style: reformat docs

R/data.R

@@ -1,11 +1,13 @@
 #' @name latrend-data
 #' @title Longitudinal dataset representation
-#' @description The [latrend estimation functions][latrend-estimation] expect univariate longitudinal data that can be represented in a `data.frame` with one row per trajectory observation:
+#' @description The [latrend estimation functions][latrend-estimation] expect univariate
+#' longitudinal data that can be represented in a `data.frame` with one row per trajectory observation:
 #' * Trajectory identifier: `numeric`, `character`, or `factor`
 #' * Observation time: `numeric`
 #' * Observation value: `numeric`
 #'
-#' In principle, any type of longitudinal data structure is supported, given that it can be transformed to the required `data.frame` format using the generic [trajectories] function.
+#' In principle, any type of longitudinal data structure is supported,
+#' given that it can be transformed to the required `data.frame` format using the generic [trajectories] function.
 #' Support can be added by implementing the [trajectories] function for the respective signature.
 #' This means that users can implement their own data adapters as needed.
 #'
@@ -52,9 +54,12 @@ NULL
 #' @name PAP.adh
 #' @title Weekly Mean PAP Therapy Usage of OSA Patients in the First 3 Months
 #' @description
-#' A simulated longitudinal dataset comprising 301 patients with obstructive sleep apnea (OSA) during their first 91 days (13 weeks) of PAP therapy.
-#' The longitudinal patterns were inspired by the adherence patterns reported by Yi et al. (2022), interpolated to weekly hours of usage.
-#' @format A `data.frame` comprising longitudinal data of 500 patients, each having 26 observations over a period of 1 year.
+#' A simulated longitudinal dataset comprising 301 patients with obstructive sleep apnea (OSA)
+#' during their first 91 days (13 weeks) of PAP therapy.
+#' The longitudinal patterns were inspired by the adherence patterns reported by Yi et al. (2022),
+#' interpolated to weekly hours of usage.
+#' @format A `data.frame` comprising longitudinal data of 500 patients, each having 26 observations
+#' over a period of 1 year.
 #' Each row represents a patient observation interval (two weeks), with columns:
 #' \describe{
 #'   \item{Patient}{`integer`: The patient identifier, where each level represents a simulated patient.}
@@ -87,11 +92,13 @@ NULL
 #' @name PAP.adh1y
 #' @title Biweekly Mean PAP Therapy Adherence of OSA Patients over 1 Year
 #' @description
-#' A simulated longitudinal dataset comprising 500 patients with obstructive sleep apnea (OSA) during their first year on CPAP therapy.
+#' A simulated longitudinal dataset comprising 500 patients with obstructive sleep apnea (OSA)
+#' during their first year on CPAP therapy.
 #' The dataset contains the patient usage hours, averaged over 2-week periods.
 #'
 #' The daily usage data underlying the downsampled dataset was simulated based on 7 different adherence patterns.
-#' The defined adherence patterns were inspired by the adherence patterns identified by Aloia et al. (2008), with slight adjustments
+#' The defined adherence patterns were inspired by the adherence patterns identified by Aloia et al. (2008),
+#' with slight adjustments
 #' @format A `data.frame` comprising longitudinal data of 500 patients, each having 26 observations over a period of 1 year.
 #' Each row represents a patient observation interval (two weeks), with columns:
 #' \describe{
@@ -105,7 +112,8 @@ NULL
 #'
 #' @note This dataset is only intended for demonstration purposes.
 #' While the data format will remain the same, the data content is subject to change in future versions.
-#' @source This dataset was generated based on the cluster-specific descriptive statistics table provided in Aloia et al. (2008),
+#' @source This dataset was generated based on the cluster-specific descriptive statistics table
+#' provided in Aloia et al. (2008),
 #' with some adjustments made in order to improve cluster separation for demonstration purposes.
 #'
 #' \insertRef{aloia2008time}{latrend}
@@ -134,7 +142,8 @@ NULL
 #' @param random Random effects formula.
 #' @param cluster Cluster effects formula.
 #' @param id Name of the strata.
-#' @param data Data with covariates to use for generation. Stratified data may be specified by adding a grouping column.
+#' @param data Data with covariates to use for generation. Stratified data may be specified by
+#' adding a grouping column.
 #' @param clusterNames A `character` vector denoting the names of the generated clusters.
 #' @param fixedCoefs Coefficients matrix for the fixed effects.
 #' @param clusterCoefs Coefficients matrix for the cluster effects.
@@ -229,7 +238,10 @@ generateLongData = function(
   )
   assert_that(ncol(clusterCoefs) == nClus)
   Xc = model.matrix(cluster, alldata)
-  assert_that(ncol(Xc) == nrow(clusterCoefs), msg = 'Missing or too many coefficients specified for cluster effects.')
+  assert_that(
+    ncol(Xc) == nrow(clusterCoefs),
+    msg = 'Missing or too many coefficients specified for cluster effects.'
+  )
   alldata[, Mu.class := rowSums(Xc * t(clusterCoefs)[Class, ])]
 
   ## Random effects

R/matrix.R

@@ -15,9 +15,16 @@
 #' @export
 #' @name trajectoryAssignments
 #' @aliases trajectoryAssignments,matrix-method
-#' @details In case `object` is a `matrix`: the posterior probability `matrix`, with the \eqn{k}th column containing the observation- or trajectory-specific probability for cluster \eqn{k}.
-#' @param clusterNames Optional `character vector` with the cluster names. If `clusterNames = NULL`, [make.clusterNames()] is used.
-setMethod('trajectoryAssignments', 'matrix', function(object, strategy = which.max, clusterNames = colnames(object), ...) {
+#' @details In case `object` is a `matrix`: the posterior probability `matrix`,
+#' with the \eqn{k}th column containing the observation- or trajectory-specific probability for cluster \eqn{k}.
+#' @param clusterNames Optional `character vector` with the cluster names.
+#' If `clusterNames = NULL`, [make.clusterNames()] is used.
+setMethod('trajectoryAssignments', 'matrix', function(
+  object,
+  strategy = which.max,
+  clusterNames = colnames(object),
+  ...
+) {
   assert_that(is_valid_postprob(object))
 
   nTraj = nrow(object)
@@ -48,7 +55,8 @@ setMethod('trajectoryAssignments', 'matrix', function(object, strategy = which.m
 #' @param time The time column name.
 #' @param response The response column name.
 #' @param ids A `vector` specifying the id names. Should match the number of rows of `data`.
-#' @param times A `numeric` `vector` specifying the times of the measurements. Should match the number of columns of `data`.
+#' @param times A `numeric` `vector` specifying the times of the measurements.
+#' Should match the number of columns of `data`.
 #' @param as.data.table Whether to return the result as a `data.table`, or a `data.frame` otherwise.
 #' @return A `data.table` or `data.frame` containing the repeated measures.
 #' @seealso [tsmatrix]
@@ -97,7 +105,8 @@ tsframe = function(
     if (all(!is.finite(times))) {
       times = seq_along(times)
       warning(
-        'supplied "times" argument failed to parse to numeric values; falling back to times = seq_along(times) instead.'
+        'supplied "times" argument failed to parse to numeric values;
+        falling back to times = seq_along(times) instead.'
       )
     } else {
       stop('some time values are non-finite (possible failed to parse to numeric?)')
@@ -119,14 +128,17 @@ tsframe = function(
 }
 
 #' @rdname tsframe
-#' @note The `meltRepeatedMeasures()` function is deprecated and will be removed in a future version, please use `tsframe()` instead.
+#' @note The `meltRepeatedMeasures()` function is deprecated and will be removed in a future version,
+#' please use `tsframe()` instead.
 meltRepeatedMeasures = tsframe
 
 #' @export
 #' @title Convert a longitudinal data.frame to a matrix
-#' @description Converts a longitudinal `data.frame` comprising trajectories with an equal number of observations, measured at identical moments in time, to a `matrix`. Each row of the matrix represents a trajectory.
+#' @description Converts a longitudinal `data.frame` comprising trajectories with an equal number of observations,
+#' measured at identical moments in time, to a `matrix`. Each row of the matrix represents a trajectory.
 #' @inheritParams tsframe
-#' @param fill A `scalar` value. If `FALSE`, an error is thrown when time series observations are missing in the data frame.
+#' @param fill A `scalar` value.
+#' If `FALSE`, an error is thrown when time series observations are missing in the data frame.
 #' Otherwise, the value used for representing missing observations.
 #' @return A `matrix` with a trajectory per row.
 #' @seealso [tsframe]
@@ -180,5 +192,6 @@ tsmatrix = function(
 }
 
 #' @rdname tsmatrix
-#' @note The `dcastRepeatedMeasures()` function is deprecated and will be removed in a future version. Please use `tsmatrix()` instead.
+#' @note The `dcastRepeatedMeasures()` function is deprecated and will be removed in a future version.
+#' Please use `tsmatrix()` instead.
 dcastRepeatedMeasures = tsmatrix

R/trajectories.R

@@ -40,9 +40,11 @@ setMethod('trajectories', 'call', function(object, ..., envir) {
 #' @param cluster The cluster assignment column
 #' @param center A function for aggregating multiple points at the same point in time
 #' @param trajectories Whether to additionally plot the original trajectories (`TRUE`),
-#' or to show the expected interval (standard deviation, standard error, range, or percentile range) of the observations at the respective moment in time.
+#' or to show the expected interval (standard deviation, standard error, range, or percentile range)
+#' of the observations at the respective moment in time.
 #'
-#' Note that visualizing the expected intervals is currently only supported for time-aligned trajectories, as the interval is computed at each unique moment in time.
+#' Note that visualizing the expected intervals is currently only supported for time-aligned trajectories,
+#' as the interval is computed at each unique moment in time.
 #' By default (`FALSE`), no information on the underlying trajectories is shown.
 #' @param facet Whether to facet by cluster. This is done by default when `trajectories` is enabled.
 #' @param id Id column. Only needed when `trajectories = TRUE`.
@@ -209,7 +211,8 @@ setMethod('plotClusterTrajectories', 'data.frame', function(object,
 #' @aliases plotTrajectories,data.frame-method
 #' @inheritParams trajectories
 #' @param response Response variable `character` name or a `call`.
-#' @param cluster Cluster variable name. If unspecified, trajectories are not grouped. Alternatively, cluster is a vector indicating cluster membership per id.
+#' @param cluster Cluster variable name. If unspecified, trajectories are not grouped.
+#' Alternatively, cluster is a vector indicating cluster membership per id.
 #' @param facet Whether to facet by cluster.
 #' @seealso [trajectories] [plotFittedTrajectories] [plotClusterTrajectories]
 #' @examples
@@ -269,7 +272,8 @@ setMethod('plotTrajectories', 'data.frame',
     response = names(dfNum)[which.max(counts)]
     message(
       sprintf(
-        'Automatically selected "%s" as the response variable. To override this, specify the "response" argument.',
+        'Automatically selected "%s" as the response variable.
+        To override this, specify the "response" argument.',
         response
       )
     )