From efec524a48901b9dcdc6cf5f9f5417e61d3edd82 Mon Sep 17 00:00:00 2001
From: Konrad1991 <konrad_kraemer@yahoo.de>
Date: Tue, 31 Mar 2026 12:19:37 +0200
Subject: [PATCH] added the possibility to calculate a variable IC value

---
 OpenStats/R/Backend_History.R                 |    1 +
 OpenStats/R/Backend_LC50.R                    |   81 +-
 OpenStats/R/Backend_V1_2_Engine.R             |   14 +-
 OpenStats/R/Server_DoseResponse.R             |    2 +-
 OpenStats/R/UI_DoseResponse.R                 |    3 +
 .../test_data/2dose_response_history.json     |    3 +
 .../inst/tinytest/test_Backend_DoseResponse.R |   14 +-
 .../inst/tinytest/test_Backend_TestEngine.R   |    1 +
 .../inst/tinytest/test_Server_DoseResponse.R  |    5 +-
 development/dose_response_vapro.R             |  137 ++
 development/dose_response_with_units.R        |    9 +-
 test_data/DoseResponse.csv                    |  434 ++---
 test_data/DoseResponseData.csv                | 1562 ++++++++---------
 13 files changed, 1214 insertions(+), 1052 deletions(-)
 create mode 100644 development/dose_response_vapro.R

diff --git a/OpenStats/R/Backend_History.R b/OpenStats/R/Backend_History.R
index ef812ce..324c6c3 100644
--- a/OpenStats/R/Backend_History.R
+++ b/OpenStats/R/Backend_History.R
@@ -146,6 +146,7 @@ eval_entry_V1_2 <- function(entry, DataModelState,
     DoseResponse = {
       res <- dose_response_V1_2$new(
         DataModelState$df,
+        entry[["IC [%]"]],
         entry[["Log transform x-axis"]],
         entry[["Log transform y-axis"]],
         entry[["Column containing the names"]],
diff --git a/OpenStats/R/Backend_LC50.R b/OpenStats/R/Backend_LC50.R
index fcff355..21a1b4a 100755
--- a/OpenStats/R/Backend_LC50.R
+++ b/OpenStats/R/Backend_LC50.R
@@ -79,7 +79,7 @@ false_discovery_rate <- function(res) {
 }
 env_lc_V1_2$false_discovery_rate <- false_discovery_rate
 
-check_fit <- function(model, min_conc, max_conc,
+check_fit <- function(model, ic_percentage, min_conc, max_conc,
                       min_abs, max_abs, substance_name, unit) {
   if (model$fit$convergence != TRUE) {
     return(errorClass$new(paste(
@@ -93,46 +93,51 @@ check_fit <- function(model, min_conc, max_conc,
   e <- coefficients(model)[4] # IC50
   RSE <- summary(model)$rseMat[1] # residual standard error estimated
   Response_lowestdose_predicted <- predict(
-    model, data.frame(concentration = min_conc),
+    model, data.frame(conc = min_conc),
     se.fit = FALSE
   )[1]
   Response_highestdose_predicted <- predict(
-    model, data.frame(concentration = max_conc),
+    model, data.frame(conc = max_conc),
     se.fit = FALSE
   )[1]
   Response_difference <- abs(
     Response_lowestdose_predicted - Response_highestdose_predicted
   )
   HillCoefficient <- b
-  IC50_relative <- e
+  ed_res <- drc::ED(
+    model,
+    respLev = ic_percentage,
+    interval = "delta",
+    level = 0.95,
+    type = "relative",
+    display = FALSE
+  )
+  IC_relative <- ed_res[1, 1]
   Problems <- ""
   if (Response_difference < 0.25) {
     Problems <- paste(Problems,
-      "Response Difference lower than 25%",
-      collapse = " , "
+      "Response Difference lower than 25%", collapse = " , "
     )
-  } else if (IC50_relative > max_conc) {
+  } else if (IC_relative > max_conc) {
     Problems <- paste(Problems,
-      "IC50 larger than highest measured concentration",
-      collapse = " , "
+      "IC larger than highest measured concentration", collapse = " , "
     )
-  } else if (IC50_relative < min_conc) {
+  } else if (IC_relative < min_conc) {
     Problems <- paste(Problems,
-      "IC50 lower than lowest measured concentration",
-      collapse = " , "
+      "IC lower than lowest measured concentration", collapse = " , "
     )
   }
-  confidence_interval <- confint(model, parm = c("e"), level = 0.95)
-  IC50_relative_lower <- confidence_interval[1]
-  IC50_relative_higher <- confidence_interval[2]
+
+  IC_relative_lower <- ed_res[1, 3]
+  IC_relative_higher <- ed_res[1, 4]
   p_value <- drc::noEffect(model)[3]
   Response_lowestdose_predicted <- env_lc_V1_2$shapenumber(Response_lowestdose_predicted)
   Response_highestdose_predicted <- env_lc_V1_2$shapenumber(Response_highestdose_predicted)
   HillCoefficient <- env_lc_V1_2$shapenumber(HillCoefficient)
-  IC50_relative <- env_lc_V1_2$shapenumber(IC50_relative)
-  IC50_relative_lower <- env_lc_V1_2$shapenumber(IC50_relative_lower)
-  IC50_relative_higher <- env_lc_V1_2$shapenumber(IC50_relative_higher)
-  pIC50 <- env_lc_V1_2$shapenumber(-log10(IC50_relative))
+  IC_relative <- env_lc_V1_2$shapenumber(IC_relative)
+  IC_relative_lower <- env_lc_V1_2$shapenumber(IC_relative_lower)
+  IC_relative_higher <- env_lc_V1_2$shapenumber(IC_relative_higher)
+  pIC <- env_lc_V1_2$shapenumber(-log10(IC_relative))
   p_value <- env_lc_V1_2$shapenumber(p_value)
   outvar <- data.frame(
     name = substance_name,
@@ -140,10 +145,16 @@ check_fit <- function(model, min_conc, max_conc,
     Response_highestdose_predicted = Response_highestdose_predicted,
     HillCoefficient = HillCoefficient,
     asymptote_one = c, asymptote_two = d,
-    IC50_relative = IC50_relative, IC50_relative_lower = IC50_relative_lower,
-    IC50_relative_higher = IC50_relative_higher, unit = unit, pIC50 = pIC50,
+    IC_relative = IC_relative, IC_relative_lower = IC_relative_lower, IC_relative_higher = IC_relative_higher,
+    unit = unit, pIC = pIC,
     RSE = RSE, p_value = p_value, Problems = Problems
   )
+  names(outvar)[7:9] <- c(
+    paste0("IC_", ic_percentage, "_relative"),
+    paste0("IC_", ic_percentage, "_relative_lower"),
+    paste0("IC_", ic_percentage, "_relative_higher")
+  )
+
   return(outvar)
 }
 env_lc_V1_2$check_fit <- check_fit
@@ -169,7 +180,7 @@ drawplot_only_raw_data <- function(df, abs_col, conc_col, title, unit) {
 env_lc_V1_2$drawplot_only_raw_data <- drawplot_only_raw_data
 
 drawplot <- function(df, abs_col, conc_col, unit, model, valid_points, title,
-                     IC50_relative, IC50_relative_lower, IC50_relative_higher,
+                     IC_relative, IC_relative_lower, IC_relative_higher,
                      islog_x, islog_y) {
 
   conc <- function() stop("Should never be called") # Please R CMD check
@@ -188,8 +199,8 @@ drawplot <- function(df, abs_col, conc_col, unit, model, valid_points, title,
   max_conc <- max(df[, conc_col]) +
     0.1 * (max(df[, conc_col]) - min(df[, conc_col]))
   min_conc <- min(df[, conc_col]) - 0.1 * min(df[, conc_col])
-  xmin <- IC50_relative_lower
-  xmax <- IC50_relative_higher
+  xmin <- IC_relative_lower
+  xmax <- IC_relative_higher
   if (!is.na(xmin) && !is.na(xmax)) {
     ymin <- min(df[, abs_col])
     ymax <- max(df[, abs_col])
@@ -228,7 +239,7 @@ drawplot <- function(df, abs_col, conc_col, unit, model, valid_points, title,
 }
 env_lc_V1_2$drawplot <- drawplot
 
-ic50_internal <- function(df, abs, conc,
+ic_internal <- function(df, ic_percentage, abs, conc,
                           title, islog_x, islog_y, unit) {
   model <- drc::drm(abs ~ conc,
     data = df, fct = drc::LL.4(),
@@ -242,17 +253,17 @@ ic50_internal <- function(df, abs, conc,
     fct = drc::LL.4(), robust = "mean",
   )
   res <- env_lc_V1_2$check_fit(
-    model, min(df[, conc]),
+    model, ic_percentage, min(df[, conc]),
     max(df[, conc]), min(df[, abs]), max(df[, abs]), title, unit
   )
   p <- env_lc_V1_2$drawplot(
-    df, abs, conc, unit, model, valid_points, title, res$IC50_relative,
-    res$IC50_relative_lower, res$IC50_relative_higher,
+    df, abs, conc, unit, model, valid_points, title,
+    res[[7]], res[[8]], res[[9]],
     islog_x, islog_y
   )
   return(list(res, p))
 }
-env_lc_V1_2$ic50_internal <- ic50_internal
+env_lc_V1_2$ic_internal <- ic_internal
 
 check_dr_df <- function(df, abs_col,
                         conc_col, substance_name_col) {
@@ -260,7 +271,7 @@ check_dr_df <- function(df, abs_col,
     !is.factor(df[, substance_name_col])) {
     return(errorClass$new("The substance names are not character"))
   }
-  substances <- unique(df[, substance_name_col]) # TODO: is this even possible?
+  substances <- unique(df[, substance_name_col]) # is this even possible?
   if (length(substances) < 1) {
     return(errorClass$new("The data for compounds seems to be missing"))
   }
@@ -279,13 +290,13 @@ transform_conc_dr <- function(conc_col) {
     ))
   }
   if (!is.numeric(temp_conc)) {
-    return(errorClass$new("The concentration data is not numerical")) # TODO: is this even possible?
+    return(errorClass$new("The concentration data is not numerical")) # is this even possible?
   }
   return(temp_conc)
 }
 env_lc_V1_2$transform_conc_dr <- transform_conc_dr
 
-ic50 <- function(df, abs_col, conc_col,
+ic <- function(df, ic_percentage, abs_col, conc_col,
                  substance_name_col, unit_col,
                  islog_x, islog_y) {
   # Checks
@@ -314,8 +325,8 @@ ic50 <- function(df, abs_col, conc_col,
 
     m <- tryCatch(
       {
-        m <- env_lc_V1_2$ic50_internal(
-          df_temp,
+        m <- env_lc_V1_2$ic_internal(
+          df_temp, ic_percentage,
           "abs", "conc",
           substances[i],
           islog_x, islog_y, unit
@@ -335,4 +346,4 @@ ic50 <- function(df, abs_col, conc_col,
   }
   return(res)
 }
-env_lc_V1_2$ic50 <- ic50
+env_lc_V1_2$ic <- ic
diff --git a/OpenStats/R/Backend_V1_2_Engine.R b/OpenStats/R/Backend_V1_2_Engine.R
index dc519cb..56c9755 100644
--- a/OpenStats/R/Backend_V1_2_Engine.R
+++ b/OpenStats/R/Backend_V1_2_Engine.R
@@ -1325,12 +1325,14 @@ dose_response_V1_2 <- R6::R6Class(
     com = NULL,
     res_df = NULL,
     res_p = NULL,
+    ic_percentage = NULL,
 
-    initialize = function(df,
+    initialize = function(df, ic_percentage,
                           is_xlog, is_ylog,
                           substance_names, unit_names,
                           formula, com = communicator_V1_2) {
       self$df <- df
+      self$ic_percentage <- ic_percentage
       self$df[, substance_names] <- self$df[, substance_names] |> as.character()
       self$is_xlog <- is_xlog
       self$is_ylog <- is_ylog
@@ -1347,7 +1349,7 @@ dose_response_V1_2 <- R6::R6Class(
         expr = {
           promise_history_entry <- self$create_history(new_name)
           ResultsState$bgp$start(
-            fun = function(df, formula, substance_names, unit_names, is_xlog, is_ylog) {
+            fun = function(df, ic_percentage, formula, substance_names, unit_names, is_xlog, is_ylog) {
               f <- as.character(formula)
               dep <- f[2]
               indep <- f[3]
@@ -1358,9 +1360,8 @@ dose_response_V1_2 <- R6::R6Class(
                 OpenStats:::env_check_ast_V1_2$check_ast(str2lang(indep), colnames(df))
                 OpenStats:::env_check_ast_V1_2$check_ast(str2lang(dep), colnames(df))
 
-                res <- OpenStats:::env_lc_V1_2$ic50(
-                  df, dep,
-                  indep, substance_names, unit_names,
+                res <- OpenStats:::env_lc_V1_2$ic(
+                  df, ic_percentage, dep, indep, substance_names, unit_names,
                   is_xlog, is_ylog
                 )
                 if (inherits(res, "errorClass")) {
@@ -1393,7 +1394,7 @@ dose_response_V1_2 <- R6::R6Class(
                 current_page = 1L)
             },
             args = list(
-              df = self$df, formula = self$formula, substance_names = self$substance_names,
+              df = self$df, ic_percentage = self$ic_percentage, formula = self$formula, substance_names = self$substance_names,
               unit_names = self$unit_names, is_xlog = self$is_xlog, is_ylog = self$is_ylog
             ),
             promise_result_name = new_name,
@@ -1411,6 +1412,7 @@ dose_response_V1_2 <- R6::R6Class(
     create_history = function(new_name) {
       list(
         type = "DoseResponse",
+        "IC [%]" = self$ic_percentage,
         "Column containing the names" = self$substance_names,
         "Column containing the units" = self$unit_names,
         "Log transform x-axis" = self$is_xlog,
diff --git a/OpenStats/R/Server_DoseResponse.R b/OpenStats/R/Server_DoseResponse.R
index 87eefcd..e82bf6e 100755
--- a/OpenStats/R/Server_DoseResponse.R
+++ b/OpenStats/R/Server_DoseResponse.R
@@ -80,7 +80,7 @@ DoseResponseServer <- function(id, DataModelState, ResultsState) {
 
     run_dr <- function(df, new_name) {
       dr <- get_dose_response()$new(
-        df, input$xTransform, input$yTransform,
+        df, input$ic_percentage, input$xTransform, input$yTransform,
         input$substanceNames, input$unitNames, DataModelState$formula
       )
       dr$eval(ResultsState, new_name)
diff --git a/OpenStats/R/UI_DoseResponse.R b/OpenStats/R/UI_DoseResponse.R
index d9f8e9a..0fd9d29 100644
--- a/OpenStats/R/UI_DoseResponse.R
+++ b/OpenStats/R/UI_DoseResponse.R
@@ -3,6 +3,9 @@ DoseResponseSidebarUI <- function(id) {
     "Dose Response analysis",
     shiny::uiOutput(shiny::NS(id, "substanceNamesUI")),
     shiny::uiOutput(shiny::NS(id, "unitNamesUI")),
+    shiny::sliderInput(shiny::NS(id, "ic_percentage"), "Percentage if IC",
+      min = 1, max = 99, value = 50
+    ),
     shiny::uiOutput(shiny::NS(id, "DoseResponseUI"))
   )
 }
diff --git a/OpenStats/inst/test_data/2dose_response_history.json b/OpenStats/inst/test_data/2dose_response_history.json
index 35b7438..6a193f4 100644
--- a/OpenStats/inst/test_data/2dose_response_history.json
+++ b/OpenStats/inst/test_data/2dose_response_history.json
@@ -17,6 +17,7 @@
   },
   {
     "type": "DoseResponse",
+    "IC [%]": 50,
     "Column containing the names": "names",
     "Column containing the units": "units",
     "Log transform x-axis": false,
@@ -26,6 +27,7 @@
   },
   {
     "type": "DoseResponse",
+    "IC [%]": 50,
     "Column containing the names": "names",
     "Column containing the units": "units",
     "Log transform x-axis": false,
@@ -35,6 +37,7 @@
   },
   {
     "type": "DoseResponse",
+    "IC [%]": 50,
     "Column containing the names": "names",
     "Column containing the units": "units",
     "Log transform x-axis": false,
diff --git a/OpenStats/inst/tinytest/test_Backend_DoseResponse.R b/OpenStats/inst/tinytest/test_Backend_DoseResponse.R
index 7a50424..c6da2b8 100755
--- a/OpenStats/inst/tinytest/test_Backend_DoseResponse.R
+++ b/OpenStats/inst/tinytest/test_Backend_DoseResponse.R
@@ -40,7 +40,7 @@ test_check_fit <- function(ic50_true) {
   abs <- "abs"
   title = "Bla"
   res <- OpenStats:::check_fit(
-    model, min(df[, conc]),
+    model, 50, min(df[, conc]),
     max(df[, conc]), min(df[, abs]), max(df[, abs]), title, "M"
   )
   expect_true(is.data.frame(res))
@@ -50,13 +50,13 @@ test_check_fit(10)
 # Test ic50 internal
 test_ic50_internal <- function(ic50_true) {
   data <- simulate("A", 7, ic50_true)
-  res <- OpenStats:::ic50_internal(data, "abs", "conc", "substance", FALSE, FALSE, "M")
+  res <- OpenStats:::ic_internal(data, 50, "abs", "conc", "substance", FALSE, FALSE, "M")
   res_df <- res[[1]]
   tol_percentage <- 0.1
   rel_error <- function(a, b) {
     abs(b - a) / b
   }
-  expect_true(rel_error(ic50_true, res_df$IC50_relative) < tol_percentage)
+  expect_true(rel_error(ic50_true, res_df$IC_50_relative) < tol_percentage)
 }
 test_ic50_internal(0.6)
 test_ic50_internal(5)
@@ -187,12 +187,12 @@ test_drawplot <- function() {
   abs <- "abs"
   title = "Bla"
   res <- OpenStats:::check_fit(
-    model, min(df[, conc]),
+    model, 50, min(df[, conc]),
     max(df[, conc]), min(df[, abs]), max(df[, abs]), title, "M"
   )
   p <- OpenStats:::drawplot(
-    df, abs, conc, "M", model, valid_points, title, res$IC50_relative,
-    res$IC50_relative_lower, res$IC50_relative_higher,
+    df, abs, conc, "M", model, valid_points, title, res$IC_50_relative,
+    res$IC_50_relative_lower, res$IC_50_relative_higher,
     FALSE, FALSE
   )
   layers <- p$layers
@@ -274,7 +274,7 @@ test_ic50 <- function() {
     names = c("A", "A", "A", "A", "A"),
     unit = "M"
   )
-  result <- OpenStats:::ic50(data, "abs", "conc", "names", "unit", FALSE, FALSE)
+  result <- OpenStats:::ic(data, 50, "abs", "conc", "names", "unit", FALSE, FALSE)
   checks[[1]] <- expect_true(is.list(result))
   checks[[2]] <- expect_true(is.data.frame(result[[1]][[1]]))
   expect_true(all(unlist(checks)))
diff --git a/OpenStats/inst/tinytest/test_Backend_TestEngine.R b/OpenStats/inst/tinytest/test_Backend_TestEngine.R
index 3d5b506..18d1771 100644
--- a/OpenStats/inst/tinytest/test_Backend_TestEngine.R
+++ b/OpenStats/inst/tinytest/test_Backend_TestEngine.R
@@ -718,6 +718,7 @@ test_dose_response <- function(in_background) {
 
   dr <- OpenStats:::dose_response_V1_2$new(
     df = df,
+    ic_percentage = 50,
     is_xlog = FALSE,
     is_ylog = FALSE,
     substance_names = "name",
diff --git a/OpenStats/inst/tinytest/test_Server_DoseResponse.R b/OpenStats/inst/tinytest/test_Server_DoseResponse.R
index a5d74c6..b350c2d 100644
--- a/OpenStats/inst/tinytest/test_Server_DoseResponse.R
+++ b/OpenStats/inst/tinytest/test_Server_DoseResponse.R
@@ -2,8 +2,8 @@ df <- read.csv(system.file("/test_data/DoseResponse.csv", package = "OpenStats")
 
 # Expected
 # =================================================================
-expected <- OpenStats:::ic50(
-  df, "abs", "conc",
+expected <- OpenStats:::ic(
+  df, 50, "abs", "conc",
   "names", "units",
   FALSE, FALSE
 )
@@ -37,6 +37,7 @@ test_dose_response <- function(app, srv) {
   shiny::testServer(srv, {
     DataModelState$df      <- df
     DataModelState$formula <- new("LinearFormula", formula = abs ~ conc)
+    session$setInputs(`DOSERESPONSE-ic_percentage` = 50)
     session$setInputs(`DOSERESPONSE-substanceNames` = "names")
     session$setInputs(`DOSERESPONSE-unitNames` = "units")
     session$setInputs(`DOSERESPONSE-yTransform` = FALSE)
diff --git a/development/dose_response_vapro.R b/development/dose_response_vapro.R
new file mode 100644
index 0000000..bb91b74
--- /dev/null
+++ b/development/dose_response_vapro.R
@@ -0,0 +1,137 @@
+simulate <- function(name, slope, true_ic50) {
+  b  <- slope         # slope
+  c  <- 0.05          # lower limit
+  d  <- 1.1           # upper limit
+  e  <- true_ic50     # IC50
+  set.seed(42)
+  conc_levels <- c(0.1, seq(2.5, 26, by = 2.5))
+  conc <- rep(conc_levels, each = 5)
+  # Generate response
+  logistic_response <- function(conc, b, c, d, e) {
+    c + (d - c) / (1 + (conc / e)^b)
+  }
+  abs <- logistic_response(conc, b, c, d, e) + rnorm(length(conc), sd = 0.05)
+  data.frame(
+    substance = rep(name, length(conc)), conc = conc, abs = abs, unit = "M"
+  )
+}
+
+ll4 <- function(b, e, df, get_params = FALSE) {
+  g <- 1 / (1 + exp(b * (log(df$conc) - log(e))))
+  X <- cbind(1 - g, g)
+  beta_hat <- qr.solve(X, df$abs)
+  c_hat <- beta_hat[[1L]]
+  d_hat <- beta_hat[[2L]]
+  if (c_hat > d_hat) {
+    tmp <- c_hat
+    c_hat <- d_hat
+    d_hat <- tmp
+    b <- -b
+    g <- 1 / (1 + exp(b * (log(df$conc) - log(e))))
+  }
+  abs_in_silico <- c_hat * (1 - g) + d_hat * g
+  if (get_params) return(c(b = b, c = c_hat, d = d_hat, e = e))
+  sum((df$abs - abs_in_silico)^2)
+}
+
+define_grid <- function(n, b_start, b_end, df, eps = 1e-6) {
+  b <- seq(b_start, b_end, length.out = n)
+  b <- b[abs(b) > eps]
+  log_e <- seq(log(min(df$conc)) - 1, log(max(df$conc)) + 1, length.out = n)
+  e <- exp(log_e)
+  expand.grid(b, e)
+}
+
+eval_grid <- function(df, grid) {
+  errors <- lapply(seq_len(nrow(grid)), function(r) {
+    pair <- grid[r, ]
+    res <- try(ll4(pair[[1L]], pair[[2L]], df), silent = TRUE)
+    if (inherits(res, "try-error")) return(NA)
+    res
+  })
+  errors_vec <- unlist(errors)
+  valid_idx <- which(!is.na(errors_vec))
+  best <- valid_idx[which.min(errors_vec[valid_idx])]
+  best <- grid[best, ]
+  params <- ll4(best[[1]], best[[2]], df, TRUE)
+  list(params = params, errors_vec = errors_vec)
+}
+
+calc_uncertainty <- function(df, errors_vec, grid) {
+  e_vals <- unique(grid$Var2)
+  profile_e <- sapply(e_vals, function(e_val) {
+    idx <- which(grid$Var2 == e_val)
+    min(errors_vec[idx], na.rm = TRUE)
+  })
+  names(profile_e) <- e_vals
+  rss_min <- min(profile_e)
+  n_obs <- nrow(df)
+  p <- 4L
+  sigma2 <- rss_min / (n_obs - p)
+  threshold <- rss_min + qchisq(0.95, df = 1) * sigma2
+  valid_e <- as.numeric(names(profile_e))[profile_e <= threshold]
+  CI_e <- range(valid_e)
+  CI_e
+}
+
+calc_xy <- function(b, e, xy_percent) {
+  p <- xy_percent / 100
+  if (p <= 0 || p >= 1) {
+    stop("xy_percent must be strictly between 0 and 100")
+  }
+  e * ((1 - p) / p)^(1 / b)
+}
+
+calc_uncertainty_xy <- function(df, grid, errors_vec, xy_percent) {
+  p <- xy_percent / 100
+  b_vals <- unique(grid$Var1)
+  ic_vals <- unique(grid$Var2)
+  
+  profile_ic <- sapply(ic_vals, function(ic_val) {
+    rss_vals <- sapply(b_vals, function(b_val) {
+      e_val <- ic_val * (p / (1 - p))^(1 / b_val)
+      if (!is.finite(e_val) || e_val <= 0) return(NA)
+      ll4(b_val, e_val, df)
+    })
+    min(rss_vals, na.rm = TRUE)
+  })
+  names(profile_ic) <- ic_vals
+  rss_min <- min(profile_ic, na.rm = TRUE)
+  n_obs <- nrow(df)
+  p_param <- 4L
+  sigma2 <- rss_min / (n_obs - p_param)
+  threshold <- rss_min + qchisq(0.95, df = 1) * sigma2
+  valid_ic <- as.numeric(names(profile_ic))[profile_ic <= threshold]
+  range(valid_ic)
+}
+
+df <- simulate("A", 7, 10)
+n <- 300L
+grid <- define_grid(n, -20, 20, df)
+res <- eval_grid(df, grid)
+params <- res$params
+params
+errors_vec <- res$errors_vec
+calc_uncertainty(df, errors_vec, grid)
+
+ll4_optim <- function(par, df) {
+  b <- par[1]
+  e <- par[2]
+  if (!is.finite(e) || e <= 0 || abs(b) < 1e-6) {
+    return(Inf)
+  }
+  ll4(b, e, df)
+}
+optim(c(5, 5), ll4_optim, df = df, method = "BFGS")
+
+calc_xy(params[["b"]], params[["e"]], 10)
+calc_xy(params[["b"]], params[["e"]], 50)
+calc_xy(params[["b"]], params[["e"]], 90)
+calc_uncertainty_xy(df, grid, errors_vec, 10)
+calc_uncertainty_xy(df, grid, errors_vec, 50)
+calc_uncertainty_xy(df, grid, errors_vec, 90)
+
+library(drc)
+fit_drc <- drm(abs ~ conc, data = df, fct = LL.4())
+coef(fit_drc)
+ED(fit_drc, c(10, 50, 90))
diff --git a/development/dose_response_with_units.R b/development/dose_response_with_units.R
index 383bbbf..012f2b9 100644
--- a/development/dose_response_with_units.R
+++ b/development/dose_response_with_units.R
@@ -15,8 +15,11 @@ backend_method_state <- R6::R6Class(
 MethodState <- backend_method_state$new()
 path <- "./development/dose_response_input.json"
 import_dose_response_json(path, DataModelState, ResultsState, MethodState)
-DataModelState$df
 
 library(ggplot2)
-res <- ic50(DataModelState$df, "values", "conc", "name", "unit", FALSE, FALSE)
-res
+res <- ic50(DataModelState$df, 10, "values", "conc", "name", "unit", FALSE, FALSE)
+df <- lapply(res, function(obj) {
+  obj[[1L]]
+})
+df <- Reduce(rbind, df)
+df
diff --git a/test_data/DoseResponse.csv b/test_data/DoseResponse.csv
index 1d174f6..b324f2d 100755
--- a/test_data/DoseResponse.csv
+++ b/test_data/DoseResponse.csv
@@ -1,217 +1,217 @@
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diff --git a/test_data/DoseResponseData.csv b/test_data/DoseResponseData.csv
index 8dff780..13a6cce 100644
--- a/test_data/DoseResponseData.csv
+++ b/test_data/DoseResponseData.csv
@@ -1,781 +1,781 @@
-substance,conc,abs
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