## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 4.5,
  message = FALSE,
  warning = FALSE
)

## ----packages-----------------------------------------------------------------
library(VCMoE)

## ----simulate-----------------------------------------------------------------
set.seed(61)

sim <- simulate_vcmoe_negbin(
  n = 320,
  k = 2,
  seed = 61,
  separation = 3.0,
  mean_count = 18,
  scenario = "well_separated"
)

head(sim$data)
summary(sim$data$y)

## ----fit----------------------------------------------------------------------
fit <- vcmoe_fit(
  y ~ z1 + offset(log_size_factor) | x1,
  data = sim$data,
  u = "u",
  family = "negative-binomial",
  k = 2,
  bandwidth = 0.60,
  u_grid = seq(0.15, 0.85, length.out = 5),
  control = list(
    maxit = 120,
    n_starts = 2,
    seed = 62,
    warn_ambiguous = FALSE,
    ridge = 1e-4,
    negbin_theta_ridge = 0.05,
    negbin_theta_target = 8
  )
)

fit

## ----coefficients-------------------------------------------------------------
coef(fit, "expert")[, , "z1"]
coef(fit, "theta")

## ----predictions--------------------------------------------------------------
head(predict(fit, type = "component"))
head(predict(fit, type = "mean"))
head(predict(fit, type = "posterior"))

## ----posterior-confidence-----------------------------------------------------
post <- predict(fit, type = "posterior")
mean(apply(post, 1, max))

## ----diagnostics--------------------------------------------------------------
diagnostics <- vcmoe_diagnostics(fit)
diagnostics[, c("u", "converged", "ambiguous", "posterior_entropy", "effective_n")]

## ----coefficient-plot---------------------------------------------------------
plot_coefficients(fit, "expert")

## ----posterior-plot-----------------------------------------------------------
plot_posterior(fit)