Revision d2eac42f58e4e0f0d07298e8c2e719ef6a30672d authored by Dominique Makowski on 19 June 2020, 08:00:07 UTC, committed by cran-robot on 19 June 2020, 08:00:07 UTC
1 parent 01482dc
region_of_practical_equivalence.R
## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
if (!requireNamespace("rstanarm", quietly = TRUE) || !requireNamespace("see", quietly = TRUE)) {
knitr::opts_chunk$set(eval = FALSE)
}
library(knitr)
options(knitr.kable.NA = '')
knitr::opts_chunk$set(comment=">")
options(digits=2)
set.seed(333)
## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------------
# library(rstanarm)
# library(bayestestR)
# library(see)
#
# data <- iris # Use the iris data
# model <- stan_glm(Sepal.Length ~ Sepal.Width, data=data) # Fit model
## ----echo=FALSE, message=FALSE, warning=FALSE, comment=">"--------------------
library(rstanarm)
library(bayestestR)
library(see)
set.seed(333)
data <- iris # Use the iris data
model <- stan_glm(Sepal.Length ~ Sepal.Width, data=data, refresh = 0)
## ----echo=TRUE, message=FALSE, warning=FALSE, comment=">"---------------------
# Compute indices
pd <- p_direction(model)
percentage_in_rope <- rope(model, ci=1)
# Visualise the pd
plot(pd)
pd
# Visualise the percentage in ROPE
plot(percentage_in_rope)
percentage_in_rope
## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------------
# data$Sepal.Width_scaled <- data$Sepal.Width / 100 # Divide predictor by 100
# model <- stan_glm(Sepal.Length ~ Sepal.Width_scaled, data=data) # Fit model
## ----echo=FALSE, message=FALSE, warning=FALSE, comment=">"--------------------
set.seed(333)
data$Sepal.Width_scaled <- data$Sepal.Width / 100
model <- stan_glm(Sepal.Length ~ Sepal.Width_scaled, data=data, refresh = 0)
## ----echo=TRUE, message=FALSE, warning=FALSE, comment=">"---------------------
# Compute indices
pd <- p_direction(model)
percentage_in_rope <- rope(model, ci=1)
# Visualise the pd
plot(pd)
pd
# Visualise the percentage in ROPE
plot(percentage_in_rope)
percentage_in_rope
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