ggpredict and glmmTMB

ggpredict_glmmTMB.R

library(glmmTMB)
library(ggeffects)

data("Salamanders")
m1 <- glmmTMB(
  count ~ mined + (1 | site),
  zi =  ~ mined,
  family = poisson,
  data = Salamanders
)

ggpredict(m1, "mined")
#> # Predicted counts of count
#> # x = mined
#> 
#> x   | Predicted |       95% CI
#> ------------------------------
#> yes |      1.09 | [0.69, 1.72]
#> no  |      3.42 | [2.86, 4.09]
#> 
#> Adjusted for:
#> * site = NA (population-level)

predict(m1, type = "conditional", newdata = data.frame(mined = c("yes", "no"), site = NA))
#> [1] 1.091875 3.420613


ggpredict(m1, "mined", type = "zero_inflated")
#> # Predicted counts of count
#> # x = mined
#> 
#> x   | Predicted |       95% CI
#> ------------------------------
#> yes |      0.26 | [0.11, 0.41]
#> no  |      2.21 | [1.79, 2.62]
#> 
#> Adjusted for:
#> * site = NA (population-level)

predict(m1, type = "response", newdata = data.frame(mined = c("yes", "no"), site = NA))
#> [1] 0.264723 2.206005