95％信頼度の結果を比較するためのRのBoxplot異なるサンプルサイズの間隔

Question

ブートストラップを使用して、95％信頼区間の境界にガンマ分布のサンプルサイズの効果を表示しようとしています。今、4つの異なるサンプルサイズの結果を1つのボックスプロット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 

Answer 1

あなたはDataTableのminとmax値を格納し、その後、私は例2のための簡単な例を与えると同様のあなたが行うことができますgeom_rect()

を使用する必要があります美容師と一緒に遊んで、より良い視界を持つことができます。

library(boot) 
pbootresults<-boot(y, gammamedian, R=1000, sim="parametric",    
        ran.gen=gengamma, mle=fit$estimate) 
pbootresults 
temp2 <-boot.ci(pbootresults, type=c("basic", "perc", "norm")) # store teh results 
pbootresults<-boot(y60, gammamedian, R=1000, sim="parametric", 
        ran.gen=gengamma, mle=fit$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 
temp <- boot.ci(pbootresults, type=c("basic", "perc", "norm")) # store the results 

datatable <- temp$percent %>% as.data.table() 
datatable <- rbind(datatable, temp2$percent %>% as.data.table()) 
datatable[, group :=.I] 

ggplot(data = datatable) + geom_rect(mapping = aes(xmin = group - 0.25, xmax = group+ 0.25, ymin = V4, ymax = V5))