## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.align = "center",
  message = FALSE,
  warning = FALSE
)

library(EpiExposure)
library(ggplot2)
library(dplyr)

## ----load-package-------------------------------------------------------------
library(EpiExposure)
library(ggplot2)

## ----load-data----------------------------------------------------------------
data("epi_data")

## ----example-data-table, echo=FALSE-------------------------------------------
knitr::kable(
  utils::head(
    epi_data,
    10L
  ),
  digits = 2,
  align = "c"
)

## ----check-history-length-----------------------------------------------------
table(
  table(
    epi_data$epi_id
  )
)

## ----message=FALSE, warning=FALSE---------------------------------------------
wetness_plot = epi_data |>  
  ggplot(aes(time,wetness, group = epi_id))+
  geom_smooth(se = F, color = "#55A868",linewidth = 0.3)+
  theme_bw()+
  labs(x = "Days after planting the crop",
       y = "Leaf wetness (%)")+
  scale_x_continuous(limits = c(0,85), expand = c(0,0),
                     breaks = pretty(epi_data$time, n = 8))+
  theme(text = element_text(size = 10))


wetness_plot

## ----message=FALSE, warning=FALSE---------------------------------------------
rain_plot = epi_data  |>  
  ggplot(aes(time,rain, group = epi_id))+
  geom_smooth(se = F, color = "#4C72B0",linewidth = 0.3)+
  theme_bw()+
  labs(x = "Days after planting the crop",
       y = "Daily precipitation (mm)")+
  scale_x_continuous(limits = c(0,85), expand = c(0,0),
                     breaks = pretty(epi_data$time, n = 8))+
  theme(text = element_text(size = 10))


rain_plot

## ----message=FALSE, warning=FALSE---------------------------------------------
tmean_plot = epi_data |> 
  ggplot(aes(time,tmean, group = epi_id))+
  geom_smooth(se = F, color = "#C44E52",linewidth = 0.3)+
  theme_bw()+
  labs(x = "Days after planting the crop",
       y = "Mean temperature (°C)")+
  scale_x_continuous(limits = c(0,85), expand = c(0,0),
                     breaks = pretty(epi_data$time, n = 8))+
  theme(text = element_text(size = 10))


tmean_plot

## ----define-exposures---------------------------------------------------------
cb <- define_exposures(
  data = epi_data,
  vars = c(
    "tmean",
    "rain",
    "wetness"
  ),
  max_lag = 85,
  df_var = 3,
  df_lag = 2
)

## ----check-identifiability----------------------------------------------------
identifiability <- check_identifiability(
  data = epi_data,
  vars = c(
    "tmean",
    "rain",
    "wetness"
  ),
  max_lag = 85,
  df_var = 3,
  df_lag = 2
)



## ----inspect-identifiability1-------------------------------------------------
knitr::kable(
  utils::head(
    identifiability$overall,
    10L
  ),
  digits = 4,
  align = "c"
)

## ----inspect-identifiability2-------------------------------------------------
knitr::kable(
  utils::head(
    identifiability$by_variable,
    10L
  ),
  digits = 4,
  align = "c"
)

## ----inspect-identifiability3-------------------------------------------------
knitr::kable(
  utils::head(
    identifiability$recommendations,
    10L
  ),
  digits = 4,
  align = "c"
)

## ----build-design-------------------------------------------------------------
dat <- build_design(
  data = epi_data,
  cb_templates = cb)

## ----design-table, echo=FALSE-------------------------------------------------
knitr::kable(
  utils::head(
    dat,
    6L
  ),
  digits = 3,
  caption = "First six rows of the epidemic-level DLNM design.",
  align = "c"
)

## ----prepare-response---------------------------------------------------------
dat <- prepare_response(
  data = dat,
  response = "y",
  family = "beta"
)

## ----fit-model----------------------------------------------------------------
fit <- fit_epidlnm(
  data = dat,
  model_engine = "glmmTMB",
  family = "beta",
  random_effect = NULL,
  epiexposure_spec = attr(
    cb,
    "spec"
  )
)

## ----model-summary------------------------------------------------------------
summary(fit)

## ----first-prediction---------------------------------------------------------
prediction <- predict_outcomes(
  fit = fit,
  profiles = list(
    tmean = rep(
      25,
      86
    ),
    rain = rep(
      5,
      86
    ),
    wetness = rep(
      15,
      86
    )
  )
)


## -----------------------------------------------------------------------------
prediction