--- /dev/null
+estimators_logic <- function(input, output, react_values) {
+ # Initialize a data frame to hold estimates.
+ react_values$estimates_table <- data.frame(Dataset = character(0))
+ # Initialize a list to hold added estimators.
+ react_values$estimators <- list()
+
+ add_id(input, output, react_values)
+ add_idea(input, output, react_values)
+ add_seq_bayes(input, output, react_values)
+ add_wp(input, output, react_values)
+
+ render_estimates(output, react_values)
+ delete_estimators(input, react_values)
+ export_estimates(output, react_values)
+}
+
+# If an estimator is added, ensure it is not a duplicate and add it to the list
+# of estimators. This function should be called at the end of each
+# estimator-specific 'add' function, after validating their parameters.
+add_estimator <- function(method, new_estimator, output, react_values) {
+ num_estimators <- length(react_values$estimators)
+
+ # Check whether the new estimator is a duplicate, and warn if so.
+ for (i in seq_len(num_estimators)) {
+ if (identical(new_estimator, react_values$estimators[[i]])) {
+ showNotification("Error: This estimator has already been added.",
+ duration = 3, id = "notify-error"
+ )
+ return()
+ }
+ }
+
+ # Add the new estimator to the list of estimators.
+ react_values$estimators[[num_estimators + 1]] <- new_estimator
+
+ showNotification("Estimator added successfully.",
+ duration = 3, id = "notify-success"
+ )
+
+ # Evaluate all the new estimator on all existing datasets and create a new
+ # column in the estimates table.
+ update_estimates_col(new_estimator, react_values)
+}
+
+# Ensure serial intervals are specified as positive numbers.
+validate_mu <- function(method, input, output) {
+ mu <- suppressWarnings(as.numeric(trimws(input[[paste0("mu_", method)]])))
+ if (is.na(mu) || mu <= 0) {
+ output[[paste0("mu_", method, "_warn")]] <- renderText(
+ "The serial interval must be a positive number."
+ )
+ return(NULL)
+ }
+ output[[paste0("mu_", method, "_warn")]] <- renderText("")
+ mu
+}
+
+# Incidence Decay (ID).
+add_id <- function(input, output, react_values) {
+ observeEvent(input$add_id, {
+ mu <- validate_mu("id", input, output)
+ if (!is.null(mu)) {
+ new_estimator <- list(
+ method = "id", mu = mu, mu_units = input$mu_id_units
+ )
+ add_estimator("id", new_estimator, output, react_values)
+ }
+ })
+}
+
+# Incidence Decay and Exponential Adjustment (IDEA).
+add_idea <- function(input, output, react_values) {
+ observeEvent(input$add_idea, {
+ mu <- validate_mu("idea", input, output)
+ if (!is.null(mu)) {
+ new_estimator <- list(
+ method = "idea", mu = mu, mu_units = input$mu_idea_units
+ )
+ add_estimator("idea", new_estimator, output, react_values)
+ }
+ })
+}
+
+# Sequential Bayes (seqB).
+add_seq_bayes <- function(input, output, react_values) {
+ observeEvent(input$add_seq_bayes, {
+ mu <- validate_mu("seq_bayes", input, output)
+
+ kappa <- trimws(input$kappa)
+ kappa <- if (kappa == "") 20 else suppressWarnings(as.numeric(kappa))
+
+ if (is.na(kappa) || kappa <= 0) {
+ output$kappa_warn <- renderText(
+ "The maximum prior must be a positive number."
+ )
+ } else if (!is.null(mu)) {
+ output$kappa_warn <- renderText("")
+ new_estimator <- list(
+ method = "seq_bayes", mu = mu,
+ mu_units = input$mu_seq_bayes_units, kappa = kappa
+ )
+ add_estimator("seq_bayes", new_estimator, output, react_values)
+ }
+ })
+}
+
+# White and Pagano (WP).
+add_wp <- function(input, output, react_values) {
+ observeEvent(input$add_wp, {
+ if (input$wp_mu_known == "Yes") {
+ mu <- validate_mu("wp", input, output)
+ if (!is.null(mu)) {
+ new_estimator <- list(method = "wp",
+ mu = mu, mu_units = input$mu_wp_units
+ )
+ add_estimator("wp", new_estimator, output, react_values)
+ }
+ } else {
+ grid_length <- trimws(input$grid_length)
+ max_shape <- trimws(input$max_shape)
+ max_scale <- trimws(input$max_scale)
+
+ suppressWarnings({
+ grid_length <- if (grid_length == "") 100 else as.numeric(grid_length)
+ max_shape <- if (max_shape == "") 10 else as.numeric(max_shape)
+ max_scale <- if (max_scale == "") 10 else as.numeric(max_scale)
+ })
+
+ valid <- TRUE
+
+ if (is.na(grid_length) || grid_length <= 0) {
+ output$grid_length_warn <- renderText(
+ "The grid length must be a positive integer."
+ )
+ valid <- FALSE
+ } else {
+ output$grid_length_warn <- renderText("")
+ }
+
+ if (is.na(max_shape) || max_shape <= 0) {
+ output$max_shape_warn <- renderText(
+ "The maximum shape must be a positive number."
+ )
+ valid <- FALSE
+ } else {
+ output$max_shape_warn <- renderText("")
+ }
+
+ if (is.na(max_scale) || max_scale <= 0) {
+ output$max_scale_warn <- renderText(
+ "The maximum scale must be a positive number."
+ )
+ valid <- FALSE
+ } else {
+ output$max_scale_warn <- renderText("")
+ }
+
+ if (valid) {
+ new_estimator <- list(method = "wp", mu = NA, grid_length = grid_length,
+ max_shape = max_shape, max_scale = max_scale
+ )
+ add_estimator("wp", new_estimator, output, react_values)
+ }
+ }
+ })
+}
+
+# Convert an estimator's specified serial interval to match the time units of
+# the given dataset.
+convert_mu_units <- function(data_units, estimator_units, mu) {
+ if (data_units == "Days" && estimator_units == "Weeks") {
+ return(mu * 7)
+ } else if (data_units == "Weeks" && estimator_units == "Days") {
+ return(mu / 7)
+ }
+ mu
+}
+
+# Add a column to the estimates table when a new estimator is added.
+update_estimates_col <- function(estimator, react_values) {
+ dataset_rows <- seq_len(nrow(react_values$data_table))
+ estimates <- dataset_rows
+
+ for (row in dataset_rows) {
+ estimate <- eval_estimator(estimator, react_values$data_table[row, ])
+ estimates[row] <- estimate
+ }
+
+ estimates <- data.frame(estimates)
+ colnames(estimates) <- estimates_col_name(estimates, estimator)
+
+ react_values$estimates_table <- cbind(
+ react_values$estimates_table, estimates
+ )
+}
+
+# Evaluate the specified estimator on the given dataset.
+eval_estimator <- function(estimator, dataset) {
+ cases <- as.integer(unlist(strsplit(dataset[, 3], ",")))
+
+ if (estimator$method == "id") {
+ mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+ estimate <- round(Rnaught::id(cases, mu), 2)
+ } else if (estimator$method == "idea") {
+ mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+ estimate <- round(Rnaught::idea(cases, mu), 2)
+ } else if (estimator$method == "seq_bayes") {
+ mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+ estimate <- round(Rnaught::seq_bayes(cases, mu, estimator$kappa), 2)
+ } else if (estimator$method == "wp") {
+ if (is.na(estimator$mu)) {
+ estimate <- Rnaught::wp(cases, serial = TRUE,
+ grid_length = estimator$grid_length,
+ max_shape = estimator$max_shape, max_scale = estimator$max_scale
+ )
+ estimated_mu <- round(sum(estimate$supp * estimate$pmf), 2)
+ estimate <- paste0(round(estimate$r0, 2), " (μ = ", estimated_mu,
+ " ", tolower(dataset[, 2]), ")"
+ )
+ } else {
+ mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+ estimate <- round(Rnaught::wp(cases, mu), 2)
+ }
+ }
+
+ return(estimate)
+}
+
+# Create the column name of an estimator when it is
+# added to the estimates table.
+estimates_col_name <- function(estimates, estimator) {
+ if (estimator$method == "id") {
+ return(paste0("ID", " (μ = ", estimator$mu, " ",
+ tolower(estimator$mu_units), ")"
+ ))
+ } else if (estimator$method == "idea") {
+ return(paste0("IDEA", " (μ = ", estimator$mu, " ",
+ tolower(estimator$mu_units), ")"
+ ))
+ } else if (estimator$method == "seq_bayes") {
+ return(paste0("seqB", " (μ = ", estimator$mu, " ",
+ tolower(estimator$mu_units), ", κ = ", estimator$kappa, ")"
+ ))
+ } else if (estimator$method == "wp") {
+ if (is.na(estimator$mu)) {
+ return(paste0("WP (", estimator$grid_length, ", ",
+ round(estimator$max_shape, 3), ", ", round(estimator$max_scale, 3), ")"
+ ))
+ } else {
+ return(paste0("WP", " (μ = ", estimator$mu, " ",
+ tolower(estimator$mu_units), ")"
+ ))
+ }
+ }
+}
+
+# Render the estimates table whenever it is updated.
+render_estimates <- function(output, react_values) {
+ observe({
+ output$estimates_table <- DT::renderDataTable(react_values$estimates_table,
+ selection = list(target = "column", selectable = c(0)),
+ escape = FALSE, rownames = FALSE,
+ options = list(
+ columnDefs = list(list(className = "dt-left", targets = "_all"))
+ ),
+ )
+ })
+}
+
+# Delete columns from the estimates table,
+# as well as the corresponding estimators.
+delete_estimators <- function(input, react_values) {
+ observeEvent(input$estimators_delete, {
+ cols_selected <- input$estimates_table_columns_selected
+ react_values$estimators <- react_values$estimators[-cols_selected]
+ react_values$estimates_table[, cols_selected + 1] <- NULL
+ })
+}
+
+# Export estimates table as a CSV file.
+export_estimates <- function(output, react_values) {
+ output$estimates_export <- downloadHandler(
+ filename = function() {
+ paste0(
+ "Rnaught_estimates_", format(Sys.time(), "%y-%m-%d_%H-%M-%S"), ".csv"
+ )
+ },
+ content = function(file) {
+ output_table <- data.frame(
+ lapply(react_values$estimates_table, sub_entity)
+ )
+ colnames(output_table) <- sub_entity(
+ colnames(react_values$estimates_table)
+ )
+ write.csv(output_table, file, row.names = FALSE)
+ }
+ )
+}
+
+# Substitute HTML entity codes with natural names.
+sub_entity <- function(obj) {
+ obj <- gsub("μ", "mu", obj)
+ obj <- gsub("κ", "kappa", obj)
+ obj
+}
git.nmode.ca / Rnaught / blobdiff
