Extract ratio posteriors from a ratio model

Compute posterior distributions of ratios from a fitted ratio model. Ratios are derived quantities: E[numerator] / E[denominator], computed in Stan's generated quantities block with full uncertainty propagation.

Usage

ratio(
  object,
  newdata = NULL,
  type = c("response", "log", "logit"),
  by = NULL,
  summary = FALSE,
  probs = c(0.025, 0.5, 0.975),
  ...
)

Arguments

object: A ratiod_fit object
newdata: Optional data frame for prediction. If NULL, uses original data.
type: Scale for ratio: "response" (default), "log", or "logit".
by: Optional grouping variable name to aggregate ratios by group.
summary: Logical; if TRUE, return summary statistics instead of full posterior draws.
probs: Quantiles to compute if summary = TRUE.
...: Ignored

Value

A ratiod_ratio object containing posterior draws or summaries.

Examples

# Create a simple family
fam <- ratiod_poisson_gamma()

# \donttest{
# Generate synthetic data
set.seed(123)
n <- 50
df <- data.frame(
  count = rpois(n, lambda = 20),
  effort = rgamma(n, shape = 10, rate = 1),
  depth = rnorm(n),
  site = sample(letters[1:5], n, replace = TRUE)
)

# Fit model
fit <- tratio(
  count | effort ~ depth + (1 | site),
  data = df,
  family = ratiod_poisson_gamma(),
  mode = "hmc",
  control = list(iter = 200, warmup = 100, chains = 1)
)
#> Inference: Exact (Tier 1)
#>   Backend: hmc
#> Fitting ratio model...
#>   Family: poisson_gamma
#>   Observations: 50
#>   Iterations: 200 (warmup: 100)
#> Running NUTS sampler...
#>   Parameters: 11
#>   Iterations: 200 (warmup: 100)
#>   Chains: 1 (cores: 1)

# Extract all observation-level ratios
r <- ratio(fit)
summary(r)
#> tulpaRatio ratio summary (response scale)
#> ===========================================
#> 
#>  obs     mean         sd     q2.5      q50    q97.5
#>    1 2.095471 0.16912117 1.817258 2.104386 2.462139
#>    2 1.994679 0.11988040 1.805203 1.991035 2.240366
#>    3 1.977096 0.11269653 1.802464 1.971168 2.201110
#>    4 2.035048 0.13819919 1.809832 2.035061 2.331845
#>    5 2.358317 0.32900598 1.820225 2.332228 3.054777
#>    6 2.036532 0.13891409 1.810017 2.036979 2.335021
#>    7 1.750532 0.09967037 1.549673 1.753678 1.923319
#>    8 1.778376 0.09260133 1.599152 1.779017 1.940970
#>    9 2.080306 0.16104306 1.815418 2.088542 2.429237
#>   10 1.853390 0.08450116 1.705722 1.854757 2.002505
#> 
#> ... and 40 more rows

# Log-ratio scale
r_log <- ratio(fit, type = "log")

# Group-level ratios
r_site <- ratio(fit, by = "site")
# }