## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

## -----------------------------------------------------------------------------
library(agriPAM)
crops <- agri_pam_example()

## -----------------------------------------------------------------------------
s <- pam_sensitivity(
  crops,
  parameter = "social_revenue",
  changes = seq(-0.20, 0.20, by = 0.05),
  index = "Paddy"
)
s$results[, c("percent_change", "social_profit", "drc", "scb")]

## -----------------------------------------------------------------------------
switching_value(
  crops,
  parameter = "social_revenue",
  metric = "drc",
  index = "Paddy"
)

switching_value(
  crops,
  parameter = "private_revenue",
  metric = "private_profit",
  index = "Paddy"
)

## -----------------------------------------------------------------------------
cv <- c(
  private_revenue = 0.08,
  private_tradable_inputs = 0.10,
  private_domestic_factors = 0.07,
  social_revenue = 0.15,
  social_tradable_inputs = 0.10,
  social_domestic_factors = 0.08
)

mc <- pam_monte_carlo(
  crops,
  cv = cv,
  n = 2000,
  seed = 2026,
  index = "Paddy"
)

mc$summary
mc$probabilities

## -----------------------------------------------------------------------------
components <- c(
  "private_revenue", "private_tradable_inputs", "private_domestic_factors",
  "social_revenue", "social_tradable_inputs", "social_domestic_factors"
)
correlation <- diag(6)
dimnames(correlation) <- list(components, components)
correlation["private_revenue", "social_revenue"] <- 0.70
correlation["social_revenue", "private_revenue"] <- 0.70

mc_correlated <- pam_monte_carlo(
  crops,
  cv = cv,
  n = 1000,
  correlation = correlation,
  seed = 2026,
  index = "Paddy"
)
mc_correlated$probabilities