Added review suggestions

Author: SofiaOtero

R/aedseo.R

@@ -13,13 +13,14 @@
 #' @param level The confidence level for parameter estimates, a numeric value
 #' between 0 and 1.
 #' @param disease_threshold An integer specifying the threshold for considering
-#' a disease outbreak. It defines the one time step disease threshold that has
-#' to be surpassed to trigger a seasonal onset alarm. If the sum of cases in a
-#' k window exceeds this threshold * k, a seasonal onset alarm is triggered.
+#' a disease outbreak. It defines the per time-step disease threshold that has
+#' to be surpassed to possibly trigger a seasonal onset alarm. If the total
+#' number of cases in a window of size k exceeds  `disease_threshold * k`,
+#' a seasonal onset alarm can be triggered.
 #' @param family A character string specifying the family for modeling.
 #' Choose between "poisson," or "quasipoisson".
-#' @param na_percentage_allowed Numeric value specifying how many percentage of
-#' the observables in the k window that are allowed to be NA.
+#' @param na_fraction_allowed Numeric value specifying the fraction of
+#' observables in the window of size k that are allowed to be NA.
 #'
 #' @return A `aedseo` object containing:
 #'   - 'reference_time': The time point for which the growth rate is estimated.
@@ -36,7 +37,7 @@
 #'   disease threshold?
 #'   - 'seasonal_onset_alarm': Logical. Is there a seasonal onset alarm?
 #'   - 'converged': Logical. Was the IWLS judged to have converged?
-#'   - 'skipped_window': Logical. Was the window skipped?
+#'   - 'skipped_window': Logical. Was the window skipped due to missing?
 #'
 #' @export
 #'
@@ -62,7 +63,7 @@
 #'   level = 0.95,
 #'   disease_threshold = 200,
 #'   family = "poisson",
-#'   na_percentage_allowed = 0.4,
+#'   na_fraction_allowed = 0.4,
 #' )
 #'
 #' # Print the AEDSEO results
@@ -77,7 +78,7 @@ aedseo <- function(
       "quasipoisson"
       # TODO: #10 Include negative.binomial regressions. @telkamp7
     ),
-    na_percentage_allowed = 0.4) {
+    na_fraction_allowed = 0.4) {
   # Throw an error if any of the inputs are not supported
   family <- rlang::arg_match(family)
 
@@ -93,7 +94,7 @@ aedseo <- function(
     obs_iter <- tsd[(i - k + 1):i, ]
 
     # Evaluate NA values in windows
-    if (sum(is.na(obs_iter)) >= k * na_percentage_allowed) {
+    if (sum(is.na(obs_iter)) >= k * na_fraction_allowed) {
       skipped_window[i] <- TRUE
       # Set fields to NA since the window is skipped
       growth_rates <- list(estimate = c(NA, NA, NA),

man/aedseo.Rd

@@ -27,15 +27,15 @@ test_that("The growth rate models converge", {
     level = 0.95,
     family = "poisson",
     disease_threshold = 20,
-    na_percentage_allowed = 0.2
+    na_fraction_allowed = 0.2
   )
   aedseo_quasipoisson <- aedseo(
     tsd = tsd_data_nbinom,
     k = 3,
     level = 0.95,
     family = "quasipoisson",
     disease_threshold = 20,
-    na_percentage_allowed = 0.2
+    na_fraction_allowed = 0.2
   )
 
   # Check if they all converge
@@ -79,18 +79,18 @@ test_that("Test if it works with weeks with NA values", {
     level = 0.95,
     family = "poisson",
     disease_threshold = 20,
-    na_percentage_allowed = 0.4
+    na_fraction_allowed = 0.4
   )
 
   # Test if correct amount of windows with NA are skipped
   k <- 3
-  na_percentage_allowed <- 0.4
+  na_fraction_allowed <- 0.4
   n <- base::nrow(tsd_data_poisson_na)
   skipped_window_count <- 0
 
   for (i in k:n) {
     obs_iter <- tsd_data_poisson_na[(i - k + 1):i, ]
-    if (sum(is.na(obs_iter)) >= k * na_percentage_allowed) {
+    if (sum(is.na(obs_iter)) >= k * na_fraction_allowed) {
       skipped_window_count <- skipped_window_count + 1
     }
   }

tests/testthat/test-aedseo.R

@@ -174,7 +174,7 @@ In the following figure, the simulated data (solid circles) is visualized alongs
 plot(tsd_data)
 ```
 
-Next, the time series data object is passed to the `aedseo()` function. Here, a window width of $k=5$ is specified, meaning that a total of 5 weeks is used in the local estimate of the exponential growth rate. $na_percentage_allowed = 0.4$ defines how many percentage of the observables in the k window that are allowed to be NA, here 0.4*5 = 20%. Additionally, a 95\% confidence interval is specified. Finally, the exponential growth rate is estimated using quasi-Poisson regression to account for overdispersion in the data.
+Next, the time series data object is passed to the `aedseo()` function. Here, a window size of $k=5$ is specified, meaning that a total of 5 weeks is used in the local estimate of the exponential growth rate. $na_fraction_allowed = 0.4$ defines how large a fraction of observables in the k window that are allowed to be NA, here 0.4*5 = 2 observables. Additionally, a 95\% confidence interval is specified. Finally, the exponential growth rate is estimated using quasi-Poisson regression to account for overdispersion in the data.
 
 ```{r}
 # Apply the 'aedseo' algorithm
@@ -184,7 +184,7 @@ aedseo_results <- aedseo(
   level = 0.95,
   disease_threshold = 2000,
   family = "quasipoisson",
-  na_percentage_allowed = 0.4
+  na_fraction_allowed = 0.4
 )
 ```