Revision

**d73e4a2afcfbd6402c11716877e8f7466f309ef4**authored by Dominique Makowski on**22 October 2020, 13:40 UTC**, committed by cran-robot on**22 October 2020, 13:40 UTC****1 parent**1b89ec8

estimate_density.Rd

```
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/estimate_density.R
\name{estimate_density}
\alias{estimate_density}
\alias{estimate_density.data.frame}
\title{Density Estimation}
\usage{
estimate_density(
x,
method = "kernel",
precision = 2^10,
extend = FALSE,
extend_scale = 0.1,
bw = "SJ",
...
)
\method{estimate_density}{data.frame}(
x,
method = "kernel",
precision = 2^10,
extend = FALSE,
extend_scale = 0.1,
bw = "SJ",
group_by = NULL,
...
)
}
\arguments{
\item{x}{Vector representing a posterior distribution, or a data frame of such
vectors. Can also be a Bayesian model (\code{stanreg}, \code{brmsfit},
\code{MCMCglmm}, \code{mcmc} or \code{bcplm}) or a \code{BayesFactor} model.}
\item{method}{Density estimation method. Can be \code{"kernel"} (default), \code{"logspline"} or \code{"KernSmooth"}.}
\item{precision}{Number of points of density data. See the \code{n} parameter in \code{density}.}
\item{extend}{Extend the range of the x axis by a factor of \code{extend_scale}.}
\item{extend_scale}{Ratio of range by which to extend the x axis. A value of \code{0.1} means that the x axis will be extended by \code{1/10} of the range of the data.}
\item{bw}{See the eponymous argument in \code{density}. Here, the default has been changed for \code{"SJ"}, which is recommended.}
\item{...}{Currently not used.}
\item{group_by}{Optional character vector. If not \code{NULL} and \code{x} is a data frame, density estimation is performed for each group (subset) indicated by \code{group_by}.}
}
\description{
This function is a wrapper over different methods of density estimation. By default, it uses the base R \code{density} with by default uses a different smoothing bandwidth (\code{"SJ"}) from the legacy default implemented the base R \code{density} function (\code{"nrd0"}). However, Deng \& Wickham suggest that \code{method = "KernSmooth"} is the fastest and the most accurate.
}
\note{
There is also a \href{https://easystats.github.io/see/articles/bayestestR.html}{\code{plot()}-method} implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}.
}
\examples{
library(bayestestR)
set.seed(1)
x <- rnorm(250, 1)
# Methods
density_kernel <- estimate_density(x, method = "kernel")
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)
df <- data.frame(replicate(4, rnorm(100)))
head(estimate_density(df))
\dontrun{
# 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)
}
}
\references{
Deng, H., & Wickham, H. (2011). Density estimation in R. Electronic publication.
}
```

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