swh:1:snp:cadb242f1f2b536450c3e647ffb173ab4a173342
Tip revision: e1fa15d202de277bb07e58bb3013557724072b2b authored by Dominique Makowski on 22 September 2019, 15:30:05 UTC
version 0.3.0
version 0.3.0
Tip revision: e1fa15d
region_of_practical_equivalence.R
## ----message=FALSE, warning=FALSE, include=FALSE-------------------------
library(knitr)
options(knitr.kable.NA = '')
knitr::opts_chunk$set(comment=">")
options(digits=2)
set.seed(333)
## ----message=FALSE, warning=FALSE, eval=FALSE----------------------------
# library(rstanarm)
# library(bayestestR)
# library(see)
#
# data <- iris # Use the iris data
# model <- stan_glm(Sepal.Length ~ Sepal.Width, data=data) # Fit model
## ----echo=FALSE, message=FALSE, warning=FALSE, comment=">"---------------
library(rstanarm)
library(bayestestR)
library(see)
set.seed(333)
data <- iris # Use the iris data
junk <- capture.output(model <- stan_glm(Sepal.Length ~ Sepal.Width, data=data))
## ----echo=TRUE, message=FALSE, warning=FALSE, comment=">"----------------
# Compute indices
pd <- p_direction(model)
percentage_in_rope <- rope(model, ci=1)
# Visualise the pd
plot(pd)
pd
# Visualise the percentage in ROPE
plot(percentage_in_rope)
percentage_in_rope
## ----message=FALSE, warning=FALSE, eval=FALSE----------------------------
# data$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
junk <- capture.output(model <- stan_glm(Sepal.Length ~ Sepal.Width_scaled, data=data))
## ----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