R/create_figure_posterior_likelihoods.R

In richelbilderbeek/wiritttea: WIRITTTE analysis

#' Create 'figure_posterior_distributions_likelihood' and 'figure_posterior_distributions_nltt'
#' @param posterior_likelihoods posterior likelihoods, as returned from read_collected_posterior_likelihoods
#' @param filename name of the file the figure will be saved to
#' @export
create_figure_posterior_likelihoods <- function(
  posterior_likelihoods,
  filename
) {

  # print("Determine sample size")
  n_sampled <- 500000
  # print(paste0("Using a sample of ", n_sampled, " of ", nrow(posterior_likelihoods), " observations"))

  # print("Sample some of the likelihoods")
  set.seed(42)
  some_posterior_likelihoods <- dplyr::sample_n(posterior_likelihoods, size = n_sampled)

  # print("Split posterior likelihoods per posterior index")
  testit::assert("pi" %in% names(posterior_likelihoods))
  `%>%` <- dplyr::`%>%`
  df <- tidyr::spread(some_posterior_likelihoods, pi, likelihood) %>% dplyr::rename(pi1 = "1", pi2 = "2")

  # print("Remove NA column")
  df <- dplyr::select(df, -starts_with("<NA>"))
  testit::assert(names(df) == c("filename", "sti", "ai", "si", "pi1", "pi2"))

  # print("Remove NAs")
  df <- stats::na.omit(df)

  # print("Group")
  df <- dplyr::group_by(.data = df, filename, sti, ai)

  safe_mann_whitney <- function(pi1, pi2)
  {
    p <- NA
    tryCatch(
        p <- stats::wilcox.test(
          pi1,
          pi2,
          correct = FALSE,
          exact = FALSE, # cannot compute exact p-value with ties
          na.action = na.omit
        )$p.value,
        error = function(cond) {} # nolint
      )
    p
  }

  df <- df %>% summarize(p_value = safe_mann_whitney(pi1, pi2))

  ggplot2::ggplot(
    stats::na.omit(df),
    ggplot2::aes(x = p_value, na.omit = TRUE)
  ) +
    ggplot2::geom_histogram(binwidth = 0.01) +
    ggplot2::geom_vline(xintercept = 0.05, linetype = "dotted") +
    ggplot2::xlab("p value") +
    ggplot2::ylab("Count") +
    ggplot2::labs(
      title = "The distribution of p values of Mann-Whitney tests\nbetween posterior likelihoods",
      caption  = filename
    ) +
    ggplot2::annotate("text", x = c(0.0, 0.125), y = 1450, label = c("different", "same")) +
    ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
  dev.off()

  # # Show posterior with low, median and high p value
  # low  <- df[which(df$p_value == min(   df$p_value)), ]
  # low_filename <- paste0(posteriors_path, "/", low$filename[1])
  # low_sti <- as.numeric(low$sti[1])
  # low_ai <- as.numeric(low$ai[1])
  # low_file <- wiritttes::read_file(low_filename)
  # low_likelihoods1 <- wiritttes::get_posterior(low_file, sti = low_sti, ai = low_ai, pi = 1)$estimates$likelihood
  # low_likelihoods2 <- wiritttes::get_posterior(low_file, sti = low_sti, ai = low_ai, pi = 2)$estimates$likelihood
  # df_low <- data.frame(
  #   pi = as.factor(c(rep(1, length(low_likelihoods1)), rep(2, length(low_likelihoods2)))),
  #   likelihood  = c(low_likelihoods1, low_likelihoods2)
  # )
  #
  # svg("~/figure_posterior_distribution_likelihoods_low.svg")
  # options(warn = 1) # Allow outliers not to be plotted
  # ggplot2::ggplot(
  #   stats::na.omit(df_low),
  #   ggplot2::aes(x = likelihood, fill = pi)
  # ) +
  #   ggplot2::geom_histogram(binwidth = 0.5, position = "identity", alpha = 0.25) +
  #   ggplot2::xlab("tree likelihood") +
  #   ggplot2::ylab("Count") +
  #   ggplot2::xlim(-59330,-59310) +
  #   ggplot2::labs(
  #     title = "The distribution of tree likelihoods of two replicate  posteriors",
  #     caption  = paste0("p value = ", low$p_value, ", figure_posterior_distribution_likelihood_low")
  #   ) +
  #   ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
  # options(warn = 2) # Be strict
  # dev.off()
  #
  #
  #
  #
  #
  #
  # high <- df[which(df$p_value == max(   df$p_value)), ]
  #
  # high_filename <- paste0(posteriors_path, "/", high$filename[1])
  # high_sti <- as.numeric(high$sti[1])
  # high_ai <- as.numeric(high$ai[1])
  # high_file <- wiritttes::read_file(high_filename)
  # high_likelihoods1 <- wiritttes::get_posterior(high_file, sti = high_sti, ai = high_ai, pi = 1)$estimates$likelihood
  # high_likelihoods2 <- wiritttes::get_posterior(high_file, sti = high_sti, ai = high_ai, pi = 2)$estimates$likelihood
  # df_high <- data.frame(
  #   pi = as.factor(c(rep(1, length(high_likelihoods1)), rep(2, length(high_likelihoods2)))),
  #   likelihood  = c(high_likelihoods1, high_likelihoods2)
  # )
  #
  # svg("~/figure_posterior_distribution_likelihoods_high.svg")
  # options(warn = 1) # Allow outliers not to be plotted
  # ggplot2::ggplot(
  #   stats::na.omit(df_high),
  #   ggplot2::aes(x = likelihood, fill = pi)
  # ) +
  #   ggplot2::geom_histogram(position = "identity", alpha = 0.25) +
  #   ggplot2::xlab("tree likelihood") +
  #   ggplot2::ylab("Count") +
  #   #ggplot2::xlim(-59330,-59310) +
  #   ggplot2::labs(
  #     title = "The distribution of tree likelihoods of two replicate posteriors",
  #     caption  = paste0("p value = ", high$p_value, ", figure_posterior_distribution_likelihood_high")
  #   ) +
  #   ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
  # options(warn = 2) # Be strict

  ggplot2::ggsave(file = filename, width = 7, height = 7)
}