Dear all,

Could someone advise if the ordinal continuation ratio model accounts for the frequency of response when estimating its conditional probability?

The count (response) and its frequency are low on all plots for some species (explanatory variable), nevertheless the model assigns almost 100% probability for Acer in response 1. At the same time, it assesses lower probability for Fagus that is frequent and abundant. Moreover, the number of species in 20 cm dbh class is low and thus, the line should decline but it is going up.

In contrast, Fagus has a higher probability for the response 2 and 3, which I assume is the correct interpretation of the observed data.

Besides, I am puzzled with marginal mean in `emmeans`

. The difference in response is huge. I cannot figure out what threshold was used in this case. I would appreciate any hints or advice.

```
inits <- list(`Intercept[1]` = 0.19,
`Intercept[2]` = 1.33,
`Intercept[3]` = 2.15,
`Intercept[4]` = 2.92,
`Intercept[5]` = 3.69,
`Intercept[6]` = 4.34,
`Intercept[7]`= 5.26,
`Intercept[8]`=6.7)
inits_list <- list(inits, inits)
p= c(prior_string("student_t(5,0,0.5)", class="Intercept"),
prior(normal(0,0.5), class=b),
prior(student_t(5,0.1,0.5), class=sd))
f=brmsformula(count1 ~ ba_sqrt + dbh_log + species.p + species.m +
position.m + status.p + y + offset(log(count_dead)) +
(y|subplot_id), center=TRUE) + lf(disc ~ 0 + status.p+y+species.p, cmc = FALSE)
fit4=brm(f, data=d1, family = cratio(link="logit"), save_pars=save_pars(all = TRUE),
cores = 2, iter = 800 + 800, warmup = 800, chains = 2, seed=123,
inits = inits_list, sample_prior="yes", silent=TRUE,
open_progress=FALSE, control = list(adapt_delta = 0.85, max_treedepth = 10))
nd <-data.frame(species.p=c("Acer platanoides", "Fagus sylvatica"),
species.m=d1$species.m, position.m=d1$position.m,
ba_sqrt=seq(2, 4, by=0.2), dbh_log = seq(2.3, 3, by=0.07),
count_dead=1, y="2015", status.p="alive", subplot_id=d1$subplot_id)
View(nd)
max_iter <- 100
p=as.data.frame(predict(fit4, newdata = nd, subset = 1:max_iter, summary = T,
re_formula = NULL, scale = "response"))
together=cbind(nd$species.p, nd$dbh_log, p)
together$gr=rep(1:368, times=1, each=11)
prep=melt(together, id=c("nd$species.p", "nd$dbh_log", "gr"))
prep$dbh_exp=exp(nd$dbh_log)
names(prep)[names(prep) == "nd$species.p"] <- "species.p"
prep %>% group_by(species.p, variable, dbh_exp, gr) %>% distinct(value)
ggplot(subset(prep, variable %in% c("P(Y = 1)", "P(Y = 2)", "P(Y = 3)")),
aes(dbh_exp, value, group=gr, color=species.p), alpha=0.2)+
stat_summary(fun = mean, geom = "line", aes(group=species.p))+
facet_wrap(.~variable, scale="free")+theme(legend.position = "top")
```

- Operating System: Windows 10
- brms Version: 2.14.6