https://github.com/cran/bayestestR
Revision d8462ad2168ad7ee61c0d7e679174e775f01a9be authored by Dominique Makowski on 18 January 2020, 07:10:02 UTC, committed by cran-robot on 18 January 2020, 07:10:02 UTC
1 parent 4936034
Tip revision: d8462ad2168ad7ee61c0d7e679174e775f01a9be authored by Dominique Makowski on 18 January 2020, 07:10:02 UTC
version 0.5.0
version 0.5.0
Tip revision: d8462ad
example1.R
## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
library(knitr)
options(knitr.kable.NA = '')
knitr::opts_chunk$set(comment=">")
options(digits=2)
set.seed(333)
## ----message=FALSE, warning=FALSE---------------------------------------------
library(rstanarm)
library(bayestestR)
library(insight)
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
model <- lm(Sepal.Length ~ Petal.Length, data=iris)
summary(model)
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
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$Petal.Length, ci=0.89)
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
library(dplyr)
# We keep only rows for which feed is meatmeal or sunflower
data <- chickwts %>%
filter(feed %in% c("meatmeal", "sunflower"))
## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------------
# model <- stan_glm(weight ~ feed, data=data)
## ----echo=FALSE, message=FALSE, warning=FALSE, comment=NA, results='hide'-----
model <- stan_glm(weight ~ feed, data=data)
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
posteriors <- insight::get_parameters(model)
ggplot(posteriors, aes(x=feedsunflower)) +
geom_density(fill = "red")
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
median(posteriors$feedsunflower)
hdi(posteriors$feedsunflower)
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
rope(posteriors$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, eval=TRUE----------------------------------
rope_value <- 0.1 * sd(data$weight)
rope_range <- c(-rope_value, rope_value)
rope_range
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
rope_value <- rope_range(model)
rope_range
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
rope(posteriors$feedsunflower, range = rope_range, ci=0.89)
## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------------
# n_positive <- posteriors %>%
# filter(feedsunflower > 0) %>% # select only positive values
# nrow() # Get length
# n_positive / nrow(posteriors) * 100
## ----echo=FALSE, message=FALSE, warning=FALSE---------------------------------
n_positive <- posteriors %>%
filter(feedsunflower > 0) %>% # select only positive values
nrow() # Get length
format(n_positive / nrow(posteriors) * 100, nsmall = 2)
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
p_direction(posteriors$feedsunflower)
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
pd <- 97.82
onesided_p <- 1 - pd / 100
twosided_p <- onesided_p * 2
twosided_p
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
lm(weight ~ feed, data=data) %>%
summary()
## ----message=FALSE, warning=FALSE, eval=TRUE----------------------------------
describe_posterior(model, test = c("p_direction","rope","bayesfactor"))
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