From: Benjamin Auder <benjamin.auder@somewhere>
Date: Sun, 9 Apr 2017 18:15:09 +0000 (+0200)
Subject: Improve documentation
X-Git-Url: https://git.auder.net/%7B%7B%20asset%28%27mixstore/css/current/git-logo.png?a=commitdiff_plain;h=102bcfda4afbb5cfee885cbee0f55545624168fd;p=talweg.git

Improve documentation
---

diff --git a/pkg/R/A_NAMESPACE.R b/pkg/R/A_NAMESPACE.R
index 1d78d9c..466293c 100644
--- a/pkg/R/A_NAMESPACE.R
+++ b/pkg/R/A_NAMESPACE.R
@@ -1,3 +1,10 @@
+#' @importFrom grDevices colors gray.colors
+#' @importFrom graphics abline hist par plot
+#' @importFrom methods hasArg is
+#' @importFrom stats quantile sd
+#' @importFrom utils getFromNamespace read.csv tail
+#' @importFrom R6 R6Class
+#'
 #' @include Data.R
 #' @include Forecast.R
 #' @include Forecaster.R
@@ -14,10 +21,4 @@
 #' @include computeError.R
 #' @include plot.R
 #'
-#' @importFrom grDevices colors gray.colors
-#' @importFrom graphics abline hist par plot
-#' @importFrom methods hasArg is
-#' @importFrom stats quantile sd
-#' @importFrom utils getFromNamespace read.csv tail
-#'
 NULL
diff --git a/pkg/R/Data.R b/pkg/R/Data.R
index 39f3837..c193f7d 100644
--- a/pkg/R/Data.R
+++ b/pkg/R/Data.R
@@ -1,51 +1,56 @@
 #' Data
 #'
-#' Data encapsulation
+#' Data encapsulation as a list (days) of lists (components).
 #'
-#' @docType class
-#' @importFrom R6 R6Class
+#' The private field .data is a list where each cell contains the variables for a period
+#' of time of 24 hours, from time P+1 until P the next day where P is an integer between
+#' 0 and 23. .data[[1]] refers to the first measured data (serie and exogenous
+#' variables): the corresponding times can be accessed through the function
+#' \code{getTime()} below. Each cell .data[[i]] is itself a list containing five slots,
+#' as described in the 'field' section.
 #'
-#' @field .data List of
+#' @field .data[[i]] List of
 #' \itemize{
 #'   \item time: vector of times
-#'   \item centered_serie: centered series
-#'   \item level: corresponding levels
+#'   \item centered_serie: centered serie
+#'   \item level: corresponding level
 #'   \item exo: exogenous variables
-#'   \item exo_hat: predicted exogenous variables
+#'   \item exo_hat: predicted exogenous variables (for next day)
 #' }
 #'
 #' @section Methods:
 #' \describe{
 #' \item{\code{getSize()}}{
-#'   Return number of series in dataset.}
+#'   Number of series in dataset.}
 #' \item{\code{getStdHorizon()}}{
-#'   Return number of time steps from serie[1] until midnight}
-#' \item{\code{appendHat(time, exo_hat)}}{
-#'   New estimated exogenous variables + time}
-#' \item{\code{append(serie, exo)}}{
-#'   New measured data; call *after* \code{appendHat()}}
+#'   Number of time steps from serie[1] until midnight}
+#' \item{\code{append(time, serie, exo, exo_hat)}}{
+#'   Measured data for given vector of times + exogenous predictions from last midgnight.
 #' \item{\code{getTime(index)}}{
-#'   Get times at specified index.}
+#'   Times (vector) at specified index.}
 #' \item{\code{getCenteredSerie(index)}}{
-#'   Get (measured or predicted) centered serie at specified index.}
+#'   Centered serie at specified index.}
 #' \item{\code{getCenteredSeries(indices)}}{
-#'   Get centered series at specified indices (in columns).}
+#'   Centered series at specified indices (in columns).}
 #' \item{\code{getLevel(index)}}{
-#'   Get level at specified index.}
+#'   Level at specified index.}
 #' \item{\code{getSerie(index)}}{
-#'   Get serie (centered+level) at specified index.}
+#'   Serie (centered+level) at specified index.}
 #' \item{\code{getSeries(indices)}}{
-#'   Get series at specified indices (in columns).}
+#'   Series at specified indices (in columns).}
 #' \item{\code{getExoHat(index)}}{
-#'   Get predicted exogenous variables at specified index.}
+#'   Predicted exogenous variables at specified index.}
 #' \item{\code{getExo(index)}}{
-#'   Get exogenous variables at specified index.}
+#'   Measured exogenous variables at specified index.}
 #' \item{\code{removeFirst()}}{
 #'   Remove first list element (if truncated).}
 #' \item{\code{removeLast()}}{
 #'   Remove last list element (if truncated).}
 #' }
 #'
+#' @docType class
+#' @format R6 class
+#'
 Data = R6::R6Class("Data",
 	private = list(
 		.data = list()
diff --git a/pkg/R/F_Average.R b/pkg/R/F_Average.R
index d7febd4..8a33111 100644
--- a/pkg/R/F_Average.R
+++ b/pkg/R/F_Average.R
@@ -1,7 +1,13 @@
 #' Average Forecaster
 #'
-#' Pointwise average of all series of the same (day of week) days in the past.
+#' Pointwise average of all the series of the same day of week in the past.
 #'
+#' For example, if the current day (argument "today") is a tuesday, then all series
+#' corresponding to wednesdays in the past (until the beginning or memory limit) are
+#' averaged to provide a smooth prediction. This forecast will most of the time be wrong,
+#' but will also look plausible enough.
+#'
+#' @docType class
 #' @format R6 class, inherits Forecaster
 #' @alias F_Average
 #'
diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 7e0cdd2..12595d9 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -2,10 +2,33 @@
 #'
 #' Predict next serie as a weighted combination of "futures of the past" days,
 #' where days in the past are chosen and weighted according to some similarity measures.
-#' See 'details' section.
 #'
-#' TODO: details.
+#' The main method is \code{predictShape()}, taking arguments data, today, memory,
+#' horizon respectively for the dataset (object output of \code{getData()}), the current
+#' index, the data depth (in days) and the number of time steps to forecast.
+#' In addition, optional arguments can be passed:
+#' \itemize{
+#'   \item local : TRUE (default) to constrain neighbors to be "same days within same
+#'     season"
+#'   \item simtype : 'endo' for a similarity based on the series only,<cr>
+#'             'exo' for a similaruty based on exogenous variables only,<cr>
+#'             'mix' for the product of 'endo' and 'exo',<cr>
+#'             'none' (default) to apply a simple average: no computed weights
+#'   \item window : A window for similarities computations; override cross-validation
+#'     window estimation.
+#' }
+#' The method is summarized as follows:
+#' \enumerate{
+#'   \item Determine N (=20) recent days without missing values, and followed by a
+#'     tomorrow also without missing values.
+#'   \item Optimize the window parameters (if relevant) on the N chosen days.
+#'   \item Considering the optimized window, compute the neighbors (with locality
+#'     constraint or not), compute their similarities -- using a gaussian kernel if
+#'     simtype != "none" -- and average accordingly the "tomorrows of neigbors" to
+#'     obtain the final prediction.
+#' }
 #'
+#' @docType class
 #' @format R6 class, inherits Forecaster
 #' @alias F_Neighbors
 #'
diff --git a/pkg/R/F_Persistence.R b/pkg/R/F_Persistence.R
index 754df07..eaeca98 100644
--- a/pkg/R/F_Persistence.R
+++ b/pkg/R/F_Persistence.R
@@ -1,8 +1,14 @@
 #' Persistence Forecaster
 #'
 #' Look for the most recent similar day in the past, and return its corresponding curve.
-#' "Similar day" means "same day in the week".
 #'
+#' There are two variations, depending whether "similar day" means "same day in the week"
+#' or "most recent day" (regardless of day type). The corresponding argument is named
+#' "same_day": a value of TRUE implies the former interpretation (same day in week).
+#' If the last similar day has missing values, the next one is searched, and so on until
+#' one full serie is found (if no one is found, NA is returned).
+#'
+#' @docType class
 #' @format R6 class, inherits Forecaster
 #' @alias F_Persistence
 #'
diff --git a/pkg/R/F_Zero.R b/pkg/R/F_Zero.R
index 3c46f2c..977cba9 100644
--- a/pkg/R/F_Zero.R
+++ b/pkg/R/F_Zero.R
@@ -1,7 +1,9 @@
 #' Zero Forecaster
 #'
-#' Flat prediction of next series of 'horizon' values.
+#' Flat prediction: always forecast a serie of zeros.
+#' This serie is then adjusted using the ".pjump" function (see \code{Forecaster} class).
 #'
+#' @docType class
 #' @format R6 class, inherits Forecaster
 #' @alias F_Zero
 #'
diff --git a/pkg/R/Forecast.R b/pkg/R/Forecast.R
index a0ddcda..6036daf 100644
--- a/pkg/R/Forecast.R
+++ b/pkg/R/Forecast.R
@@ -1,15 +1,21 @@
 #' Forecast
 #'
-#' Forecast encapsulation
+#' Forecast encapsulation as a list (days where prediction occur) of lists (components).
 #'
-#' @docType class
-#' @importFrom R6 R6Class
+#' The private field .pred is a list where each cell contains the predicted variables for
+#' a period of time of H<=24 hours, from hour P+1 until P+H, where P+1 is taken right
+#' after the end of the period designated by \code{getIndexInData()}. In other terms,
+#' \code{forecast$getForecast(i)} return forecasts for \code{data$getSerie(i+1)}.
+#' Each cell .pred[[i]] is itself a list containing three slots, as described in the
+#' 'field' section.
 #'
-#' @field .pred List with \itemize{
-#'   \item serie: forecasted serie
+#' @field .pred List with
+#' \itemize{
+#'   \item serie: the forecasted serie
 #'   \item params: corresponding list of parameters (weights, neighbors...)
-#'   \item index_in_data: corresponding index in data object}
-#' @field .dates vector of day indices where forcast occurs
+#'   \item index_in_data: corresponding index in data object
+#' }
+#' @field .dates vector of (integer) day indices where forecast occurs
 #'
 #' @section Methods:
 #' \describe{
@@ -30,6 +36,9 @@
 #'   Get index in data which corresponds to current forecast.}
 #' }
 #'
+#' @docType class
+#' @format R6 class
+#'
 Forecast = R6::R6Class("Forecast",
 	private = list(
 		.pred = list(),
diff --git a/pkg/R/Forecaster.R b/pkg/R/Forecaster.R
index cedb2b6..2efa9ba 100644
--- a/pkg/R/Forecaster.R
+++ b/pkg/R/Forecaster.R
@@ -1,27 +1,44 @@
 #' Forecaster
 #'
-#' Forecaster (abstract class, implemented by all forecasters)
+#' Forecaster (abstract class, implemented by all forecasters).
 #'
-#' @docType class
-#' @importFrom R6 R6Class
+#' A Forecaster object encapsulates parameters (which can be of various kinds, for
+#' example "Neighbors" method stores informations about the considered neighborhood for
+#' the current prediction task) and one main function: \code{predictSerie()}. This last
+#' function (by default) calls \code{predictShape()} to get a forecast of a centered
+#' serie, and then calls the "jump prediction" function -- see "field" section -- to
+#' adjust it based on the last observed values.
 #'
-#' @field .params List of computed parameters, for post-run analysis (dev)
-#' @field .pjump Function: how to predict the jump at day interface ?
+#' @field .params List of computed parameters (if applicable).
+#' @field .pjump Function: how to predict the jump at day interface? The arguments of
+#'   this function are -- in this order:
+#'   \itemize{
+#'     \item data : object output of \code{getData()},
+#'     \item today : index (integer or date) of the last known day in data,
+#'     \item memory : number of days to use in the past (including today),
+#'     \item horizon : number of time steps to predict,
+#'     \item params : optimized parameters in the main method \code{predictShape()},
+#'     \item ... : additional arguments.
+#'   }
+#'   .pjump returns an estimation of the jump after the last observed value.
 #'
 #' @section Methods:
 #' \describe{
 #' \item{\code{initialize(data, pjump)}}{
 #'   Initialize a Forecaster object with a Data object and a jump prediction function.}
 #' \item{\code{predictSerie(today,memory,horizon,...)}}{
-#'   Predict a new serie of \code{horizon} values at day index \code{today}
-#'   using \code{memory} days in the past.}
+#'   Predict a new serie of \code{horizon} values at day index \code{today} using
+#'   \code{memory} days in the past.}
 #' \item{\code{predictShape(today,memory,horizon,...)}}{
-#'   Predict a new shape of \code{horizon} values at day index \code{today}
-#'   using \code{memory} days in the past.}
+#'   Predict a new shape of \code{horizon} values at day index \code{today} using
+#'   \code{memory} days in the past.}
 #' \item{\code{getParameters()}}{
 #'   Return (internal) parameters.}
 #' }
 #'
+#' @docType class
+#' @format R6 class
+#'
 Forecaster = R6::R6Class("Forecaster",
 	private = list(
 		.params = list(),
diff --git a/pkg/R/J_Neighbors.R b/pkg/R/J_Neighbors.R
index 12fc9d4..5a818ca 100644
--- a/pkg/R/J_Neighbors.R
+++ b/pkg/R/J_Neighbors.R
@@ -1,8 +1,15 @@
-#' Obtain jump forecast by the Neighbors method
+#' getNeighborsJumpPredict
+#'
+#' Apply optimized weights on gaps observed on selected neighbors.
+#' This jump prediction method can only be used in conjunction with the Neighbors
+#' Forecaster, because it makes use of the optimized parameters to re-apply the weights
+#' on the jumps observed at days interfaces of the past neighbors.
 #'
 #' @inheritParams computeForecast
 #' @inheritParams getZeroJumpPredict
 #'
+#' @alias J_Neighbors
+#'
 getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
 {
 	first_day = max(1, today-memory)
diff --git a/pkg/R/J_Persistence.R b/pkg/R/J_Persistence.R
index a85a42a..37d05fe 100644
--- a/pkg/R/J_Persistence.R
+++ b/pkg/R/J_Persistence.R
@@ -1,8 +1,15 @@
-#' Obtain jump forecast by the Persistence method
+#' getPersistenceJumpPredict
+#'
+#' Analog of the PersistenceForecaster: predict the jump after last observed value either
+#' by re-applying the last jump between similar day and its follower (if argument
+#' "same_day" is TRUE), or by re-using the very last observed jump (when "same_day" =
+#' FALSE).
 #'
 #' @inheritParams computeForecast
 #' @inheritParams getZeroJumpPredict
 #'
+#' @alias J_Persistence
+#'
 getPersistenceJumpPredict = function(data, today, memory, horizon, params, ...)
 {
 	#return gap between end of similar day curve and first day of tomorrow (in the past)
diff --git a/pkg/R/J_Zero.R b/pkg/R/J_Zero.R
index d9140a5..065226e 100644
--- a/pkg/R/J_Zero.R
+++ b/pkg/R/J_Zero.R
@@ -1,9 +1,13 @@
-#' Just predict zero "jump" (for reference, benchmarking at least)
+#' getZeroJumpPredict
+#'
+#' Just predict zero "jump" (for reference, benchmarking at least).
 #'
 #' @inheritParams computeForecast
 #' @param today Index of the current day (predict tomorrow)
 #' @param params Optional parameters computed by the main forecaster
 #'
+#' @alias J_Zero
+#'
 getZeroJumpPredict = function(data, today, memory, horizon, params, ...)
 {
 	0
diff --git a/pkg/R/computeError.R b/pkg/R/computeError.R
index 7da1032..1fbb2a8 100644
--- a/pkg/R/computeError.R
+++ b/pkg/R/computeError.R
@@ -1,13 +1,16 @@
 #' Compute error
 #'
-#' Obtain the errors between forecast and data
+#' Compute the errors between forecasted and measured series.
 #'
-#' @param data Dataset, object of class \code{Data} output of \code{getData}
-#' @param pred Forecast object, class \code{Forecast} output of \code{computeForecast}
+#' @param data Object of class \code{Data} output of \code{getData}
+#' @param pred Object of class \code{Forecast} output of \code{computeForecast}
 #' @param horizon Horizon where to compute the error
 #'   (<= horizon used in \code{computeForecast})
 #'
-#' @return A list (abs,MAPE) of lists (day,indices)
+#' @return A list (abs,MAPE) of lists (day,indices). The "indices" slots contain series
+#'   of size L where L is the number of predicted days; i-th value is the averaged error
+#'   (absolute or MAPE) on day i. The "day" slots contain curves of errors, for each time
+#'   step, averaged on the L forecasting days.
 #'
 #' @export
 computeError = function(data, pred, horizon=data$getStdHorizon())
diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index 1d69777..23ccb04 100644
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -1,10 +1,10 @@
 #' Compute forecast
 #'
-#' Predict time-series curves for the selected days indices.
+#' Predict time-series curves ("tomorrows") at the selected days indices ("todays").
+#' This function just runs a loop over all requested indices, and stores the individual
+#' forecasts into a list which is then turned into a Forecast object.
 #'
-#' TODO: details
-#'
-#' @param data Object of type \code{Data}, output of \code{getData()}.
+#' @param data Object of class Data, output of \code{getData()}.
 #' @param indices Indices where to forecast (the day after); integers relative to the
 #'   beginning of data, or (convertible to) Date objects.
 #' @param forecaster Name of the main forecaster; more details: ?F_<forecastername>
@@ -44,7 +44,7 @@
 #'     PM10_values_of_last_24h,
 #'     exogenous_measures_of_last_24h,
 #'     exogenous_predictions_for_next_24h)
-#'   pred <- forecaster$predictSerie(data, data$getSize()-1, ...)
+#'   pred <- forecaster$predictSerie(data, data$getSize(), ...)
 #'   #do_something_with_pred
 #' }}
 #' @export
diff --git a/pkg/R/getData.R b/pkg/R/getData.R
index 268c54a..a6401f9 100644
--- a/pkg/R/getData.R
+++ b/pkg/R/getData.R
@@ -1,19 +1,20 @@
-#' @title Acquire data in a clean format
+#' getData
 #'
-#' @description Take in input data frames and/or files containing raw data, and
-#'   timezones, and output a Data object, roughly corresponding to a list where each cell
-#'   contains all value for one day (see \code{?Data}).
+#' Acquire data as a Data object; see ?Data.
+#'
+#' Since series are given in columns (database format), this function builds series one
+#' by one and incrementally grows a Data object which is finally returned.
 #'
 #' @param ts_data Time-series, as a data frame (DB style: 2 columns, first is date/time,
-#'   second is value) or a CSV file
-#' @param exo_data Exogenous variables, as a data frame or a CSV file; first comlumn is
+#'   second is value) or a CSV file.
+#' @param exo_data Exogenous variables, as a data frame or a CSV file; first column is
 #'   dates, next block are measurements for the day, and final block are exogenous
-#'   forecasts
+#'   forecasts (for the same day).
 #' @param input_tz Timezone in the input files ("GMT" or e.g. "Europe/Paris")
 #' @param date_format How date/time are stored (e.g. year/month/day hour:minutes;
-#'   see \code{strptime})
+#'   see ?strptime)
 #' @param working_tz Timezone to work with ("GMT" or e.g. "Europe/Paris")
-#' @param predict_at When does the prediction take place ? Integer, in hours. Default: 0
+#' @param predict_at When does the prediction take place? Integer, in hours. Default: 0
 #' @param limit Number of days to extract (default: Inf, for "all")
 #'
 #' @return An object of class Data
diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index 5ea4843..48b456c 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -1,8 +1,8 @@
 #' Plot curves
 #'
-#' Plot a range of curves in data
+#' Plot a range of curves in data.
 #'
-#' @param data Object of class Data
+#' @inheritParams computeError
 #' @param indices Range of indices (integers or dates)
 #'
 #' @export
@@ -22,9 +22,9 @@ plotCurves <- function(data, indices=seq_len(data$getSize()))
 
 #' Plot error
 #'
-#' Draw error graphs, potentially from several runs of \code{computeForecast}
+#' Draw error graphs, potentially from several runs of \code{computeForecast()}.
 #'
-#' @param err Error as returned by \code{computeError}
+#' @param err Error as returned by \code{computeError()}
 #' @param cols Colors for each error (default: 1,2,3,...)
 #'
 #' @seealso \code{\link{plotCurves}}, \code{\link{plotPredReal}},
@@ -74,10 +74,9 @@ plotError <- function(err, cols=seq_along(err))
 
 #' Plot measured / predicted
 #'
-#' Plot measured curve (in black) and predicted curve (in blue)
+#' Plot measured curve (in black) and predicted curve (in blue).
 #'
-#' @param data Object return by \code{getData}
-#' @param pred Object as returned by \code{computeForecast}
+#' @inheritParams computeError
 #' @param index Index in forecasts (integer or date)
 #'
 #' @export
@@ -95,9 +94,9 @@ plotPredReal <- function(data, pred, index)
 
 #' Plot similarities
 #'
-#' Plot histogram of similarities (weights)
+#' Plot histogram of similarities (weights), for 'Neighbors' method.
 #'
-#' @param pred Object as returned by \code{computeForecast}
+#' @inheritParams computeError
 #' @param index Index in forecasts (integer or date)
 #'
 #' @export
@@ -112,10 +111,10 @@ plotSimils <- function(pred, index)
 
 #' Functional boxplot
 #'
-#' Draw the functional boxplot on the left, and bivariate plot on the right
+#' Draw the functional boxplot on the left, and bivariate plot on the right.
 #'
-#' @param data Object return by \code{getData}
-#' @param indices integer or date indices to process
+#' @inheritParams computeError
+#' @inheritParams plotCurves
 #'
 #' @export
 plotFbox <- function(data, indices=seq_len(data$getSize()))
@@ -138,10 +137,10 @@ plotFbox <- function(data, indices=seq_len(data$getSize()))
 
 #' Compute filaments
 #'
-#' Get similar days in the past, as black as distances are small
+#' Obtain similar days in the past, and (optionally) plot them -- as black as distances
+#' are small.
 #'
-#' @param data Object as returned by \code{getData}
-#' @param pred Object of class Forecast
+#' @inheritParams computeError
 #' @param index Index in forecast (integer or date)
 #' @param limit Number of neighbors to consider
 #' @param plot Should the result be plotted?
@@ -197,9 +196,9 @@ computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
 
 #' Functional boxplot on filaments
 #'
-#' Draw the functional boxplot on filaments obtained by \code{computeFilaments}
+#' Draw the functional boxplot on filaments obtained by \code{computeFilaments()}.
 #'
-#' @param data Object return by \code{getData}
+#' @inheritParams computeError
 #' @param fil Output of \code{computeFilaments}
 #'
 #' @export
@@ -227,10 +226,10 @@ plotFilamentsBox = function(data, fil)
 #' Plot relative conditional variability / absolute variability
 #'
 #' Draw the relative conditional variability / absolute variability based on filaments
-#' obtained by \code{computeFilaments}
+#' obtained by \code{computeFilaments()}.
 #'
-#' @param data Object return by \code{getData}
-#' @param fil Output of \code{computeFilaments}
+#' @inheritParams computeError
+#' @inheritParams plotFilamentsBox
 #'
 #' @export
 plotRelVar = function(data, fil)
diff --git a/pkg/R/utils.R b/pkg/R/utils.R
index bbf1387..3124881 100644
--- a/pkg/R/utils.R
+++ b/pkg/R/utils.R
@@ -1,14 +1,14 @@
 #' dateIndexToInteger
 #'
-#' Transform a (potential) date index into an integer (relative to data)
+#' Transform a (potential) date index into an integer (relative to data beginning).
 #'
 #' @param index Date (or integer) index
-#' @param data Object of class \code{Data}
+#' @param data Object of class Data, output of \code{getData()}
 #'
 #' @export
 dateIndexToInteger = function(index, data)
 {
-	#works on integers too: trust input
+	# Works on integers too: trust input
 	if (is.numeric(index))
 		index = as.integer(index)
 	if (is.integer(index))
@@ -30,15 +30,14 @@ dateIndexToInteger = function(index, data)
 
 #' integerIndexToDate
 #'
-#' Transform an integer index to date index (relative to data)
+#' Transform an integer index (relative to data beginning) into a date index.
 #'
-#' @param index Date (or integer) index
-#' @param data Object of class \code{Data}
+#' @inheritParams dateIndexToInteger
 #'
 #' @export
 integerIndexToDate = function(index, data)
 {
-	#works on dates too: trust input
+	# Works on dates too: trust input
 	if (is.character(index))
 		index = as.Date(index)
 	if (is(index,"Date"))
@@ -54,7 +53,8 @@ integerIndexToDate = function(index, data)
 
 #' getSimilarDaysIndices
 #'
-#' Find similar days indices in the past.
+#' Find similar days indices in the past; at least same type of day in the week:
+#' monday=tuesday=wednesday=thursday != friday != saturday != sunday.
 #'
 #' @param index Day index (numeric or date)
 #' @param data Reference dataset, object output of \code{getData}
@@ -88,10 +88,10 @@ getSimilarDaysIndices = function(index, data, limit, same_season, days_in=NULL)
 
 # isSameSeason
 #
-# Check if two months fall in the same "season" (defined by estimated pollution rate)
+# Check if two months fall in the same "season" (defined by estimated pollution rate).
 #
-# @param month month index to test
-# @param month_ref month to compare to
+# @param month Month index to test
+# @param month_ref Month to compare to
 #
 .isSameSeason = function(month, month_ref)
 {
@@ -104,10 +104,10 @@ getSimilarDaysIndices = function(index, data, limit, same_season, days_in=NULL)
 
 # isSameDay
 #
-# Monday=Tuesday=Wednesday=Thursday ; Friday, Saturday, Sunday: specials
+# Monday=Tuesday=Wednesday=Thursday ; Friday, Saturday, Sunday: specials.
 #
-# @param day day index to test
-# @param day_ref day index to compare to
+# @param day Day index to test
+# @param day_ref Day index to compare to
 #
 .isSameDay = function(day, day_ref)
 {
@@ -122,7 +122,7 @@ getSimilarDaysIndices = function(index, data, limit, same_season, days_in=NULL)
 #'
 #' Get indices in data of no-NA series followed by no-NA, within [first,last] range.
 #'
-#' @param data Object of class Data
+#' @inheritParams dateIndexToInteger
 #' @param first First index (included)
 #' @param last Last index (included)
 #'