partly fix docs

NAMESPACE

@@ -3,7 +3,6 @@
 S3method(plot,maicplus_estimate_weights)
 S3method(print,maicplus_check_weights)
 export(bucher)
-export(calculate_weights_legend)
 export(center_ipd)
 export(check_weights)
 export(dummize_ipd)

R/matching.R

@@ -109,19 +109,23 @@ estimate_weights <- function(data, centered_colnames = NULL, start_val = 0, meth
 }
 
 
-#' Plot MAIC weights in a histogram with key statistics in legend
+#' Calculate Statistics for Weight Plot Legend
 #'
 #' Calculates ESS reduction and median weights which is used to create legend for weights plot
 #'
 #' @param weighted_data object returned after calculating weights using [estimate_weights]
 #'
 #' @return list of ESS, ESS reduction, median value of scaled and unscaled weights, and missing count
 #' @examples
+#' \dontrun{
 #' load(system.file("extdata", "weighted_data.rda", package = "maicplus", mustWork = TRUE))
 #' calculate_weights_legend(weighted_data)
-#' @export
-
+#' }
+#' @keywords internal
 calculate_weights_legend <- function(weighted_data) {
+ if (!inherits(weighted_data, "maicplus_estimate_weights")) {
+ stop("weighted_data must be class `maicplus_estimate_weights` generated by estimate_weights()")
+ }
  ess <- weighted_data$ess
  wt <- weighted_data$data$weights
  wt_scaled <- weighted_data$data$scaled_weights
@@ -241,7 +245,10 @@ plot_weights_ggplot <- function(weighted_data, bin_col, vline_col,
  ) +
  ggplot2::theme_bw() +
  ggplot2::facet_wrap(~ind, nrow = 1) +
- ggplot2::geom_text(data = legend_data, ggplot2::aes_string(label = "lab"), x = Inf, y = Inf, hjust = 1, vjust = 1, size = 3) +
+ ggplot2::geom_text(
+ data = legend_data,
+ ggplot2::aes_string(label = "lab"), x = Inf, y = Inf, hjust = 1, vjust = 1, size = 3
+ ) +
  ggplot2::theme(
  axis.title = ggplot2::element_text(size = 12),
  axis.text = ggplot2::element_text(size = 12)
@@ -300,16 +307,13 @@ plot.maicplus_estimate_weights <- function(x, ggplot = FALSE,
 #' This function checks to see if the optimization is done properly by checking the covariate averages
 #' before and after adjustment.
 #'
-#' @param match_res object returned after calculating weights using \code{\link{estimate_weights}}
+#' @param weighted_data object returned after calculating weights using \code{\link{estimate_weights}}
 #' @param processed_agd a data frame, object returned after using \code{\link{process_agd}} or
 #' aggregated data following the same naming convention
-#' @param mean_digits number of digits for rounding mean columns in the output
-#' @param prop_digits number of digits for rounding proportion columns in the output
-#' @param sd_digits number of digits for rounding mean columns in the output
 #'
 #' @examples
 #' load(system.file("extdata", "agd.rda", package = "maicplus", mustWork = TRUE))
-#' load(system.file("extdata", "match_res.rda", package = "maicplus", mustWork = TRUE))
+#' load(system.file("extdata", "weighted_data.rda", package = "maicplus", mustWork = TRUE))
 #' outdata <- check_weights(match_res, processed_agd = agd)
 #' print(outdata)
 #'
@@ -337,7 +341,8 @@ check_weights <- function(weighted_data, processed_agd) {
  sum_centered_IPD_with_weights = as.vector(num_check)
  )
  attr(outdata, "footer") <- list()
- ind_mean <- lapply(outdata$covariate, grep, x = names(processed_agd), value = TRUE) # find item that was matched by mean
+ # find item that was matched by mean
+ ind_mean <- lapply(outdata$covariate, grep, x = names(processed_agd), value = TRUE)
  ind_mean <- sapply(ind_mean, function(ii) any(grepl("_MEAN$", ii)))
  outdata$match_stat <- ifelse(grepl("_MEDIAN$", outdata$covariate), "Median",
  ifelse(grepl("_SQUARED$", outdata$covariate), "SD",
@@ -353,7 +358,10 @@ check_weights <- function(weighted_data, processed_agd) {
  covname <- outdata$covariate[ii]
  if (outdata$match_stat[ii] %in% c("Mean", "Prop")) {
  outdata$internal_trial[ii] <- mean(ipd_with_weights[[covname]], na.rm = TRUE)
- outdata$internal_trial_after_weighted[ii] <- weighted.mean(ipd_with_weights[[covname]], w = ipd_with_weights$weights, na.rm = TRUE)
+ outdata$internal_trial_after_weighted[ii] <- weighted.mean(
+ ipd_with_weights[[covname]],
+ w = ipd_with_weights$weights, na.rm = TRUE
+ )
  } else if (outdata$match_stat[ii] == "Median") {
  outdata$internal_trial[ii] <- quantile(ipd_with_weights[[covname]],
  probs = 0.5,
@@ -402,7 +410,11 @@ check_weights <- function(weighted_data, processed_agd) {
 #' @describeIn check_weights Print method for check_weights objects
 #' @export
 
-print.maicplus_check_weights <- function(x, mean_digits = 2, prop_digits = 2, sd_digits = 3, digits = getOption("digits"), ...) {
+print.maicplus_check_weights <- function(x,
+ mean_digits = 2,
+ prop_digits = 2,
+ sd_digits = 3,
+ digits = getOption("digits"), ...) {
  round_digits <- c("Mean" = mean_digits, "Prop" = prop_digits, "SD" = sd_digits)[x$match_stat]
  round_digits[is.na(round_digits)] <- digits
 
@@ -425,7 +437,7 @@ print.maicplus_check_weights <- function(x, mean_digits = 2, prop_digits = 2, sd
 #' @param width Number of characters to break string into new lines (`\n`).
 #'
 #' @return A character string
-
+#' @keywords internal
 ess_footnote_text <- function(width = 0.9 * getOption("width")) {
  text <- "An ESS reduction up to ~60% is not unexpected based on the 2021 survey of NICE's technology appraisals
 (https://onlinelibrary.wiley.com/doi/full/10.1002/jrsm.1511), whereas a reduction of >75% is less common

tests/testthat/test-matching.R

@@ -8,3 +8,18 @@ test_that("ess_footnote_text works", {
  )
  )
 })
+
+test_that("calculate_weights_legend works", {
+ load(system.file("extdata", "weighted_data.rda", package = "maicplus", mustWork = TRUE))
+ result <- calculate_weights_legend(weighted_data)
+ expect_equal(
+ result,
+ expected = list(
+ ess = 166.37,
+ ess_reduction = 66.73,
+ wt_median = 0.0594,
+ wt_scaled_median = 0.1486,
+ nr_na = 0L
+ )
+ )
+})