bayestestR (version 0.11.5)

estimate_density: Density Estimation

Description

This function is a wrapper over different methods of density estimation. By default, it uses the base R density with by default uses a different smoothing bandwidth ("SJ") from the legacy default implemented the base R density function ("nrd0"). However, Deng \& Wickham suggest that method = "KernSmooth" is the fastest and the most accurate.

Usage

estimate_density(
  x,
  method = "kernel",
  precision = 2^10,
  extend = FALSE,
  extend_scale = 0.1,
  bw = "SJ",
  ...
)
# S3 method for data.frame
estimate_density(
  x,
  method = "kernel",
  precision = 2^10,
  extend = FALSE,
  extend_scale = 0.1,
  bw = "SJ",
  ci = NULL,
  group_by = NULL,
  ...
)

Arguments

x

Vector representing a posterior distribution, or a data frame of such vectors. Can also be a Bayesian model (stanreg, brmsfit, MCMCglmm, mcmc or bcplm) or a BayesFactor model.

method

Density estimation method. Can be "kernel" (default), "logspline" or "KernSmooth".

precision

Number of points of density data. See the n parameter in density.

extend

Extend the range of the x axis by a factor of extend_scale.

extend_scale

Ratio of range by which to extend the x axis. A value of 0.1 means that the x axis will be extended by 1/10 of the range of the data.

bw

See the eponymous argument in density. Here, the default has been changed for "SJ", which is recommended.

...

Currently not used.

ci

The confidence interval threshold. Only used when method = "kernel".

group_by

Optional character vector. If not NULL and x is a data frame, density estimation is performed for each group (subset) indicated by group_by.

References

Deng, H., & Wickham, H. (2011). Density estimation in R. Electronic publication.

Examples

# NOT RUN {
library(bayestestR)

set.seed(1)
x <- rnorm(250, mean = 1)

# Basic usage
density_kernel <- estimate_density(x) # default method is "kernel"

hist(x, prob = TRUE)
lines(density_kernel$x, density_kernel$y, col = "black", lwd = 2)
lines(density_kernel$x, density_kernel$CI_low, col = "gray", lty = 2)
lines(density_kernel$x, density_kernel$CI_high, col = "gray", lty = 2)
legend("topright",
  legend = c("Estimate", "95% CI"),
  col = c("black", "gray"), lwd = 2, lty = c(1, 2)
)

# Other Methods
density_logspline <- estimate_density(x, method = "logspline")
density_KernSmooth <- estimate_density(x, method = "KernSmooth")
density_mixture <- estimate_density(x, method = "mixture")

hist(x, prob = TRUE)
lines(density_kernel$x, density_kernel$y, col = "black", lwd = 2)
lines(density_logspline$x, density_logspline$y, col = "red", lwd = 2)
lines(density_KernSmooth$x, density_KernSmooth$y, col = "blue", lwd = 2)
lines(density_mixture$x, density_mixture$y, col = "green", lwd = 2)

# Extension
density_extended <- estimate_density(x, extend = TRUE)
density_default <- estimate_density(x, extend = FALSE)

hist(x, prob = TRUE)
lines(density_extended$x, density_extended$y, col = "red", lwd = 3)
lines(density_default$x, density_default$y, col = "black", lwd = 3)

# Multiple columns
df <- data.frame(replicate(4, rnorm(100)))
head(estimate_density(df))

# Grouped data
estimate_density(iris, group_by = "Species")
estimate_density(iris$Petal.Width, group_by = iris$Species)
# }
# NOT RUN {
# rstanarm models
# -----------------------------------------------
library(rstanarm)
model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
head(estimate_density(model))

library(emmeans)
head(estimate_density(emtrends(model, ~1, "wt")))

# brms models
# -----------------------------------------------
library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
estimate_density(model)
# }
# NOT RUN {
# }