R/forest_model.R

In forestmodel: Forest Plots from Regression Models

#' Produce a forest plot based on a regression model
#'
#' @param model regression model produced by \code{\link[stats]{lm}},
#'   \code{\link[stats]{glm}}, \code{\link[survival]{coxph}}
#' @param panels \code{list} with details of the panels that make up the plot (See Details)
#' @param exponentiate whether the numbers on the x scale should be exponentiated for plotting
#' @param format_options formatting options as a list as generated by \code{\link{forest_model_format_options}}
#' @param funcs optional list of functions required for formatting \code{panels$display}
#' @param factor_separate_line whether to show the factor variable name on a separate line
#' @param theme theme to apply to the plot
#' @param limits limits of the forest plot on the X-axis (taken as the range of the data
#'   by default)
#' @param breaks breaks to appear on the X-axis (note these will be exponentiated
#'   if \code{exponentiate == TRUE})
#' @param return_data return the data to produce the plot as well as the plot itself
#' @param covariates a character vector optionally listing the variables to include in the plot
#'   (defaults to all variables)
#' @param recalculate_width \code{TRUE} to recalculate panel widths using the current device
#'   or the desired plot width in inches
#' @param recalculate_height \code{TRUE} to shrink text size using the current device
#'   or the desired plot height in inches
#' @param model_list list of models to incorporate into a single forest plot
#' @param merge_models if `TRUE`, merge all models in one section.
#' @param exclude_infinite_cis whether to exclude points and confidence intervals
#'   that go to positive or negative infinity from plotting. They will still be
#'   displayed as text. Defaults to \code{TRUE}, since otherwise plot is malformed
#'
#' @return A ggplot ready for display or saving, or (with \code{return_data == TRUE},
#'   a \code{list} with the parameters to call \code{\link{panel_forest_plot}} in the
#'   element \code{plot_data} and the ggplot itself in the element \code{plot})
#'
#' @details This function takes the model output from one of the common model functions in
#'   R (e.g. \code{\link[stats]{lm}}, \code{\link[stats]{glm}},
#'  \code{\link[survival]{coxph}}). If a \code{label} attribute was present on any of the
#'  columns in the original data (e.g. from the \code{labelled} package),
#'  this label is used in preference to the column name.
#'
#'  The \code{panels} parameter is a \code{list} of lists each of which have an element
#'  \code{width}
#'  and, optionally, \code{item}, \code{display}, \code{display_na},
#'  \code{heading}, \code{hjust} and \code{fontface}. \code{item} can be \code{"forest"} for the forest
#'  plot (exactly one required) or \code{"vline"} for a vertical line.
#'  \code{display} indicates which column to display as text. It can be a quoted variable name
#'  or a formula. The column display can include the standard ones produced by
#'  \code{\link[broom]{tidy}} and in addition
#'  \code{variable} (the term in the model; for factors this is the bare variable without the  level),
#'  \code{level} (the level of factors),
#'  \code{reference} (TRUE for the reference level of a factor). For \code{\link[survival]{coxph}}
#'  models, there will also be \code{n_events} for the number of events in the group with
#'  that level of the factor and \code{person_time} for the person-time in that group.
#'  The function \code{trans} is definded to be the
#'  transformation between the coefficients and the scales (e.g. \code{exp}). Other functions not
#'  in base R can be provided as a \code{list} with the parameter \code{funcs}.
#'  \code{display_na} allows for an alternative display for NA terms within \code{estimate}.
#'
#' @import dplyr
#' @import ggplot2
#' @import rlang
#'
#' @export
#'
#' @examples
#'
#' library("survival")
#' library("dplyr")
#' pretty_lung <- lung %>%
#'   transmute(time,
#'     status,
#'     Age = age,
#'     Sex = factor(sex, labels = c("Male", "Female")),
#'     ECOG = factor(lung$ph.ecog),
#'     `Meal Cal` = meal.cal
#'   )
#'
#' print(forest_model(coxph(Surv(time, status) ~ ., pretty_lung)))
#'
#' # Example with custom panels
#'
#' panels <- list(
#'   list(width = 0.03),
#'   list(width = 0.1, display = ~variable, fontface = "bold", heading = "Variable"),
#'   list(width = 0.1, display = ~level),
#'   list(width = 0.05, display = ~n, hjust = 1, heading = "N"),
#'   list(width = 0.05, display = ~n_events, width = 0.05, hjust = 1, heading = "Events"),
#'   list(
#'     width = 0.05,
#'     display = ~ replace(sprintf("%0.1f", person_time / 365.25), is.na(person_time), ""),
#'     heading = "Person-\nYears", hjust = 1
#'   ),
#'   list(width = 0.03, item = "vline", hjust = 0.5),
#'   list(
#'     width = 0.55, item = "forest", hjust = 0.5, heading = "Hazard ratio", linetype = "dashed",
#'     line_x = 0
#'   ),
#'   list(width = 0.03, item = "vline", hjust = 0.5),
#'   list(width = 0.12, display = ~ ifelse(reference, "Reference", sprintf(
#'     "%0.2f (%0.2f, %0.2f)",
#'     trans(estimate), trans(conf.low), trans(conf.high)
#'   )), display_na = NA),
#'   list(
#'     width = 0.05,
#'     display = ~ ifelse(reference, "", format.pval(p.value, digits = 1, eps = 0.001)),
#'     display_na = NA, hjust = 1, heading = "p"
#'   ),
#'   list(width = 0.03)
#' )
#' forest_model(coxph(Surv(time, status) ~ ., pretty_lung), panels)
#'
#' data_for_lm <- tibble(
#'   x = rnorm(100, 4),
#'   y = rnorm(100, 3, 0.5),
#'   z = rnorm(100, 2, 2),
#'   outcome = 3 * x - 2 * y + 4 * z + rnorm(100, 0, 0.1)
#' )
#'
#' print(forest_model(lm(outcome ~ ., data_for_lm)))
#'
#' data_for_logistic <- data_for_lm %>% mutate(
#'   outcome = (0.5 * (x - 4) * (y - 3) * (z - 2) + rnorm(100, 0, 0.05)) > 0.5
#' )
#'
#' print(forest_model(glm(outcome ~ ., binomial(), data_for_logistic)))
forest_model <- function(model,
                         panels = default_forest_panels(model, factor_separate_line = factor_separate_line),
                         covariates = NULL, exponentiate = NULL, funcs = NULL,
                         factor_separate_line = FALSE,
                         format_options = forest_model_format_options(),
                         theme = theme_forest(),
                         limits = NULL, breaks = NULL, return_data = FALSE,
                         recalculate_width = TRUE, recalculate_height = TRUE,
                         model_list = NULL, merge_models = FALSE, exclude_infinite_cis = TRUE) {
  mapping <- aes(estimate, xmin = conf.low, xmax = conf.high)
  if (!is.null(model_list)) {
    if (!is.list(model_list)) {
      stop("`model_list` must be a list if provided.")
    }
    if (is.null(names(model_list))) {
      model_names <- rep("", length(model_list))
    } else {
      model_names <- names(model_list)
    }
    if (any(model_names == "")) {
      need_names <- which(model_names == "")
      model_names_needed <- vapply(model_list[need_names], function(x) quo_name(x$call), character(1))
      model_names[need_names] <- model_names_needed
    }
    if (!merge_models) {
      mapping <- c(mapping, aes(section = model_name))
    }
    if (is.null(exponentiate)) {
      exponentiate <- inherits(model_list[[1]], "coxph") ||
        (inherits(model_list[[1]], "glm") && model_list[[1]]$family$link == "logit")
    }
    if (missing(panels)) {
      panels <- default_forest_panels(model_list[[1]], factor_separate_line = factor_separate_line)
    }
  } else {
    if (is.null(exponentiate)) {
      exponentiate <- inherits(model, "coxph") ||
        (inherits(model, "glm") && model$family$link == "logit")
    }
  }

  if (exponentiate) trans <- exp else trans <- I

  stopifnot(is.list(panels))

  remove_backticks <- function(x) {
    gsub("^`|`$|\\\\(?=`)|`(?=:)|(?<=:)`", "", x, perl = TRUE)
  }

  make_forest_terms <- function(model) {
    tidy_model <- broom::tidy(model, conf.int = TRUE)
    data <- stats::model.frame(model)

    forest_terms <- tibble::tibble(
      term_label = attr(model$terms, "term.labels"),
      variable = remove_backticks(term_label)
    ) %>%
      inner_join(
        tibble::tibble(
          variable = names(attr(model$terms, "dataClasses"))[-1],
          class = attr(model$terms, "dataClasses")[-1]
        ),
        by = "variable"
      )

    forest_labels <- tibble::tibble(
      variable = names(data),
      label = vapply(
        data,
        function(x) attr(x, "label", exact = TRUE) %||% NA_character_,
        character(1)
      ) %>%
        coalesce(variable)
    )

    create_term_data <- function(term_row) {
      if (!is.na(term_row$class)) {
        var <- term_row$variable
        if (term_row$class %in% c("factor", "character")) {
          tab <- table(data[, var])
          if (!any(paste0(term_row$term_label, names(tab)) %in% tidy_model$term)) {
            # Filter out terms not in final model summary (e.g. strata)
            out <- tibble::tibble(variable = NA)
          } else {
            out <- data.frame(
              term_row,
              level = names(tab),
              level_no = 1:length(tab),
              n = as.integer(tab),
              stringsAsFactors = FALSE
            )
            if (factor_separate_line) {
              out <- bind_rows(tibble::as_tibble(term_row), out)
            }
            if (inherits(model, "coxph")) {
              data_event <- bind_cols(data[, -1, drop = FALSE],
                .event_time = data[, 1][, "time"],
                .event_status = data[, 1][, "status"]
              )
              event_detail_tab <- data_event %>%
                group_by(!!as.name(var)) %>%
                summarise(
                  person_time = sum(.event_time),
                  n_events = sum(.event_status)
                )
              colnames(event_detail_tab)[1] <- "level"
              event_detail_tab$level <- as.character(event_detail_tab$level)
              out <- out %>% left_join(event_detail_tab, by = "level")
            }
          }
        } else {
          out <- data.frame(term_row,
            level = NA, level_no = NA, n = sum(!is.na(data[, var])),
            stringsAsFactors = FALSE
          )
          if (term_row$class == "logical") {
            out$term_label <- paste0(term_row$term_label, "TRUE")
          }
        }
      } else {
        out <- data.frame(term_row, level = NA, level_no = NA, n = NA, stringsAsFactors = FALSE)
      }
      out
    }
    forest_terms <- forest_terms %>%
      rowwise() %>%
      do(create_term_data(.)) %>%
      ungroup() %>%
      filter(!is.na(variable)) %>%
      mutate(term = paste0(term_label, replace(level, is.na(level), ""))) %>%
      left_join(tidy_model, by = "term") %>%
      mutate(
        reference = ifelse(is.na(level_no), FALSE, level_no == 1),
        estimate = ifelse(reference, 0, estimate),
        variable = ifelse(is.na(variable), remove_backticks(term), variable)
      ) %>%
      mutate(
        variable = ifelse(is.na(level_no) | (level_no == 1 & !factor_separate_line), variable, NA)
      ) %>%
      left_join(
        forest_labels,
        by = "variable"
      ) %>%
      mutate(
        variable = coalesce(label, variable)
      )
    if (!is.null(covariates)) {
      forest_terms <- filter(forest_terms, term_label %in% covariates)
    }

    forest_terms
  }

  if (!is.null(model_list)) {
    forest_terms <- lapply(seq_along(model_list), function(i) {
      make_forest_terms(model_list[[i]]) %>%
        mutate(model_name = model_names[i])
    }) %>%
      bind_rows()
    if (merge_models) {
      forest_terms$model_name <- NULL
    }
  } else {
    forest_terms <- make_forest_terms(model)
  }

  # #use_exp <- grepl("exp", deparse(trans))
  if (!is.null(limits)) {
    forest_terms <- forest_terms %>%
      mutate(
        arrow_tag.l = limits[1],
        arrow_tag.r = limits[2],
        arrow_tag.l = ifelse(conf.low < .data$arrow_tag.l, TRUE, FALSE),
        arrow_tag.r = ifelse(conf.high > .data$arrow_tag.r, TRUE, FALSE)
      ) %>%
      mutate(
        plot_range.low = ifelse(.data$arrow_tag.l, limits[1], conf.low),
        plot_range.high = ifelse(.data$arrow_tag.r, limits[2], conf.high)
      )
  }



  plot_data <- list(
    forest_data = forest_terms,
    mapping = mapping,
    panels = panels, trans = trans,
    funcs = funcs, format_options = format_options, theme = theme,
    limits = limits, breaks = breaks, recalculate_width = recalculate_width,
    recalculate_height = recalculate_height, exclude_infinite_cis = exclude_infinite_cis
  )
  main_plot <- do.call("panel_forest_plot", plot_data)
  if (return_data) {
    list(plot_data = plot_data, plot = main_plot)
  } else {
    main_plot
  }
}