https://github.com/cran/bayestestR
Tip revision: 85d0a0463621c30063752c5577e591be0de5ae60 authored by Dominique Makowski on 26 July 2021, 08:40:08 UTC
version 0.10.5
version 0.10.5
Tip revision: 85d0a04
example2.R
## ---- include=FALSE-----------------------------------------------------------
if (!requireNamespace("see", quietly = TRUE) ||
!requireNamespace("dplyr", quietly = TRUE) ||
!requireNamespace("ggplot2", quietly = TRUE) ||
!requireNamespace("performance", quietly = TRUE) ||
!requireNamespace("BayesFactor", quietly = TRUE) ||
!requireNamespace("rstanarm", quietly = TRUE)) {
knitr::opts_chunk$set(eval = FALSE)
}
data(iris)
library(knitr)
library(bayestestR)
options(knitr.kable.NA = "")
knitr::opts_chunk$set(
comment = ">",
message = FALSE,
warning = FALSE,
out.width = "100%"
)
options(digits = 2)
set.seed(333)
## -----------------------------------------------------------------------------
result <- cor.test(iris$Sepal.Width, iris$Sepal.Length)
result
## ---- results='hide'----------------------------------------------------------
library(BayesFactor)
result <- correlationBF(iris$Sepal.Width, iris$Sepal.Length)
## -----------------------------------------------------------------------------
describe_posterior(result)
## -----------------------------------------------------------------------------
bayesfactor(result)
## ----echo=FALSE, fig.cap="Wagenmakers' pizza poking analogy. From the great <www.bayesianspectacles.org> blog.", fig.align='center', out.width="80%"----
knitr::include_graphics("https://github.com/easystats/easystats/raw/master/man/figures/bayestestR/LetsPokeAPizza.jpg")
## -----------------------------------------------------------------------------
library(see)
plot(bayesfactor(result)) +
scale_fill_pizza()
## -----------------------------------------------------------------------------
library(dplyr)
library(ggplot2)
# Select only two relevant species
data <- iris %>%
filter(Species != "setosa") %>%
droplevels()
# Visualise distributions and observations
data %>%
ggplot(aes(x = Species, y = Sepal.Width, fill = Species)) +
geom_violindot(fill_dots = "black", size_dots = 1) +
scale_fill_material() +
theme_modern()
## -----------------------------------------------------------------------------
result <- BayesFactor::ttestBF(formula = Sepal.Width ~ Species, data = data)
describe_posterior(result)
## -----------------------------------------------------------------------------
library(rstanarm)
model <- stan_glm(Species ~ Sepal.Width, data = data, family = "binomial", refresh = 0)
## -----------------------------------------------------------------------------
library(modelbased)
vizdata <- estimate_relation(model)
ggplot(vizdata, aes(x = Sepal.Width, y = Predicted)) +
geom_ribbon(aes(ymin = CI_low, ymax = CI_high), alpha = 0.5) +
geom_line() +
ylab("Probability of being virginica") +
theme_modern()
## -----------------------------------------------------------------------------
describe_posterior(model, test = c("pd", "ROPE", "BF"))
## -----------------------------------------------------------------------------
library(performance)
model_performance(model)
## -----------------------------------------------------------------------------
library(see)
plot(rope(result))