diff --git a/NEWS.md b/NEWS.md index 8edf6755..400ba69d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,7 @@ # activAnalyzer (development version) +* Added the possibility to see a threshold highlighting abnormal values for axis 1 in the nonwear/wear graph. In addition, abnormal values for Axis 1 and dependant metrics (e.g. VM) can now be replaced by NA. + # activAnalyzer 2.1.2 * Removed an undesired comma from a req() function in app_server.R that caused an error with more recent versions of package dependencies (likely Shiny). * Updated the figures for the comparisons with norms and recommendations (legend is now correctly placed at the top). diff --git a/R/app_server.R b/R/app_server.R index f379a915..0f355eca 100644 --- a/R/app_server.R +++ b/R/app_server.R @@ -258,6 +258,15 @@ app_server <- function(input, output, session) { shinyjs::hide("warning_epoch") } }) + + # Ehcv + observeEvent(input$validate, + shinyFeedback::feedbackWarning( + "ehcv_val1", + (is.numeric(input$ehcv_val1) == FALSE | input$ehcv_val1 < 0), + "Please choose a number >= 0." + ) + ) # Frame size observeEvent(input$validate, @@ -353,9 +362,10 @@ app_server <- function(input, output, session) { }) - # Returning to default values for the wear time detection algorithm + # Returning to default values for the wear time detection algorithm and ehcv observeEvent(input$reset_nonwear, { updateNumericInput(inputId = "to_epoch", value = 60) + updateNumericInput(inputId = "ehcv_val1", value = 15000) updateSelectInput(inputId = "axis_weartime", selected = "vector magnitude") updateNumericInput(inputId = "frame_size", value = 90) updateNumericInput(inputId = "allowanceFrame_size", value = 2) @@ -407,35 +417,52 @@ app_server <- function(input, output, session) { "End time should be superior to start time." ) ) - - output$graph <- renderPlot({ - # Waiting for correct inputs - req(zoom_param$zoom_from_weartime < zoom_param$zoom_to_weartime) + observeEvent(input$update_graphic, + shinyFeedback::feedbackWarning( + "ehcv_val1", + (is.numeric(input$ehcv_val1) == FALSE | input$ehcv_val1 < 0), + "Please choose a number >= 0." + ) + ) + - # Making the plot + ehcv_val1 <- eventReactive(input$validate, input$ehcv_val1) - if (as.numeric(df()$time[2] - df()$time[1]) < 10) { - ggplot2::ggplot() + ggplot2::geom_text( - ggplot2::aes( - x = 1, - y = 1, - label = "Sorry, below 10-s epochs, we prefer \nnot to build the plot to save your time..."), - size = 10 + graph <- eventReactive(input$validate | input$update_graphic, { + + # Waiting for correct inputs + req(zoom_param$zoom_from_weartime < zoom_param$zoom_to_weartime & is.numeric(input$ehcv_val1) & input$ehcv_val1 >= 0) + + # Making the plot + if (as.numeric(df()$time[2] - df()$time[1]) < 10) { + ggplot2::ggplot() + ggplot2::geom_text( + ggplot2::aes( + x = 1, + y = 1, + label = "Sorry, below 10-s epochs, we prefer \nnot to build the plot to save your time..."), + size = 10 ) + - ggplot2::theme( - axis.title = ggplot2::element_blank(), - axis.text = ggplot2::element_blank(), - axis.ticks = ggplot2::element_blank() + ggplot2::theme( + axis.title = ggplot2::element_blank(), + axis.text = ggplot2::element_blank(), + axis.ticks = ggplot2::element_blank() + ) + } else { + plot_data( + data = df(), + metric = zoom_param$metric, + ehcv = ehcv_val1(), + zoom_from = zoom_param$zoom_from_weartime, + zoom_to = zoom_param$zoom_to_weartime ) - } else { - plot_data( - data = df(), - metric = zoom_param$metric, - zoom_from = zoom_param$zoom_from_weartime, - zoom_to = zoom_param$zoom_to_weartime - ) - } + } + + }) + + output$graph <- renderPlot({ + + graph() }, width = "auto", height = function(){ @@ -824,6 +851,14 @@ app_server <- function(input, output, session) { ) ) + # Threshold for data removal + observeEvent(input$Run, + shinyFeedback::feedbackWarning( + "ehcv_val2", + ((is.numeric(input$ehcv_val2) == FALSE | input$ehcv_val2 < 0)), + "Please provide a value >=0." + ) + ) # Intensity bins parameters observeEvent(input$Run, @@ -1375,6 +1410,14 @@ app_server <- function(input, output, session) { return(list) }) + + # Setting ehcv for dealing with the removal of abnormal epochs + ehcv_val2 <- eventReactive(input$Run, { + + if (!(input$ehcv_check)) {ehcv <- "none"} + if (input$ehcv_check) {ehcv <- input$ehcv_val2} + ehcv + }) # Building the list results_list <- eventReactive(input$Run, { @@ -1517,11 +1560,13 @@ app_server <- function(input, output, session) { period_info_12$corr_mets() >= 0, period_info_13$corr_mets() >= 0, period_info_14$corr_mets() >= 0, - period_info_15$corr_mets() >= 0 - ) - - - + period_info_15$corr_mets() >= 0, + + # Settings for abnormal epoch removal + is.numeric(input$ehcv_val2) & input$ehcv_val2 >= 0 + ) + + # Building the dataframe with intensity marks df_with_computed_metrics <- df() %>% @@ -1534,7 +1579,8 @@ app_server <- function(input, output, session) { age = input$age, weight = input$weight, sex = input$sex, - dates = input$selected_days + dates = input$selected_days, + ehcv = ehcv_val2() ) shiny::setProgress(0.5) # set progress to 50% diff --git a/R/app_ui.R b/R/app_ui.R index 3f827c97..04b01421 100644 --- a/R/app_ui.R +++ b/R/app_ui.R @@ -224,6 +224,11 @@ app_ui <- function(request) { selectInput("to_epoch", "Length of the epoch to use for analysis (s)", choices = c(60, 15, 10, 5, 1)) ), ), + fluidRow( + column(12, + numericInput("ehcv_val1", "Threshold to highlight abnormal values for Axis 1 (counts/min)", value = 15000, min = 0) + ), + ), fluidRow( column(12, shinydashboardPlus::box(id = "box-epoch", @@ -399,6 +404,24 @@ app_ui <- function(request) { ), ), + #************************* + # Removing abnormal values + #************************* + + fluidRow( + column(12, + h3("Tick the box and adjust the threshold to detect epochs corresponding to abnormal counts in Axis 1 (not mandatory)."), + ), + ), + + fluidRow( + column(6, + hr(), + checkboxInput("ehcv_check", "Use threshold to remove abnormal epochs (Detected epochs will be considered as nonwear epochs with NAs)."), + numericInput("ehcv_val2", "Threshold related to Axis 1 (counts/min)", value = 15000, min = 0)) + ), + + #************************************* # Choosing intensity bins #************************************* diff --git a/R/mark_intensity.R b/R/mark_intensity.R index 546f6ccf..2cb10ff3 100644 --- a/R/mark_intensity.R +++ b/R/mark_intensity.R @@ -37,6 +37,9 @@ #' @param weight A numeric value in kg. #' @param sex A character value. #' @param dates A character vector containing the dates to be retained for analysis. The dates must be with the "YYYY-MM-DD" format. +#' @param ehcv A numeric value to set the threshold above which Axis 1 data should be considered as extremely high (abnormal). +#' The value should be in counts/min. Default is "none". If a value is set, all Axis 1/2/3 data, VM data and related metrics corresponding to the epochs for which the counts +#' in Axis 1 are equal or higher to the threshold will be replaced by NA. #' @return A dataframe. #' @export @@ -63,6 +66,7 @@ #' age = 32, #' weight = 67, #' sex = "male", +#' ehcv = 15000 #' ) #' head(mydata_with_intensity_marks) #' @@ -81,7 +85,9 @@ mark_intensity <- function(data, age = 40, weight = 70, sex = c("male", "female", "intersex", "undefined", "prefer not to say"), - dates = NULL) { + dates = NULL, + ehcv = "none" + ) { if (is.null(dates)) {selected_dates <- attributes(as.factor(data$date))$levels} @@ -144,7 +150,28 @@ mark_intensity <- function(data, df$bout <- cumsum(c(1, as.numeric(diff(df$intensity_category_num))!= 0)) + # Setting count-based data and related data to NA for abnormal metric if required + + if (ehcv != "none" & is.numeric(ehcv) & ehcv >= 0) { + + ehcv <- ehcv / cor_factor + df$axis1 <- dplyr::if_else(df$axis1 >= ehcv, NA, df$axis1) + df$axis2 <- dplyr::if_else(df$axis1 >= ehcv, NA, df$axis2) + df$axis3 <- dplyr::if_else(df$axis1 >= ehcv, NA, df$axis3) + df$vm <- dplyr::if_else(df$axis1 >= ehcv, NA, df$vm) + df$steps <- dplyr::if_else(df$axis1 >= ehcv, NA, df$steps) + df$wearing <- dplyr::if_else(df$axis1 >= ehcv, "nw", df$wearing) + df$non_wearing_count <- dplyr::if_else(df$axis1 >= ehcv, 1, df$non_wearing_count) + df$wearing_count <- dplyr::if_else(df$axis1 >= ehcv, 0, df$wearing_count) + df$SED <- dplyr::if_else(df$axis1 >= ehcv, NA, df$SED) + df$LPA <- dplyr::if_else(df$axis1 >= ehcv, NA, df$LPA) + df$MPA <- dplyr::if_else(df$axis1 >= ehcv, NA, df$MPA) + df$VPA <- dplyr::if_else(df$axis1 >= ehcv, NA, df$VPA) + df$METS <- dplyr::if_else(df$axis1 >= ehcv, NA, df$METS) + df$kcal <- dplyr::if_else(df$axis1 >= ehcv, NA, df$kcal) + df$mets_hours_mvpa <- dplyr::if_else(df$axis1 >= ehcv, NA, df$mets_hours_mvpa) + } # Providing information about the parameters used for computing results message(paste0("You have computed intensity metrics with the mark_intensity() function using the following inputs: diff --git a/R/plot_data.R b/R/plot_data.R index 88d472dd..48b5782e 100644 --- a/R/plot_data.R +++ b/R/plot_data.R @@ -8,6 +8,8 @@ #' @param col_time A character value to indicate the name of the variable to plot time data. #' @param col_nonwear A character value to indicate the name of the variable used to count nonwear time. #' @param col_wear A character value to indicate the name of the variable used to count wear time. +#' @param ehcv A numeric value to set the threshold above which Axis 1 data should be considered as extremely high (abnormal). +#' The value should be in counts/min. #' @param zoom_from A character value with the HH:MM:SS format to set the start of the daily period to visualize. #' @param zoom_to A character value with the HH:MM:SS format to set the end of the daily period to visualize. #' @@ -34,6 +36,7 @@ #' col_time = "time", #' col_nonwear = "non_wearing_count", #' col_wear = "wearing_count", +#' ehcv = 15000, #' zoom_from = "02:00:00", #' zoom_to = "23:58:00" #' ) @@ -43,6 +46,7 @@ plot_data <- function( metric = "axis1", col_time = "time", col_nonwear = "non_wearing_count", + ehcv = 15000, col_wear = "wearing_count", zoom_from = "00:00:00", zoom_to = "23:59:59" @@ -68,6 +72,12 @@ plot_data <- function( # Getting epoch length epoch <- as.numeric(data[[col_time]][2] - data[[col_time]][1]) + + # Getting correction factor for epoch duration different from 60s + cor_factor = 60 / epoch + + # Adjusting ehcv for the considered epoch + ehcv <- ehcv / cor_factor # Creating the plot p <- @@ -128,6 +138,17 @@ plot_data <- function( geom_vline(aes(xintercept = 3600*22), linetype = "dotted", color = "grey50") + geom_vline(aes(xintercept = 3600*23), linetype = "dotted", color = "grey50") + + # Add line for highlighting abnormal values if any + max_axis1 <- max(data$axis1) + + if (metric == "axis1" && ehcv <= max_axis1) { + p <- p + + geom_hline(aes(yintercept = ehcv, linetype = "Threshold for \nabnormal values"), color = "red", linewidth = 0.7) + + scale_linetype_manual(values = 1) + + labs(linetype = NULL) + } + suppressWarnings(print(p)) } diff --git a/R/recap_by_day.R b/R/recap_by_day.R index 977baad1..2014534e 100644 --- a/R/recap_by_day.R +++ b/R/recap_by_day.R @@ -68,7 +68,11 @@ #' @param start_first_bin A numeric value to set the lower bound of the first bin of the intensity band (in counts/epoch duration). #' @param start_last_bin A numeric value to set the lower bound of the last bin of the intensity band (in counts/epoch duration). #' @param bin_width A numeric value to set the width of the bins of the intensity band (in counts/epoch duration). -#' +#' @param ehcv A numeric value to set the threshold above which Axis 1 data should be considered as extremely high (abnormal). +#' The value should be in counts/min. Default is "none". If a value is set, step-based metrics corresponding to the epochs for which the counts +#' in Axis 1 are equal or higher to the threshold will be replaced by NA. The correction of the other metrics should be done when marking the dataset +#' with the different categories of intensity. +#' #' @return A list of objects: `df_all_metrics`, `p_band`, and `p_log`. #' `df_all_metrics` is a dataframe containing all the metrics for each day. #' `p_band` is a figure that shows the distribution of time spent in the configured bins of intensity for each day of the dataset. @@ -130,7 +134,8 @@ recap_by_day <- function( sex = c("male", "female", "intersex", "undefined", "prefer not to say"), start_first_bin = 0, start_last_bin = 10000, - bin_width = 500 + bin_width = 500, + ehcv = "none" ) { sex <- match.arg(sex) @@ -191,11 +196,22 @@ recap_by_day <- function( if (as.numeric(data[[col_time]][2] - data[[col_time]][1]) == 60) { + ## Prepare dataset df_step_metrics <- data %>% dplyr::mutate(timestamp = format(timestamp, "%Y-%m-%d %H:%M:%S")) %>% tidyr::separate("timestamp", c("date", "time"), sep = " ") %>% - dplyr::mutate(date = as.Date(date), time = hms::as_hms(time)) %>% + dplyr::mutate(date = as.Date(date), time = hms::as_hms(time)) + + ## Remove epochs with abnormal counts + if (ehcv != "none" & is.numeric(ehcv) & ehcv >= 0) { + ehcv <- ehcv / cor_factor + df_step_metrics$steps <- dplyr::if_else(df_step_metrics$axis1 >= ehcv, NA, df_step_metrics$steps) + } + + ## Compute step-based metrics + df_step_metrics <- + df_step_metrics %>% dplyr::select( date, time, @@ -221,6 +237,7 @@ recap_by_day <- function( ) } else { + ## Prepare dataset df_step_metrics <- PhysicalActivity::dataCollapser( dataset = data, @@ -229,7 +246,17 @@ recap_by_day <- function( ) %>% dplyr::mutate(timestamp = format(timestamp, "%Y-%m-%d %H:%M:%S")) %>% tidyr::separate("timestamp", c("date", "time"), sep = " ") %>% - dplyr::mutate(date = as.Date(date), time = hms::as_hms(time)) %>% + dplyr::mutate(date = as.Date(date), time = hms::as_hms(time)) + + ## Remove epochs with abnormal counts + if (ehcv != "none" & is.numeric(ehcv) & ehcv >= 0) { + ehcv <- ehcv / cor_factor + df_step_metrics$steps <- dplyr::if_else(df_step_metrics$axis1 >= ehcv, NA, df_step_metrics$steps) + } + + ## Compute step-based metrics + df_step_metrics <- + df_step_metrics %>% dplyr::select( date, time, diff --git a/inst/guide/user_guide_fr.Rmd b/inst/guide/user_guide_fr.Rmd index 89378f63..31a18e8e 100644 --- a/inst/guide/user_guide_fr.Rmd +++ b/inst/guide/user_guide_fr.Rmd @@ -63,7 +63,7 @@ Ces valeurs seuils (excepté celles de Freedson et al.) ont été recommandées Ensuite, l'utilisateur peut définir les valeurs déterminant les zones d'intensité qui seront considérées pour décrire le profil de distribution de l'intensité tel qu'expliqué par Rowlands et al. [-@rowlandsCutPointsAccelerometer2018]. Les valeurs doivent être en correspondance avec les durées de périodes d'échantillonnage utilisées pour l'analyse. Par exemple, si l'utilisateur est en train d'analyser les données avec des périodes de 60 s, alors les valeurs fournies devraient être comprises comme des counts/min. Si l'utilisateur est en train d'analyser les données avec des périodes de 10s, alors les valeurs fournies devraient être comprises comme des counts/10s. -L'utilisateur a aussi la possibilité de fournir des informations relatives à de potentielles périodes d'activité physique importantes qui auraient modifié le niveau d'activité physique mais qui ne pouvaient pas être enregistrées en raison de l'enlèvement de l'accéléromètre, comme par exemple au cours d'activités de sport collectif ou de natation. L'utilisateur doit sélectionner la date à laquelle la période d'activité a été réalisée. Ensuite, deux boîtes doivent être remplies pour indiquer l'horaire de début de la période : un boîte consacrée au numéro de l'heure, et une boîte consacrée au numéro de la minute. Même chose pour l'horaire de fin de la période. Enfin, l'utilisateur doit indiquer une valeur de MET qui refléterait l'intensité moyenne de l'activité physique réalisée au cours de la période. Pour faire cela, l'utilisateur devrait utiliser le [Compendium des activités physiques](https://sites.google.com/site/compendiumofphysicalactivities/) (https://sites.google.com/site/compendiumofphysicalactivities/) qui propose une valeur de MET pour un large panel d'activités. Ces informations fournies par l'utilisateur seront utilisées pour remplacer les données initiales de l'accéléromètre. Chaque ligne avec une date sélectionnée sera utilisée. Si une période se superpose à une période précédente, les données de la dernière période seront utilisées. De manière importante, en utilisant ces informations, le temps de port lié à l'accéléromètre sera modifié : le temps relatif à la période d'activité physique rapportée sera considérée comme "port" pour les périodes de temps qui auraient été détectées comme "non-port" initialement lors de l'utilisation de l'algorithme dédié à cela. +L'utilisateur a aussi la possibilité de fournir des informations relatives à de potentielles périodes d'activité physique importantes qui auraient modifié le niveau d'activité physique mais qui ne pouvaient pas être enregistrées en raison de l'enlèvement de l'accéléromètre, comme par exemple au cours d'activités de sport collectif ou de natation. L'utilisateur doit sélectionner la date à laquelle la période d'activité a été réalisée. Ensuite, deux boîtes doivent être remplies pour indiquer l'horaire de début de la période : une boîte consacrée au numéro de l'heure, et une boîte consacrée au numéro de la minute. Même chose pour l'horaire de fin de la période. Enfin, l'utilisateur doit indiquer une valeur de MET qui refléterait l'intensité moyenne de l'activité physique réalisée au cours de la période. Pour faire cela, l'utilisateur devrait utiliser le [Compendium des activités physiques](https://sites.google.com/site/compendiumofphysicalactivities/) (https://sites.google.com/site/compendiumofphysicalactivities/) qui propose une valeur de MET pour un large panel d'activités. Ces informations fournies par l'utilisateur seront utilisées pour remplacer les données initiales de l'accéléromètre. Chaque ligne avec une date sélectionnée sera utilisée. Si une période se superpose à une période précédente, les données de la dernière période seront utilisées. De manière importante, en utilisant ces informations, le temps de port lié à l'accéléromètre sera modifié : le temps relatif à la période d'activité physique rapportée sera considérée comme "port" pour les périodes de temps qui auraient été détectées comme "non-port" initialement lors de l'utilisation de l'algorithme dédié à cela. Finalement, cette section permet à l'utilisateur de déterminer le temps de port minimum requis pour considérer un jour comme valide, ainsi que la période au cours de laquelle le temps de port, de même que les valeurs des autres indicateurs, devraient être additionnés au cours de la journée. La valeur par défaut est de 10 heures (i.e., 600 minutes) sur la journée entière, comme recommandé [@miguelesAccelerometerDataCollection2017], mais une valeur de 8 heures devrait être utilisée pour des patients BPCO [@demeyerStandardizingAnalysisPhysical2014]. Pour obtenir automatiquement une configuration recommandée établie chez des patients BPCO dans le cas où l'appareil aurait été porté la nuit [@demeyerStandardizingAnalysisPhysical2014], l'utilisateur peut cliquer sur le bouton "PROactive config. for 24-h wearing protocol". À noter que la validation de l'ensemble de la mesure est laissée à l'appréciation de l'utilisateur. Dans la littérature scientifique, il est couramment accepté qu'il faut obtenir au moins 4 jours valides pour considérer la mesure comme pouvant refléter de manière fiable ce qui a été fait par le patient au cours de la période de mesure. Cependant, plusieurs études ayant utilisé les instruments du consortium européen *PROactive* pour les patients avec une BPCO ont utilisé 3 jours [@bowlerRealworldUseRescue2019; @garcia-aymerichValidityResponsivenessDaily2021; @gimeno-santosPROactiveInstrumentsMeasure2015; @korenyPatternsPhysicalActivity2021]. Quel que soit le nombre de jours valides obtenus, il convient de garder à l'esprit qu'une semaine de mesure peut ne pas refléter correctement le comportement habituel du patient sur une période plus longue (e.g., une année). @@ -154,7 +154,7 @@ Une fois ces nouvelles variables ajoutées au jeu de données initial, l'applica Pour calculer le NAP, la dépense énergétique totale (DET) liée à la période de la journée que l'utilisateur souhaitait analyser est divisée par le MB, qui lui est calculé pour correspondre aussi à la période de la journée à analyser. La DET est obtenue en faisant la somme des kilocalories mesurées durant le temps de port et des kilocalories liées au MB dépensées durant les périodes de non-port (il est supposé que les périodes lors desquelles l'appareil n'était pas porté correspondaient à des périodes de sommeil, durant lesquelles la dépense énergétique est proche du MB), et en multipliant cette somme par 10/9 afin de prendre en compte l'effet thermique des aliments. En principe, le NAP devrait être calculé en utilisant l'ensemble de la journée, pas une période donnée de la journée. Bien sûr, de tels calculs peuvent conduire à sous-estimer la DET et le NAP si l'appareil était enlevé pendant des périodes prolongées d'activité physique, à moins que l'utilisateur ajoute manuellement des périodes d'activité physique dans les espaces de l'application prévus à cet effet comme expliqué plus haut. De plus, même si l'appareil était correctement porté, l'estimation du NAP reste très approximative étant donné qu'à la fois le MB et les kilocalories liées au temps de port étaient estimées à partir de méthodes qui peuvent ne pas être exactes au niveau individuel. -Pour déterminer le gradient d'intensité, l'application calcule le nombre de minutes accumulées dans les zones d'intensité qui ont été définies précédemment par l'utilisateur (cf. explications fournies plus haut). Ensuite, un modèle linéaire est calculé pour décrire la relation entre le logarithme népérien des minutes accumulées dans les zones d'intensité, et la logarithme népérien des valeurs médianes (en counts/min) des zones d'intensité correspondantes. Le gradient d'intensité est le coefficient de pente de ce modèle [@rowlandsCutPointsAccelerometer2018]. Moins le gradient d'intensité est négatif, plus le patient tend à passer du temps à des intensités plus élevées. +Pour déterminer le gradient d'intensité, l'application calcule le nombre de minutes accumulées dans les zones d'intensité qui ont été définies précédemment par l'utilisateur (cf. explications fournies plus haut). Ensuite, un modèle linéaire est calculé pour décrire la relation entre le logarithme népérien des minutes accumulées dans les zones d'intensité, et le logarithme népérien des valeurs médianes (en counts/min) des zones d'intensité correspondantes. Le gradient d'intensité est le coefficient de pente de ce modèle [@rowlandsCutPointsAccelerometer2018]. Moins le gradient d'intensité est négatif, plus le patient tend à passer du temps à des intensités plus élevées. Finalement, l'application calcule les moyennes et les médianes journalières des indicateurs en utilisant les jours considérés comme valides. Si l'utilisateur a analysé les données en utilisant des périodes de 60 s, alors l'application fournira en plus les indicateurs suivants relatifs à l'accumulation des périodes sédentaires et d'activité physique : diff --git a/inst/guide/user_guide_fr.log b/inst/guide/user_guide_fr.log index 50186981..e7f8c4b2 100644 --- a/inst/guide/user_guide_fr.log +++ b/inst/guide/user_guide_fr.log @@ -1,88 +1,88 @@ -This is XeTeX, Version 3.141592653-2.6-0.999994 (TeX Live 2022) (preloaded format=xelatex 2023.11.29) 26 APR 2024 17:32 +This is XeTeX, Version 3.141592653-2.6-0.999996 (TeX Live 2024) (preloaded format=xelatex 2024.10.21) 14 NOV 2024 23:33 entering extended mode restricted \write18 enabled. %&-line parsing enabled. **user_guide_fr.tex (./user_guide_fr.tex -LaTeX2e <2023-11-01> -L3 programming layer <2023-11-09> +LaTeX2e <2024-06-01> patch level 2 +L3 programming layer <2024-09-10> (c:/Users/pydemullenheim/AppData/Roaming/TinyTeX/texmf-dist/tex/latex/base/book.cls -Document Class: book 2023/05/17 v1.4n Standard LaTeX document class +Document Class: book 2024/02/08 v1.4n Standard LaTeX document class (c:/Users/pydemullenheim/AppData/Roaming/TinyTeX/texmf-dist/tex/latex/base/bk10.clo -File: bk10.clo 2023/05/17 v1.4n Standard LaTeX file (size option) +File: bk10.clo 2024/02/08 v1.4n Standard LaTeX file (size option) ) -\c@part=\count182 -\c@chapter=\count183 -\c@section=\count184 -\c@subsection=\count185 -\c@subsubsection=\count186 -\c@paragraph=\count187 -\c@subparagraph=\count188 -\c@figure=\count189 -\c@table=\count190 -\abovecaptionskip=\skip48 -\belowcaptionskip=\skip49 -\bibindent=\dimen140 +\c@part=\count190 +\c@chapter=\count191 +\c@section=\count192 +\c@subsection=\count193 +\c@subsubsection=\count194 +\c@paragraph=\count195 +\c@subparagraph=\count196 +\c@figure=\count197 +\c@table=\count198 +\abovecaptionskip=\skip49 +\belowcaptionskip=\skip50 +\bibindent=\dimen141 ) (c:/Users/pydemullenheim/AppData/Roaming/TinyTeX/texmf-dist/tex/latex/amsmath/amsmath.sty -Package: amsmath 2023/05/13 v2.17o AMS math features -\@mathmargin=\skip50 +Package: amsmath 2024/05/23 v2.17q AMS math features +\@mathmargin=\skip51 For additional information on amsmath, use the `?' option. 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LaTeX Font Info: Overwriting symbol font `largesymbols' in version `normal' -(Font) OMX/lmex/m/n --> TU/latinmodern-math.otf(3)/m/n on input line 134. +(Font) OMX/lmex/m/n --> TU/latinmodern-math.otf(3)/m/n on input line 144. LaTeX Font Info: Encoding `OMX' has changed to `TU' for symbol font -(Font) `largesymbols' in the math version `bold' on input line 134. +(Font) `largesymbols' in the math version `bold' on input line 144. LaTeX Font Info: Overwriting symbol font `largesymbols' in version `bold' -(Font) OMX/lmex/m/n --> TU/latinmodern-math.otf(3)/b/n on input line 134. +(Font) OMX/lmex/m/n --> TU/latinmodern-math.otf(3)/b/n on input line 144. +LaTeX Info: Redefining \microtypecontext on input line 144. +Package microtype Info: Applying patch `item' on input line 144. +Package microtype Info: Applying patch `toc' on input line 144. +Package microtype Info: Applying patch `eqnum' on input line 144. +Package microtype Info: Applying patch `footnote' on input line 144. +Package microtype Info: Applying patch `verbatim' on input line 144. +Package microtype Info: Character protrusion enabled (level 2). +Package microtype Info: Using protrusion set `basicmath'. +Package microtype Info: No adjustment of tracking. +Package microtype Info: No adjustment of spacing. +Package microtype Info: No adjustment of kerning. +(c:/Users/pydemullenheim/AppData/Roaming/TinyTeX/texmf-dist/tex/latex/microtype/mt-LatinModernRoman.cfg +File: mt-LatinModernRoman.cfg 2021/02/21 v1.1 microtype config. file: Latin Modern Roman (RS) +) *geometry* driver: auto-detecting *geometry* detected driver: xetex *geometry* verbose mode - [ preamble ] result: @@ -829,40 +854,48 @@ LaTeX Font Info: Overwriting symbol font `largesymbols' in version `bold' * \@reversemarginfalse * (1in=72.27pt=25.4mm, 1cm=28.453pt) -Package hyperref Info: Link coloring OFF on input line 134. +Package hyperref Info: Link coloring OFF on input line 144. 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(c:/Users/pydemullenheim/AppData/Roaming/TinyTeX/texmf-dist/tex/latex/amsfonts/umsb.fd File: umsb.fd 2013/01/14 v3.01 AMS symbols B -) [1 +) (c:/Users/pydemullenheim/AppData/Roaming/TinyTeX/texmf-dist/tex/latex/microtype/mt-msb.cfg +File: mt-msb.cfg 2005/06/01 v1.0 microtype config. file: AMS symbols (b) (RS) +) + +[1 -] [2 +] + +[2 ] (./user_guide_fr.toc LaTeX Font Info: Font shape `TU/latinmodern-math.otf(1)/m/n' will be @@ -885,16 +918,27 @@ LaTeX Font Info: Font shape `TU/latinmodern-math.otf(3)/m/n' will be \tf@toc=\write3 \openout3 = `user_guide_fr.toc'. -[3] [4 + + +[3] + +[4 ] Section 1. -[5] [6 + + +[5] + +[6 ] Section 2. + + [7] + Package fancyhdr Warning: \headheight is too small (12.0pt): (fancyhdr) Make it at least 22.65004pt, for example: (fancyhdr) \setlength{\headheight}{22.65004pt}. @@ -906,32 +950,50 @@ Package fancyhdr Warning: \headheight is too small (12.0pt): ] Section 3. -Underfull \hbox (badness 3108) in paragraph at lines 183--184 +Underfull \hbox (badness 3769) in paragraph at lines 187--188 []\TU/lmr/m/n/10 L’équation \TU/lmtt/m/n/10 Santos-Lozano et al. (2013) [Older adults] \TU/lmr/m/n/10 ([]Santos-Lozano et al., [] -[9] [10] [11] [12] [13] [14] + + +[9] [10] + +[11] + +[12] + +[13] + +[14] Section 4. + + [15 -] [16] [17 +] + +[16] + +[17 + +] -] [18] (./user_guide_fr.aux) +[18] (./user_guide_fr.aux) *********** -LaTeX2e <2023-11-01> -L3 programming layer <2023-11-09> +LaTeX2e <2024-06-01> patch level 2 +L3 programming layer <2024-09-10> *********** LaTeX Warning: There were multiply-defined labels. ) Here is how much of TeX's memory you used: - 18958 strings out of 476909 - 349169 string characters out of 5803306 - 1944189 words of memory out of 5000000 - 40576 multiletter control sequences out of 15000+600000 + 20714 strings out of 476172 + 385169 string characters out of 5782452 + 975309 words of memory out of 5000000 + 42995 multiletter control sequences out of 15000+600000 564536 words of font info for 91 fonts, out of 8000000 for 9000 - 14 hyphenation exceptions out of 8191 - 90i,12n,120p,3291b,473s stack positions out of 10000i,1000n,20000p,200000b,200000s + 36 hyphenation exceptions out of 8191 + 90i,12n,123p,3288b,539s stack positions out of 10000i,1000n,20000p,200000b,200000s Output written on user_guide_fr.pdf (18 pages). diff --git a/inst/guide/user_guide_fr.pdf b/inst/guide/user_guide_fr.pdf index 55ae9897..0d9546f6 100644 Binary files a/inst/guide/user_guide_fr.pdf and b/inst/guide/user_guide_fr.pdf differ diff --git a/man/mark_intensity.Rd b/man/mark_intensity.Rd index 72e3dcec..b9987192 100644 --- a/man/mark_intensity.Rd +++ b/man/mark_intensity.Rd @@ -18,7 +18,8 @@ mark_intensity( age = 40, weight = 70, sex = c("male", "female", "intersex", "undefined", "prefer not to say"), - dates = NULL + dates = NULL, + ehcv = "none" ) } \arguments{ @@ -50,6 +51,10 @@ dataset would be shorter than 60 s, the function will divide the cut-point value \item{sex}{A character value.} \item{dates}{A character vector containing the dates to be retained for analysis. The dates must be with the "YYYY-MM-DD" format.} + +\item{ehcv}{A numeric value to set the threshold above which vertical axis data should be considered as extremely high (abnormal). +The value should be in counts/min. Default is "none". If a value is set, all Axis 1, VM data and related metrics corresponding to the epochs for which the counts +in Axis 1 are equal or higher to the threshold will be replaced by NA.} } \value{ A dataframe. @@ -98,6 +103,7 @@ mydata_with_intensity_marks <- mark_intensity( age = 32, weight = 67, sex = "male", + ehcv = 15000 ) head(mydata_with_intensity_marks) diff --git a/man/plot_data.Rd b/man/plot_data.Rd index 03edd4af..dbf6ca7d 100644 --- a/man/plot_data.Rd +++ b/man/plot_data.Rd @@ -9,6 +9,7 @@ plot_data( metric = "axis1", col_time = "time", col_nonwear = "non_wearing_count", + ehcv = 15000, col_wear = "wearing_count", zoom_from = "00:00:00", zoom_to = "23:59:59" @@ -23,6 +24,9 @@ plot_data( \item{col_nonwear}{A character value to indicate the name of the variable used to count nonwear time.} +\item{ehcv}{A numeric value to set the threshold above which vertical axis data should be considered as extremely high (abnormal). +The value should be in counts/min.} + \item{col_wear}{A character value to indicate the name of the variable used to count wear time.} \item{zoom_from}{A character value with the HH:MM:SS format to set the start of the daily period to visualize.} @@ -54,6 +58,7 @@ plot_data( col_time = "time", col_nonwear = "non_wearing_count", col_wear = "wearing_count", + ehcv = 15000, zoom_from = "02:00:00", zoom_to = "23:58:00" ) diff --git a/man/recap_by_day.Rd b/man/recap_by_day.Rd index 1318a869..17f5885d 100644 --- a/man/recap_by_day.Rd +++ b/man/recap_by_day.Rd @@ -17,7 +17,8 @@ recap_by_day( sex = c("male", "female", "intersex", "undefined", "prefer not to say"), start_first_bin = 0, start_last_bin = 10000, - bin_width = 500 + bin_width = 500, + ehcv = "none" ) } \arguments{ @@ -46,6 +47,11 @@ recap_by_day( \item{start_last_bin}{A numeric value to set the lower bound of the last bin of the intensity band (in counts/epoch duration).} \item{bin_width}{A numeric value to set the width of the bins of the intensity band (in counts/epoch duration).} + +\item{ehcv}{A numeric value to set the threshold above which vertical axis data should be considered as extremely high (abnormal). +The value should be in counts/min. Default is "none". If a value is set, step-based metrics corresponding to the epochs for which the counts +in Axis 1 are equal or higher to the threshold will be replaced by NA. The correction of the other metrics should be done when marking the dataset +with the different categories of intensity.} } \value{ A list of objects: \code{df_all_metrics}, \code{p_band}, and \code{p_log}.