https://github.com/cran/bayestestR
Tip revision: 68a979e69aa2a1e57017730e1397470d5614d216 authored by Dominique Makowski on 02 September 2021, 23:10:30 UTC
version 0.11.0
version 0.11.0
Tip revision: 68a979e
probability_of_direction.R
## ----message=FALSE, warning=FALSE, include=FALSE------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if (!requireNamespace("see", quietly = TRUE) ||
!requireNamespace("dplyr", quietly = TRUE) ||
!requireNamespace("ggplot2", quietly = TRUE) ||
!requireNamespace("tidyr", quietly = TRUE) ||
!requireNamespace("logspline", quietly = TRUE) ||
!requireNamespace("KernSmooth", quietly = TRUE) ||
!requireNamespace("GGally", 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, echo=FALSE, fig.cap="Correlation between the frequentist p-value and the probability of direction (pd)", fig.align='center'----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(dplyr)
library(tidyr)
library(ggplot2)
library(see)
read.csv("https://raw.github.com/easystats/easystats/master/publications/makowski_2019_bayesian/data/data.csv") %>%
mutate(
effect_existence = ifelse(true_effect == 1, "Presence of true effect", "Absence of true effect"),
p_direction = p_direction * 100
) %>%
ggplot(aes(x = p_direction, y = p_value, color = effect_existence)) +
geom_point2(alpha = 0.1) +
geom_segment(aes(x = 95, y = Inf, xend = 95, yend = 0.1), color = "black", linetype = "longdash") +
geom_segment(aes(x = -Inf, y = 0.1, xend = 95, yend = 0.1), color = "black", linetype = "longdash") +
geom_segment(aes(x = 97.5, y = Inf, xend = 97.5, yend = 0.05), color = "black", linetype = "dashed") +
geom_segment(aes(x = -Inf, y = 0.05, xend = 97.5, yend = 0.05), color = "black", linetype = "dashed") +
theme_modern() +
scale_y_reverse(breaks = c(0.05, round(seq(0, 1, length.out = 11), digits = 2))) +
scale_x_continuous(breaks = c(95, 97.5, round(seq(50, 100, length.out = 6)))) +
scale_color_manual(values = c("Presence of true effect" = "green", "Absence of true effect" = "red")) +
theme(legend.title = element_blank()) +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
xlab("Probability of Direction (pd)") +
ylab("Frequentist p-value")
## ----message=FALSE, warning=FALSE, fig.align='center'-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(bayestestR)
library(logspline)
library(KernSmooth)
# Compute the correlations
data <- data.frame()
for (the_mean in runif(25, 0, 4)) {
for (the_sd in runif(25, 0.5, 4)) {
x <- rnorm(100, the_mean, abs(the_sd))
data <- rbind(
data,
data.frame(
"direct" = pd(x),
"kernel" = pd(x, method = "kernel"),
"logspline" = pd(x, method = "logspline"),
"KernSmooth" = pd(x, method = "KernSmooth")
)
)
}
}
data <- as.data.frame(sapply(data, as.numeric))
# Visualize the correlations
library(ggplot2)
library(GGally)
GGally::ggpairs(data) +
theme_classic()
## ----message=FALSE, warning=FALSE---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
data <- data.frame()
for (i in 1:25) {
the_mean <- runif(1, 0, 4)
the_sd <- abs(runif(1, 0.5, 4))
parent_distribution <- rnorm(100000, the_mean, the_sd)
true_pd <- pd(parent_distribution)
for (j in 1:25) {
sample_size <- round(runif(1, 25, 5000))
subsample <- sample(parent_distribution, sample_size)
data <- rbind(
data,
data.frame(
"sample_size" = sample_size,
"true" = true_pd,
"direct" = pd(subsample) - true_pd,
"kernel" = pd(subsample, method = "kernel") - true_pd,
"logspline" = pd(subsample, method = "logspline") - true_pd,
"KernSmooth" = pd(subsample, method = "KernSmooth") - true_pd
)
)
}
}
data <- as.data.frame(sapply(data, as.numeric))
## ----message=FALSE, warning=FALSE, fig.align='center'-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library(tidyr)
library(dplyr)
data %>%
tidyr::gather(Method, Distance, -sample_size, -true) %>%
ggplot(aes(x = sample_size, y = Distance, color = Method, fill = Method)) +
geom_point(alpha = 0.3, stroke = 0, shape = 16) +
geom_smooth(alpha = 0.2) +
geom_hline(yintercept = 0) +
theme_classic() +
xlab("\nDistribution Size")