## ----message=FALSE, warning=FALSE, include=FALSE-------------------------
library(knitr)
options(knitr.kable.NA = '')
knitr::opts_chunk$set(comment=">")
options(digits=2)
## ----message=FALSE, warning=FALSE----------------------------------------
library(ggplot2)
library(dplyr)
library(tidyr)
library(see)
df <- read.csv("https://raw.github.com/easystats/circus/master/data/bayesSim_study1.csv")
## ---- message=FALSE, warning=FALSE---------------------------------------
df %>%
select(error, true_effect, outcome_type, Coefficient, Median, Mean, MAP) %>%
gather(estimate, value, -error, -true_effect, -outcome_type) %>%
mutate(temp = as.factor(cut(error, 10, labels = FALSE))) %>%
group_by(temp) %>%
mutate(error_group = round(mean(error), 1)) %>%
ungroup() %>%
filter(value < 6) %>%
ggplot(aes(x = error_group, y = value, fill = estimate, group = interaction(estimate, error_group))) +
# geom_hline(yintercept = 0) +
# geom_point(alpha=0.05, size=2, stroke = 0, shape=16) +
# geom_smooth(method="loess") +
geom_boxplot(outlier.shape=NA) +
theme_modern() +
scale_fill_manual(values = c("Coefficient" = "#607D8B", "MAP" = "#795548", "Mean" = "#FF9800", "Median" = "#FFEB3B"),
name = "Index") +
ylab("Point-estimate") +
xlab("Noise") +
facet_wrap(~ outcome_type * true_effect, scales="free")
## ---- message=FALSE, warning=FALSE---------------------------------------
df %>%
select(sample_size, true_effect, outcome_type, Coefficient, Median, Mean, MAP) %>%
gather(estimate, value, -sample_size, -true_effect, -outcome_type) %>%
mutate(temp = as.factor(cut(sample_size, 10, labels = FALSE))) %>%
group_by(temp) %>%
mutate(size_group = round(mean(sample_size))) %>%
ungroup() %>%
filter(value < 6) %>%
ggplot(aes(x = size_group, y = value, fill = estimate, group = interaction(estimate, size_group))) +
# geom_hline(yintercept = 0) +
# geom_point(alpha=0.05, size=2, stroke = 0, shape=16) +
# geom_smooth(method="loess") +
geom_boxplot(outlier.shape=NA) +
theme_modern() +
scale_fill_manual(values = c("Coefficient" = "#607D8B", "MAP" = "#795548", "Mean" = "#FF9800", "Median" = "#FFEB3B"),
name = "Index") +
ylab("Point-estimate") +
xlab("Sample size") +
facet_wrap(~ outcome_type * true_effect, scales="free")
## ---- message=FALSE, warning=FALSE---------------------------------------
df %>%
select(sample_size, error, true_effect, outcome_type, Coefficient, Median, Mean, MAP) %>%
gather(estimate, value, -sample_size, -error, -true_effect, -outcome_type) %>%
glm(true_effect ~ outcome_type / estimate / value, data=., family="binomial") %>%
broom::tidy() %>%
select(term, estimate, p=p.value) %>%
filter(stringr::str_detect(term, 'outcome_type'),
stringr::str_detect(term, ':value')) %>%
arrange(desc(estimate)) %>%
knitr::kable(digits=2)
## ----message=FALSE, warning=FALSE----------------------------------------
df <- read.csv("https://raw.github.com/easystats/circus/master/data/bayesSim_study2.csv")
## ---- message=FALSE, warning=FALSE---------------------------------------
df %>%
select(iterations, true_effect, outcome_type, beta, Median, Mean, MAP) %>%
gather(estimate, value, -iterations, -true_effect, -outcome_type) %>%
mutate(temp = as.factor(cut(iterations, 5, labels = FALSE))) %>%
group_by(temp) %>%
mutate(iterations_group = round(mean(iterations), 1)) %>%
ungroup() %>%
filter(value < 6) %>%
ggplot(aes(x = iterations_group, y = value, fill = estimate, group = interaction(estimate, iterations_group))) +
geom_boxplot(outlier.shape=NA) +
theme_classic() +
scale_fill_manual(values = c("beta" = "#607D8B", "MAP" = "#795548", "Mean" = "#FF9800", "Median" = "#FFEB3B"),
name = "Index") +
ylab("Point-estimate of the true value 0\n") +
xlab("\nNumber of Iterations") +
facet_wrap(~ outcome_type * true_effect, scales="free")
## ---- message=FALSE, warning=FALSE---------------------------------------
df %>%
mutate(warmup = warmup / iterations) %>%
select(warmup, true_effect, outcome_type, beta, Median, Mean, MAP) %>%
gather(estimate, value, -warmup, -true_effect, -outcome_type) %>%
mutate(temp = as.factor(cut(warmup, 3, labels = FALSE))) %>%
group_by(temp) %>%
mutate(warmup_group = round(mean(warmup), 1)) %>%
ungroup() %>%
filter(value < 6) %>%
ggplot(aes(x = warmup_group, y = value, fill = estimate, group = interaction(estimate, warmup_group))) +
geom_boxplot(outlier.shape=NA) +
theme_classic() +
scale_fill_manual(values = c("beta" = "#607D8B", "MAP" = "#795548", "Mean" = "#FF9800", "Median" = "#FFEB3B"),
name = "Index") +
ylab("Point-estimate of the true value 0\n") +
xlab("\nNumber of Iterations") +
facet_wrap(~ outcome_type * true_effect, scales="free")