abikoushi

set.seed(1234)
n <- 20
x <- rbinom(1,n,0.5)
print(x)

probbeta <- function(p,x,n){
  ahat <- x + .5
  bhat <- n - x + .5
  pl <- pbeta(p, ahat, bhat, lower.tail=TRUE)
  pu <- pbeta(p, ahat, bhat, lower.tail=FALSE)

abikoushi

set.seed(1234)
n <- 12
x <- rbinom(1,n,0.5)
res_p <- prop.test(x, n, p = 0.5, correct = FALSE)

CI_score <- function(x, n, level){
  z <- qnorm(0.5*(1-level), lower.tail = FALSE)
  phat <- x/n
  t_1 <- phat+(z^2)/(2*n)
  t_2 <- sqrt(z^2+4*n*phat*(1-phat))*(z/(2*n))

abikoushi

plot.miniLineTable <- function(tab,...){
  oldpar <- graphics::par(no.readonly = TRUE)
  N <- length(tab$y)
  graphics::par(mar = c(4, 10, 0, 2), oma = rep(1, 4))
  graphics::layout(mat = cbind(N+1,1:N,1:N), respect = FALSE)
  for (i in 1:N) {
    tmpy <- tab$y[[i]]
    matplot(tmpy, type = "l", lty=1, xaxt="n", yaxt="n",
            xlab = "", ylab = "", frame.plot = FALSE,
            col=1)

abikoushi

library(deSolve)
library(ggplot2)

RNAvelo <- function(Time, State, Pars, input) {
  with(as.list(c(State, Pars)), {
    import <- input(Time)
    du   <- import  - beta * U
    ds   <- beta * U - gamma * S
    return(list(c(du, ds)))
  })

abikoushi

using Distributions
using Plots

function Chisqtest(tab, beta0)
  beta = log(tab[2,2]) - log(tab[2,1]) - (log(tab[1,2])-log(tab[1,1]))
  V = sum(inv.(tab))
  return ccdf(Chisq(1.0), (beta-beta0)^2/V)
end

function Fishertest(X, phi)

abikoushi

using Distributions
using Plots


function Fishertest(X, phi)
    cs = sum(X, dims=1)
    rs = sum(X, dims=2)
    f(x, phi) = pdf(FisherNoncentralHypergeometric(cs[1],cs[2],rs[1], exp(phi)), x)
    prob = f.(0:cs[1], phi)
    return sum(prob[prob .<= prob[X[1,1]+1]])

abikoushi

library(MCMCpack)
library(exact2x2)
library(ggplot2)
library(patchwork)

X <-matrix(c(12,5,6,12), nrow=2)
fpfun <- function(phi){exact2x2::exact2x2(x=X, or=exp(phi))$p.value}
rs <- rowSums(X)
cs <- colSums(X)
flfun <- function(phi){MCMCpack::dnoncenhypergeom(x = X[1,1], cs[1],cs[2],rs[1], exp(phi))}

abikoushi

library(ggplot2)
set.seed(123)
x <- rweibull(50,1.5) - log(2)^(1/1.5)
qweibull(0.5,1.5)- log(2)^(1/1.5) #中央値が0の分布
sx <- jitter(sort(x)) #ちょうど0をなくすため
pv_w <- sapply(sx,function(m)wilcox.test(x, mu=m)$p.value)
pv_s <- sapply(sx,function(m)binom.test(sum(x>m), length(x))$p.value)
df4p <- data.frame(x=x,sx=sx,sign=pv_s,wilcox=pv_w)

m <- median(x)

abikoushi

X <- as.matrix(iris[,-5])
d <- dist(X)
h1 <- hclust(d^2, method = "ward.D")
h2 <- hclust(d, method = "ward.D2")
h1$height <- sqrt(h1$height)

#png("dendro2.png")
par(mfrow=c(1,2))
plot(h1)
plot(h2)

abikoushi

Y<-iris[,-5]
svdout <- svd(log(Y))
cname <- hcl.colors(3, alpha = 0.1)
#png("iris_svd.png")
matplot(t(svdout$u),
        type = "l",lty=1,
        col = cname[iris$Species],
        xlab = "PC", ylab = "value")
legend("topleft", legend = levels(iris$Species),
       col=hcl.colors(3), lty=1)