无法使用 ggplot 绘制置信区间，（geom_ribbon() 参数）

Question

我正在尝试绘制一些模拟值的 95% 置信区间，但是当我尝试使用 geom_ribbon() 参数绘制 CI 时运行遇到了这样的问题。我遇到的麻烦是我的模型在绘制它们时没有显示 CI，就像这样；

如果有人知道我哪里出错了，我已经在下面包含了我的所有代码；

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

在一个 to the question, it's asked why not to logit transform the predicted values. The reason why is that the type of prediction asked for is "response". From the documentation，我强调。

type
the type of prediction required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities. The "terms" option returns a matrix giving the fitted values of each term in the model formula on the linear predictor scale.

有好办法回答，给大家看代码。

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 package (v2.0.1)}

于 2022-05-29 创建

无法使用 ggplot 绘制置信区间，（geom_ribbon() 参数）

Unable to plot confidence intervals using ggplot, (geom_ribbon() argument)

r

confidence-interval

geom-ribbon