Trial-based model where the numerator represents successes out of a known number of trials, the denominator.
Use cases:
Detection / availability (occupancy-style)
Successes / trials (hierarchical binomial)
Proportions with known denominators
Examples
# Create family object
fam <- ratiod_binomial()
print(fam)
#> tulpaRatio family: binomial_fixed
#> Binomial numerator with fixed denominator
#>
#> Numerator: binomial(logit)
#> Denominator: fixed (fixed)
# Simulate detection/availability data
set.seed(123)
n <- 50
df <- data.frame(
detections = rbinom(n, size = 10, prob = 0.4),
trials = rep(10L, n),
effort = rnorm(n),
site = factor(rep(1:10, each = 5))
)
# \donttest{
# Fit model
fit <- tratio(
detections | trials ~ effort + (1 | site),
data = df,
family = ratiod_binomial(),
control = list(iter = 400, chains = 1, seed = 1)
)
summary(fit)
#> ratio model summary
#> ===================
#>
#> Inference: Exact (Tier 1) via hmc
#> Family: binomial_fixed
#>
#> Fixed effects (numerator):
#> parameter mean sd 2.5% 97.5% rhat ess_bulk
#> num:(Intercept) -0.357 0.118 -0.571 -0.147 1.033 113
#> num:effort 0.131 0.107 -0.074 0.309 1.033 50
#>
#> Variance components:
#> parameter mean sd 2.5% 97.5% rhat ess_bulk
#> sigma_re 0.222 0.157 0.008 0.568 1.181 6
#>
#> Diagnostics:
#> Divergences: 0
#> Avg. acceptance: 0.901
#> Chains: 1
#> Iterations: 400
#> Warmup: 200
#> R-hat > 1.01 for 3 parameter(s), largest 1.181 (sigma_re).
#> Bulk ESS < 100 for 2 parameter(s), smallest 6 (sigma_re).
#> Run longer chains or reparameterise before using these estimates.
# }