---
title: "Computing plausibility functions"
format: 
  html:
    toc: true
    html-math-method: mathjax
vignette: >
  %\VignetteIndexEntry{Computing plausibility functions}
  %\VignetteEngine{quarto::html}
  %\VignetteEncoding{UTF-8}
bibliography: references.bib
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

# load the necessary packages
library(dplyr)
library(ggplot2)
library(purrr)
library(tibble)
library(tidyr)
library(flipr)

# import the data
df_mean <- readRDS(
  system.file("vignette-data/plausibility-df-mean.rds", package = "flipr")
)
df_sd <- readRDS(
  system.file("vignette-data/plausibility-df-sd.rds", package = "flipr")
)
df_fisher <- readRDS(
  system.file("vignette-data/plausibility-df-fisher.rds", package = "flipr")
)
df_tippett <- readRDS(
  system.file("vignette-data/plausibility-df-tippett.rds", package = "flipr")
)
```

```{r setup, message=FALSE}
ngrid_in <- 10
ngrid_out <- 100
nperms <- 100000
n1 <- 30
n2 <- 30
set.seed(1301)
x1 <- rnorm(n1, mean = 0, sd = 1)
x2 <- rnorm(n2, mean = 3, sd = 1)
y1 <- rnorm(n1, mean = 0, sd = 1)
y2 <- rnorm(n2, mean = 0, sd = 2)
z1 <- rnorm(n1, mean = 0, sd = 1)
z2 <- rnorm(n2, mean = 3, sd = 2)
```

The concept of plausibility functions pertains to assessing the $p$-value
of a set of null hypotheses and to plot this $p$-value surface on the
domain defined by the set of null hypotheses. The idea behind is that, if
such a plausibility function is available, you can deduce from it point
estimates or confidence interval estimates for parameters used to define
the nulls or extract a single $p$-value for a specific null of interest
[@martin2017; @fraser2019; @infanger2019]. In particular, there is
another **R** package dedicated to plausibility functions called
[**pvaluefunctions**](https://CRAN.R-project.org/package=pvaluefunctions).

## Plausibility function for the mean

```{r, eval=FALSE}
null_spec <- function(y, parameters) {
  map(y, ~ .x - parameters)
}
stat_functions <- list(stat_t)
stat_assignments <- list(delta = 1)

pf <- PlausibilityFunction$new(
  null_spec = null_spec, 
  stat_functions = stat_functions, 
  stat_assignments = stat_assignments, 
  x1, x2,  
  seed = 1234
)
pf$set_nperms(nperms)
pf$set_point_estimate(mean(x2) - mean(x1))
pf$set_parameter_bounds(
  point_estimate = pf$point_estimate, 
  conf_level = pf$max_conf_level
)
pf$set_grid(
  parameters = pf$parameters, 
  npoints = ngrid_in
)

pf$set_alternative("two_tail")
pf$evaluate_grid(grid = pf$grid)
df <- rename(pf$grid, two_tail = pvalue)

pf$set_alternative("left_tail")
pf$grid$pvalue <- NULL
pf$evaluate_grid(grid = pf$grid)
df <- bind_rows(
  df, 
  rename(pf$grid, left_tail = pvalue)
)

pf$set_alternative("right_tail")
pf$grid$pvalue <- NULL
pf$evaluate_grid(grid = pf$grid)
df <- bind_rows(
  df, 
  rename(pf$grid, right_tail = pvalue)
)

pf$set_grid(
  parameters = pf$parameters, 
  npoints = ngrid_out
)

df_mean <- tibble(
  delta = pf$grid$delta, 
  two_tail = approx(df$delta, df$two_tail, delta)$y, 
  left_tail = approx(df$delta, df$left_tail, delta)$y, 
  right_tail = approx(df$delta, df$right_tail, delta)$y, 
) %>%
  pivot_longer(-delta)
```


```{r, fig.asp=0.8, fig.width=6, out.width="97%", dpi=300}
df_mean %>%
  ggplot(aes(delta, value, color = name)) +
  geom_line() +
  labs(
    title = "P-value function for the mean", 
    subtitle = "t-statistic", 
    x = expression(delta), 
    y = "p-value", 
    color = "Type"
  ) +
  geom_hline(
    yintercept = 0.05,
    color = "black",
    linetype = "dashed"
  ) +
  geom_vline(
    xintercept = mean(x2) - mean(x1),
    color = "black"
  ) +
  geom_vline(
    xintercept = stats::t.test(x2, x1, var.equal = TRUE)$conf.int,
    color = "black",
    linetype = "dashed"
  ) +
  scale_y_continuous(breaks = seq(0, 1, by = 0.05), limits = c(0, 1))
```

## Plausibility function for the variance

```{r, eval=FALSE}
null_spec <- function(y, parameters) {
  map(y, ~ .x / parameters)
}
stat_functions <- list(stat_f)
stat_assignments <- list(rho = 1)

pf <- PlausibilityFunction$new(
  null_spec = null_spec, 
  stat_functions = stat_functions, 
  stat_assignments = stat_assignments, 
  y1, y2,  
  seed = 1234
)
pf$set_nperms(nperms)
pf$set_point_estimate(sd(y2) / sd(y1))
pf$set_parameter_bounds(
  point_estimate = pf$point_estimate, 
  conf_level = pf$max_conf_level
)
pf$set_grid(
  parameters = pf$parameters, 
  npoints = ngrid_in
)

pf$set_alternative("two_tail")
pf$evaluate_grid(grid = pf$grid)
df <- rename(pf$grid, two_tail = pvalue)

pf$set_alternative("left_tail")
pf$grid$pvalue <- NULL
pf$evaluate_grid(grid = pf$grid)
df <- bind_rows(
  df, 
  rename(pf$grid, left_tail = pvalue)
)

pf$set_alternative("right_tail")
pf$grid$pvalue <- NULL
pf$evaluate_grid(grid = pf$grid)
df <- bind_rows(
  df, 
  rename(pf$grid, right_tail = pvalue)
)

pf$set_grid(
  parameters = pf$parameters, 
  npoints = ngrid_out
)

df_sd <- tibble(
  rho = pf$grid$rho, 
  two_tail = approx(df$rho, df$two_tail, rho)$y, 
  left_tail = approx(df$rho, df$left_tail, rho)$y, 
  right_tail = approx(df$rho, df$right_tail, rho)$y, 
) %>%
  pivot_longer(-rho)
```

```{r, fig.asp=0.8, fig.width=6, out.width="97%", dpi=300}
df_sd %>%
  ggplot(aes(rho, value, color = name)) +
  geom_line() +
  labs(
    title = "P-value function for the standard deviation", 
    subtitle = "F-statistic", 
    x = expression(rho), 
    y = "p-value", 
    color = "Type"
  ) +
  geom_hline(
    yintercept = 0.05,
    color = "black",
    linetype = "dashed"
  ) +
  geom_vline(
    xintercept = sqrt(stats::var.test(y2, y1)$statistic),
    color = "black"
  ) +
  geom_vline(
    xintercept = sqrt(stats::var.test(y2, y1)$conf.int),
    color = "black",
    linetype = "dashed"
  ) +
  scale_y_continuous(breaks = seq(0, 1, by = 0.05), limits = c(0, 1))
```

## Plausibility function for both mean and variance

Assume that we have two r.v. $X$ and $Y$ that differ in distribution
only in their first two moments. Let $\mu_X$ and $\mu_Y$ be the means of
$X$ and $Y$ respectively and $\sigma_X$ and $\sigma_Y$ be the standard
deviations. We can therefore write

$$
Y = \delta + \rho X.
$$

In this case, we have

$$
\begin{cases}
\mu_Y = \delta + \rho \mu_X \\
\sigma_Y^2 = \rho^2 \sigma_X^2
\end{cases}
\Longleftrightarrow
\begin{cases}
\delta = \mu_Y - \frac{\sigma_Y}{\sigma_X} \mu_X \\
\rho = \frac{\sigma_Y}{\sigma_X}
\end{cases}
$$

In the following example, we have $\delta = 3$ and $\rho = 2$.

```{r, eval=FALSE}
null_spec <- function(y, parameters) {
  map(y, ~ (.x - parameters[1]) / parameters[2])
}
stat_functions <- list(stat_t, stat_f)
stat_assignments <- list(delta = 1, rho = 2)

pf <- PlausibilityFunction$new(
  null_spec = null_spec, 
  stat_functions = stat_functions, 
  stat_assignments = stat_assignments, 
  z1, z2,  
  seed = 1234
)
pf$set_nperms(nperms)
pf$set_point_estimate(c(
  mean(z2) - sd(z2) / sd(z1) * mean(z1), 
  sd(z2) / sd(z1)
))
pf$set_parameter_bounds(
  point_estimate = pf$point_estimate, 
  conf_level = pf$max_conf_level
)

# Fisher combining function
pf$set_aggregator("fisher")
pf$set_grid(
  parameters = pf$parameters, 
  npoints = ngrid_in
)
pf$evaluate_grid(grid = pf$grid)
grid_in <- pf$grid
pf$set_grid(
  parameters = pf$parameters, 
  npoints = ngrid_out
)
if (requireNamespace("interp", quietly = TRUE)) {
  Zout <- interp::interp(
    x = grid_in$delta,
    y = grid_in$log_rho,
    z = grid_in$pvalue,
    xo = sort(unique(pf$grid$delta)),
    yo = sort(unique(pf$grid$log_rho))
  )
  pf$grid$pvalue <- as.numeric(Zout$z)
} else
  pf$grid$pvalue <- rep(NA, nrow(pf$grid))

df_fisher <- pf$grid

# Tippett combining function
pf$set_aggregator("tippett")
pf$set_grid(
  parameters = pf$parameters, 
  npoints = ngrid_in
)
pf$evaluate_grid(grid = pf$grid)
grid_in <- pf$grid
pf$set_grid(
  parameters = pf$parameters, 
  npoints = ngrid_out
)
if (requireNamespace("interp", quietly = TRUE)) {
  Zout <- interp::interp(
    x = grid_in$delta,
    y = grid_in$log_rho,
    z = grid_in$pvalue,
    xo = sort(unique(pf$grid$delta)),
    yo = sort(unique(pf$grid$log_rho))
  )
  pf$grid$pvalue <- as.numeric(Zout$z)
} else
  pf$grid$pvalue <- rep(NA, nrow(pf$grid))

df_tippett <- pf$grid
```

```{r, fig.asp=0.85, fig.width=9, out.width="97%", dpi=300}
df_fisher %>% 
  ggplot(aes(delta, log_rho, z = pvalue)) + 
  geom_contour_filled(binwidth = 0.05) + 
  labs(
    title = "Contour plot of the p-value surface", 
    subtitle = "Using Fisher's non-parametric combination", 
    x = expression(delta), 
    y = expression(log(rho)), 
    fill = "p-value"
  ) + 
  theme_minimal()
```

```{r, fig.asp=0.85, fig.width=9, out.width="97%", dpi=300}
df_tippett %>% 
  ggplot(aes(delta, log_rho, z = pvalue)) + 
  geom_contour_filled(binwidth = 0.05) + 
  labs(
    title = "Contour plot of the p-value surface", 
    subtitle = "Using Tippett's non-parametric combination", 
    x = expression(delta), 
    y = expression(log(rho)), 
    fill = "p-value"
  ) + 
  theme_minimal()
```

## References
