Thanks in advance for all help.
I would like to produce a plot similar to the below from my model averaged results. This plot was produced from a single model using the effect() function in the effects package.
To my knowledge this cannot be achieve from model averaged results from the model.avg() function so I have tried to achieve a similar result by first predicting my model averaged results and then creating a plot.
I have built two models from this data:
igm_20 <- glmmTMB(igm_pres ~ fRHDV2_arrive_cat + fseason + sage + save_ajust_abun + fseason*fRHDV2_arrive_cat + (1 | fsite), data = test, family = binomial)
igm_21 <- glmmTMB(igm_pres ~ fRHDV2_arrive_cat + fseason + sage + save_ajust_abun + fseason*fRHDV2_arrive_cat + sage*fRHDV2_arrive_cat + (1 | fsite), data = test, family = binomial)
I can average these models like so:
mod_ave_list_1 <- list(igm_20, igm_21)
mod_ave_1 <- model.avg(mod_ave_list_1, rank = AICc)
s1 <- summary(mod_ave_1)
I have then tried to make predictions from the model averaged results for each combination of fseason*fRHDV2_arrive_cat from the dataset provided, test:
a <- as.data.frame(c("Summer", "Autumn", "Winter", "Spring", "Summer", "Autumn", "Winter", "Spring"))
a$fRHDV2_arrive_cat <- c("Pre-arrival", "Post-arrival", "Pre-arrival", "Post-arrival", "Pre-arrival", "Post-arrival", "Pre-arrival", "Post-arrival")
colnames(a) <- c("fseason", "fRHDV2_arrive_cat")
predict(mod_ave_1, full = TRUE, backtransform = TRUE, newdata = a, se.fit = TRUE)
The predict function runs fine if I do not include newdata = a, consequently I believe the data frame I supply to newdata is not of the appropriate structure.
If someone is able to help me to structure newdata properly so that I can produce predictions for each combination of fseason*fRHDV2_arrive_cat it would be much appreciated? I beleive I can produce the desired plot once I have the predictions.
My situation is very similar to that described in another post (here), which remains unanswered. Above I have described my attempt to achieve a similar plot via another means. I also note that there are other similar posts, such as here; I have not found these useful to my situation.
Your newdata must include all the explanatory variables used by the model, which it currently does not.
Variables required for prediction:
all.vars(formula(mod_ave_1)[-2]) # extracts all names from formula minus response
# [1] "fRHDV2_arrive_cat" "fseason" "sage" "save_ajust_abun"
Variables in the new data:
colnames(a)
# [1] "fseason" "fRHDV2_arrive_cat"