new structure acc. to golem

TestFileRoxygen.R

@@ -203,7 +203,7 @@ x <- 1; Div <- 3;N <- 0; res <- c(); noDil <- 7
 divFUN(x,Div,N,res,noDil)
 # [1] 0.3333333333 0.1111111111 0.0370370370 0.0123456790 0.0041152263 0.0013717421 0.0004572474
 
-
+#######
 circle <- read.csv("./tests/circle.csv")
 all_l2 <- read.csv("./tests/all_l2.csv")
 sigmoid <- c(10.0,  10.0, 110.0, 110.0, 1.0,  1.0, -3.5,  0.0)
@@ -217,4 +217,22 @@ plotT = c(114.213375,97.588663,80.963951,64.339239,47.714527,31.102604,14.477892
 PLOT <- PlotLinPLA_FUNC(circle, sigmoid, all_l2, pl_df, indS,indT)
 PlotLinTest.png
 
+############
+dat <- data.frame(R_dil1 = c(10.0651024695491, 10.9844983291817, 10.7635586089293, 10.4597656321327, 10.3898668457823, 10.8171761349909,
+10.319758021908, 10.1304854046653),
+R_dil2 = c(10.9649145494504, 10.0202868589385, 10.8424145955735, 10.9311360356894, 10.3284659026404,
+10.6890147558796, 10.3014450252305, 10.9594838595181),
+R_dil3 = c(10.4630510824383, 10.4566715089363, 10.2350765290036, 10.3300581874798, 10.9648088137065,
+10.286893755805, 10.4856643841389, 10.5275521552307),
+T_dil1 = c(12.732175566336, 12.7756403995095, 12.1672539684741, 12.7060603907892, 12.8000685682832,
+12.8800092157515, 12.7160581291873, 12.6996878912416),
+T_dil2 = c(12.3923194313831, 12.0943488144175, 12.7955302154828, 12.4825917078735, 12.6856540203788,
+12.7348548498556, 12.9222470610476, 12.1186618671252),
+T_dil3 = c(12.7899182255274, 12.9722600411128, 12.7078445380891, 12.4913523531941, 12.1718281909609,
+12.5313873615133, 12.952802332772, 12.5960321394342),
+log_dose = c(0, -1.09861228866811, -2.19722457733622, -3.29583686600433, -4.39444915467244,
+-5.49306144334055, -6.59167373200866, -7.69028602067677)) 
+p <- plotSingularity(dat)
+print(p)
 
+# plotSingularityTest.png

dev/Global.R

@@ -1,9 +1,8 @@
+################################################################################
+# All functions for plateflow
+################################################################################
 
 
-Dat <- reactiveValues()
-REP <- reactiveValues()
-REPlin <- reactiveValues()
-
 #' Levenberg Marquard fit of 4 pl
 #'
 #' Returns list of following: summary of restricted and unrestricted model fits of the 4pl function,
@@ -110,6 +109,35 @@ Fitting_FUNC <- function(ro_new, TransFlag=F) {
   return(list(s_mr, Sum_u, pot_est, potU_est, PRED, PREDu))
 }
 
+
+#' Plot when the fitting has a singularity
+#'
+#' Returns the scatter plot of the data, with REF in blue and TEST in red.
+#' One plots are returned. 
+#'
+#' @param dat The dilution series readouts for REF and TEST + 1 column log(loncentration) or concentration.
+#' @returns A grid object with 2 linearity plots, restricted and unrestricted model.
+#' @export
+#' @examples
+#' data.frame(R_dil1 = c(10.0651024695491, 10.9844983291817, 10.7635586089293, 10.4597656321327, 10.3898668457823, 10.8171761349909, 
+#' 10.319758021908, 10.1304854046653), 
+#' R_dil2 = c(10.9649145494504, 10.0202868589385, 10.8424145955735, 10.9311360356894, 10.3284659026404, 
+#' 10.6890147558796, 10.3014450252305, 10.9594838595181), 
+#' R_dil3 = c(10.4630510824383, 10.4566715089363, 10.2350765290036, 10.3300581874798, 10.9648088137065,
+#' 10.286893755805, 10.4856643841389, 10.5275521552307), 
+#' T_dil1 = c(12.732175566336, 12.7756403995095, 12.1672539684741, 12.7060603907892, 12.8000685682832, 
+#' 12.8800092157515, 12.7160581291873, 12.6996878912416), 
+#' T_dil2 = c(12.3923194313831, 12.0943488144175, 12.7955302154828, 12.4825917078735, 12.6856540203788, 
+#' 12.7348548498556, 12.9222470610476, 12.1186618671252), 
+#' T_dil3 = c(12.7899182255274, 12.9722600411128, 12.7078445380891, 12.4913523531941, 12.1718281909609,
+#' 12.5313873615133, 12.952802332772, 12.5960321394342),
+#' log_dose = c(0, -1.09861228866811, -2.19722457733622, -3.29583686600433, -4.39444915467244, 
+#' -5.49306144334055, -6.59167373200866, -7.69028602067677))  
+#'
+#' 
+#' p <- plotSingularity(dat)
+#' print(p)
+#' 
 plotSingularity <- function(dat) {  #sigmoid,det_sig, 
   CORdat <- cor(dat[,1],dat[,ncol(dat)])
   #browser() 

dev/app.R

@@ -1,3 +1,10 @@
+################################################################################
+# 4PL and Linear bioassay evaluation
+# Author: Franz Innerbichler
+# 1.4.2026
+################################################################################
+
+
 library(shiny)
 library(shinydashboard)
 library(shinyjs)
@@ -21,10 +28,6 @@ library(dplyr)
 library(scales)
 
 
-#### reactive values ----
-Dat <- reactiveValues()
-REP <- reactiveValues()
-REPlin <- reactiveValues()
 
 source("Global.R")
 
@@ -76,8 +79,12 @@ ui <- dashboardPage(
 
 #### server ----
 server <- function(input, output, session) {
-  ReportParS <- reactiveValues()
-  IPReportParS <- reactiveValues()
+  
+  #### reactive values ----
+  Dat <- reactiveValues()
+  REP <- reactiveValues()
+  REPlin <- reactiveValues()
+  
   
  #### renderUIs ---- 
   output$homePage <- renderUI({
@@ -85,7 +92,7 @@ server <- function(input, output, session) {
                tabPanel("Limit setting",
                         tags$img(src="logo.png", class="adv_logo"),
                         h4("Introduction to the bioassay software"),
-                        tags$mark("linear regression"), br(),
+                        #tags$mark("linear regression"), br(),
                         column(3,
                                tags$table(id="dose-table",
                                           numericInput("lEACdiffla","lower EAC for diff. of LA", -0.175, step=0.001),