test.Rmd

---
title: "Untitled"
author: "Fred Jaya"
date: '2024-01-18'
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(PoolTestR)
library(dplyr)
devtools::load_all("~/GitHub/PoolPoweR/")
```

## Use cases

Analyse (PoolTestR)

Design (PoolPoweR)
├
```{r}
simple <- SimpleExampleData
```

```{r}
PrevWholeDataset <- PoolPrev(simple, Result, NumInPool, bayesian = F)
PrevWholeDataset
PrevWholeDataset %>% mutate(across(is.double, round, digits = 4))
```
```{r}
PrevByRegionYear <- PoolPrev(simple, Result, NumInPool, Region, Year, bayesian = F)
PrevByRegionYear
PrevByRegionYear %>% mutate(across(is.double, round, digits = 4))
```

```{r}
PrevByHier <- HierPoolPrev(simple, Result, NumInPool,
  hierarchy = c("Village", "Site"), Year
)
PrevByHier
PrevByHier %>% mutate(across(is.double, round, digits = 4))
```

---

## PoolPoweR

```{r}
x <- optimise_sN_prevalence(
  prevalence = 0.005,
  correlation = 0.1,
  cost_unit = 5,
  cost_pool = 10,
  cost_cluster = 20,
  max_N = 20,
  max_s = 50,
  sensitivity = 1,
  specificity = 1,
  form = "logitnorm"
)

y <- as.data.frame(x) %>%
  rename(`Optimal pool size` = s, `Cost` = cost, `Catch` = catch, `Optimal number of pools` = N)
y
```



```{r}
x <- optimise_sN_prevalence(
  prevalence = 0.005,
  correlation = NA,
  cost_unit = 5,
  cost_pool = 10,
  cost_cluster = NA,
  max_N = 20,
  max_s = 50,
  sensitivity = 1,
  specificity = 1,
  form = "logitnorm"
)

y <- as.data.frame(x) %>%
  rename(`Optimal pool size` = s, `Cost` = cost, `Catch` = catch, `Optimal number of pools` = N)
y
```

```{r}
y %>%
  mutate(Cost = round(Cost, digits = 4))
```

```{r}
optimise_random_prevalence(
  # Survey
  cost_cluster = NA,
  # Cost
  cost_unit,
  cost_pool,
  cost_period,
  # Parameters
  pool_strat_family,
  catch_mean,
  catch_variance,
  prevalence,
  correlation = NA,
  # Advanced
  sensitivity = 1,
  specificity = 1,
  max_period = 10,
  form = "logitnorm",
  verbose = FALSE
)
```

```{r}
o <- optimise_random_prevalence(
  catch_mean = 10,
  catch_variance = 11,
  pool_strat_family = pool_target_number,
  prevalence = 0.01,
  cost_unit = 1,
  cost_pool = 4,
  cost_period = 10,
  cost_cluster = 20,
  correlation = 0.1,
  sensitivity = 1,
  specificity = 1,
  max_period = 10,
  form = "logitnorm",
  verbose = FALSE
)

o %>% unlist()
```
```{r}
p <- optimise_random_prevalence(
  catch_mean = 10,
  catch_variance = 11,
  pool_strat_family = pool_max_size,
  prevalence = 0.01,
  cost_unit = 1,
  cost_pool = 4,
  cost_period = 10,
  cost_cluster = 20,
  correlation = 0.1,
  sensitivity = 1,
  specificity = 1,
  max_period = 10,
  form = "logitnorm",
  verbose = FALSE
)

p
```

```{r}
q <- optimise_random_prevalence(
  catch_mean = 10,
  catch_variance = 25,
  pool_strat_family = pool_target_number,
  prevalence = 0.01,
  cost_unit = 1,
  cost_pool = 2,
  cost_period = 50,
  cost_cluster = NA,
  correlation = NA,
  sensitivity = 1,
  specificity = 1,
  max_period = 10,
  form = "logitnorm",
  verbose = FALSE
)

q$pool_strat_pars$target_number
```

```{r}
is_integer_col(simple, "NumInPool")

is.numeric(simple[["NumInPool"]])
```