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

## ----setup--------------------------------------------------------------------
library(tempodisco)

## -----------------------------------------------------------------------------
data("td_bc_single_ptpt")
mod <- kirby_score(td_bc_single_ptpt)
print(mod)

## -----------------------------------------------------------------------------
mod_exp <- kirby_score(td_bc_single_ptpt, discount_function = 'exponential')
print(mod_exp)
mod_pow <- kirby_score(td_bc_single_ptpt, discount_function = 'power')
print(mod_pow)
mod_ari <- kirby_score(td_bc_single_ptpt, discount_function = 'arithmetic')
print(mod_ari)

## -----------------------------------------------------------------------------
mod <- wileyto_score(td_bc_single_ptpt)
print(mod)

## -----------------------------------------------------------------------------
mod <- td_bclm(td_bc_single_ptpt, model = 'all')
print(mod)

## -----------------------------------------------------------------------------
mod <- td_bcnm(td_bc_single_ptpt, discount_function = 'all')
print(mod)

## -----------------------------------------------------------------------------
# Probit choice rule:
mod <- td_bcnm(td_bc_single_ptpt, discount_function = 'exponential', choice_rule = 'probit')
# Power choice rule:
mod <- td_bcnm(td_bc_single_ptpt, discount_function = 'exponential', choice_rule = 'power')

## -----------------------------------------------------------------------------
data("td_bc_study")
# Select the second participant
second_ptpt_id <- unique(td_bc_study$id)[2]
df <- subset(td_bc_study, id == second_ptpt_id)
mod <- td_bcnm(df, discount_function = 'exponential', fit_err_rate = T)
plot(mod, type = 'endpoints', verbose = F)
lines(c(0, 1), c(0, 0), lty = 2)
lines(c(0, 1), c(1, 1), lty = 2)
cat(sprintf("epsilon = %.2f\n", coef(mod)['eps']))

## -----------------------------------------------------------------------------
mod <- td_bcnm(df, discount_function = 'exponential', fixed_ends = T)
plot(mod, type = 'endpoints', verbose = F, del = 50, val_del = 200)