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ブートストラップを使用して、95% 信頼区間の境界に対するガンマ分布のサンプル サイズの影響を示そうとしています。ここで、4 つの異なるサンプル サイズの結果を 1 つの箱ひげ図にまとめる必要があります。R コードは次のとおりです。

y <- rgamma(30,1,1) + rnorm(30,0,0.01)
y60 <- rgamma(60,1,1) + rnorm(60,0,0.01)
y100 <- rgamma(100,1,1) + rnorm(100,0,0.01)
y200 <- rgamma(200,1,1) + rnorm(200,0,0.01)
minusL <- function(params, data) {
-sum(log(dgamma(data, params[1], params[2])))
}
fit <- nlm(minusL, c(1,1), data=y)
fit
gammamedian<-function(data) {
fit <- nlm(minusL, c(1,1), data=data)
qgamma(.5, fit$estimate[1], fit$estimate[2])
}
gammamedian(y)
gammamedian(y60)
gammamedian(y100)
gammamedian(y200)
gengamma<- function(data, params){
rgamma(length(data), params[1], params[2])}
library(boot)
pbootresults<-boot(y, gammamedian, R=1000, sim="parametric",            
ran.gen=gengamma, mle=fit$estimate)
pbootresults
boot.ci(pbootresults, type=c("basic", "perc", "norm"))
pbootresults<-boot(y60, gammamedian, R=1000, sim="parametric",  
ran.gen=gengamma, mle=fit$estimate)
pbootresults
boot.ci(pbootresults, type=c("basic", "perc", "norm"))
pbootresults<-boot(y100, gammamedian, R=1000, sim="parametric",     
ran.gen=gengamma, mle=fit$estimate)
pbootresults
boot.ci(pbootresults, type=c("basic", "perc", "norm"))
pbootresults<-boot(y200, gammamedian, R=1000, sim="parametric",  
ran.gen=gengamma, mle=fit$estimate)
pbootresults
boot.ci(pbootresults, type=c("basic", "perc", "norm"))

 [An Excel image example ][1]


 [1]: https://i.stack.imgur.com/JXR6P.jpg
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