Posterior summaries
- The two models lead to similar conclusions for the relationship with age and oxygen use.
# Get trend summaries for both models
climb_model_1_mean <- tidy(climb_model_1, effects = "fixed")
climb_model_2_mean <- tidy(climb_model_2, effects = "fixed")
# Combine the summaries for both models
climb_model_1_mean %>%
right_join(., climb_model_2_mean, by ="term",
suffix = c("_model_1", "_model_2")) %>%
select(-starts_with("std.error"))
term estimate_model_1 estimate_model_2
<chr> <dbl> <dbl>
1 (Intercept) -1.41 -1.53
2 age -0.0475 -0.0474
3 oxygen_usedTRUE 5.79 6.18
- Different accounting of the variability in success rates.
term estimate_model_1 estimate_model_2
<chr> <dbl> <dbl>
1 sd_(Intercept).expedition_id 3.63 3.09
2 sd_(Intercept).peak_name NA 1.85
- This makes sense, not only are some expeditions more successeful , some peaks are easier to climb.