https://github.com/cran/bayestestR
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
probability_of_direction.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, 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")