無法使用 ggplot 繪制置信區間，（geom_ribbon() 參數）

Question

我試圖在一些模擬值上繪制 95% 的置信區間，但是當我嘗試使用 geom_ribbon() 參數繪制 CI 時遇到了這樣的問題。 我遇到的麻煩是，當我繪制它們時，我的模型沒有顯示 CI，就像這樣；

如果有人知道我在這里出錯的地方，我已經在下面包含了我的所有代碼；

set.seed(20220520)  
#simulating 200 values between 0 and 1 from a uniform distribution
x = runif(200, min = 0, max = 1) 

lam = exp(0.3+5*x)

y = rpois(200, lambda = lam)

#before we do this each Yi may contain zeros so we need to add a small constant
y <- y + .1 
#combining x and y into a dataframe so we can plot
df = data.frame(x, y)

#fitting a Poisson GLM
model2 <- glm(y ~ x, 
          data = df,
          family = poisson(link='log'))

#make predictions (this may be the same as predictions_mod2)
preds <- predict(model2, type = "response")

#making CI predictions
predictions_mod2 = predict(model2, df, se.fit = TRUE, type = 'response')

#calculate confidence intervals limit
upper_mod2 = predictions_mod2$fit+1.96*predictions_mod2$se.fit 
lower_mod2 = predictions_mod2$fit-1.96*predictions_mod2$se.fit

#transform the CI limit to get one at the level of the mean
upper_mod2 = exp(upper_mod2)/(1+exp(upper_mod2)) 
lower_mod2 = exp(lower_mod2)/(1+exp(lower_mod2))

#combining into a df
predframe = data.frame(lwr=lower_mod2,upr=upper_mod2, x = df$x, y = df$y)

#plot model with 95% confidence intervals using ggplot
ggplot(df, aes(x, y)) +
  geom_ribbon(data = predframe, aes(ymin=lwr, ymax=upr), alpha = 0.4) +
  geom_point() +
  geom_line(aes(x, preds2), col = 'blue')

Answer 1

在對該問題的評論中，有人問為什么不對預測值進行 logit 轉換。 原因是要求的預測類型是"response" 。 從文檔中，我強調。

類型
所需的預測類型。 默認值在線性預測變量的范圍內； 另一種“響應”在響應變量的范圍內。 因此，對於默認二項式模型，默認預測是對數賠率（logit 標度上的概率），type = "response" 給出預測概率。 "terms" 選項返回一個矩陣，給出模型公式中每個項在線性預測尺度上的擬合值。

有一個很好的回答方法，顯示代碼。

library(ggplot2, quietly = TRUE)

set.seed(20220520)  
#simulating 200 values between 0 and 1 from a uniform distribution
x = runif(200, min = 0, max = 1) 

lam = exp(0.3+5*x)

y = rpois(200, lambda = lam)

#before we do this each Yi may contain zeros so we need to add a small constant
y <- y + 0.1 
#combining x and y into a dataframe so we can plot
df = data.frame(x, y)

#fitting a Poisson GLM
suppressWarnings(
  model2 <- glm(y ~ x, 
                data = df,
                family = poisson(link='log'))
)
#make predictions (this may be the same as predictions_mod2)
preds <- predict(model2, type = "response")

#making CI predictions
predictions_mod2 = predict(model2, df, se.fit = TRUE, type = 'response')

#calculate confidence intervals limit
upper_mod2 = predictions_mod2$fit+1.96*predictions_mod2$se.fit 
lower_mod2 = predictions_mod2$fit-1.96*predictions_mod2$se.fit

#combining into a df
predframe = data.frame(lwr=lower_mod2,upr=upper_mod2, x = df$x, y = df$y)

#plot model with 95% confidence intervals using ggplot
ggplot(df, aes(x, y)) +
  geom_ribbon(data = predframe, aes(ymin=lwr, ymax=upr), alpha = 0.4) +
  geom_point() +
  geom_line(aes(x, preds), col = 'blue')

^{由reprex 包於 2022-05-29 創建 (v2.0.1)}