create test problem for population means

DESCRIPTION

@@ -24,18 +24,17 @@ Depends:
 Imports:
  utils,
  stats,
- sf,
+ generics,
+ methods,
  ggplot2,
  tidyr,
  dplyr,
  purrr,
  tibble,
  rlang,
  broom,
- hardhat,
  parsnip,
- generics,
- methods,
+ hardhat,
 Suggests:
  kableExtra,
  knitr,

R/coding.R

@@ -1,10 +1,9 @@
 #' Center for Medicare and Medicaid Services (CMS) Procedure Codes
 #'
-#' @description
-#' This is a generative function used to call CMS procedure codes. It is used to
-#' create a dataset that can be generally used to map procedure codes to their
-#' descriptions, allowing for understanding of interventions performed. The
-#' currently supported codes are explained in the details.
+#' @description This is a generative function used to call CMS procedure codes.
+#' It is used to create a dataset that can be generally used to map procedure
+#' codes to their descriptions, allowing for understanding of interventions
+#' performed. The currently supported codes are explained in the details.
 #'
 #' The following procedure codes are currently supported:
 #'
@@ -16,11 +15,10 @@
 #'
 #' * CPT procedure codes, most recently updated on `2023-11-29`
 #'
-#' @details
-#' CMS will usually release updated version of these codes on an annual basis.
-#' Each dataset that is supported below can be identified by the year it was
-#' published (not the _go-live_ date, but the _publically-available_ date). The
-#' previous versions that are included in the package are as below.
+#' @details CMS will usually release updated version of these codes on an annual
+#' basis. Each dataset that is supported below can be identified by the year it
+#' was published (not the _go-live_ date, but the _publically-available_ date).
+#' The previous versions that are included in the package are as below.
 #'
 #' * ICD9: 2014
 #'
@@ -30,11 +28,17 @@
 #'
 #' * CPT: 2023
 #'
-#' @param format <character> The format of the procedure codes. Currently supported formats
-#' are: `c("icd9", "icd10", "hcpcs", "cpt")` (case-insensitive).
+#' @param format The format of the procedure codes, written as a `character`.
+#' Currently supported formats are: `c("icd9", "icd10", "hcpcs", "cpt")`
+#' (case-insensitive).
 #'
-#' @param version <character> The version of the procedure codes. Currently supported:
-#' `c("2014", "2023")`
+#' @param version The version of the procedure codes, which are written as a
+#' `character` although the majority are listed as a year. Currently
+#' supported: `c("2014", "2023")`
+#'
+#' @returns A `tbl_df` with two columns: `code` and `description`. The `code`
+#' refers to the procedure code, while the `description` refers to the
+#' description of the procedure.
 #'
 #' @name procedure_codes
 #' @export

R/cosinor-constructor.R

@@ -10,25 +10,25 @@
 #' @param t Represents the _ordered_ time indices that provide the positions for the
 #' cosine wave. Depending on the context:
 #'
-#' - A __data frame__ of a time-based predictor/index.
+#' - A `data frame` of a time-based predictor/index.
 #'
-#' - A __matrix__ of time-based predictor/index.
+#' - A `matrix` of time-based predictor/index.
 #'
-#' - A __recipe__ specifying a set of preprocessing steps
+#' - A `recipe` specifying a set of preprocessing steps
 #' created from [recipes::recipe()].
 #'
-#' @param y When `t` is a __data frame__ or __matrix__, `y` is the outcome
+#' @param y When __t__ is a `data frame` or `matrix`, __y__ is the outcome
 #' specified as:
 #'
-#' - A __data frame__ with 1 numeric column.
+#' - A `data frame` with 1 numeric column.
 #'
-#' - A __matrix__ with 1 numeric column.
+#' - A `matrix` with 1 numeric column.
 #'
-#' - A numeric __vector__.
+#' - A numeric `vector`.
 #'
-#' @param data When a __recipe__ or __formula__ is used, `data` is specified as:
+#' @param data When a `recipe` or `formula` is used, __data__ is specified as:
 #'
-#' - A __data frame__ containing both the predictors and the outcome.
+#' - A `data frame` containing both the predictors and the outcome.
 #'
 #' @param formula A formula specifying the outcome terms on the left-hand side,
 #' and the predictor terms on the right-hand side.
@@ -37,23 +37,23 @@
 #' number of elements in the vector determine the number of components (e.g.
 #' single versus multiple cosinor).
 #'
-#' - A __vector__ with a single element = single-component cosinor, e.g.
+#' - A `vector` with a single element = single-component cosinor, e.g.
 #' period = c(24)
-#' - A __vector__ with multiple elements = multiple-component
+#' - A `vector` with multiple elements = multiple-component
 #' cosinor, e.g. period = c(24, 12)
 #'
 #' @param population Represents the population to be analyzed with a
 #' population-mean cosinor. Defaults to NULL, assuming individual cosinors are
-#' being generated. When a __recipe__ or __formula__ is used, `population` is
+#' being generated. When a `recipe` or `formula` is used, __population__ is
 #' specified as:
 #'
-#' - A __character__ name of the column contained in `data` that contains
+#' - A `character` name of the column contained in __data__ that contains
 #' identifiers for each subject. Every row will have a subject name which
 #' should be duplicated for each time index given.
 #'
-#' When a __data frame__ or __matrix__ is used, `population` is specified as:
+#' When a `data frame` or `matrix` is used, __population__ is specified as:
 #'
-#' - A __vector__ of the same length as `t`, with values representing each
+#' - A `vector` of the same length as __t__, with values representing each
 #' subject at the correct indices.
 #'
 #' @param ... Not currently used, but required for extensibility.
@@ -73,7 +73,7 @@ cosinor <- function(t, ...) {
  UseMethod("cosinor")
 }
 
-# Cosinor Methods {{{ ----
+# Cosinor Methods ----
 
 ## Default method
 
@@ -125,15 +125,13 @@ cosinor.recipe <- function(t, data, tau, population = NULL, ...) {
  cosinor_bridge(processed, tau, population, data, ...)
 }
 
-# }}}
-
-# Cosinor Construction {{{ ----
+# Cosinor Construction ----
 
 ## Bridging Function
 
 #' @description Bridging function takes user-facing call, after it is processed,
-#' and moves it to `cosinor_bridge`, which then calls both `cosinor_impl`, the
-#' fitting algorithm, and `new_cosinor`, the constructor for a new type of S3
+#' and moves it to `cosinor_bridge()`, which then calls both `cosinor_impl()`, the
+#' fitting algorithm, and `new_cosinor()`, the constructor for a new type of S3
 #' class. This also bridges to population-mean cosinor implementation if
 #' needed.
 #' @noRd
@@ -254,9 +252,7 @@ new_cosinor <- function(
  )
 }
 
-# }}}
-
-# Cosinor Parsnip Methods {{{ ----
+# Cosinor Parsnip Methods ----
 
 # Wrapper function to load parsnip model
 make_cosinor_reg <- function() {
@@ -327,8 +323,8 @@ make_cosinor_reg <- function() {
 #' @examples
 #' library(parsnip)
 #' data(twins)
-#' cosinor_reg(period = 24) %>%
-#' set_engine("card") %>%
+#' cosinor_reg(period = 24) |>
+#' set_engine("card") |>
 #' fit(rDYX ~ hour, data = twins)
 #' @export
 cosinor_reg <- function(mode = "regression", period = NULL) {
@@ -404,9 +400,7 @@ print.cosinor_reg <- function(x, ...) {
  invisible(x)
 }
 
-# }}}
-
-# Cosinor Generic S3 Methods {{{ ----
+# Cosinor Generic S3 Methods ----
 
 ## Print Method
 
@@ -487,10 +481,7 @@ plot.cosinor <- function(x, ...) {
  plot(model$t, model$yhat)
 }
 
-
-# }}}
-
-# Cosinor Tidiers {{{ ----
+# Cosinor Tidiers ----
 
 ## Tidy Method
 
@@ -525,17 +516,17 @@ tidy.cosinor <- function(x, conf.int = FALSE, conf.level = 0.95, ...) {
 
  # Tibble it
  result <-
- mat %>%
- dplyr::as_tibble(rownames = "term") %>%
+ mat |>
+ dplyr::as_tibble(rownames = "term") |>
  dplyr::rename(estimate = coefs, std.error = se)
 
  if (conf.int) {
 
  ci <- val$ci
  colnames(ci) <- c("conf.low", "conf.high")
  result <-
- ci %>%
- dplyr::as_tibble(rownames = "term") %>%
+ ci |>
+ dplyr::as_tibble(rownames = "term") |>
  dplyr::left_join(result, ., by = "term")
 
  }
@@ -571,5 +562,4 @@ augment.cosinor <- function(x, ...) {
  # Return
  return(result)
 
-}
-
+}

R/cosinor-fit.R

@@ -2,7 +2,7 @@
 
 ## Single Component Cosinor Implementation
 
-#' @description Model fitting algorithm for cosinor. Results in output that define the new S3 class, as seen by the [hardhat::new_model], which generates the `new_cosinor` function.
+#' @description Model fitting algorithm for cosinor. Results in output that define the new S3 class, as seen by the [hardhat::new_model], which generates the `new_cosinor()` function.
 #' @noRd
 cosinor_impl <- function(predictors, outcomes, tau) {
 
@@ -857,9 +857,9 @@ cosinor_features <- function(object, population = TRUE, ...) {
 
  # Fits are based on individuals, already made from original pop-cosinor
  results <-
- aug %>%
- tidyr::nest(models = -population) %>%
- dplyr::mutate(purrr::map_df(models, features)) %>%
+ aug |>
+ tidyr::nest(models = -population) |>
+ dplyr::mutate(purrr::map_df(models, features)) |>
  dplyr::select(-models)
 
  }

R/cosinor-plots.R

@@ -452,7 +452,7 @@ ggmulticosinor <- function(object, labels, ...) {
  # Merge together
  aug <- dplyr::bind_rows(aug, .id = ".id")
  features <-
- dplyr::bind_rows(features, .id = ".id") %>%
+ dplyr::bind_rows(features, .id = ".id") |>
  tibble::add_column(mesor = unlist(mesor))
 
  # Plot

R/cosinor-predict.R

@@ -1,18 +1,20 @@
 # Predict ----
 
 #' Predict from a `cosinor`
+#'
 #' @param object A `cosinor` object.
+#'
 #' @param new_data A data frame or matrix of new predictors.
-#' @param type A single character. The type of predictions to generate.
-#' Valid options are:
+#'
+#' @param type A single character. The type of predictions to generate. Valid
+#' options are:
 #'
 #' - `"numeric"` for numeric predictions.
 #'
-#' @param ... Not used, but required for extensibility.
-#' @return
+#' @param ... Additional arguments passed to the prediction function
 #'
-#' A tibble of predictions. The number of rows in the tibble is guaranteed
-#' to be the same as the number of rows in `new_data`.
+#' @return A tibble of predictions. The number of rows in the tibble is
+#' guaranteed to be the same as the number of rows in `new_data`.
 #'
 #' @export
 predict.cosinor <- function(object, new_data, type = "numeric", ...) {

R/data.R

@@ -30,7 +30,7 @@
 #' Clinical data is also available for visualization and comparison. Other HRV measures are used here for comparison and testing out functions.
 #'
 #' @docType data
-#' @format An tibble data frame
+#' @format A `tbl_df`
 #' @keywords datasets
 "triplets"