R/plot_helpers.R

In weibulltools: Statistical Methods for Life Data Analysis

# Helper function to set the distribution-specific grid:
plot_layout_helper <- function(x,
                               y = NULL,
                               distribution
) {

  # Define x-ticks as logarithm to the base of 10 for log-location-scale distributions:
  if (distribution %in% c("weibull", "lognormal", "loglogistic")) {

    # Layout depends on x, using a function to build helpful sequences:
    x_base <- function(xb) floor(log10(xb))
    xlog10_range <- (x_base(min(x)) - 1):x_base(max(x))
    # x-ticks and x-labels:
    x_ticks <- sapply(
      xlog10_range,
      function(z) seq(10 ^ z, 10 ^ (z + 1), 10 ^ z),
      simplify = TRUE
    )
    x_ticks <- round(as.numeric(x_ticks), digits = 10)
    x_ticks <- x_ticks[!duplicated(x_ticks)]
    x_labels <- x_ticks
    x_labels[c(rep(F, 3), rep(T, 6))] <- " "
  } else {
    # We don't need these values, therefore we return NULL:
    x_ticks <- x_labels <- NULL
  }

  # y-ticks and y-labels:
  ## Hard coded but it's okay since range is always between 0 and 1:
  y_s <- c(.0000001, .000001, .00001, .0001, .001, .005, .01, .05, .1,
           .2, .3, .5, .6, .7, .8, .9, .95, .99, .999, .9999, .99999)

  ## If argument y is not `NULL` y is used to narrow down the range of y_s:
  if (!purrr::is_null(y)) {
    ### y range:
    ymin <- min(y, na.rm = TRUE)
    ymax <- max(y, na.rm = TRUE)

    ### Determine adjacent indices, i.e. min(y)_(i-1) and max(y)_(i+1) if exist:
    ind_min <- max(which(y_s < ymin), 1L)
    ind_max <- min(which(y_s > ymax), length(y_s))

    y_s <- y_s[ind_min:ind_max]
  }

  y_ticks <- q_std(y_s, distribution)

  y_labels <- y_s * 100

  # Plot:

  l <- list(
    x_ticks = x_ticks,
    x_labels = x_labels,
    y_ticks = y_ticks,
    y_labels = y_labels
  )

  l
}



# Helper function to compute the distribution-specific plotting positions:
plot_prob_helper <- function(tbl,
                             distribution
) {
  tbl <- tbl %>%
    dplyr::filter(.data$status == 1) %>%
    dplyr::arrange(.data$x)

  tbl$q <- q_std(tbl$prob, distribution)

  tbl
}



# Helper function to compute distribution-specific points of the regression line:
plot_mod_helper <- function(x,
                            dist_params,
                            distribution,
                            cdf_estimation_method = NA_character_
) {

  if (length(x) == 2) {
    if (std_parametric(distribution) %in% c("weibull", "lognormal", "loglogistic")) {
      x_p <- 10 ^ seq(log10(x[1]), log10(x[2]), length.out = 100)
    } else {
      x_p <- seq(x[1], x[2], length.out = 100)
    }
  }

  if (length(x) > 2) {
    if (
      length(x) < 30 &&
      distribution %in% c("weibull3", "lognormal3", "loglogistic3")
    ) {
      warning(
        "'x' has less than 30 values and distribution is three-parametric.
        Consider using 'x = range(x)' to avoid visual kinks in regression line.
        ",
        call. = FALSE
      )
    }

    x_p <- x
  }

  y_p <- predict_prob(
    q = x_p,
    dist_params = dist_params,
    distribution = distribution
  )

  tbl_pred <- tibble::tibble(x_p = x_p, y_p = y_p)

  q <- q_std(y_p, std_parametric(distribution))

  # Preparation of plotly hovers:
  n_par <- length(dist_params)
  ## Values:
  param_val <- rep(NA_character_, 3)
  param_val[1:n_par] <- format(dist_params, digits = 3)

  ## Labels:
  ### Enforce length 3:
  if (std_parametric(distribution) == "exponential") {
    param_label <- c("\u03B8:", NA_character_, NA_character_)
  } else {
    param_label <- c("\u03BC:", "\u03C3:", NA_character_)
  }

  if (has_thres(distribution)) {
    param_label[n_par] <- "\u03B3:"
  }

  tbl_pred <- tbl_pred %>%
    dplyr::mutate(
      param_val = list(param_val),
      param_label = list(param_label),
      cdf_estimation_method = cdf_estimation_method,
      group = NA_character_,
      q = q
    )
}



# Helper function to compute distribution-specific points of the regression line:
plot_mod_mix_helper <- function(model_estimation,
                                cdf_estimation_method,
                                group
) {

  distribution <- model_estimation$distribution
  data <- model_estimation$data %>%
    dplyr::filter(.data$status == 1)

  x_min <- min(data$x, na.rm = TRUE)
  x_max <- max(data$x, na.rm = TRUE)

  x_p <- seq(x_min, x_max, length.out = 200)
  y_p <- predict_prob(
    q = x_p,
    dist_params = model_estimation$coefficients,
    distribution = distribution
  )

  param_1 <- format(model_estimation$coefficients[[1]], digits = 3)
  param_2 <- format(model_estimation$coefficients[[2]], digits = 3)
  label_1 <- "\u03BC:"
  label_2 <- "\u03C3:"

  tbl_p <- tibble::tibble(
    x_p = x_p,
    y_p = y_p,
    param_val = list(c(param_1, param_2)),
    param_label = list(c(label_1, label_2)),
    cdf_estimation_method = cdf_estimation_method,
    group = group
  )

  tbl_p$q <- q_std(tbl_p$y_p, distribution)

  tbl_p
}



# Helper function for S3 method plot_conf.wt_confint:
plot_conf_helper_2 <- function(confint) {
  direction <- attr(confint, "direction", exact = TRUE)
  distribution <- attr(confint, "distribution", exact = TRUE)

  tbl_upper <- if (hasName(confint, "upper_bound")) {
    if (direction == "x") {
      tibble::tibble(
        x = confint$upper_bound,
        y = confint$prob,
        bound = "Upper",
        cdf_estimation_method = confint$cdf_estimation_method
      )
    } else {
      tibble::tibble(
        x = confint$x,
        y = confint$upper_bound,
        bound = "Upper",
        cdf_estimation_method = confint$cdf_estimation_method
      )
    }
  }

  tbl_lower <- if (hasName(confint, "lower_bound")) {
    if (direction == "x") {
      tibble::tibble(
        x = confint$lower_bound,
        y = confint$prob,
        bound = "Lower",
        cdf_estimation_method = confint$cdf_estimation_method
      )
    } else {
      tibble::tibble(
        x = confint$x,
        y = confint$lower_bound,
        bound = "Lower",
        cdf_estimation_method = confint$cdf_estimation_method
      )
    }
  }

  tbl_p <- dplyr::bind_rows(tbl_upper, tbl_lower)

  tbl_p$q <- q_std(tbl_p$y, std_parametric(distribution))

  tbl_p <- dplyr::group_by(tbl_p, .data$bound)

  tbl_p
}



# Helper function for S3 method plot_conf.default:
plot_conf_helper <- function(tbl_mod,
                             x,
                             y,
                             direction,
                             distribution
) {

  # Construct x, y from x/y, upper/lower bounds (depending on direction and bounds)
  lst <- Map(tibble::tibble, x = x, y = y)
  tbl_p <- dplyr::bind_rows(lst, .id = "bound")

  if (direction == "y") {
    tbl_p$bound <- ifelse(test = tbl_p$y < tbl_mod$y_p, yes = "Lower", no = "Upper")
  } else {
    tbl_p$bound <- ifelse(test = tbl_p$x < tbl_mod$x_p, yes = "Lower", no = "Upper")
  }

  tbl_p$q <- q_std(tbl_p$y, std_parametric(distribution))

  tbl_p <- dplyr::group_by(tbl_p, .data$bound)
  tbl_p$cdf_estimation_method <- NA_character_

  tbl_p
}



# Helper function for `plot_pop()`:
plot_pop_helper <- function(x,
                            dist_params_tbl,
                            distribution,
                            tol = 1e-6
) {

  # Determine equidistant x positions if needed:
  x_s <- if (length(x) == 2) {
    10 ^ seq(log10(x[1]), log10(x[2]), length.out = 200)
  } else {
    x
  }

  # Set groups, since every row is its own distribution:
  tbl_pop <- dist_params_tbl %>%
    dplyr::mutate(group = as.character(dplyr::row_number()))

  # Map predict_prob over tbl_pop:
  tbl_pop <- purrr::pmap_dfr(
    tbl_pop,
    x_s = x_s,
    distribution = distribution,
    function(loc = NA, sc, thres = NA, group, x_s, distribution) {
      # Replace NA with NULL, so that loc and thres are ignored in c():
      dist_params <- c(loc %NA% NULL, sc, thres %NA% NULL)

      if (!is_std_parametric(distribution, dist_params)) {
        # Threshold models:
        distribution <- paste0(distribution, length(dist_params))
      }

      tibble::tibble(
        loc = loc,
        sc = sc,
        thres = thres,
        group = group,
        x_s = x_s,
        y_s = predict_prob(
          q = x_s,
          dist_params = dist_params,
          distribution = distribution
        )
      )
    }
  )

  tbl_pop <- tbl_pop %>%
    dplyr::filter(.data$y_s < 1, .data$y_s > 0)

  tbl_pop$q <- q_std(tbl_pop$y_s, distribution)
  # Set values and labels for plotlys hoverinfo:
  tbl_pop <- tbl_pop %>%
    dplyr::mutate(
      param_val_1 = ifelse(is.na(.data$loc), NA, format(.data$loc, digits = 3)),
      param_val_2 = format(.data$sc, digits = 3),
      param_val_3 = ifelse(is.na(.data$thres), NA, format(.data$thres, digits = 3)),
      param_label_1 = ifelse(is.na(.data$loc), NA, "\u03BC:"),
      param_label_2 = if (distribution == "exponential") "\u03B8:" else "\u03C3:",
      param_label_3 = ifelse(is.na(.data$thres), NA, "\u03B3:")
    ) %>%
    dplyr::rowwise() %>%
    dplyr::mutate(
      param_val = list(
        c(.data$param_val_1, .data$param_val_2, .data$param_val_3)
      ),
      param_label = list(
        c(.data$param_label_1, .data$param_label_2, .data$param_label_3)
      )
    ) %>%
    dplyr::ungroup() %>%
    dplyr::select("x_s", "y_s", "q", "param_val", "param_label", "group") %>%
    dplyr::filter(.data$y_s <= 1 - tol, .data$y_s >= tol)

  tbl_pop
}