Hi,
I have a question about model interpetation. I originally posed a question about the type of model I should run, and Solomon was very kind to help Setting priors and interpreting ordered logistic model).
I am trying to determine whether three different continents (Africa, Asia, Europe) have different numbers of life expectancies at different levels (low, med, high) (this data and question are entirely fabricated)
Essentially: does continent predict how many low/med/high life expectancies in that group?
My data:
#load libraries
library(brms)
library("gapminder")
library("dplyr")
#Edit data
data(gapminder)
gapminder <-gapminder
#select columns of interest
gapminder <-gapminder %>%
select(continent,lifeExp, pop)
#select continents of interest
gapminder <-gapminder %>%
filter(continent == c("Africa","Asia","Europe"))
#Make ordered factors
gapminder$lifeExp<- cut(gapminder$lifeExp, 3, labels=c("low","med","high"), ordered_result=TRUE)
After advice, I have settled on an ordinal cumulative model with probit link:
mod2selection_fit <- brm(
formula = lifeExp ~ 1 + continent,
data = gapminder,
family = cumulative(probit),
prior = c(
prior(normal(0, 2), class = Intercept),
prior(normal(0, 1), class = b)))
which gives:
Family: cumulative
Links: mu = probit; disc = identity
Formula: lifeExp ~ 1 + continent
Data: gapminder (Number of observations: 460)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept[1] 0.10 0.08 -0.06 0.26 1.00 3951 2911
Intercept[2] 1.47 0.11 1.26 1.69 1.00 2764 3112
continentAsia 1.17 0.13 0.91 1.42 1.00 2899 2919
continentEurope 3.03 0.21 2.64 3.44 1.00 2482 2857
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
disc 1.00 0.00 1.00 1.00 NA NA NA
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
My question is about interpreting this, what it means to the average person (me!)
Lets taker the continent Asia as an Example, the estimate is 1.17, and the 95%CI do not cross 0. Could I interpret this as Asia has 1.17 years more life expectancy than Africa? Or should I transform this as it is a probit link?Or is it in terms of risk? I’m trying to understand exactly what the numbers in the output are telling me.
Also, what if the 95%CI did NOT cross 0, then I couldn’t be confident in the estimate?
Finally, does anyone have any suggestions for how one would report this in a paper, is there a Bayes equivalent of the frequentist (p value, confidence intervals) reporting?
Thank you so much and Happy new year to all!