stat_poly_eq不计算每个系数的置信区间（如果您遵循?stat_poly_eq中的插图，则可以使用debug模式来显示其中的内容）。我认为最平滑的方法是从外部计算利息系数，然后加上：

library(tidyverse)
library(ggpmisc)

model <- iris |>
  lm(Sepal.Length ~ Sepal.Width, data = _) 

coef(model)['Sepal.Width']
#> Sepal.Width 
#>  -0.2233611

coef_label <- model |> 
  confint() |> 
  _['Sepal.Width',] |> 
  sprintf("%.2f", x = _) |> 
  paste(collapse = " to ") |> 
  paste0("paste(beta[1], \" = ", sprintf("%.2f", coef(model)['Sepal.Width']), " (", x = _, ")\")")

iris |>
  ggplot(aes(Sepal.Width, Sepal.Length)) +
  geom_point() +
  geom_smooth(method = "lm") +
  stat_poly_eq(
    method = "lm",
    aes(label = paste(
      after_stat(rr.label),
      after_stat(p.value.label),
      coef_label,
      sep = "*\", \"*"
    )),
    label.x = "right", label.y = "bottom", size = 4
  )
#> `geom_smooth()` using formula = 'y ~ x'

编辑答案-使用geom_text

您可以对每个组执行相同的操作，并使用geom_text将其绘制在分面图上。为了清晰起见，我已经删除了stat_poly_eq（因为它们很难不重叠，而且它们在面中也不完全粘贴在一起）

library(tidyverse)
library(gapminder)

gap_data <- gapminder |> 
  filter(continent != "Oceania", year == 2007) |> 
  mutate(gdpPercap = gdpPercap / 1000)

coef_labels <- gap_data |>
  nest(data = -continent) |>
  mutate(model = map(data, \(data) lm(lifeExp ~ gdpPercap, data = data)),
         coef_label = map_chr(model, \(model) {

           # This code is the label-pasting code from above
           # Creates a separate label per facet

           model |>
             confint() |>
             _['gdpPercap', ] |>
             sprintf("%.2f", x = _) |>
             paste(collapse = " to ") |>
             paste0("paste(beta[1], \" = ",
                    sprintf("%.2f", coef(model)['gdpPercap']),
                    " (", x = _, ")\")")

         })) |> 
  select(continent, coef_label)

gap_data |> 
  ggplot(aes(gdpPercap, lifeExp)) +
  geom_point() +
  geom_smooth(method = "lm") +
  geom_text(data = coef_labels,
            aes(x = Inf, y = -Inf, label = coef_label),
            hjust = 1,
            vjust = -1,
            parse = TRUE) +
  facet_wrap(~ continent, scales = "free") +
  xlab("GDP per capita ($1,000s)")
#> `geom_smooth()` using formula = 'y ~ x'