# Does it make sense to see if success of one thing determines the success of another

I have modeled whether a bird is detected by an antenna (1=yes, 0=no) with the following predictor variables: length of visit, species, and site. Individual ID is a random effect.

I am not also wondering, and think it could be valuable to see whether a bird took a seed(1=yes, 0=no) would also determine whether a bird is detected by an antenna.

So the model would look like this:

mod <- glmer(success_rfid ~ length + species + site + success_seed + (1|id), family = binomial( link = "logit"), data = data)


The output looks like this:

summary(mod)
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept)    -4.4283     0.9780  -4.528 5.95e-06 ***
length          0.1921     0.1205   1.594   0.1110
speciesTUTI     1.5201     0.7887   1.927   0.0539 .
speciesWBNU     1.2716     0.8844   1.438   0.1505
siteL3         -0.7220     0.7591  -0.951   0.3415
siteYB2        -0.1024     0.8637  -0.119   0.9057
success_seed1   3.1105     0.7296   4.263 2.01e-05 ***


My initial reaction is "Oh, no I can't do this. Length of a visit predicts success of taking a seed. Those are correlated and couldn't be in the model."

And then I'm left wondering what to do.

• Why can't correlated explanatory variables be included in the model?
– whuber
Nov 19, 2019 at 21:24