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

## ----data---------------------------------------------------------------------
library(tidycreel)
data(example_aerial_glmm_counts)
head(example_aerial_glmm_counts)

## ----design-------------------------------------------------------------------
aerial_cal <- unique(example_aerial_glmm_counts[, c("date", "day_type")])
aerial_cal <- aerial_cal[order(aerial_cal$date), ]

design <- creel_design(
  aerial_cal,
  date        = date,
  strata      = day_type,
  survey_type = "aerial",
  visibility_correction = "none",
  angler_ratio = 1,
  angler_ratio_se = 0,
  h_open      = 14
)

design <- add_counts(design, example_aerial_glmm_counts, count_col = n_anglers)
print(design)

## ----glmm-default-------------------------------------------------------------
glmm_result <- estimate_effort_aerial_glmm(design, time_col = time_of_flight)
print(glmm_result)

## ----glmm-boot, eval = FALSE--------------------------------------------------
# # Bootstrap CIs — use nboot = 500 for production analyses
# glmm_boot <- estimate_effort_aerial_glmm(
#   design,
#   time_col = time_of_flight,
#   boot = TRUE,
#   nboot = 100L
# )
# print(glmm_boot)

## ----downstream---------------------------------------------------------------
# Build a complementary design with matching interview dates
aerial_int_cal <- unique(example_aerial_counts[, c("date", "day_type")])
aerial_int_cal <- aerial_int_cal[order(aerial_int_cal$date), ]

design_int <- creel_design(
  aerial_int_cal,
  date        = date,
  strata      = day_type,
  survey_type = "aerial",
  visibility_correction = "none",
  angler_ratio = 1,
  angler_ratio_se = 0,
  h_open      = 14
)
design_int <- add_counts(design_int, example_aerial_counts)
design_int <- add_interviews(design_int, example_aerial_interviews,
  catch       = walleye_catch,
  effort      = hours_fished,
  trip_status = trip_status
)
catch_rate <- estimate_catch_rate(design_int)
print(catch_rate)

total_catch <- estimate_total_catch(design_int)
print(total_catch)

## ----comparison---------------------------------------------------------------
# Daily means for the simple estimator: the flights are four looks at each day,
# so they aggregate rather than accumulate.
design_daily <- add_counts(
  creel_design(
    aerial_cal,
    date        = date,
    strata      = day_type,
    survey_type = "aerial",
    visibility_correction = "none",
    angler_ratio = 1,
    angler_ratio_se = 0,
    h_open      = 14
  ),
  example_aerial_glmm_counts,
  count_col      = n_anglers,
  count_time_col = time_of_flight
)

# Simple aerial estimator — no diurnal correction
simple_result <- estimate_effort(design_daily)

# GLMM result from above
# glmm_result already computed

# Side-by-side comparison. Both estimators report a total across the sampled
# days, so the two numbers answer the same question and the gap between them is
# the diurnal correction.
comparison <- data.frame(
  method = c("GLMM", "Simple"),
  estimate = c(
    glmm_result$estimates$estimate,
    simple_result$estimates$estimate
  ),
  target = c(
    glmm_result$effort_target,
    simple_result$effort_target
  ),
  stringsAsFactors = FALSE
)

print(comparison)

## ----custom-formula-----------------------------------------------------------
glmm_linear <- estimate_effort_aerial_glmm(
  design,
  time_col = time_of_flight,
  formula  = n_anglers ~ time_of_flight + (1 | date)
)
print(glmm_linear)