Working on update_curve_data function.

R/plot_curves.R

@@ -12,7 +12,8 @@
 #'   for each facet. One of \code{"fixed"} (default), \code{"free_y"}, \code{"free_x"},
 #'   or \code{"free"}.
 #' @param rug If \code{TRUE} (default) will add a "rug" of tickmarks to the x and y
-#'   axes to denote Unknown and Control samples.
+#'   axes to denote Unknown and Control samples. Setting this to \code{FALSE} may speed
+#'   up rendering.
 #' @param model_limits If \code{TRUE} (default) will annotate the plots with lower
 #'   limits of quantitation (LLoQ), limits of detection (LoD), and minimum
 #'   detectable dose (MDD) if available.
@@ -34,7 +35,7 @@ plot_curves <- function(.intelliframe, analytes = NULL, type = "individual", int
 
   if(!is.null(analytes)) {
     stopifnot("Analyte not found in data" = all(analytes %in% unique(dat$Analyte)))
-    dat <- dplyr::filter(dat, "Analyte" %in% analytes)
+    dat <- dplyr::filter(dat, .data[["Analyte"]] %in% analytes)
   }
 
   standard_data <- dplyr::filter(dat, .data[["Type"]] == "Standard")
@@ -44,7 +45,7 @@ plot_curves <- function(.intelliframe, analytes = NULL, type = "individual", int
   curve_data <- S7::`@`(.intelliframe, "curve_data")
 
   if(!is.null(analytes)) {
-    curve_data <- dplyr::filter(curve_data, "Analyte" %in% analytes)
+    curve_data <- dplyr::filter(curve_data, .data[["Analyte"]] %in% analytes)
   }
 
   curve_data_long <- curve_data |>
@@ -75,7 +76,6 @@ plot_curves <- function(.intelliframe, analytes = NULL, type = "individual", int
       ggplot2::aes(.data[["Result"]], .data[["Predicted"]]),
       inherit.aes = FALSE
     ) +
-    # ggplot2::scale_colour_brewer(type = "qual", palette = "Set1") +
     ggplot2::scale_colour_manual(values = c("Control" = "#E41A1C", "Standard" = "#377EB8", "Unknown" = "#4DAF4A")) +
     suppressWarnings(
       ggplot2::geom_point(

R/refit_curves.R

@@ -105,7 +105,7 @@ refit_curves <- function(
 
   standard_list <- split(standard_list, standard_list$Analyte)
 
-  fits <- lapply(standard_list, function(analyte) {
+  fits <<- lapply(standard_list, function(analyte) {
 
     if(!use_excluded) {
       analyte <- analyte[-excluded_wells, ]

R/update_curve_data.R

@@ -1,68 +1,96 @@
-lapply(names(fits), function(x) {
+update_well_data <- function(.summary_data, .well_data, .fits, .standard_list, .silent) {
+
+lapply(names(.fits), function(x) {
 
   lloq <- dplyr::filter(
-    u5plex@summary_data,
+    .summary_data,
     Analyte == x,
     Type == "Standard",
     Result_CV <= 0.2,
+    dplyr::n() > 1,
     Result_Avg / Expected > 0.8,
     Result_Avg / Expected < 1.2
   ) |>
     dplyr::pull(Expected) |>
     min()
 
   blank <- dplyr::filter(
-    u5plex@well_data,
+    .summary_data,
     Analyte == x,
     Type == "Standard",
     Expected == 0
-  ) |>
-    dplyr::pull(MFI)
+  )
 
-  blank_targets <-
-    (mean(blank) + 2.5*sd(blank)) /
-    max(dplyr::filter(u5plex@well_data, Analyte == x)$MFI)
+  analyte  <- dplyr::filter(.well_data, Analyte == x)
+  analyte  <- analyte[!analyte$Excluded, ]
+  standard <- dplyr::filter(analyte, Type == "Standard")
+
+  blank_targets <- (blank$MFI_Avg + 2.5 * blank$MFI_SD) / max(standard$MFI)
 
   suppressWarnings({
     suppressMessages({
       mdd <- nplr::getEstimates(
-        fits[[x]],
+        .fits[[x]],
         targets = blank_targets
       )$x
     })
   })
 
   df_low <- data.frame(
-    x = 10^(fits[[x]]@x),
-    y = fits[[x]]@y
-  )[10^(fits[[x]]@x) %in% unique(10^(fits[[x]]@x))[1:3],]
+    x = 10^(.fits[[x]]@x),
+    y = .fits[[x]]@y
+  )[10^(.fits[[x]]@x) %in% unique(10^(.fits[[x]]@x))[2:4],]
 
   fit_low <- nplr::nplr(
     x        = df_low$x,
-    y        = df_low$y#,
-    # npars    = npars,
-    # method   = weight_method,
-    # LPweight = LPweight,
-    # silent   = silent
+    y        = df_low$y,
+    silent   = TRUE
   )
 
   suppressWarnings({
     suppressMessages({
-      lod <- nplr::getEstimates(
-        fit_low,
-        targets = blank_targets
-      )$x
+      lod <- nplr::getEstimates(fit_low, targets = blank_targets)$x
     })
   })
 
+  if(!silent) {
+    warning("LLoQ, MDD, and LoD may be calculated differently than in Belysa. See ?refit_curves for details.")
+  }
+
+  if(.fits[[x]]@weightMethod == "res") {
+    weight_meth <- paste0("(1/residual)^", .fits[[x]]@LPweight)
+  } else if(.fits[[x]]@weightMethod == "sdw") {
+    weight_meth <- "1/Var(y)"
+  } else if(.fits[[x]]@weightMethod == "gw") {
+    weight_meth <- paste0("1/fitted^", .fits[[x]]@LPweight)
+  }
+
+  model_eqn <- paste0(
+    "y = ",
+    .fits[[x]]@pars["bottom"],
+    " + (",
+    .fits[[x]]@pars["top"],
+    " - ",
+    .fits[[x]]@pars["bottom"],
+    ") / (1 + 10^(",
+    .fits[[x]]@pars["bottom"],
+    " * (",
+    .fits[[x]]@pars["xmid"],
+    " - x)))^",
+    .fits[[x]]@pars["s"]
+  )
+
   tibble::tibble(
+    Group       = analyte$Group,
     Analyte     = x,
-    Fit         = paste0("nplr ", fits[[x]]@npars, "PL"),
+    Fit         = paste0("nplr ", .fits[[x]]@npars, "PL"),
     LLoQ        = lloq,
     MDD         = mdd,
     LoD         = lod,
-    `R Squared` = fits[[x]]@goodness$gof,
-    Slope       = fits[[x]]@pars$s
+    `R Squared` = .fits[[x]]@goodness$gof,
+    Slope       = .fits[[x]]@pars$s,
+    Weighting   = weight_meth,
+    Equation    = model_eqn
   )
 }) |> dplyr::bind_rows()
-
+}