# 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$estimators[[i]])) {
- showNotification("Error: This estimator has already been added.",
- duration = 3, id = "notify-error"
+ showNotification(
+ "Error: This estimator has already been added.", duration = 3
)
return()
}
# Add the new estimator to the list of estimators.
react_values$estimators[[num_estimators + 1]] <- new_estimator
- showNotification("Estimator added successfully.",
- duration = 3, id = "notify-success"
- )
+ showNotification("Estimator added successfully.", duration = 3)
# Evaluate the new estimator on all existing datasets and create a new row in
# the estimates table.
kappa <- trimws(input$kappa)
kappa <- if (kappa == "") 20 else suppressWarnings(as.numeric(kappa))
- if (is.na(kappa) || kappa <= 0) {
+ if (is.na(kappa) || kappa < 1) {
output$kappa_warn <- renderText(
- "The maximum prior must be a positive number."
+ "The maximum prior must be a number greater than or equal to 1."
)
} else if (!is.null(mu)) {
output$kappa_warn <- renderText("")
eval_estimator <- function(estimator, dataset) {
cases <- as.integer(unlist(strsplit(dataset[, 3], ",")))
- if (estimator$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$r0, 2), " (SI = ", estimated_mu,
- " ", tolower(dataset[, 2]), ")"
+ tryCatch(
+ {
+ if (estimator$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)
+ mu_units <- if (dataset[, 2] == "Days") "day(s)" else "week(s)"
+ estimate <- paste0(
+ round(estimate$r0, 2), " (SI = ", estimated_mu, " ", mu_units, ")"
+ )
+ } else {
+ mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+ estimate <- round(Rnaught::wp(cases, mu), 2)
+ }
+ }
+
+ return(estimate)
+ }, error = function(e) {
+ showNotification(
+ paste0(toString(e),
+ " [Estimator: ", sub(" .*", "", estimator_name(estimator)),
+ ", Dataset: ", dataset[, 1], "]"
+ ), duration = 6
)
- } else {
- mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
- estimate <- round(Rnaught::wp(cases, mu), 2)
+ return("—")
}
- }
-
- return(estimate)
+ )
}
# Create the name of an estimator to be added to the first column of the
if (is.na(estimator$mu)) {
return("—")
}
- paste(estimator$mu, tolower(estimator$mu_units))
+ mu_units <- if (estimator$mu_units == "Days") "day(s)" else "week(s)"
+ paste(estimator$mu, mu_units)
}
# Render the estimates table whenever it is updated.