layout of explore & optimize

R/Global.R

@@ -23,26 +23,6 @@ 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),]
-  #browser()
-  return(cor(df2[,1],df2[,2]))
-}
 
 #' Levenberg Marquard fit of 4 pl
 #'
@@ -66,9 +46,8 @@ COR_FUNC <- function(vec1, vec2) {
 #' Dat <- list()
 #' te <- Fitting_FUNC(dat, TransF)
 #' print(te)
-Fitting_FUNC <- function(ro_new, TransFlag = FALSE, nameWS ="") {
+Fitting_FUNC <- function(ro_new, TransFlag = FALSE) {
-  #browser()
+  CORro <- cor(ro_new[, 1], ro_new[, ncol(ro_new)])
-  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)
@@ -90,7 +69,6 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE, nameWS ="") {
       },
       warning = function(e) {
         mr <<- "In nlsModel singular gradient matrix"
-        
       }
     )
     # Stop if singular gradient matrix
@@ -104,12 +82,6 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE, nameWS ="") {
       },
       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 {
@@ -126,7 +98,7 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE, nameWS ="") {
   if (!TransFlag) {
     startlistmu <- list(
       as = min(ro_new[, 2]), bs = SLOPE, ds = max(ro_new[, 2]), cs = mean(all_l$log_dose),
-      at = min(ro_new[, 4]), bt = SLOPE, dt = max(ro_new[, 4]), r = 0
+      at = min(ro_new[, 2]), bt = SLOPE, dt = max(ro_new[, 2]), r = 0
     )
     tryCatch(
       {
@@ -148,19 +120,13 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE, nameWS ="") {
         summary(mu)
       },
       error = function(msg) {
-        showModal(modalDialog(
-          title = "4PL fit",
-          paste("fit not possible: EC50 outside concentration range for dataset", nameWS),
-          easyClose = TRUE,
-          footer = NULL
-        ))
         return(0)
       }
     )
   } else {
     startlistmu <- list(
       as = log(min(ro_new[, 2])), bs = SLOPE, ds = log(max(ro_new[, 2])), cs = mean(all_l$log_dose),
-      at = log(min(ro_new[, 4])), bt = SLOPE, dt = log(max(ro_new[, 4])), r = 0
+      at = log(min(ro_new[, 2])), bt = SLOPE, dt = log(max(ro_new[, 2])), r = 0
     )
     tryCatch(
       {
@@ -186,28 +152,17 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE, nameWS ="") {
       }
     )
   }
-  
-#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))
 }
 
@@ -257,7 +212,7 @@ Fitting_FUNC <- function(ro_new, TransFlag = FALSE, nameWS ="") {
 #' p <- plotSingularity(dat)
 #' print(p)
 plotSingularity <- function(dat) { # sigmoid,det_sig,
-  CORdat <- COR_FUNC(dat[, 1], dat[, ncol(dat)])
+  CORdat <- cor(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)
@@ -317,7 +272,7 @@ plotSingularity <- function(dat) { # sigmoid,det_sig,
 #' p <- plot_f(dat, TransFlag)
 #' print(p)
 plot_f <- function(dat, TransFlag = FALSE) { # sigmoid,det_sig,
-  CORdat <- COR_FUNC(dat[, 1], dat[, ncol(dat)])
+  CORdat <- cor(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)
@@ -711,7 +666,6 @@ ANOVAlintests <- function(ro_new, circles, Lim, PureErrFlag) {
   all_l$isRef <- isRef
   all_l$isSample <- isSample
   all_l$Conc <- exp(all_l$log_dose)
-  all_l <- all_l[complete.cases(all_l),]
   all_lA <- all_l[all_l$isSample == 1, ] # TEST
   all_lB <- all_l[all_l$isSample == 0, ] # REF
   # browser()
@@ -799,40 +753,40 @@ ANOVAlintests <- function(ro_new, circles, Lim, PureErrFlag) {
   }
 
   # treatment
-  SStreat <- print(sum((predict(lm(readout ~ factor(log_dose) * isSample, circ_ABl)) - mean(circ_ABl$readout, na.rm = T))^2, na.rm = T))
+  SStreat <- print(sum((predict(lm(readout ~ factor(log_dose) * isSample, circ_ABl)) - mean(circ_ABl$readout))^2))
   F_treat <- (SStreat / dfTreat) / (SSRes / dfRes)
   # Preparation
-  SSprep <- print(sum((predict(lm(readout ~ isSample, circ_ABl)) - mean(circ_ABl$readout, na.rm = T))^2, na.rm = T))
+  SSprep <- print(sum((predict(lm(readout ~ isSample, circ_ABl)) - mean(circ_ABl$readout))^2))
   F_prep <- (SSprep / dfTreat) / (SSRes / dfRes)
   # Regression
   # ANOVA tape II SS of regression
   SSreg <- Anova(lm(readout ~ log_dose + isSample, circ_ABl))[1, 1]
   # Non-parallelism
   # diff of RSS of restricted and unrestricted model
-  SSnonpar <- sum(resid(modAB)^2, na.rm = T) - sum(resid(modABu)^2, na.rm = T)
+  SSnonpar <- sum(resid(modAB)^2) - sum(resid(modABu)^2)
-  F_nonpar <- SSnonpar / (sum(resid(lm(readout ~ factor(log_dose) * isSample, circ_ABl))^2, na.rm = T) / (lenCirc - 4))
+  F_nonpar <- SSnonpar / (sum(resid(lm(readout ~ factor(log_dose) * isSample, circ_ABl))^2) / (lenCirc - 4))
 
   # non-linearity
-  SSnonlin <- sum((predict(modABu) - predict(lm(readout ~ as.factor(log_dose) * isSample, circ_ABl)))^2, na.rm = T)
+  SSnonlin <- sum((predict(modABu) - predict(lm(readout ~ as.factor(log_dose) * isSample, circ_ABl)))^2)
   # = RSS-SSE
   # Total SS
-  SStot <- sum((circ_ABl$readout - mean(circ_ABl$readout, na.rm = T))^2, na.rm=T)
+  SStot <- sum((circ_ABl$readout - mean(circ_ABl$readout))^2)
   # Significance of R^2 F-ratio
   # MSR/MSE
   # sample A
-  F_R2_A <- sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Al)) - mean(predict(modA), na.rm = T))^2 - (predict(modA) - mean(circ_Al$readout, na.rm = T))^2, na.rm = T) /
+  F_R2_A <- sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Al)) - mean(predict(modA)))^2 - (predict(modA) - mean(circ_Al$readout))^2) /
-    (sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Al)) - circ_Al$readout)^2, na.rm = T) / (nrow(circ_Al) - 3))
+    (sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Al)) - circ_Al$readout)^2) / (nrow(circ_Al) - 3))
   pFR2_A <- round(pf(F_R2_A, 1, 6), 4)
   # sample B
-  F_R2_B <- sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Bl)) - mean(predict(modB), na.rm = T))^2 - (predict(modB) - mean(circ_Bl$readout))^2, na.rm = T) /
+  F_R2_B <- sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Bl)) - mean(predict(modB)))^2 - (predict(modB) - mean(circ_Bl$readout))^2) /
-    (sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Bl)) - circ_Bl$readout)^2, na.rm = T) / (nrow(circ_Bl) - 3))
+    (sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Bl)) - circ_Bl$readout)^2) / (nrow(circ_Bl) - 3))
   pFR2_B <- round(pf(F_R2_B, 1, 6), 4)
   # sign of non-lin with pure error: MSSnonlin/MSSE
   F_nonlin <- (SSnonlin / 2) / (SSE / dfPureE)
 
   # sign of slope
-  F_slope_B <- sum((predict(modB) - mean(circ_Bl$readout, na.rm = T))^2) / (sum((circ_Bl$readout - predict(modB))^2, na.rm = T) / (nrow(circ_Bl) - 2))
+  F_slope_B <- sum((predict(modB) - mean(circ_Bl$readout))^2) / (sum((circ_Bl$readout - predict(modB))^2) / (nrow(circ_Bl) - 2))
-  F_slope_A <- sum((predict(modA) - mean(circ_Al$readout, na.rm = T))^2) / (sum((circ_Al$readout - predict(modA))^2, na.rm = T) / (nrow(circ_Al) - 2))
+  F_slope_A <- sum((predict(modA) - mean(circ_Al$readout))^2) / (sum((circ_Al$readout - predict(modA))^2) / (nrow(circ_Al) - 2))
   # F-test on regression: MSSreg/MSSE
   if (is.na(F_nonlin)) F_nonlin <- 0
   if (F_nonlin > 0) {
@@ -945,12 +899,11 @@ PlotLinPLA_FUNC <- function(circle, sigmoid, all_l2, pl_df, indS, indT) {
   }
 
 
-
   p <- ggplot(all_l2, aes(x = log_dose, y = readout, color = factor(isRef))) +
     geom_point(size = 2) +
     # labs(title=paste("linear regression model", indS,indT), color="product") +
     scale_colour_manual(labels = c("test", "reference"), values = c("#C2173F", "#4545BA")) +
-    
+    ylim(min(all_l2$readout), max(all_l2$readout)) +
     scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
     scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
     theme_bw()
@@ -984,7 +937,6 @@ PlotLinPLA_FUNC <- function(circle, sigmoid, all_l2, pl_df, indS, indT) {
     x = log_dose, y = readout, shape = factor(isRef),
     size = 5, alpha = 0.2
   ), col = c("black"), inherit.aes = FALSE) +
-    ylim(min(all_l2$readout), max(all_l2$readout)) +
     scale_shape_manual(labels = c("test", "reference"), values = c(21, 21))
   # fit intercept for test and ref and common slope
 
@@ -1021,7 +973,6 @@ PlotLinPLA_FUNC <- function(circle, sigmoid, all_l2, pl_df, indS, indT) {
       title = paste("restricted linear regression model"),
       subtitle = paste("Regression on highlighted points")
     ) +
-    ylim(min(all_l2$readout), max(all_l2$readout)) +
     theme(legend.position = "none", axis.text = element_text(size = 14))
   pr3 <- pr2 + geom_point(circle, mapping = aes(
     x = log_dose, y = readout, shape = factor(isRef),
@@ -1067,7 +1018,7 @@ pot4plFUNC <- function(ro_new, PureErrFlag) {
   all_l$readout[all_l$readout < 0] <- 0.01
   all_l$readouttrans <- log(all_l$readout)
   # browser()
-  CORdat <- COR_FUNC(ro_new[, 1], ro_new[, ncol(ro_new)])
+  CORdat <- cor(ro_new[, 1], ro_new[, ncol(ro_new)])
   if (CORdat < 0) SLOPE <- -1 else SLOPE <- 1
   #
   FITs <- Fitting_FUNC(ro_new, TransFlag = FALSE)
@@ -1161,8 +1112,8 @@ 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_xs - log_xt - qt(Conf, DFs) * se_log_ratio
+  lower_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
+  upper_log_ratio <- log_xt - log_xs + qt(Conf, DFs) * se_log_ratio
   ci_ratio <- exp(c(lower_log_ratio, upper_log_ratio))
   return(ci_ratio)
 }
@@ -1192,9 +1143,6 @@ 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)
@@ -1202,7 +1150,6 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   all_l$isSample <- isSample
   all_l$Conc <- exp(all_l$log_dose)
   all_l$readout[all_l$readout < 0] <- 0.01
-  all_l <- all_l[complete.cases(all_l),]
   # browser()
   FITs <- Fitting_FUNC(ro_new = ro_new, TransFlag = FALSE)
   if (is.character(FITs)) {
@@ -1225,7 +1172,7 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   VCOVpure <- V_V * meanPureErr
   DFsPure <- FitAnova[4, 1]
 
-#browser()
+
   testPOTr <- logical()
   if (POTr_CI[1] * 100 > Lim[[9]] & POTr_CI[2] * 100 < Lim[[10]]) testPOTr <- 0 else testPOTr <- 1
 
@@ -1241,19 +1188,19 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   noConc <- length(unique(all_l$Conc))
   nofitted <- noConc
   AnovaDFs <- c(nofitted - 1, 1, 3, nofitted - 4 - 1, nrow(all_l) - nofitted, nofitted, nrow(all_l) - 2 * nofitted, nrow(all_l) - 1)
-  SStreat <- round(sum((predPotU - mean(all_l$readout, na.rm = T))^2, na.rm = T), 5)
+  SStreat <- round(sum((predPotU - mean(all_l$readout))^2), 5)
-  SSregr <- round(sum((predPot - mean(all_l$readout, na.rm=T))^2, na.rm=T), 5)
+  SSregr <- round(sum((predPot - mean(all_l$readout))^2), 5)
   # non-parallelism
-  SSnonparall <- round(sum(smr$residuals^2, na.rm=T) - sum(smu$residuals^2, na.rm=T), 5)
+  SSnonparall <- round(sum(smr$residuals^2) - sum(smu$residuals^2), 5)
-  SSprep <- round(sum((predict(lm(readout ~ isSample, all_l)) - mean(all_l$readout, na.rm=T))^2, na.rm=T), 5)
+  SSprep <- round(sum((predict(lm(readout ~ isSample, all_l)) - mean(all_l$readout))^2), 5)
-# browser()
+
-  RSS <- round(sum(smu$residuals^2, na.rm=T), 5)
+  RSS <- round(sum(smu$residuals^2), 5)
   RSS_df <- AnovaDFs[5]
   MSEunr <- RSS / RSS_df
   RMSEunr <- sqrt(RSS / RSS_df)
   # Pure Err
   FitAnova <- anova(lm(readout ~ factor(Conc) * isSample, all_l))
-  SSE <- sum(resid(lm(readout ~ factor(Conc) * isSample, all_l))^2, na.rm=T) # =FitAnova[4,2]
+  SSE <- sum(resid(lm(readout ~ factor(Conc) * isSample, all_l))^2) # =FitAnova[4,2]
   SSE_df <- FitAnova[4, 1]
   PureMSE <- SSE / SSE_df
   RMSE_pure <- sqrt(PureMSE)
@@ -1278,12 +1225,12 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
 
   test_a <- test_b <- test_d <- test_ad <- logical()
 
-  RSS_r <- round(sum(smr$residuals^2, na.rm=T), 5)
+  RSS_r <- round(sum(smr$residuals^2), 5)
   MSE_r <- RSS_r / (nrow(all_l) - 5)
   RMSE_r <- round(sqrt(MSE_r), 6)
-  DatL$RMSE_r <- RMSE_r
+  Dat$RMSE_r <- RMSE_r
-  DatL$RMSE_pure <- RMSE_pure
+  Dat$RMSE_pure <- RMSE_pure
-  DatL$RMSE_unr <- round(RMSEunr, 6)
+  Dat$RMSE_unr <- round(RMSEunr, 6)
 
   coeffs <- smu$coefficients[, 1]
   # browser()
@@ -1295,7 +1242,6 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
   lCI_laDiff <- lAs_diff - qt(0.975, smu$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"]
@@ -1308,12 +1254,11 @@ 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(ds, dt, se_dt, se_ds, CoVarlog_d, DFs, Conf = 0.975)
+  uAsCI2 <- ParamCI_F(dt, ds, 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(dt / ds, 5)
+  estUppA <- round(at / as, 5)
 
-  DatL$uAsCI <- uAsCI2
+  Dat$uAsCI <- uAsCI2
-# browser()
 
   #### EQ test on slope ratio ----
   # bs <- coeffs["bs"]
@@ -1326,11 +1271,11 @@ 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(bs, bt, se_bt, se_bs, CoVarlog_b, DFs, Conf = 0.975)
+  slopeCI2 <- ParamCI_F(bt, bs, 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
-  estSlope <- round(abs(bt) / abs(bs), 5)
+  estUppA <- round(at / as, 5)
 
-  DatL$slopeRatioCI <- slopeCI2
+  Dat$slopeRatioCI <- slopeCI2
 
   #### EQ test on lower As ratio ----
 
@@ -1342,11 +1287,11 @@ 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(as, at, se_at, se_as, CoVarlog_a, DFs, Conf = 0.975)
+  lAsCI2 <- ParamCI_F(at, as, 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)
 
-  DatL$lAsCI <- lAsCI2
+  Dat$lAsCI <- lAsCI2
 
   #### EQtest on ratio of As difference ----
   AsDiffRatio <- (dt - at) / (ds - as)
@@ -1360,11 +1305,11 @@ 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( ds_as,dt_at, se_dt_at, se_ds_as, CoVar = 0, DFs, Conf = 0.975)
+  AsDiffCI2 <- ParamCI_F(dt_at, ds_as, 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
-  estDiffA <- round(dt_at /ds_as, 5)
+  estLowA <- round(at / as, 5)
 
-  Dat$estDiffA <- estDiffA
+  Dat$up_lowAs <- abs(ds - as)
 
   lowerCIlowerA <- lAsCI2[1]
   lowerCIupperA <- uAsCI2[1]
@@ -1392,8 +1337,8 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
     ),
     estimate = c(
       round(p_F_regr, 3), round(lAs_diff, 5),
-      estLowA, estSlope, estUppA, p_F_nonlin,
+      estLowA, round(bs / bt, 5), estUppA, p_F_nonlin,
-      estDiffA, round(potAll2[1] * 100, 2), round(potAllU2[1] * 100, 2)
+      round(dt_at / ds_as, 5), 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]]),
@@ -1430,10 +1375,7 @@ tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
 #' ANOVA4plUnresfunc(ro_new)
 #' 
 ANOVA4plUnresfunc <- function(ro_new) {
-  
   all_l <- melt(data.frame(ro_new), id.vars = "log_dose", variable.name = "replname", value.name = "readout")
-  
-  #browser()
   all_len <- nrow(all_l)
   isRef <- rep(c(1, 0), 1, each = all_len / 2)
   isSample <- rep(c(0, 1), 1, each = all_len / 2)
@@ -1441,8 +1383,6 @@ ANOVA4plUnresfunc <- function(ro_new) {
   all_l$isSample <- isSample
   all_l$Conc <- exp(all_l$log_dose)
   all_l$readout[all_l$readout < 0] <- 0.01
-  all_l <- all_l[complete.cases(all_l),]
-  
 
   FITs <- Fitting_FUNC(ro_new = ro_new, TransFlag = FALSE)
   smr <- FITs[[1]]

dev/BioassayReportROUT.Rmd

@@ -1,335 +0,0 @@
----
-output:
-  pdf_document:
-    extra_dependencies: ["float"]
-    number_sections: true
-    toc: true
-    toc_depth: 3
-header_includes:
-  -\usepackage{fancyheadr}
-  -\setlength{\headheight}{22pt}%
-  -\usepackage{lastpage}
-  -\pagestyle{fancy}
-  -\usepackage{pdflscape}
-  -\usepackage{longtable}
-  -\rhead{\includegraphics[width=.15\textwidth]{`r getwd()`/logov2.png}}
-params:
-  FileName: NA
-  author: NA
-  NoP: NA
-  Assay: NA
-  REP: NA
-  coeffs: NA
-author: "Author: `r params$author`"
-title: |
-    | ![](logov2.png){width=1in}
-    | 4PL bioassay evaluation
-subtitle: |
-  `r params$FileName`
-  
-  <left> Unique time: </left> <right> `r Sys.time()`</right>
-date: "`r paste(params$NoP, params$Assay)`"
-
----
-
-<!-- \fancyfoot[C]{\thepage\ of \pageref{LastPage}} -->
-<!-- \newpage -->
-
-<!-- \newpage -->
-
-```{r setup, include=FALSE}
-
-knitr::opts_chunk$set(echo = TRUE)
-
-library(knitr)
-library(DT)
-library(kableExtra)
-
-REP <- params$REP
-author <- params$author
-coeffs <- params$coeffs
-
-all_l <- REP$all_l
-#ANOVAXLS <- REP$ANOVAXLS
-#XLplot4pl <- REP$XLplot4pl
-DiagnTable <- REP$DiagnTable
-UnRPLAausw <- REP$UnRPLAausw
-UnRPLBend <- REP$UnRPLBend
-PLAausw <- REP$PLAausw
-PLbend <- REP$PLBend
-pottab4plXL <- REP$pottab4plXL
-Lim <- REP$Lim
-XLdat2 <- REP$XLdat2
-PureErr <- REP$PureErr
-ro_newROUT <- REP$ro_newROUT
-ROUTplot <- REP$ROUTplot
-ANOVA_ROUT <- REP$ANOVA_ROUT
-
-CIplot <- REP$CIplot
-testsTabROUT <- REP$testsTabROUT
-relpotTestPlot <- REP$relpotTestPlot
-
-#browser()
-
-```
-
-
-# Introduction
-
-Bioassay potency estimation uses statistical methods to quantify the strength of a biological product or drug by comparing its response to that of a reference standard. Because biological responses are inherently variable, affected by assay conditions, cell systems or organisms, and measurement noise, the 4-parametric logistic regression is used to obtain reliable potency values.  
-USP<1034> recommends calculation of standard errors of ratios of the parameters using Fieller's theorem [1] or using the "delta" method (for a discussion about the "delta" method see [3]). The gradient approach using the differences on the log-scale is mathematically more stable und thus preferable compared to a ratio approach [2].
-
-# Raw data
-
-All data used for the 4PL evaluation is shown in table 1:
-
-```{r alll, echo=FALSE, warning=FALSE, results='asis'}
-
-kable(XLdat2, format = "markdown", caption= "Uploaded data (test and reference) ", digits=3)
-
-if (!is.null(ro_newROUT)) {
-  kable(ro_newROUT, format = "markdown", caption= "Data after exclusion of suspected outliers (see [6]) ", digits=3)
-}
-
-```
-
-
-# Results
-
-## Overall result
-
-```{r Over_all, echo=FALSE, comment=NA, warning=NA, message=NA}
-
-browser()
-potFlag <- 0
-if (pottab4plXL["test_result"][[1]][1]=="failed") potFlag <- 1
-AnalysisFlag <- FALSE
-if (potFlag==1 | sum(testsTabROUT$test_results)>0) AnalysisFlag <- TRUE
-
-colFmt <- function() {
-  
-  outputFormat <- knitr::opts_knit$get("rmarkdown.pandoc.to")
- if(AnalysisFlag) {
-    text <- paste("\\textcolor{red}{Analysis failed}",sep="")
-  } else  {
-    text <- paste("\\textcolor{black}{Analysis succeeded}",sep="")
-  }  
-  return(text)
-}
-
-
-```
-
-`r colFmt()`
-
-
-## 4pl-regression
-
-Relative potency (absolute and relative confidence limits) are shown in Table 3. `r if(PureErr) {"Pure Error is used for calculations."}` 
-`r if (!PureErr) {"RMSE of restricted model is used for confidence limit calculation."}`
-
-```{r Pot_tab4pl, echo=FALSE, comment=NA, warning=NA, message=NA}
-
-#browser()
-if (pottab4plXL["test_result"][[1]][1]==1) { cat(paste("FAILED: relative potency CL result of restricted model outside limits: ", Lim[[9]], "to" ,Lim[[10]] ))}
-if (pottab4plXL["test_result"][[1]][1]==0) { cat(paste("PASSED: relative potency CL result of restricted model within limits: ", Lim[[9]], "to" ,Lim[[10]] ))}
-kable(pottab4plXL, format = "markdown", caption= "Relative potency with absolute and relative CLs ", digits=3, row.names = F) %>%
-  kable_styling(latex_options = "hold_position")
-
-
-```
-NOTE: results of unrestricted model for Information only.
-
-
-## Plot of the data and models
-
-Plots in Figure 1 shows the restricted model.
-
-
-```{r XLplot, echo=FALSE, warning=FALSE, fig.height=4, fig.width=6, fig.cap="Plot of models", fig.align='left', comment=F, message=F, results='asis', fig.pos='H'}
-
-plot(ROUTplot)
-
-
-```
-
-
-## ANOVA table
-
-The ANOVA of the unconstrained model is listed in table 4. Bates and Watts [4] proposed a test on parallelism which compares the residual sum of squares of the restricted model (ResRSSE) with the residual sum of squares of the unrestricted model (UnresRSSE). If the UnresRSSE is significantly smaller than the ResRSSE, the p-value of "Non-parallelism" is smaller than 0.05 (line 4 in table 4). This test is for information only as it may be overly sensitive in case of small overall variability of the data. 
-
-```{r anovaxls, echo=FALSE, warning=FALSE, results='asis'}
-
-kable(ANOVA_ROUT, format = "markdown", caption= "Analysis of variance", digits=3) %>%
-  kable_styling(latex_options = "hold_position")
-
-
-```
-
-## Assay suitability tests
-
-Table 5 lists the chosen suitability test results with confidence limits, where applicable. F-tests should be read with caution, if the overall variability is small, as the test gets overly sensitive.
-
-
-```{r SST_ergebn, echo=FALSE, cache=FALSE, warning=FALSE, message=FALSE, tidy=TRUE}
-
-kable(testsTabROUT, row.names = F, format = "markdown", caption="Assay suitability results", digits=4)
-
-
-```
-
-
-\footnotesize
-
-```{r Fussnote, echo=F, comment=NA}
-
-cat("*...The estimate for F-test on regression and on non-linearity is the p-value")
-cat(  "F-test on regression passes if F-value > F-crit and thus p < 0.05")
-cat(  "F-test on non-linearity passes if F-value < F-crit and thus p > 0.05")
-cat(  "Test results outcome:")
-cat("  0 ... test passed (for EQ tests: CL within limits);")
-cat("  1 ... test failed (for EQ tests: CL not within limits);")
-
-
-  
-```
-
-\normalsize
-
-
-```{r AST_Ergebn, echo=FALSE, cache=FALSE, warning=FALSE, message=FALSE, tidy=TRUE}
-
-TestsTabFlag <- FALSE
-if (sum(testsTabROUT$test_results)>0) TestsTabFlag <- TRUE
-colFmt2 <- function() {
-  
-  outputFormat <- knitr::opts_knit$get("rmarkdown.pandoc.to")
- if(TestsTabFlag) {
-    text <- paste("\\textcolor{red}{Assay suitability tests failed}",sep="")
-  } else  {
-    text <- paste("\\textcolor{black}{Assay suitability tests succeeded}",sep="")
-  }  
-  return(text)
-}
-
-
-```
-
-
-`r colFmt2()`
-
-
-
-
-## Fitting results with curve points
-
-The results of the non-linear fitting procedure for the restricted model (5 parameters) is listed in table 5:
-
-```{r PLAausw, echo=FALSE, warning=FALSE, results='asis'}
-
-kable(PLAausw, format = "markdown", caption= "Restricted 4PL model", digits=3, row.names = F)
-
-
-```
-
-
-
-Sebaugh et al proposed bend points for test and reference samples, that define the points with highest turning behavior. Table 6 lists these bendpoints as well as asymptote points ~ twice as far from the center as the bendpoints.
-
-```{r PLBend, echo=FALSE, warning=FALSE, results='asis'}
-
-kable(PLbend, format = "markdown", caption= "Bendpoints and asymptote points of restricted 4PL model", digits=3)
-
-
-```
-
-The results of the non-linear fitting procedure for the unrestricted model (8 parameters) is listed in table 7:
-
-```{r UnRPLAausw, echo=FALSE, warning=FALSE, results='asis'}
-
-kable(UnRPLAausw, format = "markdown", caption= "Unrestricted 4PL model", digits=3, row.names = F)
-
-```
-
-
-
-# Signature
-
-
-\vspace{1.5cm} 
-\noindent
-\begin{tabular}{p{6cm}p{1cm}p{6cm}}
-  \cline{1-1} \cline{3-3}
-  Date & & Signature 
-\end{tabular}
-
-
-\newpage
-
-# Appendix: Formulas
-
-## 4PL regression
-
-$$
-Y = D + \frac{A-D} {1+(\frac{C} {x})^B } + \epsilon
-$$
-
-where: x ... concentration of the analyte
-
-A: upper asymptote
-
-B: slope
-
-D: lower asymptote
-
-C ... EC50
-
-
-## log-logistic 4P regression
-
-$$
-Y = D + \frac{A-D} {1+e^{(B*(C - log(x))) }} + \epsilon
-$$
-
-
-
-## Intercept for slope at EC50
-
-$$
-I = A+\frac{D-A}{2}-B_{true}*EC50
-$$
-
-## Slope at EC50
-
-$$
-B_{true}=B*\frac{D-A}{4}
-$$
-
-## Confidence intervals
-
-In general, the confidence intervals are calculated as follows:
-$$
- CI = \hat\theta\pm se(\hat\theta)*q^{t_{n-p}}_{1-\frac{\alpha}{2}}
-$$
-…where $\hat\theta$ is a fitted parameter or a linear combination thereof, q is the 1-alpha/2 quantile of the Student’s t-distribution with n-p degrees of freedom and se is the standard error derived from any covariance matrix.
-
-Let $\theta$ be the 4+1 parameters of the fit (a, b, d, EC50 of reference and EC50 difference). It can be shown that the least squares estimator $\hat\theta$ is normally distributed with asymptotic covariance matrix. The gradient method provides one of several ways to calculate the covariance matrix:
-
-$$
-\hat{V(\theta)}= \sigma^2(A(\hat\theta)^T*A(\hat\theta))^{-1}
-$$
-where A($\theta$) is the n x p matrix of the first partial derivatives for each parameter (i.e. gradient) realized at the fitted parameter estimates. The RMSE of the model or the pure error is used as estimate of $\sigma$. The square root of the diagonals of $\hat{V(\theta)}$ gives the standard errors and with that confidence intervals (CI) can be computed.
-
-# Literature
-
-[1] Finney, D.J.: (1978) Statistical Method in Biological Assay, London: Charles Griffin House, 3rd edition (pp. 80-82)
-
-[2] Franz, V.H.: Ratios: A short guide to confidence limits and proper use. arXiv:0710.2024v1, 10 Oct 2007
-
-[3] VerHoef, J.M.: Who invented the Delta Method? The American Statistician, 2012, 66:2, 124-127 DOI: 10.1080/00031305.2012.687494
-
-[4] Bates, D.M., Watts, D.G. (1988). Comparing models. In: Nonlinear Regression Analysis and Its Applications. New York: Wiley, pp 103-108
-
-[5] Bates, D.M., Watts, D.G. (1988) 2. In: Nonlinear Regression Analysis and Its Applications. New York: Wiley, pp 52-58
-
-[6] Motulsky, Brown Outlier testing

dev/ROUT.R

@@ -1,267 +0,0 @@
-################################################################################
-# F.Innerbichler
-# Jun 2026
-# Robust Outlier detection 
-################################################################################
-
-# Code acc. to Mutulsky and Brown hpptps://pubmed.ncbi.nlm.nih.gov/16526949/
-# except line 175 p <- 2*(1-pt(z, length(residuals)-5)) was exchanged with p <- 2*(1-pcauchy(z)) to be robust
-
-library(minpack.lm)
-library(ggplot2)
-library(rstudioapi)
-
-# Getting path for current open file
-current_path = rstudioapi::getActiveDocumentContext()$path
-setwd(dirname(current_path))
-
-outsPlot_FUN <- function(all_l,OUTs_, TS, PROC = "not specified", PROBE="", Q=0.01, par) {
-#browser()
-  #all_l_rout <- all_l[KEEP,]
-  klein <- min(log(all_l$conc))
-  gross <- max(log(all_l$conc))
-  x_seq <- seq(klein, gross, (gross-klein)/100)
-  samTRUE <- f4pl(x=x_seq, bottom=par["a"], top=par["d"],hill=par["b"], logEC50=par["cs"]-par["r"])
-  refTRUE <- f4pl(x=x_seq, bottom=par["a"], top=par["d"],hill=par["b"], logEC50=par["cs"])
-  
-  pl_T <- data.frame(cbind(x_seq, refTRUE, samTRUE))
-  p <- ggplot(all_l) +
-    geom_point(aes(x=log(conc), y=y, shape=factor(isRef)), size=2) +
-    theme_bw()
-
-  p2 <- p + geom_point(OUTs_, mapping=aes(x=log(conc), y=y, shape=factor(isRef)),
-                       size=5, color="violetred", stroke=2, inherit.aes = FALSE) +
-    scale_shape_manual(label=c("R",TS), values = c(21,25)) +
-    ggtitle(paste(PROC, PROBE, TS, "Threshold=", Q)) +
-    theme(legend.title=element_blank())
-  p3 <- p2 + geom_line(data=pl_T, aes(x=x_seq, y=refTRUE), color="blue", inherit.aes = F) +
-    geom_line(data=pl_T, aes(x=x_seq, y=samTRUE), color="red", inherit.aes = F)
-  p3
-  
-}
-
-#### 4PL model ----
-f4pl <- function(x, bottom, top, logEC50, hill) {
-  bottom+(top-bottom)/(1+exp((logEC50-x)*hill))
-}
-
-#### residuals for joint REF/TEST fit ----
-resid_4pl_joint <- function(par,x,y, is_ref) {
-  bottom <- par["bottom"]
-  top <- par["top"]
-  hill <- par["hill"]
-  le50_r <- par["logEC50_ref"]
-  le50_t <- par["logEC50_test"]
-  
-  le50 <- ifelse(is_ref, le50_r, le50_t)
-  yhat <- f4pl(x, bottom, top, le50, hill)
-  y - yhat
-}
-
-
-jac_4pl_joint <- function(par, x,y,is_ref) {
-  bottom <- par["bottom"]
-  top <- par["top"]
-  hill <- par["hill"]
-  le50_r <- par["logEC50_ref"]
-  le50_t <- par["logEC50_test"]
-  
-  le50 <- ifelse(is_ref, le50_r, le50_t)
-  
-  # useful intermediates
-  
-  texp <- exp((le50-x)*hill) # t = 10^((le50-x)*hill))
-  den <- (1-texp)
-  frac <- (top-bottom)/den
-  
-  dyhat_dbottom <- 1-1/den
-  dyhat_dtop <- 1/den
-  
-  d_invden_dle50 <- -(texp*log(2.718282)*hill)/(den^2)
-  
-  d_invden_dhill <- -(texp*log(2.718282)*(le50-x))/(den^2)
-  
-  dyhat_dle50 <- (top-bottom)* d_invden_dle50
-  dyhat_dhill <- (top-bottom)* d_invden_dhill
-  
-  J <- cbind(
-    bottom = -dyhat_dbottom,
-    topm = -dyhat_dtop,
-    hill = -dyhat_dhill,
-    logEC50_ref = ifelse(is_ref, -dyhat_dle50,0),
-    logEC50_test = ifelse(!is_ref, -dyhat_dle50,0)
-  )
-  J
-}
-
-
-robust_fit_irls <- function(par_start, x,y, is_ref,
-                            max_outer = 30, control = nls.lm.control(maxiter=200, ftol=1e-10, ptol=1e-10)) {
-  par <- par_start
- #browser() 
-  for (k in seq_len(max_outer)) {
-    r <- resid_4pl_joint(par,x,y,is_ref)
-    
-    # Mot Brown
-    rSD <- quantile(r, 0.6827)*length(is_ref)/(length(is_ref)-5)
-    # Robust scale (MAD); fallback if MAD ~0
-    # s <- median(abs(r))/0.6745
-    
-    # Lorentzian / Cauchy weights acc. Mot/Brown
-    w <- 1/log(1+(abs(r)/rSD)^2)
-    # weighted residual function lor LM: sqrt(w)*r
-    fn_w <- function(p) {
-      rr <- resid_4pl_joint(p,x,y,is_ref)
-      sqrt(w)*rr
-      # w*rr
-    }
-    jac_w <- function(p) {
-      JJ <- jac_4pl_joint(p,x,y,is_ref)
-      JJ*sqrt(w) # row-wise scaling
-      # JJ*w
-    }
-    
-    #browser()
-    fit <- tryCatch(nls.lm(par=par, fn=fn_w, jac=jac_w, control=control),
-                    error=function(e) {
-                      paste("error at robust fit irls", k)
-                      # tau <<- tau *10
-                      #Return zero delta to avoid NaN updates
-                      return(rep(0, length(par)))
-                    })
-    
-    if (max(abs(fit$par -par)) < 1e-8) {
-      par <- fit$par
-      break
-    }
-    par <- fit$par
-  }
-  r_final <- resid_4pl_joint(par,x,y,is_ref)
-  # s_final <- median(abs(r_final))/0.6745
-  rSD <- quantile(r, 0.6827)*length(r)/(length(r)-5)
-  # Robust scale (MAD); fallback if MAD ~0
-  # s <- median(abs(r))/0.6745
-  list(par=par, residuals=r_final, scale=rSD)
-  
-}
-
- # ROUT outlier detection via BH-FDR [1] https://cran.r-project.org/web//packages/minpack.lm/refman/minpack.lm.html)[6]
-
-rout_detect <- function(residuals, scale, Q=0.01) {
-  z <- abs(residuals/scale)
-  p <- 2*(1 - pcauchy(z))
-  # p <- 2*(1-pt(z, length(residuals)-5))
-  
-  o <- order(p)
-  p_sorted <- p[o]
-  m <- length(p)
-  # thresh <- (seq_len(m)/m) # *Q
-  keep <- p_sorted <= Q # thresh
-  (out_idx <- o[keep])
-  sort(out_idx)
-  
-}
-
-# Final OLS LM refit after removing outliers
-
-ols_refit <- function(par_start, x,y,is_ref, keep_idx,
-                      control = nls.lm.control(maxiter=400, ftol=1e-12, ptol=1e-12)) {
-  xk <- x[keep_idx]; yk <- y[keep_idx]; rk <- is_ref[keep_idx]
-  
-  fn_w <- function(p) resid_4pl_joint(p,xk,yk,rk)
-  jac_w <- function(p) jac_4pl_joint(p,xk,yk,rk)
-#browser()  
-  fit <- tryCatch(nls.lm(par=par_start, fn=fn_w, jac=jac_w, control=control),
-                  error=function(e) {
-                    paste("error at ols_refits")
-                    # tau <<- tau *10
-                    #Return zero delta to avoid NaN updates
-                    return(rep(0, length(par)))
-                  })
-  
-  list(par = fit$par, residuals = fn_w(fit$par), keep=keep_idx)
-}
-
-#. high-level ROUT 4PL analysis
-
-rout_4pl_potency <- function(df, Q=0.01, par_start=NULL, max_outer = 30) {
-  stopifnot(all(c("sample","conc","y") %in% names(df))) # "rep",
- #browser() 
-  x <- log(df$conc)
-  y <- df$y
-  is_ref <- df$isRef == 1
-  
-  # start values if not provided
-  if (is.null(par_start)) {
-    bottom0 = min(y, na.rm = T)
-    top0 = max(y, na.rm = T)
-    if (df$y[1]>df$y[8]) hill0 <- 1 else hill0 <- -1
-    le50_r0 <- median(x[is_ref], na.rm = T)
-    le50_t0 <- median(x[!is_ref], na.rm = T)
-    par_start <- c(bottom=bottom0, top=top0, hill=hill0, logEC50_ref = le50_r0 , logEC50_test = le50_t0)
-  }
-  # Step1 : robust fit
-  robust <- robust_fit_irls(par_start, x,y,is_ref, max_outer = max_outer)
-  # Step 2: outlier detection
-  out_idx <- rout_detect(robust$residuals, robust$scale, Q=Q)
-  keep_idx <- setdiff(seq_len(nrow(df)), out_idx)
-  
-  # Step 3: OLS refit
-  ols <- ols_refit(robust$par, x,y,is_ref, keep_idx)
-  
-  # Relative potency
-  rp <- exp(ols$par["logEC50_ref"] - ols$par["logEC50_test"])*100
-  
-  list(Q=Q, outliers=out_idx,
-       kept=keep_idx,
-       par_robust = robust$par,
-       par_final = ols$par,
-       rp_percent = rp,
-       df=df)
-  
-}
-
-# sheets <- openxlsx::getSheetNames("~/plateflow/outlierFUB.xlsx")
-# all_dat <- lapply(sheets, openxlsx::read.xlsx, xlsxFile="~/plateflow/outlierFUB.xlsx")
-# names(all_dat) <- sheets
-# 
-# Plate <- 1
-# 
-# PlanteN <- sheets[Plate]
-# NoDils <- 8
-# Nreps <- 3
-# DAT <- all_dat[[Plate]]
-# colnames(DAT) <- DAT[2,]
-# REF <- DAT[3:10,1:4]
-# SAM1 <- DAT[13:20,1:4]
-# SAM2 <- DAT[23:30,1:4]
-# SAM3 <- DAT[33:40,1:4]
-# 
-# REF_ <- sapply(REF, as.numeric)
-# colnames(REF_) <- colnames(REF)
-# SAM1_ <- sapply(SAM1, as.numeric)
-# colnames(SAM1_) <- colnames(SAM1)
-# SAM2_ <- sapply(SAM2, as.numeric)
-# colnames(SAM2_) <- colnames(SAM2)
-# SAM3_ <- sapply(SAM3, as.numeric)
-# colnames(SAM3_) <- colnames(SAM3)
-# 
-# plot_df <- rbind(REF_, SAM1_, SAM2_,SAM3_)
-# 
-# plot_df_ <- cbind(as.data.frame(plot_df), sample=c(rep("R", NoDils),rep("S1R", NoDils),rep("S2", NoDils),rep("S3", NoDils)))
-# test_df <- plot_df_[1:16,]
-# test_df2 <- plot_df_[c(1:8, 17:24),]
-# test_df3 <- plot_df_[c(1:8, 25:32),]
-# 
-# all_l <- melt(test_df, id.vars = c("sample","Dose"), variable.name="replname", value.name="readout")
-# colnames(all_l) <- c("sample","conc","rep","y")
-# 
-# for (Q in c(0.01,0.015,0.02)) {
-#   res <- rout_4pl_potency(all_l, Q)
-#   OUTs_ <- all_l[res$outliers,]
-#   print(outsPlot_FUN(all_l, OUTs_, TS=all_l$sample[9], PROC="ROUT",PROBE="Sheet 1",Q,par=res$par_final))
-# }
-
-
-
-

dev/explore_page.R

@@ -0,0 +1,143 @@
+explore_4pl <- tagList(
+  div(),#empty, just for spacing
+  navset_pill(
+    id = "tab_menu_2",
+  
+    nav_panel("normal option TODO", 
+      tags$div("graphs 4pl TODO"),
+      accordion(
+        open = FALSE,
+        accordion_panel(
+          title = "4PL-Tests",
+          p("This is the content inside the expandable box."),
+        )
+      ),
+      accordion(
+        open = FALSE,
+        accordion_panel(
+          title = "unrestricted ANOVA for 4PL",
+          p("This is the content inside the expandable box."),
+        )
+      ),
+      accordion(
+        open = FALSE,
+        accordion_panel(
+          title = "Error calculations:", #TODO NAME?
+          p("This is the content inside the expandable box."),
+        )
+      )
+    ),
+    
+    
+    nav_panel("ln-transformed y", 
+      tags$div("graphs 4pl ln transformed y TODO"),
+      accordion(
+        open = FALSE,
+        accordion_panel(
+          title = "4PL-Tests for ln-transformed y",
+          p("This is the content inside the expandable box."),
+        )
+      )
+    ),
+  ),
+)
+
+explore_linear <- tagList(
+  tags$p("graphs linear TODO"), 
+  accordion(
+    open = FALSE,
+    accordion_panel(
+      title = "Tests for linear PLA:",
+      p("This is the content inside the expandable box."),
+    )
+  ),
+  accordion(
+    open = FALSE,
+    accordion_panel(
+      title = "ANOVA for parallel line assay:",
+      p("This is the content inside the expandable box."),
+    )
+  ),
+  accordion(
+    open = FALSE,
+    accordion_panel(
+      title = "Unrestricted linear model (SSSI):",
+      p("This is the content inside the expandable box."),
+    )
+  ),
+  accordion(
+    open = FALSE,
+    accordion_panel(
+      title = "Restricted linear model (CSSI):",
+      p("This is the content inside the expandable box."),
+    )
+  )
+)
+
+
+
+explore_page <- layout_sidebar(
+  sidebar = sidebar(
+    class = "sidebar",
+    tags$div(  class = "sidebar_collector",
+      tags$h5( class = "sidebar_collector_header",
+        "Exploration Settings"
+      ),
+      sliderInput("sdfac", "Variability of lower to upper asymptote [%]:", min = 0, max = 100, value = 50),
+      sliderInput("potencydiff", "Potency of test [%]:", min = 0, max = 200, value = 100),
+      checkboxInput("PureErrMeta","Use pure error", FALSE),
+      checkboxInput("heterosked","Heteroskedastic noise", FALSE),
+    ),
+    tags$div( class = "sidebar_collector",
+      tags$h5( class = "sidebar_collector_header", "Curve Settings"),
+      numericInput("lowAsymptREF", "lower asymptote REF", 10, step = 1, min = 0),
+      numericInput("lowAsymptTEST", "lower asymptote TEST", 10, step = 1, min = 0),
+      numericInput("uppAsymptREF", "upper asymptote REF", 110, step = 1, min = 0),
+      numericInput("uppAsymptTEST", "upper asymptote TEST", 110, step = 1, min = 0),
+      numericInput("slopeREF", "slope REF", 1, step = 0.1, min = -10),
+      numericInput("slopeTEST", "slope TEST", 1, step = 0.1, min = -10),
+      numericInput("EC50", "EC50 REF", -3.5),
+      numericInput("potDiff", "potency difference", 0)
+      ),
+    tags$div( class = "sidebar_collector",
+              tags$h5( class = "sidebar_collector_header", "Dilutions"),
+              numericInput("CONC1", "highest concentration", 0.3, min = -3.5),
+              numericInput("CONC2", "2nd concentration", 0.15),
+              numericInput("CONC3", "3rd concentration", 0.075),
+              numericInput("CONC4", "4th concentration", 0.0375),
+              numericInput("CONC5", "5th concentration", 0.01875),
+              numericInput("CONC6", "6th concentration", 0.00938),
+              numericInput("CONC7", "7th concentration", 0.00469),
+              numericInput("CONC8", "8thd concentration", 0.00235),
+              numericInput("CONC9", "9thd concentration", value = NA),
+              numericInput("CONC10", "10th concentration", value = NA),
+              numericInput("CONC11", "11th concentration", value = NA),
+              numericInput("CONC12", "lowest concentration", NA)
+    ),
+    tags$div( class = "sidebar_collector",
+              tags$h5( class = "sidebar_collector_header", "Geometric Dilution Scheme"),
+              numericInput("ConcStart", "starting concentration", value = NA, min = 0),
+              numericInput("dilutionFac", "dilution factor", value = NA, min = 0, max = 10),
+              numericInput("NoDil", "no. of dilutions", value = NA, min = 8),
+              numericInput("NoDilSer", "no. of dil. series", value = NA, min = 0), #TODO 1?
+    ),
+  ),
+  
+  
+  # Main content goes here (can be multiple elements)
+  navset_pill(
+    id = "tab_menu_1",
+    nav_panel("4PL", explore_4pl),
+    nav_panel("LINEAR", explore_linear)
+  ),
+  accordion(
+    open = FALSE,
+    accordion_panel(
+      title = "Input-Data with added random noise:",
+      box( # TODO
+        title = "Simulated data per log-concentration", solidHeader = TRUE, width = 12, "incl. mean, sd and CV%",
+        DT::dataTableOutput("ConctabMeta") 
+      ),
+    )
+  )
+)

dev/home_page.R

@@ -0,0 +1,44 @@
+home_page <- tagList(
+  tags$div( style = "align-self: center",
+            tags$div( class = "home-text-section",
+                      tags$h3("Welcome to Plateflow"),
+                      tags$p("Plateflow allows you to EXPLORE, INSPECT and OPTIMIZE your data in 
+           the context of a 4 PL fit or a linear regression fit. "),
+            ),
+            
+            tags$div( class = "home-text-section",
+                      tags$h4("Readable formats and expected structure"),
+                      tags$p("Plateflow is optimized for uploadfiles in the folowing formats: 
+             .xlsx, .csv and .numbers."),
+                      tags$ul(
+                        tags$li("1 column with the dilution concentrations (first or last column) is expected"),
+                        tags$li("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."), 
+                        tags$li("2 columns of reference and test sample readouts, respectively, in this order, are expected"),
+                        tags$li("The column names for reference and test are free to set, but must differ for all columns"),
+                      ),
+                      tags$p("You can download an example", tags$a("TestFile.xlsx ", target = "self", href = "TestFile.xlsx"), "File here."),
+                      tags$h6("Example filestructure:"),
+                      tags$img(src= "example_file.png", width = "800px",   alt="example filestructure for upload", style = " max-width:800px"),
+                      tags$br(),
+            ),
+            
+            tags$div( class = "home-text-section",
+                      tags$br(),
+                      tags$h4("Information on dilution settings"), #TODO
+                      tags$p("Bend points are calculated according to following formula:",
+                             withMathJax(" $$bp_{1/2} = \\pm\\frac{1.31696}{Hill's slope}$$"),
+                             "Please refer to this",  a(href = "ADONIS.pdf", "Whitepaper", download = NA, target = "_blank"), "for further details.", #TODO this link does not work for me
+                      ),
+                      tags$br(),
+            ),
+            
+            tags$div( class = "home-text-section",
+                      tags$h4("Plateflow Point of Contact"),
+                      tags$p("At InnerAnalytics, we are happy to hear from you. You can contact us under: ",
+                             tags$a("info@inner-analytics.eu", href = "mailto:info@inner-analytics.eu")),
+                      tags$p("If you would like to report a bug, we would appreciate it if you sent us the following infos to help us solve your request as fast as possible: "),
+                      verbatimTextOutput("sessioninfo"), #TODO actually output this
+            ),
+  )
+)

dev/optimize_page.R

@@ -0,0 +1,61 @@
+optimize_4pl <- tagList(
+  tags$p("graphs optimize 4pl TODO"), 
+  accordion(
+    open = FALSE,
+    accordion_panel(
+      title = "Tests for linear PLA:",
+      p("This is the content inside the expandable box."),
+    )
+  ),
+)
+
+dilution_slider <- tagList(
+  tags$h2("Finder for optimal dilutions"), 
+  tags$p("graphs optimize dilution-slider TODO"),
+  accordion(
+    open = FALSE,
+    accordion_panel(
+      title = "Adjusted dilution factors:",
+      p("This is the content inside the expandable box."),
+    )
+  ),
+)
+
+histograms <- tagList(
+  accordion(
+    open = FALSE,
+    accordion_panel(
+      title = "Parameter Histograms",
+      p("This is the content inside the expandable box."),
+    )
+  ),
+)
+
+optimize_report <- tagList(
+  tags$p("download optimize report TODO"), 
+) 
+
+
+
+
+optimize_page <- layout_sidebar(
+  sidebar = sidebar(
+    class = "sidebar",
+    tags$div(  class = "sidebar_collector",
+       tags$h5( class = "sidebar_collector_header",
+                "Optimization Settings"
+       ),
+       sliderInput("TODO", "Adjust the dilutions (+- change in %):", min = -100, max = 100, value = 0),
+       checkboxInput("TODO2","Fix highest concentration instead of center", FALSE),
+       p("Wider dilution ranges increase the CIs of the relative potency, and decrease the CIs of the upper and lower asymptotes ratios, as well as the Hill's slope ratios."),
+    ),
+  ),
+  
+  
+  # Main content goes here (can be multiple elements)
+  optimize_4pl,
+  dilution_slider,
+  histograms,
+  optimize_report
+  
+)

www/styles.css

@@ -0,0 +1,107 @@
+:root {
+  color-scheme: light;
+  --text-dark: #000000;
+  --text-light: #ffffff;
+
+  --grey-900: #1f2937;
+  --grey-800: #2b3441;
+  --grey-200: #b1b9c5;
+  --grey-100: #e7f1ff;
+  --grey-white: #ffffff;
+
+  --blue-light: #7faeff;
+  --blue: #5757fd;
+  --blue-dark: #4545ba;
+
+}
+
+body {
+  background: var(--grey-white);
+  color: var(--text-dark);
+}
+
+/*------------------------header-------------------------------------------*/
+.navbar {
+  background-color: var(--grey-100); 
+  border-bottom: 1px solid var(--blue-light) !important;
+  align-items: center;
+  width: 100;
+}
+
+/* The app title */
+.navbar-brand {
+  color: var(--text-dark);
+  font-size: 1.4rem;
+}
+
+/* Nav container */
+.navbar-nav {
+  display: flex;
+  justify-content: space-evenly;
+  align-self: center;
+}
+
+/*Header buttons*/
+.navbar-nav .nav-link {
+  padding: 0.2rem 1.2rem !important; 
+  margin: 0.4rem 0rem;
+  border: none;
+  border-radius: 5px; 
+} 
+
+/*----------------------------------------------------------------------------*/
+
+.navbar-nav .nav-link.active,
+.tab-button:hover, 
+.navbar-nav .nav-link.focus,
+.tab-button:focus, 
+#tab_menu_1  .nav-link.active,
+#tab_menu_2  .nav-link.active{
+  background-color: var(--blue-light);
+  color: var(--text-light)
+}
+
+/* Center the tab buttons */
+#tab_menu_1,
+#tab_menu_2{
+  justify-content: center;
+}
+
+/* Style individual tab buttons */
+#tab_menu_1  .nav-link,
+#tab_menu_2  .nav-link{
+  background-color: var(--grey-100);
+  border-radius: 4px;
+  margin: 0rem 1rem;
+  color: var(--text-dark)
+}
+
+
+
+/*----------------------------------------------------------------------------*/
+
+.sidebar {
+  background-color: var(--grey-100) !important;
+  border-right: 1px solid var(--blue-light) !important; 
+}
+
+.sidebar_collector{
+  border: 1px solid var(--blue-light);
+  border-radius: 4px;
+  padding: 10px;
+}
+
+.sidebar_collector_header{
+  border-bottom: 1px dashed var(--blue-light);
+  padding: 0 0 15px 0; 
+}
+/*----------------------------------------------------------------------------*/
+.home-text-section{
+  background-color: var(--grey-white);
+  padding: 10px;
+  border-radius: 4px; 
+  max-width: 900px;
+  align-self: start;
+}
+
+