Posterior predictive check

Generate posterior predictive checks for a fitted ratio model. Compares observed data to replicated data from the posterior predictive distribution.

Usage

pp_check(object, ...)

# S3 method for class 'ratiod_fit'
pp_check(
  object,
  type = c("dens_overlay", "scatter", "intervals", "stat"),
  component = c("numerator", "denominator", "both"),
  stat = mean,
  ndraws = 50,
  ...
)

Arguments

object

A ratiod_fit object

...

Additional arguments passed to bayesplot functions

type

Type of check: "dens_overlay", "scatter", "intervals", "stat"

component

Which component: "numerator", "denominator", or "both"

stat

Function for "stat" type (default: mean)

ndraws

Number of posterior draws to use (default: 50 for plots)

Value

A ggplot object

Examples

# pp_check requires a fitted model with posterior predictive draws
# See tratio() examples for fitting models

# \donttest{
# Simulate data and fit model (slow, not run on CRAN)
set.seed(123)
n <- 50
df <- data.frame(
  count = rnbinom(n, size = 5, mu = 15),
  total = rnbinom(n, size = 8, mu = 100),
  x = rnorm(n),
  site = factor(rep(1:10, each = 5))
)
fit <- tratio(
  count | total ~ x + (1 | site),
  data = df,
  family = ratiod_negbin_negbin(),
  control = list(iter = 200, warmup = 100, chains = 1)
)
#> Inference: Exact (Tier 1)
#>   Backend: hmc
#>   Reason: default (full MCMC)
#> Fitting ratio model...
#>   Family: negbin_negbin
#>   Observations: 50
#>   Iterations: 200 (warmup: 100)
#> Running NUTS sampler...
#>   Parameters: 17
#>   Iterations: 200 (warmup: 100)
#>   Chains: 1 (cores: 1)
# Density overlay (requires bayesplot package)
# pp_check(fit, type = "dens_overlay")
# }