在r图中手动导入置信区间

我在R中有一个散点图，我想为每个值加上90%的置信区间。我已经有了间隔的所有值。我只需要关于如何在图片中绘制它们的指南

---

例如，我有一个坐标为

（1,2）

的点，其置信区间为

[1.9,2.1

]（垂直）。如何为该点绘制该间隔？

以下是一个可能对您有所帮助的实现

data <- structure(list(growth = c(12L, 10L, 8L, 11L, 6L, 7L, 2L, 3L, 
3L), tannin = 0:8), .Names = c("growth", "tannin"), class = "data.frame", row.names = c(NA, 
-9L))

attach(data)
names(data)


plot(tannin,growth,pch=16,ylim=c(0,12))
model<-lm(growth ~ tannin)
abline(model)


ci.lines<-function(model,conf= .95 ,interval = "confidence"){
  x <- model[[12]][[2]]
  y <- model[[12]][[1]]
  xm<-mean(x)
  n<-length(x)
  ssx<- sum((x - mean(x))^2)
  s.t<- qt(1-(1-conf)/2,(n-2))
  xv<-seq(min(x),max(x),(max(x) - min(x))/100)
  yv<- coef(model)[1]+coef(model)[2]*xv

  se <- switch(interval,
        confidence = summary(model)[[6]] * sqrt(1/n+(xv-xm)^2/ssx),
        prediction = summary(model)[[6]] * sqrt(1+1/n+(xv-xm)^2/ssx)
              )
  # summary(model)[[6]] = 'sigma'

  ci<-s.t*se
  uyv<-yv+ci
  lyv<-yv-ci
  limits1 <- min(c(x,y))
  limits2 <- max(c(x,y))

  predictions <- predict(model, level = conf, interval = interval)

  insideCI <- predictions[,'lwr'] < y & y < predictions[,'upr']

  x_name <- rownames(attr(model[[11]],"factors"))[2]
  y_name <- rownames(attr(model[[11]],"factors"))[1]

  points(x[!insideCI],y[!insideCI], pch = 16, col = 'red')

  lines(xv,uyv,lty=2,col=ifelse(interval=="confidence",3,4))
  lines(xv,lyv,lty=2,col=ifelse(interval=="confidence",3,4))
}

ci.lines(model, conf= .95 , interval = "confidence")

---

data以下是一个可能对您有所帮助的实现

data <- structure(list(growth = c(12L, 10L, 8L, 11L, 6L, 7L, 2L, 3L, 
3L), tannin = 0:8), .Names = c("growth", "tannin"), class = "data.frame", row.names = c(NA, 
-9L))

attach(data)
names(data)


plot(tannin,growth,pch=16,ylim=c(0,12))
model<-lm(growth ~ tannin)
abline(model)


ci.lines<-function(model,conf= .95 ,interval = "confidence"){
  x <- model[[12]][[2]]
  y <- model[[12]][[1]]
  xm<-mean(x)
  n<-length(x)
  ssx<- sum((x - mean(x))^2)
  s.t<- qt(1-(1-conf)/2,(n-2))
  xv<-seq(min(x),max(x),(max(x) - min(x))/100)
  yv<- coef(model)[1]+coef(model)[2]*xv

  se <- switch(interval,
        confidence = summary(model)[[6]] * sqrt(1/n+(xv-xm)^2/ssx),
        prediction = summary(model)[[6]] * sqrt(1+1/n+(xv-xm)^2/ssx)
              )
  # summary(model)[[6]] = 'sigma'

  ci<-s.t*se
  uyv<-yv+ci
  lyv<-yv-ci
  limits1 <- min(c(x,y))
  limits2 <- max(c(x,y))

  predictions <- predict(model, level = conf, interval = interval)

  insideCI <- predictions[,'lwr'] < y & y < predictions[,'upr']

  x_name <- rownames(attr(model[[11]],"factors"))[2]
  y_name <- rownames(attr(model[[11]],"factors"))[1]

  points(x[!insideCI],y[!insideCI], pch = 16, col = 'red')

  lines(xv,uyv,lty=2,col=ifelse(interval=="confidence",3,4))
  lines(xv,lyv,lty=2,col=ifelse(interval=="confidence",3,4))
}

ci.lines(model, conf= .95 , interval = "confidence")

数据
例如，我有一个坐标为（1,2）的点，其置信区间为[1.9,2.1]（垂直）

工作示例：

plot(rnorm(20, 1), rnorm(20,2))
points( 1,2, col="blue", cex=2)

segments(0.9, 1.9, 1.1, 1.9, col="red") # lower limit
segments(0.9, 2.1, 1.1, 2.1, col="red") # upper
segments(1 , 1.9, 1, 2.1, col="red", lty=3) # dashed vertical line connecting

例如，我有一个坐标为（1,2）的点，其置信区间为[1.9,2.1]（垂直）

工作示例：

plot(rnorm(20, 1), rnorm(20,2))
points( 1,2, col="blue", cex=2)

segments(0.9, 1.9, 1.1, 1.9, col="red") # lower limit
segments(0.9, 2.1, 1.1, 2.1, col="red") # upper
segments(1 , 1.9, 1, 2.1, col="red", lty=3) # dashed vertical line connecting

漂亮的我认为这是有用的，但我认为它没有回答问题。我认为这是有用的，但我认为它没有回答问题。