Plot your own generated confidence interval with ggplot2 in R-CodePudding

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Thank you very much for the help, if I am missing to give some informations do not hesitate to ask, I will edit my post.

CodePudding user response：

As mentioned in the comments, you can do this with geom_ribbon(), you just need to merge the CI data with the model coefficient data.

CIs_to_plot <- map_dfr(1:dim(CI_mod1)[3], ~as_tibble(CI_mod1[,,.x], rownames = "pctle"), 
                       .id = "Quantile") %>% 
  pivot_wider(names_from = "pctle", values_from = c("V1", "V2")) %>% 
  rename("intercept.low" = `V1_2.5%`, "intercept.high" = `V1_97.5%`,
         "julian_day.low" =`V2_2.5%`, "julian_day.high" = `V2_97.5%`) %>% 
  pivot_longer(-Quantile, names_sep = "\\.", names_to = c("beta", ".value")) %>% 
  mutate(Quantile = parse_number(Quantile))
  
model_to_plot <- model1 %>% 
  mutate(Quantile=row_number()) %>% 
  pivot_longer(!Quantile,names_to="beta",values_to = "Coefficient")

model_to_plot %>% 
  left_join(CIs_to_plot, by = c("Quantile", "beta")) %>% 
  ggplot(aes(Quantile,Coefficient))   
  geom_ribbon(aes(ymin = low, ymax = high), fill = "grey", alpha = .5)  
  geom_line(aes(color=beta))  
  facet_wrap(~beta, scales="free_y")  
  theme_minimal()