bayestestR: estimate_density – R documentation

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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 class '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`.

Note

There is also a plot()-method implemented in the see-package.

References

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

Examples

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)

## End(Not run)

bayestestR

Understand and Describe Bayesian Models and Posterior Distributions

v0.10.0

GPL-3

Authors

Dominique Makowski [aut, cre] (<https://orcid.org/0000-0001-5375-9967>, @Dom_Makowski), Daniel Lüdecke [aut] (<https://orcid.org/0000-0002-8895-3206>, @strengejacke), Mattan S. Ben-Shachar [aut] (<https://orcid.org/0000-0002-4287-4801>, @mattansb), Indrajeet Patil [aut] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets), Michael D. Wilson [aut] (<https://orcid.org/0000-0003-4143-7308>), Paul-Christian Bürkner [rev], Tristan Mahr [rev] (<https://orcid.org/0000-0002-8890-5116>), Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>), Quentin F. Gronau [ctb] (<https://orcid.org/0000-0001-5510-6943>), Sam Crawley [ctb] (<https://orcid.org/0000-0002-7847-0411>)

Initial release