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

## ----simulated-data-----------------------------------------------------------
set.seed(128)
n <- 750
latent <- rlnorm(n, log(14000), 1.0)
limits <- sample(c(20000, 50000, 100000, Inf), n, replace = TRUE)
dat <- data.frame(ground_up_loss = pmin(latent, limits),
                  censor_limit = limits)

## ----candidate-fit------------------------------------------------------------
candidates <- fit_severity_candidates(dat, loss = "ground_up_loss",
  limit = "censor_limit", distributions = c("lognormal", "gamma", "weibull"),
  hessian = FALSE)
print(candidates$comparison)
candidates$comparison[order(candidates$comparison$AIC), ]
fit_lognormal <- candidates$fits$lognormal
summary(fit_lognormal)

## ----candidate-plots, fig.width=8, fig.height=5-------------------------------
plot_candidate_fits(candidates, type = "histogram",
  x_scale = "log10", xlim = c(500, 200000),
  x_text_angle = 45, x_text_size = 8)

## ----candidate-data-----------------------------------------------------------
# Deterministic summaries: the very same bins drawn in the default plot.
head(candidate_plot_data(candidates, output = "plot_ready",
  type = "histogram", xlim = c(500, 200000)))

# Old row-level simulations remain available when requested.
head(candidate_plot_data(candidates, nsim = 1, seed = 22, output = "raw"))

## ----candidate-factors--------------------------------------------------------
factors <- severity_factors(candidates, deductible = c(500, 1000),
  limit = c(25000, 50000, Inf), base_deductible = 500, base_limit = 25000)
head(factors)
compare_factors(candidates, metric = "rating_factor",
  deductible = c(500, 1000), limit = c(25000, 50000),
  base_deductible = 500, base_limit = 25000)

## ----anomaly-audit------------------------------------------------------------
bad <- dat
bad$ground_up_loss[2] <- bad$censor_limit[2] + 1000
# Use a guaranteed finite limit in the example.
bad$censor_limit[2] <- 10000
bad$ground_up_loss[2] <- 10500
fit <- suppressWarnings(fit_severity(bad, loss = "ground_up_loss",
  limit = "censor_limit", distribution = "lognormal", hessian = FALSE))
fit$excluded_rows
fit$excluded_details

## ----optimizer-table----------------------------------------------------------
candidates$comparison[, c("distribution", "elapsed_seconds",
  "function_evaluations", "gradient_evaluations", "convergence_code")]