Difference between insight::get_predicted and ggeffects::ggpredict when calculating PI

[mroumet]

Hi,

I'm quite new and would like to apologise if this is not the correct place to post my question.
I'm currently trying to compute model prediction and associated prediction intervals. My model is a Poisson regression. I currently use 2 methods to derive the prediction intervals: insight::get_predicted and ggeffects::ggpredict. The two methods lead to quite different PI. Moreover the PI obtained when using ggpredict are extremely large (in some cases the upper limit > the maximum observed value). How is that possible? I'm missing something?

I get the same issue when using a very simple model (see code below). I would greatly appreciate some help.

Many thanks in advance,
Marie

A basic example

data("Salamanders")

m <- glm( count ~ mined , family = poisson(link = "log"), data = Salamanders)

d <- data_grid(m, terms = c("mined"))

Using insight::get_predicted

cbind(d,insight::get_predicted(mm, d, ci = 0.95,predict = "prediction") )

mined Predicted CI_low CI_high
1 yes 0.2954545 2.220446e-16 2
2 no 2.2648810 2.220446e-16 6

Using ggpredict

ggpredict(m, terms = c("mined"), interval = "prediction" )

mined | Predicted | 95% CI

yes | 0.30 | [0.01, 6.41]
no | 2.26 | [0.11, 48.83]