1172 lines
55 KiB
Plaintext
1172 lines
55 KiB
Plaintext
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library(shiny)
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#library(shinyjs)
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#library(shinyAce)
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library(shinydashboard)
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#library(shinycssloaders)
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library(purrr)
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library(gslnls)
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library(tidyverse)
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library(ggplot2)
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library(reshape2)
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library(openxlsx)
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library(DT)
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library(ggpubr)
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library(gridExtra)
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library(drc)
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library(twopartm)
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library(car)
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library(dplyr)
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Dat <- reactiveValues()
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REP <- reactiveValues()
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dilFUN2 <- function(cs_,dils,Faktor) {
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av <- cs_
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dils_av <- dils_av
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dils_avsc <- dils_av*Faktor
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dils2 <- dils_avsc+av
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dilfactors <- 1/exp(dils2-lag(dils2))
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return(dilfactors)
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}
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plot_f <- function(dat, sigmoid,det_sig) {
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CORdat <- cor(dat[,1],dat[,ncol(dat)])
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#browser()
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all_l <- melt(data.frame(dat), id.vars="log_dose", variable.name="replname", value.name = "readout")
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isRef <- rep(c(1,0),1,each=nrow(all_l)/2)
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isSample <- rep(c(0,1),1,each=nrow(all_l)/2)
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all_l2 <- cbind(all_l, isRef, isSample)
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#browser()
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if(is.null(det_sig)) {
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if (CORdat<0) {
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startlist <- list(a=sigmoid[1], b=-sigmoid[5],cs=sigmoid[7],
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d=sigmoid[3],r=sigmoid[8])
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} else {
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startlist <- list(a=sigmoid[1],b=sigmoid[5],cs=sigmoid[7],
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d=sigmoid[3],r=sigmoid[8])
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}
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} else {
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startlist <- list(a=det_sig[5], b=det_sig[1],cs=det_sig[7],
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d=det_sig[3],r=det_sig[7] - det_sig[8])
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}
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mr <- gsl_nls(fn = readout ~ a+(d-a)/(1+exp(b*(log_dose-(cs-r*isSample)))),
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data=all_l,
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start=startlist,
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control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
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s_mr <- tryCatch({
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s_mr <- summary(mr)
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},
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error = function(err) {
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s_mr <- NULL
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})
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a <- s_mr$coefficients[1,1]
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b <- s_mr$coefficients[2,1]
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cs <- s_mr$coefficients[3,1]
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d <- s_mr$coefficients[4,1]
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r <- s_mr$coefficients[5,1]
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log_dose <- unique(all_l$log_dose)
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seq_x <- seq(min(log_dose),max(log_dose),0.1)
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SAMPLE <- a+(d-a)/(1+exp(b*(seq_x-(cs-r))))
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REF <- a+(d-a)/(1+exp(b*(seq_x-(cs))))
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if (is.null(det_sig)) {
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SAMPLEtrue <- sigmoid[2] + (sigmoid[4] -sigmoid[2])/(1+exp(sigmoid[6]*((sigmoid[7]-sigmoid[8]-seq_x))))
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REFtrue <- sigmoid[1] + (sigmoid[3] -sigmoid[1])/(1+exp(sigmoid[5]*((sigmoid[7]-seq_x))))
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} else {
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SAMPLEtrue <- det_sig[4] + (det_sig[6] -det_sig[4])/(1+exp(det_sig[2]*(det_sig[8]-seq_x)))
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REFtrue <- det_sig[3] + (det_sig[5] -det_sig[3])/(1+exp(det_sig[1]*(det_sig[7]-seq_x)))
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}
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#browser()
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pl_df <- cbind(seq_x, SAMPLE, REF, SAMPLEtrue, REFtrue)
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all_l2$readout[all_l2$readout < 0] <- 0.01
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all_l2$readouttrans <- log(all_l2$readout)
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slopeEC50 <- b*(a-d)/4
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Xbendl3 <- cs-(1.31696/b)
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Xbendu3 <- cs+(1.31696/b)
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XbendlT <- cs-r-(1.31696/b)
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XbenduT <- cs-r+(1.31696/b)
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bendpoints <- c(bendREF_lower = round(Xbendl3,3), bendREF_upper=round(Xbendu3,3),
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bendSAMPLE_lower = round(XbendlT,3), bendSAMPLE_upper=round(XbenduT,3))
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Dat$bendpoints <- bendpoints
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Dat$cfordils <- cs
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p <- ggplot(all_l2, aes(x=log_dose, y=readout, color=factor(isRef))) +
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geom_point(shape=factor(isRef), alpha=0.8) +
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labs(title = paste("restricted 4pl; bendp:", round(Xbendl3,3),round(Xbendu3,3),round(XbendlT,3),round(XbenduT,3)),
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color="product") +
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scale_color_manual(labels=c("test","reference"), values=c("red","blue")) +
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scale_shape_manual(labels=c("test","reference")) +
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theme_bw() +
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theme(axis.text = element_text(size=14))
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p2 <- p + geom_line(data=as.data.frame(pl_df), aes(x=seq_x, y=SAMPLE), color="red",
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inherit.aes = F) +
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geom_line(data=as.data.frame(pl_df), aes(x=seq_x, y=REF), color="blue",
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inherit.aes = F) +
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geom_line(data=as.data.frame(pl_df), aes(x=seq_x, y=SAMPLEtrue), color="red", linetype=2, alpha=0.4,
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inherit.aes = F) +
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geom_line(data=as.data.frame(pl_df), aes(x=seq_x, y=REFtrue), color="blue", linetype=2, alpha=0.4,
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inherit.aes = F) +
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geom_vline(xintercept=c(Xbendl3, Xbendu3), col="blue",linetype=2) +
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geom_vline(xintercept=c(XbendlT, XbenduT), col="red",linetype=2) +
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annotate("text", x=cs, y=a+(d-a)/2, label="0", size=5) +
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theme(legend.position="none")
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Dat$p2 <- p2
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# transformed plots
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p_rt <- ggplot(all_l2, aes(x=log_dose, y=readouttrans, color=factor(isRef))) +
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geom_point(shape=factor(isRef), alpha=0.8) +
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labs(title = paste("restricted transformed 4pl"), color="product") +
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scale_color_manual(labels=c("test","reference"), values=c("red","blue")) +
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theme_bw()
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mrt <- gsl_nls(fn = readouttrans ~ a+(d-a)/(1+exp(b*(log_dose-(cs-r*isSample)))),
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data=all_l2,
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start=startlist,
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control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
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s_mrt <- summary(mrt)
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a_trans <- s_mrt$coefficients[1,1]
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b_trans <- s_mrt$coefficients[2,1]
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cs_trans <- s_mrt$coefficients[3,1]
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d_trans <- s_mrt$coefficients[4,1]
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r_trans <- s_mrt$coefficients[5,1]
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XbendlTrans <- cs_trans-(1.31696/b_trans)
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XbenduTrans <- cs_trans+(1.31696/b_trans)
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XbendlTransT <- cs_trans-r_trans-(1.31696/b_trans)
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XbenduTransT <- cs_trans-r_trans+(1.31696/b_trans)
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bendpointsTRANS <- c(bendREF_lower = round(XbendlTrans,3), bendREF_upper=round(XbenduTrans,3),
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bendSAMPLE_lower = round(XbendlTransT,3), bendSAMPLE_upper=round(XbenduTransT,3))
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Dat$bendpointsTRANS <- bendpointsTRANS
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SAMPLEtrans <- a_trans+(d_trans-a_trans)/(1+exp(b_trans*(seq_x-(cs_trans-r_trans))))
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REFtrans <- a_trans+(d_trans-a_trans)/(1+exp(b_trans*(seq_x-(cs_trans))))
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pl_df_trans <- cbind(seq_x, SAMPLEtrans, REFtrans)
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p_rt2 <- p_rt + geom_line(data=as.data.frame(pl_df_trans), aes(x=seq_x, y=SAMPLEtrans), color="red",
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inherit.aes = F) +
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geom_line(data=as.data.frame(pl_df_trans), aes(x=seq_x, y=REFtrans), color="blue",
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inherit.aes = F) +
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geom_vline(xintercept=c(XbendlTrans, XbenduTrans), col="blue",linetype=2) +
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geom_vline(xintercept=c(XbendlTransT, XbenduTransT), col="red",linetype=2) +
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theme(legend.position = "none", axis.text=element_text(size=14))
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if (is.null(det_sig)) {
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unrestr <- drm(readout ~ exp(log_dose), isSample, data=all_l2, fct=LL.4(),
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pmodels=data.frame(isSample, isSample,isSample,isSample))
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Sum_u <- summary(unrestr)
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ast <- Sum_u$coefficients[3,1]
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ate <- Sum_u$coefficients[4,1]
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bst <- Sum_u$coefficients[1,1]
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bte <- Sum_u$coefficients[2,1]
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cst <- log(Sum_u$coefficients[7,1])
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cte <- log(Sum_u$coefficients[8,1])
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dst <- Sum_u$coefficients[5,1]
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dte <- Sum_u$coefficients[6,1]
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} else {
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ast <- det_sig[5]
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ate <- det_sig[6]
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bst <- det_sig[1]
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bte <- det_sig[2]
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cst <- det_sig[7]
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cte <- det_sig[8]
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dst <- det_sig[3]
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dte <- det_sig[4]
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}
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REFu <- ast + (dst-ast)/(1+exp(bst*(seq_x-cst)))
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SAMPLEu <- ate + (dte-ate)/(1+exp(bte*(seq_x-cte)))
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pl_df2 <- cbind(seq_x, SAMPLEu, REFu)
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#browser()
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pu <- ggplot(all_l2, aes(x=log_dose, y=readout, color=factor(isRef))) +
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geom_point() +
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labs(title="unrestricted 4_pl-Model", color="product") +
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scale_color_manual(labels = c("test","reference"), values=c("red","blue")) +
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theme_bw()
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pu2 <- pu + geom_line(data=as.data.frame(pl_df2), aes(x=seq_x, y=SAMPLEu),
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color="red", inherit.aes = F) +
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geom_line(data=as.data.frame(pl_df2), aes(x=seq_x, y=REFu),
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color="blue", inherit.aes = F,
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show.legend = F)
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pu2_ <- pu2 +
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theme(legend.position = "none", axis.text = element_text(size=14))
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putrans <- ggplot(all_l2, aes(x=log_dose, y=readouttrans, color=factor(isRef))) +
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geom_point() +
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labs(title="unrestricted transformed 4_pl-Model", color="product") +
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scale_color_manual(labels = c("test","reference"), values=c("red","blue")) +
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theme_bw()
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unrestr_trans <- drm(readouttrans ~ exp(log_dose), isSample, data=all_l2, fct=LL.4(),
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pmodels=data.frame(isSample, isSample,isSample,isSample))
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Sum_ut <- summary(unrestr_trans)
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ast_t <- Sum_ut$coefficients[3,1]
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ate_t <- Sum_ut$coefficients[4,1]
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bst_t <- Sum_ut$coefficients[1,1]
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bte_t <- Sum_ut$coefficients[2,1]
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cst_t <- log(Sum_ut$coefficients[7,1])
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cte_t <- log(Sum_ut$coefficients[8,1])
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dst_t <- Sum_ut$coefficients[5,1]
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dte_t <- Sum_ut$coefficients[6,1]
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REFu_trans <- ast_t + (dst_t-ast_t)/(1+exp(bst_t*(seq_x-cst_t)))
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SAMPLEu_trans <- ate_t + (dte_t-ate_t)/(1+exp(bte_t*(seq_x-cte_t)))
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pl_df2u_t <- cbind(seq_x, SAMPLEu_trans, REFu_trans)
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pu2_t <- putrans + geom_line(data=as.data.frame(pl_df2u_t), aes(x=seq_x, y=SAMPLEu_trans),
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color="red", inherit.aes = F) +
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geom_line(data=as.data.frame(pl_df2u_t), aes(x=seq_x, y=REFu_trans),
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color="blue", inherit.aes = F,
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show.legend = F)
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pu3_t <- pu2_t
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grid.arrange(p2,p_rt2,pu2_,pu3_t, nrow=2)
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}
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Calc_DilRes <- function(as=3, bs=1, cs=-4, ds=10, at=3, bt=1, dt=10,r=0.0001,ct=cs-r,
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sd_fac=0.1, gt=1, gs=1, log_conc,
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heteroNoise=FALSE, noDilSeries, noDils) {
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yAxfac <- (ds-as)
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log_dose <- log_conc
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isRef <- rep(c(1,0),1,each=length(log_conc)*noDilSeries)
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isSample <- rep(c(0,1),1,each=length(log_conc)*noDilSeries)
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#browser()
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av <- as*isRef + at*isSample + (ds*isRef + dt*isSample - as*isRef - at*isSample)/
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(1+isRef*exp(bs*(cs - log_dose)) + isSample*exp(bt*(ct-log_dose)))
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if (heteroNoise) {
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# heterosc noise
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ro_jit <- matrix(unlist(map(av, function(x) x+rnorm(1,0,x*sd_fac/100))), nrow=noDils, ncol=noDilSeries*2)
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} else {
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# homosc noise
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ro_jit <- matrix(unlist(map(av, function(x) x+rnorm(1,0,sd_fac*yAxfac/100))), nrow=noDils, ncol=noDilSeries*2)
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}
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ro_jit <- abs(ro_jit)
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ro_jit2 <- cbind(ro_jit, log_dose)
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if (noDilSeries==3) {
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colnames(ro_jit2) <- c("R_dil1","R_dil2","R_dil3","T_dil1","T_dil2","T_dil3", "log_dose")
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} else {
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colnames(ro_jit2) <- c("R_dil1","R_dil2","T_dil1","T_dil2", "log_dose")
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}
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return(ro_jit2)
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}
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LinPotTab <- function(circles, Lim, PureErrFlag) {
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circ_ABl <- circles
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circ_Al <- circ_ABl[circ_ABl$isSample ==1,]
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circ_Al <- circ_ABl[circ_ABl$isSample ==0,]
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# restr CSSI model
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modAB <- lm(readout ~ log_dose + isSample, circ_ABl)
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coeffs <- modAB$coefficients
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SU_modAB <- tryCatch({
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SU_modAB <- summary(modAB)
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}, error = function(msg) {
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return(NA)
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})
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# Intercept diff/slope modAB
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linPot <- exp(modAB$coefficients[3]/modAB$coefficients[2])
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if(PureErrFlag) {
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FitAnova <- anova(lm(readout ~ factor(log_dose)*isSample, circ_ABl))
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meanPureErr <- FitAnova[4,3]
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DFsPure <- FitAnova[4,1]
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VCOV <- vcov(modAB)
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V_V <- VCOV/SU_modAB$sigma^2
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VCOVpure <- V_V*meanPureErr
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SEsPure <- sqrt(diag(V_V)*meanPureErr)
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}
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log_pot_delta <- deltaMethod(modAB, "isSample/log_dose")
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if (PureErrFlag) {
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V_ <- log_pot_delta$SE^2/SU_modAB$sigma^2
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V_p <- V_*meanPureErr
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potDeltaPureSE <- sqrt(V_p)
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CI_log_low <- log_pot_delta$Estimate - qt(0.975, DFsPure)*potDeltaPureSE
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CI_log_up <- log_pot_delta$Estimate + qt(0.975, DFsPure)*potDeltaPureSE
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} else {
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CI_log_low <- log_pot_delta$Estimate - qt(0.975, df.residual(modAB))*log_pot_delta$SE
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CI_log_up <- log_pot_delta$Estimate + qt(0.975, df.residual(modAB))*log_pot_delta$SE
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}
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#browser()
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ExpLinPot <- exp(c(log_pot_delta$Estimate, CI_log_low, CI_log_up))
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if (ExpLinPot[2]*100>Lim[[9]] & ExpLinPot[3]*100<Lim[[10]]) test_potCI <- 0 else test_potCI <- 1
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# Rel pot CI
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relLinpotCI <- ExpLinPot/linPot*100
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pottab <- cbind(round(linPot*100,3), round(ExpLinPot[2]*100,3), round(ExpLinPot[3]*100,3),
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round(test_potCI,3), round(relLinpotCI[2],3),round(relLinpotCI[3],3))
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colnames(pottab) <- c("Potency","lower 95%CI", "upper 95%CI", "test_result", "lowerRel95%CI","upperRel95%CI")
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return(pottab)
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}
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ANOVAlintests <- function(ro_new, circles, Lim, PureErrFlag) {
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all_l <- melt(data.frame(ro_new), id.vars="log_dose", variable.name = "replname", value.name = "readout")
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isRef <- rep(c(1,0),1,each=nrow(all_l)/2)
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isSample <- rep(c(0,1),1,each=nrow(all_l)/2)
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all_l$isRef <- isRef
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all_l$isSample <- isSample
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all_l$Conc <- exp(all_l$log_dose)
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all_lA <- all_l[all_l$isSample == 1,] # TEST
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all_lB <- all_l[all_l$isSample == 0,] # REF
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circ_ABl <- circles
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circ_Al <- circ_ABl[circ_ABl$isSample ==1,]
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circ_Bl <- circ_ABl[circ_ABl$isSample ==0,]
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# restr CSSI model
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modAB <- lm(readout ~ log_dose + isSample, circ_ABl)
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# unrestr with interact term SSSI
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modABu <- lm(readout ~ log_dose + isSample + log_dose*isSample, circ_ABl)
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modA <- lm(readout ~ log_dose + isSample, circ_Al)
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modB <- lm(readout ~ log_dose + isSample, circ_Bl)
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log_pot_delta <- deltaMethod(modAB, "isSample/log_dose")
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CI_log_low <- log_pot_delta$Estimate - qt(0.975, df.residual(modAB))*log_pot_delta$SE
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CI_log_up <- log_pot_delta$Estimate + qt(0.975, df.residual(modAB))*log_pot_delta$SE
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ExpLinPot <- exp(c(log_pot_delta$Estimate, CI_log_low, CI_log_up))
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if (ExpLinPot[2]*100>Lim[9] & ExpLinPot[3]*100>Lim[10]) test_potCI <- 0 else test_potCI <- 1
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su_mod <- summary(modAB)$coefficients
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su_mod2 <- cbind(data.frame(parameter = c("intercept REF","slope REF","intercepts diff.")), su_mod)
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su_modU <- summary(modABu)$coefficients
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su_modU2 <- cbind(data.frame(parameter = c("intercept REF","slope REF","intercepts diff.","slope difference")), su_modU)
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uCI_SloDiff <- su_modU[4,1] + qt(0.975,8)*su_modU[4,2]
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lCI_SloDiff <- su_modU[4,1] - qt(0.975,8)*su_modU[4,2]
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SlopeDiffCI <- c(su_modU[4,1], lCI_SloDiff,uCI_SloDiff)
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lenCirc <- nrow(circ_ABl)
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dfTreat <- 3
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dfPrep <- 1
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dfReg <- 1
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dfnonP <- 1
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dfRMSE <- c(lenCirc-3-1)
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dfTotal <- lenCirc-1
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dfPureE <- lenCirc-6
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dfNonLin <- dfRMSE-dfPureE
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RSS <- sum(resid(lm(readout ~ log_dose*isSample, circ_ABl))^2)
|
|
MSE <- RSS/dfRMSE
|
|
SSE <- sum(resid(lm(readout ~ factor(log_dose)*isSample, circ_ABl))^2)
|
|
MSpure <- SSE/dfPureE
|
|
|
|
if (PureErrFlag) {
|
|
FitAnova <- anova(lm(readout ~ factor(log_dose)*isSample, circ_ABl))
|
|
meanPureErr <- FitAnova[4,3]
|
|
SU_modAB <- tryCatch({
|
|
SU_modAB <- summary(modAB)
|
|
}, error = function(msg) {
|
|
return(NA)
|
|
})
|
|
if (length(SU_modAB)>1) s_modABcoeffs <- summary(modAB)$coefficients
|
|
|
|
DFsPure <- FitAnova[4,1]
|
|
VCOV <- vcov(modAB)
|
|
V_V <- VCOV/SU_modAB$sigma^2
|
|
VCOVpure <- V_V*meanPureErr
|
|
SEsPure <- sqrt(diag(V_V)*meanPureErr)
|
|
su_mod2[,3] <- SEsPure
|
|
su_mod2[,4] <- su_mod2[,2]/su_mod2[,3]
|
|
su_mod2[,5] <- 2*(1-pt(abs(su_mod[,4]), FitAnova[4,1]))
|
|
|
|
s_mu <- summary(modABu)$coefficients
|
|
SU_modABu <- summary(modABu)
|
|
VCOVu <- vcov(modABu)
|
|
V_Vu <- VCOVu/SU_modABu$sigma^2
|
|
SEsPureU <- sqrt(diag(V_Vu)*meanPureErr)
|
|
|
|
su_modU2[,3] <- SEsPureU
|
|
su_modU2[,4] <- su_modU2[,2]/su_modU2[,3]
|
|
su_modU2[,5] <- 2*(1-pt(abs(su_modU2[,4]), FitAnova[4,1]))
|
|
|
|
uCI_SloDiffP <- su_modU[4,1] + qt(0.975,8)*SEsPureU[4]
|
|
lCI_SloDiffP <- su_modU[4,1] - qt(0.975,8)*SEsPureU[4]
|
|
SlopeDiffCI <- c(su_modU[4,1], lCI_SloDiffP,uCI_SloDiffP)
|
|
|
|
SSRes <- SSE
|
|
dfRes <- dfPureE
|
|
|
|
} else {
|
|
SSRes <- RSS
|
|
dfRes <- dfRMSE
|
|
}
|
|
|
|
# treatment
|
|
SStreat <- print(sum((predict(lm(readout ~ factor(log_dose)*isSample, circ_ABl))-mean(circ_ABl$readout))^2))
|
|
F_treat <- (SStreat/dfTreat)/(SSRes/dfRes)
|
|
# Preparation
|
|
SSprep <- print(sum((predict(lm(readout ~ isSample, circ_ABl))-mean(circ_ABl$readout))^2))
|
|
F_prep <- (SSprep/dfTreat)/(SSRes/dfRes)
|
|
# Regression
|
|
# ANOVA tape II SS of regression
|
|
SSreg <- Anova(lm(readout ~log_dose + isSample, circ_ABl))[1,1]
|
|
# Non-parallelism
|
|
# diff of RSS of restricted and unrestricted model
|
|
SSnonpar <- sum(resid(modAB)^2) - sum(resid(modABu)^2)
|
|
F_nonpar <- SSnonpar/(sum(resid(lm(readout ~ factor(log_dose)*isSample, circ_ABl))^2)/(lenCirc-4))
|
|
|
|
# non-linearity
|
|
SSnonlin <- sum((predict(modABu)-predict(lm(readout ~ as.factor(log_dose)*isSample, circ_ABl)))^2)
|
|
# = RSS-SSE
|
|
# Total SS
|
|
SStot <- sum((circ_ABl$readout-mean(circ_ABl$readout))^2)
|
|
# Significance of R^2 F-ratio
|
|
# MSR/MSE
|
|
# sample A
|
|
F_R2_A <- sum((predict(lm(readout ~ log_dose+ I(log_dose^2), circ_Al)) - mean(predict(modA)))^2 - (predict(modA) - mean(circ_Al$readout))^2)/
|
|
(sum((predict(lm(readout ~ log_dose+ I(log_dose^2), circ_Al)) - circ_Al$readout)^2)/(nrow(circ_Al)-3))
|
|
pFR2_A <- round(pf(F_R2_A,1,6),4)
|
|
# sample B
|
|
F_R2_B <- sum((predict(lm(readout ~ log_dose+ I(log_dose^2), circ_Bl)) - mean(predict(modB)))^2 - (predict(modB) - mean(circ_Bl$readout))^2)/
|
|
(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))^2)/(sum((circ_Bl$readout - predict(modB))^2)/(nrow(circ_Bl)-2))
|
|
F_slope_A <- sum((predict(modA) - mean(circ_Al$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) {
|
|
p_F_nonlin <- round(pf(F_nonlin,2,dfPureE, lower.tail = F),5)
|
|
} else { p_F_nonlin <- "SSnonlin neg or 0"; }
|
|
|
|
# significances
|
|
F_regr <- (SSreg/1)/(SSRes/dfRes)
|
|
p_F_regr <- round(pf(F_regr,1,dfRes, lower.tail = F),3)
|
|
p_F_treat <- round(pf(F_treat,3,dfRes, lower.tail = F),3)
|
|
p_F_prep <- round(pf(F_prep,1,dfRes, lower.tail = F),3)
|
|
p_F_slope_A <- round(pf(F_slope_A,1,(nrow(circ_Al)-2), lower.tail = F),3)
|
|
p_F_slope_B <- round(pf(F_slope_B,1,(nrow(circ_Bl)-2), lower.tail = F),3)
|
|
p_F_nonp <- round(pf(F_nonpar,1,dfRes, lower.tail = F),3)
|
|
p_F_LoF <- p_F_nonlin
|
|
|
|
res_tab_lin <- data.frame(test = c("F-test on sign. of regression", "F_test on non-lin",
|
|
"F-test on R^2 A","F_test on R^2 B",
|
|
"F-test on slope A","F-test on slope B",
|
|
"F-test on non-parallelism","F-test on preparation"),
|
|
test_results = c(ifelse(p_F_regr<0.05,0,1),ifelse(p_F_nonlin<0.05,1,0),
|
|
ifelse(pFR2_A<0.05,1,0),ifelse(pFR2_B<0.05,1,0),
|
|
ifelse(p_F_slope_A<0.05,0,1),ifelse(p_F_slope_B<0.05,0,1),
|
|
ifelse(p_F_nonp<0.05,1,0),ifelse(p_F_prep<0.05,0,1)),
|
|
estimate = c(p_F_regr, p_F_nonlin,pFR2_A,pFR2_B,p_F_slope_A,
|
|
p_F_slope_B,p_F_nonp,p_F_prep),
|
|
Source = c("Treatment","Preparation","Regression","Non-parallelism",
|
|
"Resid Error","Non-linearity","Pure error", "Total"),
|
|
df = c(dfTreat,1,1,1,dfRMSE,2,dfPureE,lenCirc-1),
|
|
SumSquares = c(round(SStreat,5),round(SSprep,5),round(SSreg,5),
|
|
round(SSnonpar,5),round(RSS,5),round(SSnonlin,5),
|
|
round(SSE,5),round(SStot,5)),
|
|
MS = c(round(SStreat/dfTreat,5),round(SSprep,5),round(SSreg,5),
|
|
round(SSnonpar,5),round(RSS/dfRMSE,5),round(SSnonlin/2,5),
|
|
round(SSE/dfPureE,5),round(SStot/dfTotal,5)),
|
|
"F-value" = c(round(F_treat,5), round(F_prep,5),round(F_regr,5),
|
|
round(F_nonpar,5),"",round(F_nonlin,5),"",""),
|
|
"p-value" = c(p_F_treat, p_F_prep, p_F_regr, p_F_nonp, "", p_F_LoF, "",""))
|
|
RET <- list(res_tab_lin, su_modU2, SlopeDiffCI, su_mod2)
|
|
return(RET)
|
|
}
|
|
|
|
pot4plFUNC <- function(ro_new, PureErrFlag) {
|
|
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)
|
|
all_l$isRef <- isRef
|
|
all_l$isSample <- isSample
|
|
all_l$Conc <- exp(all_l$log_dose)
|
|
all_l$readout[all_l$readout < 0] <- 0.01
|
|
all_l$readouttrans <- log(all_l$readout)
|
|
|
|
CORdat <- cor(ro_new[,1],ro_new[,ncol(ro_new)])
|
|
if (CORdat<0) SLOPE <- -1 else SLOPE <- 1
|
|
|
|
startlist <- list(a=min(ro_new[,2]), b=SLOPE, d=max(ro_new[,2]), cs=mean(all_l$log_dose),r=0)
|
|
tryCatch({
|
|
mr <- gsl_nls(fn = readout ~ a+(d-a)/(1+exp(b*(cs-r*isSample-log_dose))),
|
|
data=all_l,
|
|
start=startlist,
|
|
control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
|
|
},
|
|
error = function(err) {
|
|
err$message
|
|
})
|
|
|
|
startlistmu <- list(as=max(ro_new[,2]), bs=SLOPE, ds=min(ro_new[,2]), cs=mean(all_l$log_dose),
|
|
at=max(ro_new[,2]), bt=SLOPE, dt=min(ro_new[,2]), r=0)
|
|
|
|
tryCatch({
|
|
mu <- gsl_nls(fn = readout ~ as*isRef + at*isSample + (ds*isRef + dt*isSample - as*isRef - at*isSample)/
|
|
(1+isRef*exp(bs*(cs - log_dose)) + isSample*exp(bt*(cs-r*isSample-log_dose))),
|
|
data=all_l,
|
|
start=startlistmu,
|
|
control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
|
|
},
|
|
error = function(err) {
|
|
err$message
|
|
})
|
|
|
|
if (!PureErrFlag) {
|
|
pot_est <- exp(confintd(mr, "r", method="asymptotic"))
|
|
potU_est <- exp(confintd(mu, "r", method="asymptotic"))
|
|
} else {
|
|
FitAnova <- anova(lm(readout ~ factor(Conc)*isSample, all_l))
|
|
meanPureErr <- FitAnova[4,3]
|
|
SU_mr <- tryCatch({
|
|
SU_mr <- summary(mr)
|
|
}, error = function(msg) {
|
|
return()
|
|
})
|
|
|
|
#browser()
|
|
if (length(SU_mr)>1) {
|
|
s_mr <- SU_mr$coefficients
|
|
} else { SU_mr <- rep(NA,5) }
|
|
|
|
VCOV <- vcov(mr)
|
|
V_V <- VCOV/SU_mr$sigma^2
|
|
SEsPure <- sqrt(diag(V_V)*meanPureErr)
|
|
pot_est <- c(exp(s_mr[5,1]),exp(s_mr[5,1]-qt(0.975,nrow(all_l)-5)*SEsPure[5]),
|
|
exp(s_mr[5,1]+qt(0.975,nrow(all_l)-5)*SEsPure[5]))
|
|
# unrestricted
|
|
s_mu <- summary(mu)$coefficients
|
|
SU_mu <- summary(mu)
|
|
VCOVu <- vcov(mu)
|
|
V_Vu <- VCOVu/SU_mu$sigma^2
|
|
SEsPureU <- sqrt(diag(V_Vu)*meanPureErr)
|
|
potU_est <- c(exp(s_mu[7,1]),exp(s_mu[7,1]-qt(0.975,nrow(all_l)-8)*SEsPureU[7]),
|
|
exp(s_mu[7,1]+qt(0.975,nrow(all_l)-8)*SEsPureU[7]))
|
|
} # PureErrFlag
|
|
|
|
startlistmr_log <- list(a=max(all_l$readouttrans), b=SLOPE, d=min(all_l$readouttrans), cs=mean(all_l$log_dose),r=0)
|
|
|
|
tryCatch({
|
|
mr_log <- gsl_nls(fn = readouttrans ~ a+(d-a)/(1+exp(b*(cs-r*isSample-log_dose))),
|
|
data=all_l,
|
|
start=startlistmr_log,
|
|
control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
|
|
},
|
|
error = function(err) {
|
|
err$message
|
|
})
|
|
|
|
|
|
startlistmu_log <- list(as=max(ro_new[,2]), bs=SLOPE, ds=min(ro_new[,2]), cs=mean(all_l$log_dose),
|
|
at=max(ro_new[,2]), bt=SLOPE, dt=min(ro_new[,2]), r=0)
|
|
|
|
tryCatch({
|
|
mu_log <- gsl_nls(fn = readouttrans ~ as*isRef + at*isSample + (ds*isRef + dt*isSample - as*isRef - at*isSample)/
|
|
(1+isRef*exp(bs*(cs - log_dose)) + isSample*exp(bt*(cs-r*isSample-log_dose))),
|
|
data=all_l,
|
|
start=startlistmu_log,
|
|
control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
|
|
},
|
|
error = function(err) {
|
|
err$message
|
|
})
|
|
|
|
pot_est_log <- exp(confintd(mr_log, "r", method="asymptotic"))
|
|
potU_est_log <- exp(confintd(mu_log, "r", method="asymptotic"))
|
|
colnames(pot_est_log) <- c("estimate","lowerCI2","upperCI")
|
|
colnames(potU_est_log) <- c("estimate","lowerCI2","upperCI")
|
|
#browser()
|
|
su_mr_log <- summary(mr_log)
|
|
Dat$RMSE_Rlog <- su_mr_log$sigma
|
|
su_mu_log <- summary(mu_log)
|
|
Dat$RMSE_Ulog <- su_mu_log$sigma
|
|
Dat$up_lowAslog <- su_mu_log$coefficients[1,1] - su_mu_log$coefficients[4,1]
|
|
potALL <- rbind(pot_est, potU_est, pot_est_log, potU_est_log)
|
|
|
|
potALL2 <- cbind(c("restricted","unrestricted","transformed restr","untransf restr"), potALL)
|
|
return(potALL2)
|
|
}
|
|
|
|
ParamCI_F <- function(xt,xs,se_xt, se_xs, CoVarlog,DFs, Conf=0.975) {
|
|
log_xs <- log(abs(xs))
|
|
log_xt <- log(abs(xt))
|
|
var_log_xs <- (se_xs/xs)^2 # approximate variance of log(bs)
|
|
var_log_xt <- (se_xt/xt)^2
|
|
se_log_ratio <- sqrt(var_log_xs + var_log_xt) #-2*CoVarlog)
|
|
|
|
lower_log_ratio <- log_xt-log_xs - 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)
|
|
}
|
|
|
|
tests_FUNC <- function(ro_new, Lim, PureErrFlag) {
|
|
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)
|
|
all_l$isRef <- isRef
|
|
all_l$isSample <- isSample
|
|
all_l$Conc <- exp(all_l$log_dose)
|
|
all_l$readout[all_l$readout < 0] <- 0.01
|
|
|
|
pot <- drm(readout ~ Conc, isSample, data=all_l, fct=LL.4(names=c("b","d","a","c")),
|
|
pmodels=data.frame(1,1,1,isSample))
|
|
compParm(pot, "c",display=T)
|
|
ED50 <- ED(pot,c(50), interval="delta")
|
|
PotEst <- ED50[1,1]/ED50[2,1]
|
|
potAll <- EDcomp(pot, percVec=c(50,50), interval="delta", display=FALSE)
|
|
potAll2 <- potAll[1:3]
|
|
|
|
CORro <- cor(ro_new[,1], ro_new[,ncol(ro_new)])
|
|
|
|
if (CORro<0) SLOPE <- -1 else SLOPE <- 1
|
|
startlist <- list(a=max(ro_new[,2]), b=SLOPE, d=min(ro_new[,2]), cs=mean(all_l$log_dose),r=0)
|
|
tryCatch({
|
|
mr <- gsl_nls(fn = readout ~ a+(d-a)/(1+exp(b*(cs-r*isSample-log_dose))),
|
|
data=all_l,
|
|
start=startlist,
|
|
control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
|
|
},
|
|
error = function(err) {
|
|
err$message
|
|
})
|
|
|
|
startlistmu <- list(as=max(ro_new[,2]), bs=SLOPE, ds=min(ro_new[,2]), cs=mean(all_l$log_dose),
|
|
at=max(ro_new[,2]), bt=SLOPE, dt=min(ro_new[,2]), r=0)
|
|
tryCatch({
|
|
mu <- gsl_nls(fn = readout ~ as*isRef + at*isSample + (ds*isRef + dt*isSample - as*isRef - at*isSample)/
|
|
(1+isRef*exp(bs*(cs - log_dose)) + isSample*exp(bt*(cs-r*isSample-log_dose))),
|
|
data=all_l,
|
|
start=startlistmu,
|
|
control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
|
|
},
|
|
error = function(err) {
|
|
err$message
|
|
})
|
|
|
|
smu <- tryCatch({ summary(mu) },
|
|
error=function(err) {
|
|
return(0)
|
|
})
|
|
|
|
POTr_CI <- potAll2[2:3]
|
|
|
|
FitAnova <- anova(lm(readout ~ factor(Conc)*isSample, all_l))
|
|
# pure error
|
|
pureSS <- FitAnova[4,2]
|
|
pureSS_df <- FitAnova[4,1]
|
|
meanPureErr <- FitAnova[4,3]
|
|
vcovMU <- vcov(mu)
|
|
V_V <- vcovMU/smu$sigma^2
|
|
SEsPure <- sqrt(diag(V_V)*meanPureErr)
|
|
VCOVpure <- V_V*meanPureErr
|
|
DFsPure <- FitAnova[4,1]
|
|
|
|
|
|
|
|
testPOTr <- logical()
|
|
if (POTr_CI[1]*100>Lim[[9]] & POTr_CI[2]*100<Lim[[10]] ) testPOTr <- 0 else testPOTr <- 1
|
|
|
|
potU <- drm(readout ~ Conc, isSample, data=all_l, fct=LL.4(names=c("b","d","a","c")),
|
|
pmodels=data.frame(isSample, isSample,isSample,isSample))
|
|
potAllU <- EDcomp(potU, percVec=c(50,50), interval="delta", display=FALSE)
|
|
potAllU2 <- potAllU[1:3]
|
|
sum_potU <- summary(potU)
|
|
coeffs <- potU$coefficients
|
|
coeffs[1] <- ifelse(CORro<0, -coeffs[1], coeffs[1])
|
|
coeffs[2] <- ifelse(CORro<0, -coeffs[2], coeffs[2])
|
|
names(coeffs) <- c("bs","bt","ds","dt","as","at","cs","ct")
|
|
|
|
e_c_ref <- coeffs[7]
|
|
e_c_test <- coeffs[8]
|
|
coeffs[7:8] <- log(coeffs[7:8])
|
|
test_c <- logical()
|
|
if((potAllU2[2] >Lim[[9]]/100 & potAllU2[3] <Lim[[10]]/100)) test_c <- 0 else test_c <- 1
|
|
|
|
#### ANOVA ----
|
|
noConc <- length(unique(all_l$Conc))
|
|
nofitted <- noConc
|
|
AnovaDFs <- c(nofitted-1,1,3,nofitted-4-1,nrow(all_l)-nofitted, nofitted,nrow(all_l)-2*nofitted,nrow(all_l)-1)
|
|
SStreat <- round(sum((predict(potU)-mean(all_l$readout))^2),5)
|
|
SSregr <- round(sum((predict(pot)-mean(all_l$readout))^2),5)
|
|
# non-parallelism
|
|
SSnonparall <- round(sum(resid(pot)^2)-sum(resid(potU)^2),5)
|
|
SSprep <- round(sum((predict(lm(readout ~ isSample, all_l))-mean(all_l$readout))^2),5)
|
|
|
|
RSS <- round(sum(potU$predres[,2]^2),5)
|
|
RSS_df <- AnovaDFs[5]
|
|
MSEunr <- RSS/RSS_df
|
|
RMSEunr <- sqrt(RSS/RSS_df)
|
|
# Pure Err
|
|
FitAnova <- anova(lm(readout ~ factor(Conc)*isSample, all_l))
|
|
SSE <- sum(resid(lm(readout ~ factor(Conc)*isSample, all_l))^2) # =FitAnova[4,2]
|
|
SSE_df <- FitAnova[4,1]
|
|
PureMSE <- SSE/SSE_df
|
|
RMSE_pure <- sqrt(PureMSE)
|
|
## non-lin = LoF
|
|
if (PureErrFlag) { ERR <- PureMSE; ERR_df <- SSE_df } else { ERR <- MSEunr; ERR_df <- RSS_df }
|
|
SSnonlin <- sum((predict(lm(readout ~ factor(Conc)*isSample, all_l))-predict(potU))^2)
|
|
LoF_df <- FitAnova[1,1]+FitAnova[2,1]
|
|
F_regr <- (SSregr/AnovaDFs[3])/ERR
|
|
p_F_regr <- round(pf(F_regr, AnovaDFs[3], ERR_df, lower.tail = F),5)
|
|
if (ncol(ro_new)<4) F_nonlin <- 0 else F_nonlin <- (SSnonlin/AnovaDFs[6])/ERR
|
|
if (F_nonlin > 0) {
|
|
p_F_nonlin <- round(pf(F_nonlin, AnovaDFs[6], ERR_df, lower.tail = F),5)
|
|
} else { p_F_nonlin <- "SSnonlin neg or single dilutions" }
|
|
|
|
test_a <- test_b <- test_d <- test_ad <- logical()
|
|
|
|
RSS_r <- round(sum(pot$predres[,2]^2),5)
|
|
MSE_r <- RSS_r/(nrow(all_l)-5)
|
|
RMSE_r <- round(sqrt(MSE_r),6)
|
|
Dat$RMSE_r <- RMSE_r
|
|
Dat$RMSE_pure <- RMSE_pure
|
|
Dat$RMSE_unr <- round(RMSEunr,6)
|
|
#browser()
|
|
## EQ test on lower As diff
|
|
ds <- coeffs["ds"]
|
|
dt <- coeffs["dt"]
|
|
lAs_diff <- (dt-ds)
|
|
uCI_laDiff <- lAs_diff+qt(0.975,df.residual(mu))*sqrt(sum_potU$coefficients[3,2]^2+sum_potU$coefficients[4,2]^2)
|
|
lCI_laDiff <- lAs_diff-qt(0.975,df.residual(mu))*sqrt(sum_potU$coefficients[3,2]^2+sum_potU$coefficients[4,2]^2)
|
|
if (uCI_laDiff < Lim[[2]] & lCI_laDiff > Lim[[1]]) test_la_diff <- 0 else test_la_diff <- 1
|
|
|
|
#### EQ test on upper asymptote ratio ----
|
|
as <- coeffs["as"]
|
|
at <- coeffs["at"]
|
|
uAsRatio <- compParm(potU, "a","/",display=F)
|
|
uAsCI <- c(uAsRatio[1]-qt(0.975,RSS_df)*uAsRatio[2], uAsRatio[1]+qt(0.975,RSS_df)*uAsRatio[2])
|
|
#browser()
|
|
ds <- smu$coefficients["ds",1]
|
|
dt <- smu$coefficients["dt",1]
|
|
if (PureErrFlag) se_ds <- sqrt(VCOVpure["ds","ds"]) else se_ds <- smu$coefficients["ds",2]
|
|
if (PureErrFlag) se_dt <- sqrt(VCOVpure["dt","dt"]) else se_dt <- smu$coefficients["dt",2]
|
|
if (PureErrFlag) CoVarlog_d <- VCOVpure["dt","ds"] else CoVarlog_d <- vcovMU["dt","ds"]
|
|
if (PureErrFlag) DFs <- DFsPure else DFs <- nrow(all_l)-noConc
|
|
uAsCI2 <- ParamCI_F(dt,ds,se_dt, se_ds,CoVarlog_d, DFs, Conf=0.9975)
|
|
if (uAsCI2[1] > Lim[[7]] & uAsCI2[2] < Lim[[8]]) test_a <- 0 else test_a <- 1
|
|
estUppA <- round(at/as,5)
|
|
|
|
Dat$uAsCI <- uAsCI2
|
|
|
|
#### EQ test on slope ratio ----
|
|
bs <- coeffs["bs"]
|
|
bt <- coeffs["bt"]
|
|
slopeRatio <- compParm(potU, "b","/",display=F)
|
|
slopeCI <- c(slopeRatio[1,1]-qt(0.975,RSS_df)*slopeRatio[1,2], slopeRatio[1,1]+qt(0.975,RSS_df)*slopeRatio[1,2])
|
|
|
|
bs <- smu$coefficients["bs",1]
|
|
bt <- smu$coefficients["bt",1]
|
|
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)
|
|
if (slopeCI2[1] > Lim[[5]] & slopeCI2[2] < Lim[[6]]) test_b <- 0 else test_b <- 1
|
|
estUppA <- round(at/as,5)
|
|
|
|
Dat$slopeRatioCI <- slopeCI
|
|
|
|
#### EQ test on lower As ratio ----
|
|
|
|
lAsRatio <- compParm(potU, "d","/",display=F)
|
|
slopeCI <- c(lAsRatio[1,1]-qt(0.975,RSS_df)*lAsRatio[1,2], lAsRatio[1,1]+qt(0.975,RSS_df)*lAsRatio[1,2])
|
|
|
|
as <- smu$coefficients["as",1]
|
|
at <- smu$coefficients["at",1]
|
|
if (PureErrFlag) se_as <- sqrt(VCOVpure["as","as"]) else se_as <- smu$coefficients["as",2]
|
|
if (PureErrFlag) se_at <- sqrt(VCOVpure["at","at"]) else se_at <- smu$coefficients["at",2]
|
|
if (PureErrFlag) CoVarlog_a <- VCOVpure["at","as"] else CoVarlog_a <- vcovMU["at","as"]
|
|
lAsCI2 <- ParamCI_F(at,as,se_at, se_as,CoVarlog_a, DFs, Conf=0.975)
|
|
if (lAsCI2[1] > Lim[[3]] & lAsCI2[2] < Lim[[4]]) test_d <- 0 else test_d <- 1
|
|
estLowA <- round(at/as,5)
|
|
|
|
Dat$lAsCI <- lAsCI2
|
|
|
|
#### EQtest on ratio of As difference ----
|
|
AsDiffRatio <- (at-dt)/(as-ds)
|
|
|
|
at_dt <- (at-dt)
|
|
as_ds <- (as-ds)
|
|
se_ds_asPure <- sqrt(VCOVpure["as","as"]+VCOVpure["ds","ds"]-2*VCOVpure["as","ds"])
|
|
se_dt_atPure <- sqrt(VCOVpure["at","at"]+VCOVpure["dt","dt"]-2*VCOVpure["at","dt"])
|
|
se_ds_asRMSE <- sqrt(vcovMU["as","as"]+vcovMU["ds","ds"]-2*vcovMU["as","ds"])
|
|
se_dt_atRMSE <- sqrt(vcovMU["at","at"]+vcovMU["dt","dt"]-2*vcovMU["at","dt"])
|
|
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(at_dt,as_ds,se_dt_at, se_ds_as,CoVarlog=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)
|
|
|
|
Dat$up_lowAs <- (as-ds)
|
|
|
|
lowerCIlowerA <- lAsCI2[1]; lowerCIupperA <- uAsCI2[1]; upperCIlowerA <- lAsCI2[2]; upperCIupperA <- uAsCI2[2]
|
|
test_lowA <- test_d; test_uppA <- test_a
|
|
#browser()
|
|
res_tab <- data.frame(test= c("F-test on sign. of regression*",
|
|
"EQ test on lower asymptotes difference",
|
|
"EQ test ratio of lower asymptotes",
|
|
"EQ test ratio of Hill slopes",
|
|
"EQ test ratio of upper asymptotes",
|
|
"F-test on non-linearity*",
|
|
"EQ test ratio of asymptote difference",
|
|
"geom. rel. CI restr. model",
|
|
"geom. rel. CI unrestr. model"),
|
|
test_results = c(ifelse(p_F_regr<0.05,0,1), test_la_diff, test_lowA, test_b, test_uppA,
|
|
ifelse(p_F_nonlin>1,1, ifelse(p_F_nonlin<0.05,1,0)), test_ad,
|
|
testPOTr, test_c),
|
|
estimate = c(round(p_F_regr, 3), round(lAs_diff, 5),
|
|
estLowA, round(bs/bt,5), estUppA, p_F_nonlin,
|
|
round(at_dt/as_ds, 5), round(potAll2[1]*100,2),round(potAllU2[1]*100,2)),
|
|
lower_limit = c("-",Lim[[1]],Lim[[3]],Lim[[5]],Lim[[7]],"-",Lim[[11]],Lim[[9]],Lim[[9]]),
|
|
upper_limit = c("-",Lim[[2]],Lim[[4]],Lim[[6]],Lim[[8]],"-",Lim[[12]],Lim[[10]],Lim[[10]]),
|
|
lower_CI = c(RMSE_r, round(lCI_laDiff,3), round(lAsCI2[1],5), round(slopeCI2[1],5),
|
|
round(uAsCI2[1],5), "-", round(AsDiffCI2[1],5), round(potAll2[2],2), round(potAllU2[2],2)),
|
|
upper_CI = c(RMSE_pure, round(uCI_laDiff,3), round(lAsCI2[2],5), round(slopeCI2[2],5),
|
|
round(uAsCI2[2],5), "-", round(AsDiffCI2[2],5), round(potAll2[3],2), round(potAllU2[3],2))
|
|
)
|
|
return(res_tab)
|
|
}
|
|
|
|
ANOVA4plUnresfunc <- function(ro_new, sigmoid) {
|
|
all_l <- melt(data.frame(ro_new), id.vars="log_dose", variable.name="replname", value.name = "readout")
|
|
all_len <- nrow(all_l)
|
|
isRef <- rep(c(1,0),1,each=all_len/2)
|
|
isSample <- rep(c(0,1),1,each=all_len/2)
|
|
all_l$isRef <- isRef
|
|
all_l$isSample <- isSample
|
|
all_l$Conc <- exp(all_l$log_dose)
|
|
all_l$readout[all_l$readout < 0] <- 0.01
|
|
|
|
pot <- drm(readout ~ Conc, isSample, data=all_l2, fct=LL.4(names=c("b","d","a","c")),
|
|
pmodels=data.frame(1,1,1,isSample))
|
|
potU <- drm(readout ~ Conc, isSample, data=all_l, fct=LL.4(names=c("b","d","a","c")),
|
|
pmodels=data.frame(isSample, isSample,isSample,isSample))
|
|
SStreat <- round(sum((potU$predres[,1] - mean(all_l$readout))^2),5)
|
|
SStreat_df <- length(unique(all_l$log_dose))-1
|
|
SSregr <- round(sum((predict(pot)-mean(all_l$readout))^2),5)
|
|
## Non-parallel
|
|
SSnonparallel <- round(sum(resid(pot)^2) - sum(resid(potU)^2),5)
|
|
## Preparation
|
|
SSprep <- round(sum((predict(lm(readout ~ isSample, all_l)) - mean(all_l$readout))^2),5)
|
|
## Resid Err
|
|
RSS <- round(sum(potU$predres[,2]^2),5)
|
|
RSS_df <- nrow(all_l)-SStreat_df-1
|
|
FitAnova <- anova(lm(readout ~ factor(Conc)*isSample, all_l))
|
|
# PureErr
|
|
SSE <- FitAnova[4,3]
|
|
SSE_df <- FitAnova[4,1]
|
|
# Non-Linearity
|
|
SSnonlin <- round(sum((predict(lm(readout ~ factor(Conc)*isSample, all_l)) - predict(potU))^2),4)
|
|
LoF_df <- FitAnova[1,1]+FitAnova[2,1]
|
|
## Total
|
|
SStot <- round(sum((all_l$readout -mean(all_l$readout))^2),5)
|
|
MSE <- RSS/RSS_df
|
|
noConc <- length(unique(all_l$Conc))
|
|
AnovaDFs <- c(noConc-1, 1,3,noConc-4-1, nrow(all_l)-noConc, noConc, nrow(all_l)-noConc-noConc, nrow(all_l)-1)
|
|
p_SStreat <- round(pf((SStreat/AnovaDFs[1])/MSE, AnovaDFs[1],RSS_df, lower.tail = F),3)
|
|
p_SSprep <- round(pf((SSprep/AnovaDFs[2])/MSE, AnovaDFs[2],RSS_df, lower.tail = F),3)
|
|
p_SSregr <- round(pf((SSregr/AnovaDFs[3])/MSE, AnovaDFs[3],RSS_df, lower.tail = F),3)
|
|
p_SSnonp <- round(pf((SSnonparallel/AnovaDFs[4])/MSE, AnovaDFs[3],RSS_df, lower.tail = F),3)
|
|
p_SSLoF <- round(pf((SSnonlin/LoF_df)/(SSE/SSE_df), LoF_df,SSE_df, lower.tail = F),5)
|
|
|
|
ANOVAtab <- data.frame(Source = c("Treatment","Preparation","Regression",
|
|
"Non-Parallelism","Residual Error","Non-linearity",
|
|
"Pure Error","Total"),
|
|
DF = AnovaDFs,
|
|
SumSquares = c(SStreat, SSprep,SSregr, SSnonparallel,
|
|
RSS, SSnonlin,SSE, SStot),
|
|
MeanSquares = c(round(SStreat/AnovaDFs[1],3), SSprep, round(SStreat/AnovaDFs[3],3),round(SSnonparallel/AnovaDFs[4],3),
|
|
round(MSE,5), round(SSnonlin/LoF_df,5), round(SSE/SSE_df,5),""),
|
|
"F-value" = c(round((SStreat/AnovaDFs[1])/MSE,5), round((SSprep/AnovaDFs[2])/MSE,5),
|
|
round((SSregr/AnovaDFs[3])/MSE,5),round((SSnonparallel/AnovaDFs[4])/MSE,5),
|
|
"",round((SSnonlin/LoF_df)/(SSE/SSE_df),5),"",""),
|
|
"p_value" = c(round(p_SStreat,3), p_SSprep, round(p_SSregr,3), p_SSnonp,"",p_SSLoF,"","")
|
|
)
|
|
|
|
return(ANOVAtab)
|
|
}
|
|
|
|
perConcTab <- function(ro_new, noDilSeries) {
|
|
Reftab <- ro_new[,c(1:noDilSeries)]
|
|
Testtab <- ro_new[,c((noDilSeries+1):(2*noDilSeries))]
|
|
tReftab <- t(Reftab)
|
|
colnames(tReftab) <- round(ro_new[,ncol(ro_new)],5)
|
|
|
|
avs <- apply(tReftab,2,mean)
|
|
sds <- apply(tReftab,2,sd)
|
|
cv <- sds/avs*100
|
|
tReftab2 <- rbind(tReftab, avs,sds,cv)
|
|
|
|
tTesttab <- t(Testtab)
|
|
colnames(tTesttab) <- round(ro_new[,ncol(ro_new)],5)
|
|
|
|
avs_test <- apply(tTesttab,2,mean)
|
|
sds_test <- apply(tTesttab,2,sd)
|
|
cv_test <- sds_test/avs_test*100
|
|
tTesttab2 <- rbind(tTesttab, avs_test,sds_test,cv_test)
|
|
concTab <- rbind(tReftab2, tTesttab2)
|
|
return(concTab)
|
|
|
|
}
|
|
|
|
divFUN <- function(x,Div,N,res,noDil) {
|
|
N <- N+1
|
|
y <- x/Div
|
|
res <- c(res,y)
|
|
if (N==noDil) { return(res) }
|
|
divFUN(y,Div,N,res,noDil)
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
server = function(input,output,session) {
|
|
ReportParS <- reactiveValues()
|
|
IPReportParS <- reactiveValues()
|
|
|
|
|
|
output$homePage <- renderUI({
|
|
navbarPage("Home",
|
|
tabPanel("Introduction",
|
|
tags$img(src="logo.png", class="adv_logo"),
|
|
h4("Introduction to the bioassay software"),
|
|
tags$mark("linear regression"), br(),
|
|
|
|
),
|
|
tabPanel("Documentation",
|
|
h4("Introduction "),
|
|
h4("Information on dilution setting"),
|
|
"(for details see:", a(href="ADONIS.pdf","Whitepaper", download=NA, target="_blank"),")",tags$br(),
|
|
"Bend points are calculated according to following formula:",
|
|
withMathJax(" $$bp_{1/2} = \\pm\\frac{1.31696}{Hill's slope}$$")),
|
|
tabPanel("Configuration",
|
|
verbatimTextOutput("sessioninfo"))
|
|
)
|
|
})
|
|
|
|
output$Dataupload <- renderUI({
|
|
navbarPage(title="Information",
|
|
tabPanel(title = "Upload all worksheets",
|
|
box(title = "Upload", status="warning",solidHeader = T, width=4, "Please upload your EXCEL file here",
|
|
fileInput("XLfile",'',accept=".xlsx")),
|
|
"Two possibilities of data structures...",
|
|
img(src="Screenshot.png", width=400),
|
|
tags$head(tags$style(HTML("label {font-size:80%;margin-bottom: 3px;margin-top: 3px;}"))),
|
|
column(2,
|
|
tags$image(src="logo.png", class="adv_logo"),
|
|
h4("upload EXCEL"),
|
|
fileInput(inputId = "iFile", label = "", accept="ms-excel"),
|
|
uiOutput(outputId = "sheetName"),
|
|
div(checkboxInput("PureErr", "Should pure error be used for calculation of CIs?", FALSE),
|
|
style = "font-size: 24px !important;color: red"),
|
|
verbatimTextOutput("stats"),
|
|
h5("\n\n\n Author: Franz Innerbichler, InnerAnalytics")),
|
|
div(id="parameter",
|
|
column(1,style = "background: lightgrey",
|
|
#actionButton("StartCalc", "Click, when calculations to be started"),
|
|
h4("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)
|
|
),
|
|
column(1,style = "background: lightgrey",
|
|
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)
|
|
),
|
|
column(1,style = "background: lightgreen",
|
|
h4("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)
|
|
),
|
|
column(1,style = "background: lightgreen",
|
|
|
|
numericInput("CONC7", "7th concentration",0.00469),
|
|
numericInput("CONC8", "8thd concentration",0.00235),
|
|
numericInput("CONC9", "9thd concentration",NA),
|
|
numericInput("CONC10", "10th concentration",NA),
|
|
numericInput("CONC11", "11th concentration",NA),
|
|
|
|
numericInput("CONC12", "lowest concentration",NA)
|
|
),
|
|
column(1,style = "background: lightblue",
|
|
h4("geometric dilution scheme"),
|
|
numericInput("ConcStart", "starting concentration",NA),
|
|
numericInput("dilutionFac", "dilution factor",NA),
|
|
numericInput("NoDil", "no. of dilutions",NA),
|
|
numericInput("NoDilSer", "no. of dil. series",NA),
|
|
verbatimTextOutput("dilutions")
|
|
),
|
|
column(4,
|
|
h4("log-dilution scheme"),
|
|
verbatimTextOutput("logdil"),
|
|
h4("User help"),
|
|
h5("If new dilutions are entered, please adjust EC50 to avoid calculation errors"))
|
|
)
|
|
|
|
))
|
|
})
|
|
|
|
output$fourPL <- renderUI({
|
|
navbarPage(title="4PL",
|
|
tabPanel("Analysis and Plots",
|
|
sidebarLayout(
|
|
sidebarPanel(
|
|
width=3,
|
|
fluidRow(
|
|
column(6,
|
|
numericInput("Limits",p("limit to be >", bsButton("q4",label="", icon=icon("info"), style="primary", size="extra-small")),
|
|
bsPopover(id="q4", title="", content="The calculated limits ...")))
|
|
)),
|
|
mainPanel(
|
|
tabsetPanel(id="tabs",
|
|
tabPanel("4pl",
|
|
box(title="ANOVA table", status="primary",solidHeader = T, width=12,
|
|
tableOutput("Anovatab")),
|
|
column(2,
|
|
h5("Diagnostics only shown if EXCEL is uploaded"),
|
|
htmlOutput("PureErrW2"),
|
|
tags$head(tags$style("#PureErrW2{color: red;
|
|
font-size: 16px;
|
|
font_style: italic;}")),
|
|
tableOutput("FileSAmpl"),
|
|
downloadButton("downloadXLReport", label="Download PDF report", class="butt"),
|
|
tags$style(type="text/css","#downloadXLReport {background-color: orange; color: black;font-family: COurier New}"),
|
|
plotOutput("relpotTestPlot", width="300px", height="150px"),
|
|
h4("Akaike Information Criterion"),
|
|
tableOutput("AIC"),
|
|
h5("First row: restricted model; 2nd row: unrestricted model"),
|
|
h5("Smaller values of AIC indicate better fit to the data"),
|
|
tableOutput("VarDiagn")
|
|
),
|
|
column(4,
|
|
plotOutput("XLplot"),
|
|
column(6,
|
|
br(),
|
|
"Regression results restricted",
|
|
tableOutput("coeffs_r"),
|
|
"Bend points restricted",
|
|
tableOutput("coeffs_r2")),
|
|
column(6,
|
|
br(),
|
|
"Regression results unrestricted",
|
|
tableOutput("coeffs_unr"))),
|
|
column(4,
|
|
plotOutput("diagnplot"),
|
|
column(6,
|
|
tableOutput("logcoeffs_r"),
|
|
tableOutput("coeffs_unr2")),
|
|
column(6,
|
|
tableOutput("logcoeffs_unr"))),
|
|
column(2,
|
|
tableOutput("ANOVAXLS"),
|
|
DT::renderDataTable("EQtests"))
|
|
),
|
|
tabPanel("Report",
|
|
h4("Settings for report"),
|
|
))
|
|
)
|
|
)))
|
|
})
|
|
|
|
output$pla <- renderUI({
|
|
navbarPage(title="pla",
|
|
tabPanel("Analysis and Plots",
|
|
sidebarLayout(
|
|
sidebarPanel(
|
|
width=3,
|
|
fluidRow(
|
|
column(6,
|
|
numericInput("Limits",p("limit to be >", bsButton("q4",label="", icon=icon("info"), style="primary", size="extra-small")),
|
|
bsPopover(id="q4", title="", content="The calculated limits ...")))
|
|
)),
|
|
mainPanel(
|
|
tabsetPanel(id="tabs",
|
|
tabPanel("4pl",
|
|
box(title="ANOVA table", status="primary",solidHeader = T, width=12,
|
|
tableOutput("Anovatab")),
|
|
column(6,
|
|
htmlOutput("PureErrW3"),
|
|
tags$head(tags$style("#PureErrW2{color: red;
|
|
font-size: 16px;
|
|
font_style: italic;}")),
|
|
plotOutput("plotLin"),
|
|
"Delta method is used for potency CIs",
|
|
DT::dataTableOutput("pottab"),
|
|
h4("Unrestricted linear model (SSSI):"),
|
|
tableOutput("SummaryModABu"),
|
|
h4("Restricted linear model (CSSI):"),
|
|
tableOutput("SummaryModAB")),
|
|
column(3,
|
|
h3("Tests for linear PLA):"),
|
|
DT::dataTableOutput("TESTSlin"),
|
|
h5("The estimate is the p-value of the test"),
|
|
h5("F-tests on regression, significance of slopes, and preparation need to have a p-value <0.05 to pass"),
|
|
h5("All other tests pass if p-value > 0.05"),
|
|
"SST CI for difference of slopes:",
|
|
tableOutput("SlopeDiffCI")),
|
|
column(3,
|
|
h3("ANOVA for parallel line assay"),
|
|
DT::dataTableOutput("ANOVAlin"))),
|
|
tabPanel("Report",
|
|
h4("Settings for report")
|
|
))
|
|
)
|
|
)))
|
|
})
|
|
|
|
output$wizard <- renderUI({
|
|
navbarPage(title="Dilution setting",
|
|
tabPanel("Plots",
|
|
sidebarLayout(
|
|
sidebarPanel(
|
|
width=3,
|
|
fluidRow(
|
|
column(6,
|
|
numericInput("Limits",p("limit to be >", bsButton("q4",label="", icon=icon("info"), style="primary", size="extra-small")),
|
|
bsPopover(id="q4", title="", content="The calculated limits ...")))
|
|
)),
|
|
mainPanel(
|
|
tabsetPanel(id="tabs",
|
|
tabPanel("4pl",
|
|
box(title="ANOVA table", status="primary",solidHeader = T, width=12,
|
|
tableOutput("Anovatab")),
|
|
column(4,
|
|
h3("Confidence intervals"),
|
|
tableOutput("CIs"),
|
|
"The confidence intrval table is interaactive for changes in: variability slider (%SD), potency of test-slider,
|
|
and 'Adjust the dilutions'-slider",
|
|
tableOutput("optimalDils"),
|
|
selectInput(inputId="scenario", label= "Select an 'optimal' scenario:", choices = c("scenario 2","scenario 3","scenario 6","steep slope"))),
|
|
column(5,
|
|
plotOutput("plotfordilutions"),
|
|
h4("in grey: most extreme bend point lines of theoretical samples with 50% and 200% potency"),
|
|
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),
|
|
h5("Dilution factors"),
|
|
tableOutput("adjlogdil"),
|
|
"Short guidance: wider dilution ranges increase the CIs of rel. potency, and decrease the CIs of upper and lower asymptote ratios, as well as Hill's slope ratios", br(),
|
|
"Narrower dilution ranges decrease the CIs of rel. potency, and increase the CIs of upper and lower asymptote ratios, ands Hill's slope ratios",),
|
|
column(3,
|
|
h3("Bend points"),
|
|
tableOutput("bps"),
|
|
tableOutput("extremebps"),
|
|
h4("Explanation of the plots")
|
|
)),
|
|
tabPanel("Report",
|
|
h4("Settings for report")
|
|
))
|
|
)
|
|
)))
|
|
})
|
|
|
|
|
|
}
|
|
|
|
|