https://github.com/cran/bayestestR
Revision 40f7c88ddf855896018cf20ec8a7ac5fbd0ea2fb authored by Dominique Makowski on 27 January 2020, 05:30 UTC, committed by cran-robot on 27 January 2020, 05:30 UTC
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Tip revision: 40f7c88ddf855896018cf20ec8a7ac5fbd0ea2fb authored by Dominique Makowski on 27 January 2020, 05:30 UTC
version 0.5.1
Tip revision: 40f7c88
example1.R
## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
library(knitr)
library(insight)
options(knitr.kable.NA = '')
knitr::opts_chunk$set(comment=">")
options(digits=2)

set.seed(333)

if (!requireNamespace("rstanarm", quietly = TRUE) &&
    !requireNamespace("dplyr", quietly = TRUE) &&
    !requireNamespace("ggplot2", quietly = TRUE)) {
  knitr::opts_chunk$set(eval = FALSE)
}

format_percent <- function(x, digits = 0, ...) {
  paste0(format_value(x*100, digits = digits, ...), "%")
}

## ----message=FALSE, warning=FALSE---------------------------------------------
library(rstanarm)
library(bayestestR)
library(insight)

## ----message=FALSE, warning=FALSE---------------------------------------------
model <- lm(Sepal.Length ~ Petal.Length, data=iris)
summary(model)

## ----message=FALSE, warning=FALSE---------------------------------------------
library(ggplot2)  # Load the package

# The ggplot function takes the data as argument, and then the variables 
# related to aesthetic features such as the x and y axes.
ggplot(iris, aes(x=Petal.Length, y=Sepal.Length)) +
  geom_point() +  # This adds the points
  geom_smooth(method="lm") # This adds a regression line

## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------------
#  model <- stan_glm(Sepal.Length ~ Petal.Length, data=iris)

## ----echo=FALSE, message=FALSE, warning=FALSE, comment=NA, results='hide'-----
library(rstanarm)
set.seed(333)

model <- stan_glm(Sepal.Length ~ Petal.Length, data=iris)

## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------------
#  posteriors <- insight::get_parameters(model)
#  
#  head(posteriors)  # Show the first 6 rows

## ----message=FALSE, warning=FALSE, echo=FALSE---------------------------------
posteriors <- insight::get_parameters(model)

head(posteriors)  # Show the first 6 rows

## ----message=FALSE, warning=FALSE---------------------------------------------
nrow(posteriors)  # Size (number of rows)

## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------------
#  model <- stan_glm(Sepal.Length ~ Petal.Length, data=iris, chains = 2, iter = 1000, warmup = 250)
#  
#  nrow(insight::get_parameters(model))  # Size (number of rows)

## ----echo=FALSE, message=FALSE, warning=FALSE, comment=NA, echo=FALSE---------
model <- stan_glm(Sepal.Length ~ Petal.Length, data=iris, chains = 2, iter = 1000, warmup = 250, refresh = 0)
nrow(insight::get_parameters(model))  # Size (number of rows)

## ----message=FALSE, warning=FALSE---------------------------------------------
ggplot(posteriors, aes(x = Petal.Length)) +
  geom_density(fill = "orange")

## ----message=FALSE, warning=FALSE---------------------------------------------
mean(posteriors$Petal.Length)

## ----message=FALSE, warning=FALSE---------------------------------------------
median(posteriors$Petal.Length)

## ----message=FALSE, warning=FALSE---------------------------------------------
map_estimate(posteriors$Petal.Length)

## ----message=FALSE, warning=FALSE---------------------------------------------
ggplot(posteriors, aes(x = Petal.Length)) +
  geom_density(fill = "orange") +
  # The mean in blue
  geom_vline(xintercept=mean(posteriors$Petal.Length), color="blue", size=1) +
  # The median in red
  geom_vline(xintercept=median(posteriors$Petal.Length), color="red", size=1) +
  # The MAP in purple
  geom_vline(xintercept=map_estimate(posteriors$Petal.Length), color="purple", size=1)

## ----message=FALSE, warning=FALSE---------------------------------------------
range(posteriors$Petal.Length)

## ----message=FALSE, warning=FALSE---------------------------------------------
hdi(posteriors$Petal.Length, ci=0.89)

## ----message=FALSE, warning=FALSE---------------------------------------------
library(dplyr)

# We keep only rows for which feed is meatmeal or sunflower
data <- chickwts %>% 
  filter(feed %in% c("meatmeal", "sunflower"))

## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------------
#  model <- stan_glm(weight ~ feed, data=data)

## ----echo=FALSE, message=FALSE, warning=FALSE, comment=NA, results='hide'-----
model <- stan_glm(weight ~ feed, data=data)

## ----message=FALSE, warning=FALSE---------------------------------------------
posteriors <- insight::get_parameters(model)

ggplot(posteriors, aes(x=feedsunflower)) +
  geom_density(fill = "red")

## ----message=FALSE, warning=FALSE---------------------------------------------
median(posteriors$feedsunflower)
hdi(posteriors$feedsunflower)

## ----message=FALSE, warning=FALSE---------------------------------------------
rope(posteriors$feedsunflower, range = c(-20, 20), ci=0.89)

## ----echo=FALSE, fig.cap="Prof. Sanders giving default values to define the Region of Practical Equivalence (ROPE).", fig.align='center', out.width="75%"----
knitr::include_graphics("https://github.com/easystats/easystats/raw/master/man/figures/bayestestR/profsanders.png")

## ----message=FALSE, warning=FALSE---------------------------------------------
rope_value <- 0.1 * sd(data$weight)
rope_range <- c(-rope_value, rope_value)
rope_range

## ----message=FALSE, warning=FALSE---------------------------------------------
rope_value <- rope_range(model)
rope_range

## ----message=FALSE, warning=FALSE---------------------------------------------
rope(posteriors$feedsunflower, range = rope_range, ci=0.89)

## ----message=FALSE, warning=FALSE---------------------------------------------
n_positive <- posteriors %>% 
  filter(feedsunflower > 0) %>% # select only positive values
  nrow() # Get length
n_positive / nrow(posteriors) * 100

## ----message=FALSE, warning=FALSE---------------------------------------------
p_direction(posteriors$feedsunflower)

## ----message=FALSE, warning=FALSE---------------------------------------------
pd <- 97.82
onesided_p <- 1 - pd / 100  
twosided_p <- onesided_p * 2
twosided_p

## ----message=FALSE, warning=FALSE---------------------------------------------
lm(weight ~ feed, data=data) %>% 
  summary()

## ----message=FALSE, warning=FALSE---------------------------------------------
describe_posterior(model, test = c("p_direction","rope","bayesfactor"))

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