## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 4.5,
  fig.align = "center"
)

## ----message=FALSE, warning=FALSE---------------------------------------------
library(AddiVortes)

## -----------------------------------------------------------------------------
set.seed(2026)
n <- 120
x <- data.frame(
  x1 = runif(n, -1, 1),
  x2 = runif(n, -1, 1)
)
y <- factor(ifelse(x$x1 + x$x2 > 0, "active", "inactive"),
            levels = c("inactive", "active"))

fit_bin <- AddiVortes(
  y, x,
  m = 200,
  totalMCMCIter = 2000,
  mcmcBurnIn = 500,
  showProgress = FALSE
)

fit_bin$task
fit_bin$classLevels
fit_bin$inSampleAccuracy

## -----------------------------------------------------------------------------
p_hat <- predict(fit_bin, x, type = "response", showProgress = FALSE)
y_hat <- predict(fit_bin, x, type = "class", showProgress = FALSE)
p_int <- predict(fit_bin, x,
  type = "quantile",
  quantiles = c(0.05, 0.95),
  showProgress = FALSE
)

mean(y_hat == y)
range(p_hat)
head(cbind(prob = round(p_hat, 3), lower = round(p_int[, 1], 3),
           upper = round(p_int[, 2], 3), class = as.character(y_hat)))

## -----------------------------------------------------------------------------
plot(p_hat, as.integer(y) - 1,
     xlab = "Predicted P(active)",
     ylab = "Observed class",
     pch = 19, col = "steelblue")
abline(v = 0.5, lty = 2, col = "grey40")

## -----------------------------------------------------------------------------
set.seed(2026)
n3 <- 90
x3 <- data.frame(
  x1 = rnorm(n3),
  x2 = rnorm(n3)
)
y3 <- cut(x3$x1, breaks = c(-Inf, -0.5, 0.5, Inf), labels = c("low", "mid", "high"))

fit_multi <- AddiVortes(
  y3, x3,
  m = 200,
  totalMCMCIter = 2000,
  mcmcBurnIn = 500,
  showProgress = FALSE
)

fit_multi$task
fit_multi$nLatents
fit_multi$inSampleAccuracy

## -----------------------------------------------------------------------------
p3 <- predict(fit_multi, x3, type = "response", showProgress = FALSE)
cls3 <- predict(fit_multi, x3, type = "class", showProgress = FALSE)

head(round(p3, 3))
mean(abs(rowSums(p3) - 1) < 1e-8)
table(predicted = cls3, observed = y3)