: Adjust your expectations accordingly! #> Chain 2: #> Chain 2: #> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup) #> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup) #> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 2: #> Chain 2: Elapsed Time: 0.288 seconds (Warm-up) #> Chain 2: 0.245 seconds (Sampling) #> Chain 2: 0.533 seconds (Total) #> Chain 2: #> #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3). #> Chain 3: #> Chain 3: Gradient evaluation took 0 seconds #> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. #> Chain 3: Adjust your expectations accordingly! #> Chain 3: #> Chain 3: #> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup) #> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup) #> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 3: #> Chain 3: Elapsed Time: 0.252 seconds (Warm-up) #> Chain 3: 0.244 seconds (Sampling) #> Chain 3: 0.496 seconds (Total) #> Chain 3: #> #> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4). #> Chain 4: #> Chain 4: Gradient evaluation took 0 seconds #> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. #> Chain 4: Adjust your expectations accordingly! #> Chain 4: #> Chain 4: #> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup) #> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup) #> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 4: #> Chain 4: Elapsed Time: 0.261 seconds (Warm-up) #> Chain 4: 0.262 seconds (Sampling) #> Chain 4: 0.523 seconds (Total) #> Chain 4:#># }
