removed nplr warnings (#190)

* Change warnings to message for handle_datetime

* Fix warnings with bottom asymptote for plots

* Manually handle values outside the asymptotes

* Add test covering the warning

R/classes-model.R

@@ -151,16 +151,37 @@ Model <- R6::R6Class(
     #' seen in standard curve samples \eqn{RAU_{max}}. Defaults to 0.
     #' If the value of the predicted RAU is above \eqn{RAU_{max} + \text{over_max_extrapolation}},
     #' the value is censored to the value of that sum.
+    #' @param eps (`numeric(1)`)\cr
+    #' A small value used to avoid numerical issues close to the asymptotes
     #'
     #' @return (`data.frame()`)\cr
     #' Dataframe with the predicted RAU values for given MFI values
     #' The columns are named as follows:
     #' - `RAU` - the Relative Antibody Units (RAU) value
     #' - `MFI` - the predicted MFI value
     #'
-    predict = function(mfi, over_max_extrapolation = 0) {
+    predict = function(mfi, over_max_extrapolation = 0, eps = 1e-6) {
       private$assert_model_fitted()
       original_mfi <- mfi
+      # Extrapolation maximum
+      max_sc_rau <- max(dilution_to_rau(self$dilutions), na.rm = TRUE)
+      rau_threshold <- max_sc_rau + over_max_extrapolation
+      top_asymptote_transformed <- private$mfi_transform(self$top_asymptote - eps)
+      rau_at_top_asymptote <- nplr::getEstimates(self$model,
+        targets = top_asymptote_transformed
+      )[1, "x"]
+      if (rau_threshold >= rau_at_top_asymptote) {
+        warning(
+          "Extrapolation above the top asymptote is not allowed.
+          Samples with MFI values above the top asymptote will be censored to the top asymptote.
+          Consider using a smaller value for `over_max_extrapolation`."
+        )
+      }
+      # Handle mfi outside asymptotes
+      mfi <- clamp(mfi,
+        lower = self$bottom_asymptote + eps,
+        upper = self$top_asymptote - eps
+      )
       mfi <- private$mfi_transform(mfi)
       # Example columns: y, x,
       df <- nplr::getEstimates(self$model, mfi)
@@ -170,11 +191,7 @@ Model <- R6::R6Class(
       # Convert to RAU
       df[, "x"] <- dilution_to_rau(df[, "x"])
       # Censor values or extrapolate
-      max_sc_rau <- max(dilution_to_rau(self$dilutions), na.rm = TRUE)
-      max_allowed_value <- max_sc_rau + over_max_extrapolation
-      df[, "x"] <- ifelse(
-        df[, "x"] > max_allowed_value, max_allowed_value, df[, "x"]
-      )
+      df[, "x"] <- ifelse(df[, "x"] > rau_threshold, rau_threshold, df[, "x"])
       # Rename columns before returning
       colnames(df) <- sub("x", "RAU", colnames(df))
       colnames(df) <- sub("y", "MFI", colnames(df))
@@ -191,7 +208,7 @@ Model <- R6::R6Class(
       private$assert_model_fitted()
       upper_bound <- nplr::getPar(self$model)$params$top
       upper_bound <- (upper_bound + 1) / 2
-      lower_bound <- private$mfi_transform(5) # Scaled MFI for MFI = 5
+      lower_bound <- private$mfi_transform(10) # Scaled MFI for MFI = 10
 
       uniform_targets <- seq(lower_bound, upper_bound, length.out = 100)
       df <- nplr::getEstimates(self$model, targets = uniform_targets)

R/parser.R

@@ -80,11 +80,11 @@ handle_datetime <- function(datetime_str, file_format = "xPONENT") {
   if (!is.na(first_attempt)) {
     return(first_attempt)
   } else {
-    warning("Could not parse datetime string using default datetime format. Trying other possibilies.")
+    message("Could not parse datetime string using default datetime format. Trying other possibilies.")
     for (order in possible_orders[-1]) {
       datetime <- lubridate::parse_date_time2(datetime_str, orders = order, tz = "")
       if (!is.na(datetime)) {
-        warning("Successfully parsed datetime string using order: ", order)
+        message("Successfully parsed datetime string using order: ", order)
         return(datetime)
       }
     }

tests/testthat/test-parser.R

@@ -8,7 +8,7 @@ test_that("Test handle_datetime with seen date formats", {
   )
 
   # Automatic recovery with xPONENT datetime format DD/MM/YYYY HH:MM
-  expect_warning(dt <- handle_datetime("26/02/2014 16:07", "xPONENT"))
+  expect_message(dt <- handle_datetime("26/02/2014 16:07", "xPONENT"))
   expect_equal(dt, as.POSIXct("2014-02-26 16:07:00", tz = ""))
 
   # Default INTELLIFLEX datetime format YYYY-MM-DD HH:MM:SS AM/PM

tests/testthat/test-standard_curve.R

@@ -39,3 +39,12 @@ test_that("Test plotting the full plot", {
   model <- create_standard_curve_model_analyte(plate, "Spike_6P_IPP")
   expect_no_error(plot_standard_curve_analyte_with_model(plate, model))
 })
+
+test_that("Test over max extrapolation", {
+  plate <- get_test_plate()
+  model <- create_standard_curve_model_analyte(plate, "Spike_6P_IPP")
+  expect_warning(plot_standard_curve_analyte_with_model(
+    plate, model,
+    over_max_extrapolation = Inf
+  ))
+})