https://github.com/cran/bayestestR
Tip revision: 40f7c88ddf855896018cf20ec8a7ac5fbd0ea2fb authored by Dominique Makowski on 27 January 2020, 05:30:28 UTC
version 0.5.1
version 0.5.1
Tip revision: 40f7c88
credible_interval.R
## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
library(knitr)
options(knitr.kable.NA = '')
knitr::opts_chunk$set(comment=">")
options(digits=2)
if (!(requireNamespace("ggplot2", quietly = TRUE) &&
requireNamespace("dplyr", quietly = TRUE)
)) {
knitr::opts_chunk$set(eval = FALSE)
}
set.seed(333)
## ----warning=FALSE, message=FALSE---------------------------------------------
library(bayestestR)
library(dplyr)
library(ggplot2)
# Generate a normal distribution
posterior <- distribution_normal(1000)
# Compute HDI and ETI
ci_hdi <- ci(posterior, method = "HDI")
ci_eti <- ci(posterior, method = "ETI")
# Plot the distribution and add the limits of the two CIs
posterior %>%
estimate_density(extend=TRUE) %>%
ggplot(aes(x=x, y=y)) +
geom_area(fill="orange") +
theme_classic() +
# HDI in blue
geom_vline(xintercept=ci_hdi$CI_low, color="royalblue", size=3) +
geom_vline(xintercept=ci_hdi$CI_high, color="royalblue", size=3) +
# Quantile in red
geom_vline(xintercept=ci_eti$CI_low, color="red", size=1) +
geom_vline(xintercept=ci_eti$CI_high, color="red", size=1)
## ----warning=FALSE, message=FALSE---------------------------------------------
library(bayestestR)
library(dplyr)
library(ggplot2)
# Generate a beta distribution
posterior <- distribution_beta(1000, 6, 2)
# Compute HDI and Quantile CI
ci_hdi <- ci(posterior, method = "HDI")
ci_eti <- ci(posterior, method = "ETI")
# Plot the distribution and add the limits of the two CIs
posterior %>%
estimate_density(extend=TRUE) %>%
ggplot(aes(x=x, y=y)) +
geom_area(fill="orange") +
theme_classic() +
# HDI in blue
geom_vline(xintercept=ci_hdi$CI_low, color="royalblue", size=3) +
geom_vline(xintercept=ci_hdi$CI_high, color="royalblue", size=3) +
# Quantile in red
geom_vline(xintercept=ci_eti$CI_low, color="red", size=1) +
geom_vline(xintercept=ci_eti$CI_high, color="red", size=1)
## ----warning=FALSE, message=FALSE---------------------------------------------
prior <- distribution_normal(1000, mean = 0, sd = 1)
posterior <- distribution_normal(1000, mean = .5, sd = .3)
si_1 <- si(posterior, prior, BF = 1)
si_3 <- si(posterior, prior, BF = 3)
ggplot(mapping = aes(x=x, y=y)) +
theme_classic() +
# The posterior
geom_area(fill = "orange",
data = estimate_density(posterior, extend = TRUE)) +
# The prior
geom_area(color = "black", fill = NA, size = 1, linetype = "dashed",
data = estimate_density(prior, extend = TRUE)) +
# BF = 1 SI in blue
geom_vline(xintercept=si_1$CI_low, color="royalblue", size=1) +
geom_vline(xintercept=si_1$CI_high, color="royalblue", size=1) +
# BF = 3 SI in red
geom_vline(xintercept=si_3$CI_low, color="red", size=1) +
geom_vline(xintercept=si_3$CI_high, color="red", size=1)