abikoushi

library(DiagrammeR)
library(DiagrammeRsvg)
library(rsvg)
library(dplyr)
library(dqrng)
library(ggplot2)

g <- grViz("digraph{
                     graph[rankdir = TB]
                     node[shape = none]

abikoushi

library(dagitty)
library(ggdag)
library(ggplot2)
library(gridExtra)

g1 <- dagitty("dag{U -> X; U ->Y; X -> Y}")
exposures(g1) <- "X"
outcomes(g1) <- "Y"

plot(g1)

abikoushi

library(ggplot2)

NB_p <- function(p,x,r){
  pnbinom(x, size = r, prob = p,lower.tail = TRUE)  
}

B_p <- function(p,x,r){
  pbinom(r-1, size = r+x, prob = p, lower.tail = FALSE)  
}

abikoushi

library(rootSolve)
findall_q = function(q_ini, a, b, ...){
  dp = dbeta(q_ini, shape1 = a, shape2 = b)
  sol = rootSolve::uniroot.all(function(X)dbeta(X, shape1 = a, shape2 = b)-dp, interval = c(0, 1), ...)
  sort( sol )
}

prob_diff <- function(q, a, b){
  p = pbeta(q, shape1 = a, shape2 = b)
  diff( p )

abikoushi

#install.packages("ggalluvial")
library("ggalluvial")
library("dplyr")
V_row=matrix(rexp(100*10),100,10)
V_col=matrix(rexp(100*10),100,10)

W_row = matrix(rexp(10*5),10,5)
cl_row = apply(V_row%*%W_row,1,which.max)

W_col = matrix(rexp(10*7),10,7)

abikoushi

# https://en.wikipedia.org/wiki/Wallenius%27_noncentral_hypergeometric_distribution#Multivariate_distribution
# https://markheckmann.github.io/notes/wallenius-distribution.html
library(dplyr)
library(tibble)
library(BiasedUrn)
set.seed(1234); R = rexp(10)

ressim = t(replicate(1e5L,sort(sample.int(10, size = 3, prob = R))))
pmf = data.frame(ressim) %>% 
  group_by_all() %>% 

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\documentclass[tikz, border=14pt]{standalone}
\usepackage{tikz}
\usetikzlibrary{bayesnet}
\begin{document}
\begin{tikzpicture}
    \node[obs] (x) {$X$};
    \node[obs, above right = of x] (z) {$Z$};
    \node[obs, below right = of z] (y) {$Y$};
    \edge{x}{y};
    \edge{z}{y};

abikoushi

#browseURL("https://en.wikipedia.org/wiki/Weibull_distribution")

median_weibull <- function(shape, scale=1){
  ln2 = log(2)
  scale * (ln2^(1/shape))  
}

relativepoverty <- function(shape, scale=1){
  pweibull(0.5*median_weibull(shape, scale), shape, scale)
}

abikoushi

########
## 佐藤俊哉『宇宙怪人しまりす統計よりも重要なことを学ぶ』（朝倉書店）
## Chapter 1: 再現性
########
library(dplyr)
library(ggplot2)
TPratio = function(true_ratio,
                           beta = 0.8,
                           alpha = 0.05){
  study1 =cbind(true_ratio*c(beta,1-beta), 

abikoushi

########
## 佐藤俊哉『宇宙怪人しまりす統計よりも重要なことを学ぶ』（朝倉書店）
## Chapter 1: 再現性
########
library(dplyr)
library(ggplot2)
reproducibility = function(true_ratio,
                           beta = 0.8,
                           alpha = 0.05){
  study1 =cbind(true_ratio*c(beta,1-beta),