bugfix: 4PL fit made robust

R/Global.R

@@ -23,6 +23,25 @@ library(car)
 library(dplyr)
 library(scales)
 
+#' Estimate correlations
+#'
+#' returns the correlation of 2 vectors
+#'
+#' @param vec1 The 1st vector.
+#' @param vec2 The 2nd vector. 
+#' @returns A float as correlatioin estimate
+#' @export
+#' @examples
+#' suppressMessages(source("../../dev/setup.R"))
+#' vector1 <- c(1,2,3,4,5)
+#' vector2 <- c(5.1,4.3,NA,1.9,1.2)
+#' te <- COR_FUNC(vector1,vector2)
+#' print(te)
+COR_FUNC <- function(vec1, vec2) {
+  df <- data.frame(v1 = vec1, v2 = vec2)
+  df2 <- df[complete.cases(df),]
+  return(cor(df2[,1],df2[,2]))
+}
 
 #' Levenberg Marquard fit of 4 pl
 #'
@@ -46,8 +65,9 @@ library(scales)
 #' Dat <- list()
 #' te <- Fitting_FUNC(dat, TransF)
 #' print(te)
-Fitting_FUNC <- function(ro_new, TransFlag = FALSE) {
+Fitting_FUNC <- function(ro_new, TransFlag = FALSE, nameWS ="") {
-  CORro <- cor(ro_new[, 1], ro_new[, ncol(ro_new)])
+  #browser()
+  CORro <- COR_FUNC(ro_new[, 1], ro_new[, ncol(ro_new)])
   # browser()
   all_l <- melt(data.frame(ro_new), id.vars = "log_dose", variable.name = "replname", value.name = "readout")
   isRef <- rep(c(1, 0), 1, each = nrow(all_l) / 2)
@@ -69,6 +89,7 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE) {
       },
       warning = function(e) {
         mr <<- "In nlsModel singular gradient matrix"
+        
       }
     )
     # Stop if singular gradient matrix
@@ -82,6 +103,12 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE) {
       },
       error = function(err) {
         s_mr <- NULL
+        showModal(modalDialog(
+          title = " fit",
+          paste("fit not possible: EC50 outside concentration range for dataset", nameWS),
+          easyClose = TRUE,
+          footer = NULL
+        ))
       }
     )
   } else {
@@ -98,7 +125,7 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE) {
   if (!TransFlag) {
     startlistmu <- list(
       as = min(ro_new[, 2]), bs = SLOPE, ds = max(ro_new[, 2]), cs = mean(all_l$log_dose),
-      at = min(ro_new[, 2]), bt = SLOPE, dt = max(ro_new[, 2]), r = 0
+      at = min(ro_new[, 4]), bt = SLOPE, dt = max(ro_new[, 4]), r = 0
     )
     tryCatch(
       {
@@ -120,13 +147,19 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE) {
         summary(mu)
       },
       error = function(msg) {
+        showModal(modalDialog(
+          title = "4PL fit",
+          paste("fit not possible: EC50 outside concentration range for dataset", nameWS),
+          easyClose = TRUE,
+          footer = NULL
+        ))
         return(0)
       }
     )
   } else {
     startlistmu <- list(
       as = log(min(ro_new[, 2])), bs = SLOPE, ds = log(max(ro_new[, 2])), cs = mean(all_l$log_dose),
-      at = log(min(ro_new[, 2])), bt = SLOPE, dt = log(max(ro_new[, 2])), r = 0
+      at = log(min(ro_new[, 4])), bt = SLOPE, dt = log(max(ro_new[, 4])), r = 0
     )
     tryCatch(
       {
@@ -152,17 +185,28 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE) {
       }
     )
   }
+  
+#browser()
   if (!TransFlag) {
+#browser()
+    if (length(s_mr) ==1 | length(Sum_u) ==1) { 
+      return("failed")
+      } else {
         pot_est <- exp(confintd(mr, "r", method = "asymptotic"))
         potU_est <- exp(confintd(mu, "r", method = "asymptotic"))
         PRED <- predict(mr)
         PREDu <- predict(mu)
+      }
+  } else {
+    if (length(s_mr) ==1 | length(Sum_u) ==1) { 
+      return("failed")
       }else {
         pot_est <- exp(confintd(mrT, "r", method = "asymptotic"))
         potU_est <- exp(confintd(muT, "r", method = "asymptotic"))
         PRED <- predict(mrT)
         PREDu <- predict(muT)
       }
+  }
   return(list(s_mr, Sum_u, pot_est, potU_est, PRED, PREDu))
 }
 
@@ -212,7 +256,7 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE) {
 #' p <- plotSingularity(dat)
 #' print(p)
 plotSingularity <- function(dat) { # sigmoid,det_sig,
-  CORdat <- cor(dat[, 1], dat[, ncol(dat)])
+  CORdat <- COR_FUNC(dat[, 1], dat[, ncol(dat)])
   # browser()
   all_l <- melt(data.frame(dat), id.vars = "log_dose", variable.name = "replname", value.name = "readout")
   isRef <- rep(c(1, 0), 1, each = nrow(all_l) / 2)
@@ -272,7 +316,7 @@ plotSingularity <- function(dat) { # sigmoid,det_sig,
 #' p <- plot_f(dat, TransFlag)
 #' print(p)
 plot_f <- function(dat, TransFlag = FALSE) { # sigmoid,det_sig,
-  CORdat <- cor(dat[, 1], dat[, ncol(dat)])
+  CORdat <- COR_FUNC(dat[, 1], dat[, ncol(dat)])
   # browser()
   all_l <- melt(data.frame(dat), id.vars = "log_dose", variable.name = "replname", value.name = "readout")
   isRef <- rep(c(1, 0), 1, each = nrow(all_l) / 2)
@@ -1018,7 +1062,7 @@ pot4plFUNC <- function(ro_new, PureErrFlag) {
   all_l$readout[all_l$readout < 0] <- 0.01
   all_l$readouttrans <- log(all_l$readout)
   # browser()
-  CORdat <- cor(ro_new[, 1], ro_new[, ncol(ro_new)])
+  CORdat <- COR_FUNC(ro_new[, 1], ro_new[, ncol(ro_new)])
   if (CORdat < 0) SLOPE <- -1 else SLOPE <- 1
   #
   FITs <- Fitting_FUNC(ro_new, TransFlag = FALSE)
@@ -1112,8 +1156,8 @@ ParamCI_F <- function(xt, xs, se_xt, se_xs, CoVar, DFs, Conf = 0.975) {
   var_log_xt <- (se_xt / xt)^2
   se_log_ratio <- sqrt(var_log_xs + var_log_xt) #-2*CoVar/(xs*xt)
 
-  lower_log_ratio <- log_xt - log_xs - qt(Conf, DFs) * se_log_ratio
+  lower_log_ratio <- log_xs - log_xt - qt(Conf, DFs) * se_log_ratio
-  upper_log_ratio <- log_xt - log_xs + qt(Conf, DFs) * se_log_ratio
+  upper_log_ratio <- log_xs - log_xt + qt(Conf, DFs) * se_log_ratio
   ci_ratio <- exp(c(lower_log_ratio, upper_log_ratio))
   return(ci_ratio)
 }
@@ -1143,6 +1187,9 @@ ParamCI_F <- function(xt, xs, se_xt, se_xs, CoVar, DFs, Conf = 0.975) {
 #'
 #' tests_FUNC(ro_new=dat, Lim, PureErrF)
 tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
+  
+  DatL <- list()
+  
   all_l <- melt(data.frame(ro_new), id.vars = "log_dose", variable.name = "replname", value.name = "readout")
   isRef <- rep(c(1, 0), 1, each = nrow(all_l) / 2)
   isSample <- rep(c(0, 1), 1, each = nrow(all_l) / 2)
@@ -1228,9 +1275,9 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   RSS_r <- round(sum(smr$residuals^2), 5)
   MSE_r <- RSS_r / (nrow(all_l) - 5)
   RMSE_r <- round(sqrt(MSE_r), 6)
-  Dat$RMSE_r <- RMSE_r
+  DatL$RMSE_r <- RMSE_r
-  Dat$RMSE_pure <- RMSE_pure
+  DatL$RMSE_pure <- RMSE_pure
-  Dat$RMSE_unr <- round(RMSEunr, 6)
+  DatL$RMSE_unr <- round(RMSEunr, 6)
 
   coeffs <- smu$coefficients[, 1]
   # browser()
@@ -1242,6 +1289,7 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   lCI_laDiff <- lAs_diff - qt(0.975, smu$df[2]) * sqrt(smu$coefficients["ds", 2]^2 + smu$coefficients["dt", 2]^2)
   if (uCI_laDiff < Lim[[2]] & lCI_laDiff > Lim[[1]]) test_la_diff <- 0 else test_la_diff <- 1
 
+  #browser()
   #### EQ test on upper asymptote ratio ----
   # as <- coeffs["as"]
   # at <- coeffs["at"]
@@ -1254,11 +1302,12 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   if (PureErrFlag) se_dt <- sqrt(VCOVpure["dt", "dt"]) else se_dt <- smu$coefficients["dt", 2]
   if (PureErrFlag) CoVarlog_d <- VCOVpure["dt", "ds"] else CoVarlog_d <- vcovMU["dt", "ds"]
   if (PureErrFlag) DFs <- DFsPure else DFs <- nrow(all_l) - 8
-  uAsCI2 <- ParamCI_F(dt, ds, se_dt, se_ds, CoVarlog_d, DFs, Conf = 0.975)
+  uAsCI2 <- ParamCI_F(ds, dt, se_dt, se_ds, CoVarlog_d, DFs, Conf = 0.975)
   if (uAsCI2[1] > Lim[[7]] & uAsCI2[2] < Lim[[8]]) test_a <- 0 else test_a <- 1
-  estUppA <- round(at / as, 5)
+  estUppA <- round(dt / ds, 5)
 
-  Dat$uAsCI <- uAsCI2
+  DatL$uAsCI <- uAsCI2
+# browser()
   
   #### EQ test on slope ratio ----
   # bs <- coeffs["bs"]
@@ -1271,11 +1320,11 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   if (PureErrFlag) se_bs <- sqrt(VCOVpure["bs", "bs"]) else se_bs <- smu$coefficients["bs", 2]
   if (PureErrFlag) se_bt <- sqrt(VCOVpure["bt", "bt"]) else se_bt <- smu$coefficients["bt", 2]
   if (PureErrFlag) CoVarlog_b <- VCOVpure["bt", "bs"] else CoVarlog_b <- vcovMU["bt", "bs"]
-  slopeCI2 <- ParamCI_F(bt, bs, se_bt, se_bs, CoVarlog_b, DFs, Conf = 0.975)
+  slopeCI2 <- ParamCI_F(bs, bt, se_bt, se_bs, CoVarlog_b, DFs, Conf = 0.975)
   if (slopeCI2[1] > Lim[[5]] & slopeCI2[2] < Lim[[6]]) test_b <- 0 else test_b <- 1
-  estUppA <- round(at / as, 5)
+  estSlope <- round(abs(bt) / abs(bs), 5)
 
-  Dat$slopeRatioCI <- slopeCI2
+  DatL$slopeRatioCI <- slopeCI2
 
   #### EQ test on lower As ratio ----
 
@@ -1287,11 +1336,11 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   if (PureErrFlag) se_as <- sqrt(VCOVpure["as", "as"]) else se_as <- smu$coefficients["as", 2]
   if (PureErrFlag) se_at <- sqrt(VCOVpure["at", "at"]) else se_at <- smu$coefficients["at", 2]
   if (PureErrFlag) CoVarlog_a <- VCOVpure["at", "as"] else CoVarlog_a <- vcovMU["at", "as"]
-  lAsCI2 <- ParamCI_F(at, as, se_at, se_as, CoVarlog_a, DFs, Conf = 0.975)
+  lAsCI2 <- ParamCI_F(as, at, se_at, se_as, CoVarlog_a, DFs, Conf = 0.975)
   if (lAsCI2[1] > Lim[[3]] & lAsCI2[2] < Lim[[4]]) test_d <- 0 else test_d <- 1
   estLowA <- round(at / as, 5)
 
-  Dat$lAsCI <- lAsCI2
+  DatL$lAsCI <- lAsCI2
 
   #### EQtest on ratio of As difference ----
   AsDiffRatio <- (dt - at) / (ds - as)
@@ -1305,11 +1354,11 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   if (PureErrFlag) se_ds_as <- se_ds_asPure else se_ds_as <- se_ds_asRMSE
   if (PureErrFlag) se_dt_at <- se_dt_atPure else se_dt_at <- se_dt_atRMSE
 
-  AsDiffCI2 <- ParamCI_F(dt_at, ds_as, se_dt_at, se_ds_as, CoVar = 0, DFs, Conf = 0.975)
+  AsDiffCI2 <- ParamCI_F( ds_as,dt_at, se_dt_at, se_ds_as, CoVar = 0, DFs, Conf = 0.975)
   if (AsDiffCI2[1] > Lim[[11]] & AsDiffCI2[2] < Lim[[12]]) test_ad <- 0 else test_ad <- 1
-  estLowA <- round(at / as, 5)
+  estDiffA <- round(dt_at /ds_as, 5)
 
-  Dat$up_lowAs <- abs(ds - as)
+  Dat$estDiffA <- estDiffA
 
   lowerCIlowerA <- lAsCI2[1]
   lowerCIupperA <- uAsCI2[1]
@@ -1337,8 +1386,8 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
     ),
     estimate = c(
       round(p_F_regr, 3), round(lAs_diff, 5),
-      estLowA, round(bs / bt, 5), estUppA, p_F_nonlin,
+      estLowA, estSlope, estUppA, p_F_nonlin,
-      round(dt_at / ds_as, 5), round(potAll2[1] * 100, 2), round(potAllU2[1] * 100, 2)
+      estDiffA, round(potAll2[1] * 100, 2), round(potAllU2[1] * 100, 2)
     ),
     lower_limit = c("-", Lim[[1]], Lim[[3]], Lim[[5]], Lim[[7]], "-", Lim[[11]], Lim[[9]], Lim[[9]]),
     upper_limit = c("-", Lim[[2]], Lim[[4]], Lim[[6]], Lim[[8]], "-", Lim[[12]], Lim[[10]], Lim[[10]]),

dev/app.R

@@ -28,6 +28,7 @@ library(twopartm)
 library(car)
 library(dplyr)
 library(scales)
+library(tolerance)
 
 source("../R/Global.R")
 
@@ -116,7 +117,7 @@ server <- function(input, output, session) {
           "It needs to contain 1 column with the  dilution concentrations (first or last column) and at least 2 columns of reference  and test sample readouts, respectively.",
           "The reference readout columns have to be before the test sample readout columns. The column names for reference and test are free to set, but different for all columns.",
           "The column name of the dilution concentrations can be as follows: concentration, dose, log_concentration, log_dose (first letter can be capital)",
-          "It is assumed, that the concentrations are in anti-log or in natural log mode.",
+          "If the concentrations are in logarithmized, any log base can be used.",
         ),
         column(6, )
       ),
@@ -159,10 +160,11 @@ server <- function(input, output, session) {
               ),
 
               # actionLink("selectall","SelectAll"),
-              h5("\n\n\n Author: Franz Innerbichler, InnerAnalytics")
+              #h5("\n\n\n Author: Franz Innerbichler, InnerAnalytics")
             ),
             column(
               4,
+              
               h4("Suitability tests for 4-parametric logistic regression"),
               "(potency CI test is set per default)",
               checkboxGroupInput("selectedSSTs", "Which suitability tests to be used?",
@@ -206,6 +208,11 @@ server <- function(input, output, session) {
               numericInput("uEACdiffla", "upper EAC for diff. of LA", 0.189, step = 0.001)
             )
           ),
+          
+          tabPanel(
+            "Uploaded data",
+            tableOutput("XLdata")
+          ),
           tabPanel(
             "4pl-Analysis",
             tags$style(HTML("pre { color: black; background-color: #FFE1FF;
@@ -376,7 +383,7 @@ server <- function(input, output, session) {
         mainPanel(
           width = 12,
           tabsetPanel(
-            id = "tabs",
+            id = "tabs2",
             tabPanel(
               "Settings",
               h4("Settings of 4PL regression"),
@@ -588,12 +595,15 @@ server <- function(input, output, session) {
                   fileInput("MiFile", "", accept = ".xlsx")
               ),
               sliderInput("dilslider", "Adjust the dilutions(+-change in %)", min = -100,max=100, value=0, step=1, round=0),
-              checkboxInput("fixupper","Fix highest concentration (if unticked, the center is fixed)",FALSE)
+              #checkboxInput("fixupper","Fix highest concentration (if unticked, the center is fixed)",FALSE),
+              sliderInput("dilmover", "Move the dilutions(+-move in log-units)", min = -3,max=3, value=0, step=0.1, round=1),
+              numericInput("TolConf","confidence", value=0.95, step=0.01),
+              numericInput("TolPop","population", value=0.9, step=0.01)
             )
           ),
           mainPanel(
             tabsetPanel(
-              id = "tabs",
+              id = "tabs3",
               tabPanel("4pl",
                 
 
@@ -736,17 +746,19 @@ server <- function(input, output, session) {
           if (length(logI) > 0 & length(logDoseI) == 0) {
             XLdat$log_dose <- XLdat[, logI]
             XLdat2 <- XLdat[, -logI]
-            CORro <- cor(XLdat$log_dose, XLdat[, 3])
+            CORro <- COR_FUNC(XLdat$log_dose, XLdat[, 3])
           } else if (length(logI) == 0 & length(logDoseI) == 0) {
             Ind <- grep(".ilution|.ose|.onc", cn)
             XLdat$log_dose <- log(XLdat[, Ind])
-            CORro <- cor(XLdat[, Ind], XLdat[, 3])
+            CORro <- COR_FUNC(XLdat[, Ind], XLdat[, 3])
             XLdat2 <- XLdat[, -Ind]
           } else if (length(logI) > 0 & length(logDoseI) > 0) {
             XLdat2 <- XLdat
-            CORro <- cor(XLdat[, logI], XLdat[, 3])
+            CORro <- COR_FUNC(XLdat[, logI], XLdat[, 3])
           }
           Dat$EXCEL <- XLdat2
+          output$XLdata <- renderTable({ XLdat2 })
+          
           PureErrFlag <- input$PureErr
           warning_text2 <- reactive({
             ifelse(PureErrFlag, "Pure Error is selected", "")
@@ -775,7 +787,7 @@ server <- function(input, output, session) {
 
           #### XLSX eval ----
           #if (CORro < 0) SLOPE <- -1 else SLOPE <- 1
-          FITs <- Fitting_FUNC(XLdat2, TransFlag = FALSE)
+          FITs <- Fitting_FUNC(XLdat2, TransFlag = FALSE, nameWS="")
 
           #### if no 4pl fit is possible ----
           if (!is.null(FITs)) {
@@ -2130,27 +2142,28 @@ server <- function(input, output, session) {
     for (N_WS in 1:length(AllXL)) {
    
       datWS <- as.data.frame(AllXL[[N_WS]])
-      
+      nameWS <- names(AllXL)[N_WS]
       cn <- colnames(datWS)
       logI <- grep("log|ln", cn)
       logDoseI <- grep("log_dose", cn)
       if (length(logI) > 0 & length(logDoseI) == 0) {
         datWS$log_dose <- datWS[, logI]
         datWS2 <- datWS[, -logI]
-        CORro <- cor(datWS$log_dose, datWS[, 3])
+        CORro <- COR_FUNC(datWS$log_dose, datWS[, 3])
       } else if (length(logI) == 0 & length(logDoseI) == 0) {
         Ind <- grep(".ilution|.ose|.onc", cn)
         datWS$log_dose <- log(datWS[, Ind])
-        CORro <- cor(datWS[, Ind], datWS[, 3])
+
+        CORro <- COR_FUNC(datWS[, Ind], datWS[, 3])
         datWS2 <- datWS[, -Ind]
       } else if (length(logI) > 0 & length(logDoseI) > 0) {
         datWS2 <- datWS
-        CORro <- cor(datWS[, logI], datWS[, 3])
+        CORro <- COR_FUNC(datWS[, logI], datWS[, 3])
       }
       Dat$datWS2 <- datWS2
       
-      FITs <- Fitting_FUNC(datWS2, TransFlag = F)
+      FITs <- Fitting_FUNC(datWS2, TransFlag = F, nameWS = nameWS)
-   
+      if (!is.character(FITs)) {
         pot_est <- FITs[[3]]
         potEstL[[N_WS]] <- pot_est
         potU_est <- FITs[[4]]
@@ -2178,7 +2191,7 @@ server <- function(input, output, session) {
         
         if (!exists("SIGrefDF")) SIGrefDF <- dfPlotsigRef else SIGrefDF <- rbind(SIGrefDF, dfPlotsigRef)
         if (!exists("SIGtestDF")) SIGtestDF <- dfPlotsigTest else SIGtestDF <- rbind(SIGtestDF,dfPlotsigTest)
-
+      }
     } #for N_WS
     
     #browser()   
@@ -2211,11 +2224,15 @@ server <- function(input, output, session) {
     
     #browser()
     BoxDF <- data.frame(EC50REF = EC50REF, EC50TEST = EC50TEST, LasREF = LasREF, UasREF = UasREF)
+    UasParTolREF <- normtol.int(x = UasREF, alpha = 1-input$TolConf, P = input$TolPop, side = 2)
+    LasParTolREF <- normtol.int(x = LasREF, alpha = 1-input$TolConf, P = input$TolPop, side = 2)
     
     p1 <- ggplot(SIGrefDF, aes(x=X, y=sigRef, col=as.factor(Sheet))) +
       geom_line() +
       annotate("text", label="x", x=x_UA, y=UasREF, alpha=0.2) +
       annotate("text", label="o", x=x_LA, y=LasREF, alpha=0.2) +
+      geom_hline(yintercept = c(UasParTolREF[[4]], UasParTolREF[[5]]), linetype=2, col="grey") +
+      geom_hline(yintercept = c(LasParTolREF[[4]], LasParTolREF[[5]]), linetype=2, col="grey") +
       geom_vline(xintercept = EC50REF, alpha = 0.2) +
       scale_x_continuous(expand = c(0, 0)) + 
       scale_y_continuous(expand = c(0, 0)) + 
@@ -2229,70 +2246,19 @@ server <- function(input, output, session) {
       theme(axis.text = element_text(face = "bold", size = 15),
             plot.title = element_text(size = 15, face = "bold"),
             plot.margin = unit(c(0.2, 0.2, 0.5, 0.5), "lines"))
-    # Horizontal marginal boxplot - to appear at the top of the chart
-    pBox_hor <- ggplot( BoxDF, aes(x = factor(1), y = EC50REF)) + 
-      geom_boxplot(outlier.colour = NA) + 
-      geom_jitter(position = position_jitter(width = 0.05)) + 
-      scale_y_continuous(expand = c(0, 0)) + 
-      expand_limits(y = c(min(SIGrefDF$X) - 0.1 * diff(range(SIGrefDF$X)), 
-                          max(SIGrefDF$X) + 0.1 * diff(range(SIGrefDF$X)))) + 
-      coord_flip() + 
-      theme_bw() +
-      theme(axis.text = element_blank(), 
-            axis.title = element_blank(), 
-            axis.ticks = element_blank(), 
-            plot.margin = unit(c(1, 0.2, -0.5, 0.5), "lines"))
-    
-    # Vertical marginal boxplot - to appear at the right of the chart
-    pBox_ver <- ggplot(BoxDF, aes(x = factor(1), y = UasREF)) + 
-      geom_boxplot(outlier.colour = NA) + 
-      geom_jitter(position = position_jitter(width = 0.05)) + 
-      scale_y_continuous(expand = c(0, 0)) + 
-      expand_limits(y = c(min(SIGrefDF$sigRef) - 0.1 * diff(range(SIGrefDF$sigRef)), 
-                          max(SIGrefDF$sigRef) + 0.1 * diff(range(SIGrefDF$sigRef)))) + 
-      theme_bw() +
-      theme(axis.text = element_blank(), 
-            axis.title = element_blank(), 
-            axis.ticks = element_blank(), 
-            plot.margin = unit(c(0.2, 1, 0.5, -0.5), "lines"))
-    
-    #browser()
-    gt1 <- ggplot_gtable(ggplot_build(p1))
-    gt2 <- ggplot_gtable(ggplot_build(pBox_hor))
-    gt3 <- ggplot_gtable(ggplot_build(pBox_ver))
-    
-    # Get maximum widths and heights
-    maxWidth <- unit.pmax(gt1$widths[2:3], gt2$widths[2:3])
-    maxHeight <- unit.pmax(gt1$heights[4:5], gt3$heights[4:5])
-    
-    # Set the maximums in the gtables for gt1, gt2 and gt3
-    gt1$widths[2:3] <- as.list(maxWidth)
-    gt2$widths[2:3] <- as.list(maxWidth)
-    
-    gt1$heights[4:5] <- as.list(maxHeight)
-    gt3$heights[4:5] <- as.list(maxHeight)
-    # Create a new gtable
-    gt <- gtable(widths = unit(c(7, 1), "null"), height = unit(c(1, 7), "null"))
-    
-    # Instert gt1, gt2 and gt3 into the new gtable
-    gt <- gtable_add_grob(gt, gt1, 2, 1)
-    gt <- gtable_add_grob(gt, gt2, 1, 1)
-    gt <- gtable_add_grob(gt, gt3, 2, 2)
-    
-    # grid.rect(x = 0.5, y = 0.5, height = 0.995, width = 0.995, default.units = "npc", 
-    #           gp = gpar(col = "black", fill = NA, lwd = 1))
-    # And render the plot
-    grid.newpage()
-#browser()
-    
-    output$sigPlotREF <- renderPlot({ grid.draw(gt) })
     
+    output$sigPlotREF <- renderPlot({ p1 })
     Dat$sigPlotREF <- p1
-    # 
+    
+    UasParTolTEST <- normtol.int(x = UasTEST, alpha = 1-input$TolConf, P = input$TolPop, side = 2)
+    LasParTolTEST <- normtol.int(x = LasTEST, alpha = 1-input$TolConf, P = input$TolPop, side = 2)
+   
     p2 <- ggplot(SIGtestDF, aes(x=X, y=sigTest, col=as.factor(Sheet))) +
       geom_line() +
       annotate("text", label="x", x=x_UA, y=UasTEST, alpha=0.2) +
+      geom_hline(yintercept = c(UasParTolTEST[[4]], UasParTolTEST[[5]]), linetype=2, col="grey") +
       annotate("text", label="o", x=x_LA, y=LasTEST, alpha=0.2) +
+      geom_hline(yintercept = c(LasParTolTEST[[4]], LasParTolTEST[[5]]), linetype=2, col="grey") +
       geom_vline(xintercept = EC50TEST, alpha = 0.2) +
       xlab("dilutions") +
       ggtitle("Calculated test sample fits (unrestricted model, in gray vertical lines: EC50)") +
@@ -2354,23 +2320,23 @@ server <- function(input, output, session) {
     dils <- tab$log_dose
     min_y <- min(tab[, 1:2])
     max_y <- max(tab[, 1:2])
-
+#browser()
-    if (input$fixupper) {
+    # if (input$fixupper) {
-      dils_av <- dils - max(dils)
+    #   dils_av <- dils - max(dils)
-      dils_av_ <- dils_av * (input$dilslider / 100 + 1)
+    #   dils_av_ <- dils_av * (input$dilslider / 100 + 1) + input$dilmover
-      dils2 <- round(dils_av_ + max(dils), 4)
+    #   dils2 <- round(dils_av_ + max(dils), 4)
-      dilfactors <- 1 / exp(dils2 - lag(dils2))
+    #   dilfactors <- 1 / exp(dils2 - lag(dils2))
-    } else {
+    # } else {
       if (!is.null(EC50TEST)) {
         av <- mean(EC50TEST, na.rm = TRUE)
       } else {
         av <- (min(dils) + max(dils)) / 2
       }
       dils_av <- dils - av
-      dils_avsc <- dils_av * (input$dilslider / 100 + 1)
+      dils_avsc <- dils_av * (input$dilslider / 100 + 1) + input$dilmover
       dils2 <- dils_avsc + av
       dilfactors <- 1 / exp(dils2 - lag(dils2))
-    }
+    #}
  
     
    Dat$newDils <- dils2