Differences between get_predicted and get_predicted_ci for mixed models #797

library(tidyverse)
library(lme4)
library(glmmTMB)
library(insight)

m1 <- lmer(Petal.Width ~ Sepal.Length + (1 | Species), iris)
get_predicted(m1, ci = 0.95) %>% as.data.frame() %>% head
predictions <- predict(m1)
get_predicted_ci(m1, predictions) %>% as.data.frame() %>% head 
#' Matches. Currently, both of these standard errors are actually wrong because they 
#' do not account for the uncertainty in the predicted random effects. 
#' This is an lme4 thing though, and Ben Bolker among others is aware of this. 


m2 <- glmmTMB(Petal.Width ~ Sepal.Length + (1 | Species), iris, REML = TRUE)
get_predicted(m2, ci = 0.95) %>% as.data.frame() %>% head 
predictions <- predict(m2)
get_predicted_ci(m2, predictions) %>% as.data.frame() %>% head 
#' Does not match. The former gets the TMB prediction error,  which is better. 
#' But the latter reverts back to the lme4 incorrect prediction error. 

m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial)
get_predicted(m1, ci = 0.95) %>% as.data.frame() %>% head
predictions <- predict(m1, type = "response")
get_predicted_ci(m1, predictions) %>% as.data.frame() %>% head 
#' These two do not match (the latter is conisderably larger); not sure why.


m2 <- glmmTMB(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial)
get_predicted(m2, ci = 0.95) %>% as.data.frame() %>% head
predictions <- predict(m2, type = "response")
get_predicted_ci(m2, predictions) %>% as.data.frame() %>% head 
#' These two also do not match (the latter is conisderably larger); not sure why. 
#' Also, note the latter gets very similar results to get_predicted_ci(m1, predictions), 
#' while the former is not the same as get_predicted(m1, ci = 0.95)