3241 lines
151 KiB
R
3241 lines
151 KiB
R
library(shiny)
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library(shinydashboard)
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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(shinyBS)
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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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#### reactive values ----
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Dat <- reactiveValues()
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REP <- reactiveValues()
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#### functions ----
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# dilFUN2 <- function(cs_,dils,Faktor) {
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# av <- cs_
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# dils_av <- dils
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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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#' Levenberg Marquard fit of 4 pl
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#'
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#' Returns list of following: summary of restricted and unrestricted model fits of the 4pl function,
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#' potency estimates and respective CIs of restricted and unrestricted models, and the predictions thereof.
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#'
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#' @param ro_new The dilution series readouts for REF and TEST + 1 column log(loncentration) or concentration.
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#' @param TransFlag A boolean if the plot should be with natural log as readout (TRUE) or not (FALSE).
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#' @returns Returns list of following: summary of restricted and unrestricted model fits of the 4pl function,
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#' potency estimates and respective CIs of restricted and unrestricted models, and the predictions thereof.
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#' @export
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#' @examples
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#' dat <- data.frame(REF1=c(1547, 1620, 1644, 2504, 3426, 3512, 3401, 3787), REF2=c(1492, 1536, 1384, 2286, 3046, 3479, 3516, 3497),
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#' REF3=c(1468, 1827, 1558, 2252, 3002, 3349, 2945, 3665),
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#' TEST1=c(1405, 1523, 1502, 1474, 2383, 3221, 3589, 3445), TEST2=c(1420, 1516, 1544, 1512, 2226, 3219, 3327, 3591),
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#' TEST3=c(1399, 1376, 1588, 1475, 2148, 3083, 2942, 3466), log_dose=c(5.01,3.401,2.708,2.015,1.32176,0.62861,-0.0645385,-1.6739764))
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#' TransF<- FALSE
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#' Dat<- list()
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#' te <- Fitting_FUNC(dat,TransF)
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#' print(te)
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Fitting_FUNC <- function(ro_new, TransFlag=F) {
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CORro <- cor(ro_new[,1],ro_new[,ncol(ro_new)])
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#browser()
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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_l2 <- cbind(all_l, isRef, isSample)
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#browser()
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if (CORro<0) SLOPE <- -1 else SLOPE <- 1
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if (!TransFlag) {
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startlist <- list(a=min(ro_new[,2]), b=SLOPE, d=max(ro_new[,2]), cs=mean(all_l$log_dose),r=0)
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mr <- gsl_nls(fn = readout ~ a+(d-a)/(1+exp(b*((cs-r*isSample)-log_dose))),
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data=all_l2,
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start=startlist,#trace=T,
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control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
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s_mr <- tryCatch({
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s_mr <- summary(mr)
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},
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error = function(err) {
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s_mr <- NULL
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})
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} else {
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startlist <- list(a=log(min(ro_new[,2])), b=SLOPE, d=log(max(ro_new[,2])), cs=mean(all_l$log_dose),r=0)
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mrT <- gsl_nls(fn = log(readout) ~ a+(d-a)/(1+exp(b*((cs-r*isSample)-log_dose))),
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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_mr <- summary(mrT)
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}
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if (!TransFlag) {
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startlistmu <- list(as=min(ro_new[,2]), bs=SLOPE, ds=max(ro_new[,2]), cs=mean(all_l$log_dose),
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at=min(ro_new[,2]), bt=SLOPE, dt=max(ro_new[,2]), r=0)
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tryCatch({
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mu <- gsl_nls(fn = readout ~ as*isRef + at*isSample + (ds*isRef + dt*isSample - as*isRef - at*isSample)/
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(1+isRef*exp(bs*(cs - log_dose)) + isSample*exp(bt*(cs-r*isSample-log_dose))),
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data=all_l,
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start=startlistmu,
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control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
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},
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error = function(msg){
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return(0) })
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Sum_u <- tryCatch({ summary(mu) },
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error=function(msg){
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return(0) })
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} else {
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startlistmu <- list(as=log(min(ro_new[,2])), bs=SLOPE, ds=log(max(ro_new[,2])), cs=mean(all_l$log_dose),
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at=log(min(ro_new[,2])), bt=SLOPE, dt=log(max(ro_new[,2])), r=0)
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tryCatch({
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muT <- gsl_nls(fn = log(readout) ~ 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*(cs-r*isSample-log_dose))),
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data=all_l,
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start=startlistmu,
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control=gsl_nls_control(xtol=1e-6,ftol=1e-6, gtol=1e-6))
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},
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error = function(msg){
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return(0) })
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Sum_u <- tryCatch({ summary(muT) },
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error=function(msg){
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return(0) })
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}
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if (!TransFlag) {
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pot_est <- exp(confintd(mr, "r", method="asymptotic"))
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potU_est <- exp(confintd(mu, "r", method="asymptotic"))
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PRED <- predict(mr)
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PREDu <- predict(mu)
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} else {
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pot_est <- exp(confintd(mrT, "r", method="asymptotic"))
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potU_est <- exp(confintd(muT, "r", method="asymptotic"))
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PRED <- predict(mrT)
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PREDu <- predict(muT)
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}
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return(list(s_mr, Sum_u, pot_est, potU_est, PRED, PREDu))
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}
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#' Plot sigmoidal curve
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#'
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#' Returns the final plots of the 4pl function as sigmoidal lines, and the single readouts as scatter, with REF in blue and TEST in red.
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#' Two plots are returned in a grid object: the unrestricted model and the restricted model.
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#'
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#' @param dat The dilution series readouts for REF and TEST + 1 column log(loncentration) or concentration.
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#' @param sigmoid The parameters of the 4pl fit of unrestricted model from EXCEL input.
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#' @param det_sig The parameters of the 4pl fit of unrestricted model from the metadata input.
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#' @param TransFlag A boolean if the plot should be with natural log as readout (TRUE) or not (FALSE).
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#' @returns A grid object either of the original scale or the natural log of the readouts.
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#' @export
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#' @examples
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#' dat <- data.frame(REF1=c(1547, 1620, 1644, 2504, 3426, 3512, 3401, 3787), REF2=c(1492, 1536, 1384, 2286, 3046, 3479, 3516, 3497),
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#' REF3=c(1468, 1827, 1558, 2252, 3002, 3349, 2945, 3665),
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#' TEST1=c(1405, 1523, 1502, 1474, 2383, 3221, 3589, 3445), TEST2=c(1420, 1516, 1544, 1512, 2226, 3219, 3327, 3591),
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#' TEST3=c(1399, 1376, 1588, 1475, 2148, 3083, 2942, 3466), log_dose=c(5.01,3.401,2.708,2.015,1.32176,0.62861,-0.0645385,-1.6739764))
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#' sigmoid <- c(0.7163324, 0.5636804, 10.6156340 , 9.9784160, -0.7504673, -0.7108692, -3.5788141, -0.6662962)
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#' det_sig <- FALSE
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#' TransF<- FALSE
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#' Dat<- list()
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#' p <- plot_f(dat,sigmoid,det_sig,TransF)
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#' print(p)
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plot_f <- function(dat, TransFlag=F) { #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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MODLS <- Fitting_FUNC(dat, TransFlag=F)
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s_mr <- MODLS[[1]]
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a <- s_mr$coefficients["a",1]
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b <- s_mr$coefficients["b",1]
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cs <- s_mr$coefficients["cs",1]
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d <- s_mr$coefficients["d",1]
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r <- s_mr$coefficients["r",1]
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log_dose <- unique(all_l$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*((cs-r)-seq_x)))
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REF <- a+(d-a)/(1+exp(b*((cs)-seq_x)))
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s_mu <- MODLS[[2]]
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as <- s_mu$coefficients["as",1]
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bs <- s_mu$coefficients["bs",1]
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cs <- s_mu$coefficients["cs",1]
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ds <- s_mu$coefficients["ds",1]
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at <- s_mu$coefficients["at",1]
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bt <- s_mu$coefficients["bt",1]
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ct <- s_mu$coefficients["cs",1]-s_mu$coefficients["r",1]
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dt <- s_mu$coefficients["dt",1]
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r <- s_mu$coefficients["r",1]
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SAMPLEtrue <- at + (dt -at)/(1+exp(bt*((cs-r-seq_x))))
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REFtrue <- as + (ds -as)/(1+exp(bs*((cs-seq_x))))
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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*(d-a)/4
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Intercept <- a+(d-a)/2-b*(d-a)/4*cs
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#browser()
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Xbendl3 <- cs-(1.5434/b)
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Xbendu3 <- cs+(1.5434/b)
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XbendlT <- cs-r-(1.5434/b)
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XbenduT <- cs-r+(1.5434/b)
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XasymplS <- cs-(3/b)
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XasympuS <- cs+(3/b)
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XasymplT <- cs-r-(3/b)
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XasympuT <- cs-r+(3/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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asympREF_lower = round(XasymplS,3), asympREF_upper=round(XasympuS,3),
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asympSAMPLE_lower = round(XasymplT,3), asympSAMPLE_upper=round(XasympuT,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), size=3,alpha=0.8) +
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labs(title = paste("restricted 4pl"),
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color="product") +
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scale_color_manual(labels=c("test","reference"), values=c("#C2173F","#4545BA")) +
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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="#C2173F",
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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="#4545BA",
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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="#C2173F", 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="#4545BA", linetype=2, alpha=0.4,
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inherit.aes = F) +
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geom_vline(xintercept=c(Xbendl3, Xbendu3), col="#4545BA",linetype=2) +
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geom_vline(xintercept=c(XbendlT, XbenduT), col="#C2173F",linetype=2) +
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geom_vline(xintercept=c( XasymplS, XasympuS), col="#4545BA55",linetype=2) +
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geom_vline(xintercept=c(XasymplT, XasympuT), col="#C2173F55",linetype=2) +
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annotate("text", x=cs, y=a+(d-a)/2, label="0", size=5) +
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geom_abline(slope = slopeEC50, intercept = Intercept) +
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theme(legend.position="none")
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Dat$p2 <- p2
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# transformed plots
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p_rt <- ggplot(all_l2, aes(x=log_dose, y=readouttrans, color=factor(isRef))) +
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geom_point(shape=factor(isRef), size=3,alpha=0.8) +
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labs(title = paste("restricted transformed 4pl"), color="product") +
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scale_color_manual(labels=c("test","reference"), values=c("#C2173F","#4545BA")) +
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theme_bw()
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MODLStrans <- Fitting_FUNC(dat,TransFlag = TRUE)
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s_mrt <- MODLStrans[[1]]
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a_trans <- s_mrt$coefficients["a",1]
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b_trans <- s_mrt$coefficients["b",1]
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cs_trans <- s_mrt$coefficients["cs",1]
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d_trans <- s_mrt$coefficients["d",1]
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r_trans <- s_mrt$coefficients["r",1]
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XbendlTrans <- cs_trans-(1.5434/b_trans)
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XbenduTrans <- cs_trans+(1.5434/b_trans)
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XbendlTransT <- cs_trans-r_trans-(1.5434/b_trans)
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XbenduTransT <- cs_trans-r_trans+(1.5434/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$bendpointsTRANS <- bendpointsTRANS
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SAMPLEtrans <- a_trans+(d_trans-a_trans)/(1+exp(b_trans*((cs_trans-r_trans)-seq_x)))
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REFtrans <- a_trans+(d_trans-a_trans)/(1+exp(b_trans*((cs_trans)-seq_x)))
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pl_df_trans <- cbind(seq_x, SAMPLEtrans, REFtrans)
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p_rt2 <- p_rt + geom_line(data=as.data.frame(pl_df_trans), aes(x=seq_x, y=SAMPLEtrans), color="#C2173F",
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inherit.aes = F) +
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geom_line(data=as.data.frame(pl_df_trans), aes(x=seq_x, y=REFtrans), color="#4545BA",
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inherit.aes = F) +
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geom_vline(xintercept=c(XbendlTrans, XbenduTrans), col="#4545BA",linetype=2) +
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geom_vline(xintercept=c(XbendlTransT, XbenduTransT), col="#C2173F",linetype=2) +
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theme(legend.position = "none", axis.text=element_text(size=14))
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#browser()
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Sum_u <- MODLS[[2]]
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#browser()
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#if (is.null(det_sig)) {
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ast <- Sum_u$coefficients["as",1]
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ate <- Sum_u$coefficients["at",1]
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bst <- Sum_u$coefficients["bs",1]
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bte <- Sum_u$coefficients["bt",1]
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cst <- Sum_u$coefficients["cs",1]
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cte <- Sum_u$coefficients["cs",1]-Sum_u$coefficients["r",1]
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dst <- Sum_u$coefficients["ds",1]
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dte <- Sum_u$coefficients["dt",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*(cst-seq_x)))
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SAMPLEu <- ate + (dte-ate)/(1+exp(bte*(cte-seq_x)))
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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(size=3) +
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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("#C2173F88","#4545BA88")) +
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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="#C2173F", 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="#4545BA", 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( size=3) +
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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("#C2173FAA","#4545BAAA")) +
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theme_bw()
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Sum_ut <- MODLStrans[[2]]
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ast_t <- Sum_ut$coefficients["as",1]
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ate_t <- Sum_ut$coefficients["at",1]
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bst_t <- Sum_ut$coefficients["bs",1]
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bte_t <- Sum_ut$coefficients["bt",1]
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cst_t <- Sum_ut$coefficients["cs",1]
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cte_t <- Sum_ut$coefficients["cs",1]-Sum_ut$coefficients["r",1]
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dst_t <- Sum_ut$coefficients["ds",1]
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dte_t <- Sum_ut$coefficients["dt",1]
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REFu_trans <- ast_t + (dst_t-ast_t)/(1+exp(bst_t*(cst_t-seq_x)))
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SAMPLEu_trans <- ate_t + (dte_t-ate_t)/(1+exp(bte_t*(cte_t-seq_x)))
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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="#C2173F", 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="#4545BA", inherit.aes = F,
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show.legend = F)
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pu3_t <- pu2_t
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if (TransFlag) grid.arrange(p_rt2,pu3_t, nrow=1) else grid.arrange(p2,pu2_, nrow=1)
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}
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#' Simulate a well-plate readout
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#'
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#' Returns a possible well-plate result based on the metadata provided and a variability.
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#' Homo- and heteroscedasticity can be simulated.
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#'
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#' @param as, bs, cs, ds, at, bt, dt,r,ct, gt, gs are the parameter of the 5pl logistic regression.
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#' @param sd_fac The standard deviation with units in % of upper-lower asymptote.
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#' @param log_conc The natural log of the concentrations.
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#' @param heteroNoise Boolean if heteroscedstic noise should be added.
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#' @param noDilSeries A number >1 indicating how many dilution series per product should be simulated.
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#' @param noDils A number >7 indicating how many dilutions steps per product should be simulated.
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#' @returns A data-frame with readouts and natural log of concentrations.
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#' @export
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#' @examples
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#' as=3; bs=1; cs=-4; ds=10; at=3; bt=1; dt=10;r=0.0001;ct=cs-r;sd_fac=0.1; gt=1; gs=1;
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#' lnConc=c(1,0,-1,-2,-3,-4,-5,-6)
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#' heteroNoise <- FALSE
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#' noDilS <- 3
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#' noD <- 8
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#' Calc_DilRes(as,bs,cs,ds,at,bt,dt,r,ct,sd_fac,gt,gs,log_conc=lnConc, heteroNoise, noDilS, noD)
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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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#' Calculate the potency of linear PLA
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#'
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#' The delta Method is used for calculating the potency using a restricted linear model.
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#' Absolute and relative CIs are calculated. The model RMSE or the Pure Error can be used.
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#'
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#' @param circles Data frame with the 3 consecutive concentrations and the respective readouts.
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#' @param Lim The limits to check if the relative CI is within the limits.
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#' @param PureErrFlag Boolean if Pure Error should be used.
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#' @returns A data-frame with potency estimate, absolute CIs, test result, relative CIs.
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#' @export
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#' @examples
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#' CIRC <- data.frame(log_dose = c(-2.5,-2.5,-2.5, -3.2,-3.2,-3.2,-3.9,-3.9,-3.9,
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#' -3.2,-3.2,-3.2,-3.9,-3.9,-3.9,-4.7,-4.7,-4.7),
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#' replname= c("R_dil1","R_dil1","R_dil1", "R_dil2","R_dil2","R_dil2", "R_dil3","R_dil3","R_dil3",
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#' "T_dil1","T_dil1","T_dil1", "T_dil2","T_dil2","T_dil2", "T_dil3","T_dil3","T_dil3"),
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#' readout = c(72.1,75.8,76.04,59.8,61,62.7,43.6,45,41.5,53.5,62.2,65.9,48.3,43.8,43.14,28.17,29.2,31.2),
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#' isRef=c(rep(1,9), rep(0,9)),
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#' isSample = c(rep(0,9), rep(1,9)))
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#' Lim <- c(rep(0,8),70,130) # only Lim 9 and 10 relevant
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#' PureErrF <- TRUE
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#'
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#'
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#' LinPotTab(circles=CIRC, Lim, PureErrF)
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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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#' Calculate the ANOVA of linear PLA and restricted and unrestr. model summaries
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#'
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#' The delta Method is used for calculating the potency using a restricted linear model.
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#' Absolute and relative CIs are calculated. The model RMSE or the Pure Error can be used.
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#'
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#' @param circles Data frame with the 3 consecutive concentrations and the respective readouts.
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#' @param Lim The limits to check if the relative CI is within the limits.
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#' @param PureErrFlag Boolean if Pure Error should be used.
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#' @returns A data-frame with 1) data.frame with F-tests ANOVA and test results
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#' 2) summary of unrestricted linear model 3) Slope difference +CIs
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#' 4) summary of restricted linear model.
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#' @export
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#' @examples
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#' dat <- data.frame(R_dil1 =c(10221, 18258, 31993, 49336, 68332, 83527, 95584, 102229),
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#' R_dil2=c( 10136, 19078, 31925, 49003, 68034, 83776, 95495, 101608),
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#' T_dil1=c( 10830, 19891, 33915, 52131, 70617, 85784, 95937, 102791),
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#' T_dil2=c( 11169, 20153, 34007, 52179, 69962, 85543, 96439, 102655),
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#' log_dose=c( -1.2029, -1.89712, -2.590267, -3.2834, -3.97656, -4.66917, -5.362323, -6.05334))
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#' CIRC <- data.frame(log_dose=c( -2.590267, -2.590267, -3.2834 , -3.2834, -3.97656, -3.97656, -2.590267, -2.590267,-3.2834, -3.2834, -3.97656, -3.97656),
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#' replname= c("R_dil1","R_dil2","R_dil1", "R_dil2","R_dil1","R_dil2", "T_dil1","T_dil2","T_dil1", "T_dil2","T_dil1","T_dil2"),
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#' readout = c(31993, 31925, 49336, 49003 , 68332, 68034 , 33915, 34007, 52131, 52179 , 70617, 69962),
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#' isRef=c(rep(1,6), rep(0,6)),
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#' isSample = c(rep(0,6), rep(1,6)))
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#' Lim <- c(rep(0,8),70,130) # only Lim 9 and 10 relevant
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#' PureErrF <- TRUE
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#'
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#'
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#' ANOVAlintests(ro_new, circles, Lim, PureErrF)
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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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#browser()
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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)
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MSE <- RSS/dfRMSE
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SSE <- sum(resid(lm(readout ~ factor(log_dose)*isSample, circ_ABl))^2)
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MSpure <- SSE/dfPureE
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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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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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if (length(SU_modAB)>1) s_modABcoeffs <- summary(modAB)$coefficients
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DFsPure <- FitAnova[4,1]
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VCOV <- vcov(modAB)
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V_V <- VCOV/SU_modAB$sigma^2
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VCOVpure <- V_V*meanPureErr
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SEsPure <- sqrt(diag(V_V)*meanPureErr)
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su_mod2[,3] <- SEsPure
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su_mod2[,4] <- su_mod2[,2]/su_mod2[,3]
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su_mod2[,5] <- 2*(1-pt(abs(su_mod[,4]), FitAnova[4,1]))
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s_mu <- summary(modABu)$coefficients
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SU_modABu <- summary(modABu)
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VCOVu <- vcov(modABu)
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V_Vu <- VCOVu/SU_modABu$sigma^2
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SEsPureU <- sqrt(diag(V_Vu)*meanPureErr)
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|
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su_modU2[,3] <- SEsPureU
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su_modU2[,4] <- su_modU2[,2]/su_modU2[,3]
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su_modU2[,5] <- 2*(1-pt(abs(su_modU2[,4]), FitAnova[4,1]))
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|
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uCI_SloDiffP <- su_modU[4,1] + qt(0.975,8)*SEsPureU[4]
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lCI_SloDiffP <- su_modU[4,1] - qt(0.975,8)*SEsPureU[4]
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SlopeDiffCI <- c(su_modU[4,1], lCI_SloDiffP,uCI_SloDiffP)
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|
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SSRes <- SSE
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dfRes <- dfPureE
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|
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} else {
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SSRes <- RSS
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dfRes <- dfRMSE
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}
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|
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# treatment
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SStreat <- print(sum((predict(lm(readout ~ factor(log_dose)*isSample, circ_ABl))-mean(circ_ABl$readout))^2))
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F_treat <- (SStreat/dfTreat)/(SSRes/dfRes)
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# Preparation
|
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SSprep <- print(sum((predict(lm(readout ~ isSample, circ_ABl))-mean(circ_ABl$readout))^2))
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F_prep <- (SSprep/dfTreat)/(SSRes/dfRes)
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# Regression
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# ANOVA tape II SS of regression
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SSreg <- Anova(lm(readout ~log_dose + isSample, circ_ABl))[1,1]
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# Non-parallelism
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# diff of RSS of restricted and unrestricted model
|
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SSnonpar <- sum(resid(modAB)^2) - sum(resid(modABu)^2)
|
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F_nonpar <- SSnonpar/(sum(resid(lm(readout ~ factor(log_dose)*isSample, circ_ABl))^2)/(lenCirc-4))
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|
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# non-linearity
|
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SSnonlin <- sum((predict(modABu)-predict(lm(readout ~ as.factor(log_dose)*isSample, circ_ABl)))^2)
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# = RSS-SSE
|
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# Total SS
|
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SStot <- sum((circ_ABl$readout-mean(circ_ABl$readout))^2)
|
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# Significance of R^2 F-ratio
|
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# MSR/MSE
|
|
# sample A
|
|
F_R2_A <- sum((predict(lm(readout ~ log_dose+ I(log_dose^2), circ_Al)) - mean(predict(modA)))^2 - (predict(modA) - mean(circ_Al$readout))^2)/
|
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(sum((predict(lm(readout ~ log_dose+ I(log_dose^2), circ_Al)) - circ_Al$readout)^2)/(nrow(circ_Al)-3))
|
|
pFR2_A <- round(pf(F_R2_A,1,6),4)
|
|
# sample B
|
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F_R2_B <- sum((predict(lm(readout ~ log_dose+ I(log_dose^2), circ_Bl)) - mean(predict(modB)))^2 - (predict(modB) - mean(circ_Bl$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)
|
|
}
|
|
|
|
PlotLinPLA_FUNC <-function(circle, sigmoid, all_l2, pl_df, indS, indT) {
|
|
|
|
mLin <- gsl_nls(readout ~ (intS+r)*isSample + intS*isRef + k*log_dose,
|
|
data=circle,
|
|
start=list(intS = 0, k=1,r=0),
|
|
control = gsl_nls_control(xtol=1e-10,ftol=1e-10,gtol=1e-10))
|
|
# alternativ: modAB <- lm(readout ~ log_dose+isSample, circle)
|
|
sum_mLin <- summary(mLin)
|
|
|
|
log_dose <- unique(all_l2$log_dose)
|
|
seq_x <- seq(min(log_dose), max(log_dose),0.1)
|
|
SAMPLEtrue <- sigmoid[5] + (sigmoid[7]-sigmoid[5])/(1+exp(sigmoid[6]*((sigmoid[4]-sigmoid[8]-seq_x))))
|
|
REFtrue <- sigmoid[1] + (sigmoid[3]-sigmoid[1])/(1+exp(sigmoid[2]*((sigmoid[4]-seq_x))))
|
|
|
|
truePL_df <- cbind(seq_x,SAMPLEtrue, REFtrue)
|
|
|
|
p <- ggplot(all_l2,aes(x=log_dose,y=readout, color=factor(isRef))) +
|
|
geom_point() +
|
|
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)) +
|
|
theme_bw()
|
|
p2 <- p + geom_line(data=pl_df,aes(x=lnC,y=plotS),color="#4545BA",
|
|
inherit.aes = F) +
|
|
geom_line(data=pl_df,aes(x=lnC,y=plotT),color="#C2173F",
|
|
inherit.aes = F) +
|
|
geom_line(data=data.frame(truePL_df),aes(x=seq_x,y=SAMPLEtrue),color="#C2173F", linetype=2,alpha=0.4,
|
|
inherit.aes = F) +
|
|
geom_line(data=data.frame(truePL_df),aes(x=seq_x,y=REFtrue),color="#4545BA", linetype=2,alpha=0.4,
|
|
inherit.aes = F) +
|
|
labs(title = paste("unrestricted linear regression model",indS,indT), color="product") +
|
|
theme(legend.position="none", axis.text = element_text(size=14))
|
|
p3 <- p2 + geom_point(circle, mapping=aes(x=log_dose, y=readout, shape=factor(isRef),
|
|
size=5,alpha=0.2), inherit.aes = FALSE) +
|
|
scale_shape_manual(labels=c("test","reference"), values=c(21,21))
|
|
# fit intercept for test and ref and common slope
|
|
|
|
|
|
pl_restS <- sum_mLin$coefficients[1,1] + sum_mLin$coefficients[2,1]*log_dose
|
|
pl_restT <- sum_mLin$coefficients[1,1] + sum_mLin$coefficients[3,1] + sum_mLin$coefficients[2,1]*log_dose
|
|
pl_rest <- data.frame(lnC=log_dose, plotS=pl_restS, plotT=pl_restT)
|
|
|
|
pr2 <- p + geom_line(data=pl_rest,aes(x=lnC,y=plotS),color="#4545BA",
|
|
inherit.aes = F) +
|
|
geom_line(data=pl_rest,aes(x=lnC,y=plotT),color="#C2173F",
|
|
inherit.aes = F) +
|
|
geom_line(data=data.frame(truePL_df),aes(x=seq_x,y=SAMPLEtrue),color="#C2173F", linetype=2,alpha=0.4,
|
|
inherit.aes = F) +
|
|
geom_line(data=data.frame(truePL_df),aes(x=seq_x,y=REFtrue),color="#4545BA", linetype=2,alpha=0.4,
|
|
inherit.aes = F) +
|
|
labs(title = paste("restricted linear regression model",indS,indT), color="product") +
|
|
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),
|
|
size=5,alpha=0.2), inherit.aes = FALSE) +
|
|
scale_shape_manual(labels=c("test","reference"), values=c(21,21))
|
|
return(grid.arrange(p3,pr3,nrow=1))
|
|
}
|
|
|
|
|
|
|
|
#' Calculates the potency of 4PL PLA of all models model
|
|
#'
|
|
#' The gradient method is used for calculating the potency for a restricted model, an unrestricteed model,
|
|
#' and the log-transformations thereof.
|
|
#' Absolute and relative CIs are calculated. The model RMSE or the Pure Error can be used.
|
|
#'
|
|
#' @param ro_new The dilution series readouts for REF and TEST + 1 column log(concentration).
|
|
#' @param PureErrFlag Boolean if Pure Error should be used.
|
|
#' @returns A data-frame with 1) data.frame with F-tests ANOVA and test results
|
|
#' 2) summary of unrestricted linear model 3) Slope difference +CIs
|
|
#' 4) summary of restricted linear model.
|
|
#' @export
|
|
#' @examples
|
|
#' ro_new <- data.frame(R_dil1 =c(10221, 18258, 31993, 49336, 68332, 83527, 95584, 102229),
|
|
#' R_dil2=c( 10136, 19078, 31925, 49003, 68034, 83776, 95495, 101608),
|
|
#' T_dil1=c( 10830, 19891, 33915, 52131, 70617, 85784, 95937, 102791),
|
|
#' T_dil2=c( 11169, 20153, 34007, 52179, 69962, 85543, 96439, 102655),
|
|
#' log_dose=c( -1.2029, -1.89712, -2.590267, -3.2834, -3.97656, -4.66917, -5.362323, -6.05334))
|
|
#' PureErrF <- TRUE
|
|
#'
|
|
#'
|
|
#' pot4plFUNC(ro_new, PureErrF)
|
|
|
|
|
|
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$readout)
|
|
#browser()
|
|
CORdat <- cor(ro_new[,1],ro_new[,ncol(ro_new)])
|
|
if (CORdat<0) SLOPE <- -1 else SLOPE <- 1
|
|
#
|
|
FITs <- Fitting_FUNC(ro_new, TransFlag = F)
|
|
if (!PureErrFlag) {
|
|
pot_est <- FITs[[3]]
|
|
potU_est <- FITs[[4]]
|
|
} else {
|
|
FitAnova <- anova(lm(readout ~ factor(Conc)*isSample, all_l))
|
|
meanPureErr <- FitAnova[4,3]
|
|
# SU_mr <- tryCatch({
|
|
# SU_mr <- summary(mr)
|
|
# }, error = function(msg) {
|
|
# return()
|
|
# })
|
|
SU_mr <- FITs[[1]]
|
|
SU_mrCoeff <- SU_mr$coefficients
|
|
# if (length(SU_mr)>1) {
|
|
# SU_mrCoeff <- SU_mr$coefficients
|
|
# } else { SU_mrCoeff <- rep(NA,5) }
|
|
#te2$cov.unscaled[1,1]*te2$sigma^2
|
|
#VCOV <- vcov(mr)
|
|
#V_V <- VCOV/SU_mr$sigma^2
|
|
V_V <- SU_mr$cov.unscaled
|
|
SEsPure <- sqrt(diag(V_V)*meanPureErr)
|
|
pot_est <- c(exp(SU_mrCoeff['r',1]),exp(SU_mrCoeff['r',1]-qt(0.975,nrow(all_l)-5)*SEsPure['r']),
|
|
exp(SU_mrCoeff['r',1]+qt(0.975,nrow(all_l)-5)*SEsPure['r']))
|
|
# unrestricted
|
|
SU_mu <- FITs[[2]]
|
|
s_muCoeff <- SU_mu$coefficients
|
|
#SU_mu <- summary(mu)
|
|
#VCOVu <- vcov(mu)
|
|
V_Vu <- SU_mu$cov.unscaled
|
|
SEsPureU <- sqrt(diag(V_Vu)*meanPureErr)
|
|
potU_est <- c(exp(s_muCoeff['r',1]),exp(s_muCoeff['r',1]-qt(0.975,nrow(all_l)-8)*SEsPureU['r']),
|
|
+ exp(s_muCoeff['r',1]+qt(0.975,nrow(all_l)-8)*SEsPureU['r']))
|
|
} # PureErrFlag
|
|
|
|
FITstrans <- Fitting_FUNC(ro_new, TransFlag = TRUE)
|
|
|
|
|
|
# pot_est_log <- exp(confintd(mr_log, "r", method="asymptotic"))
|
|
# potU_est_log <- exp(confintd(mu_log, "r", method="asymptotic"))
|
|
pot_est_log <- FITstrans[[3]]
|
|
potU_est_log <- FITstrans[[4]]
|
|
colnames(pot_est_log) <- c("estimate","lowerCI2","upperCI")
|
|
colnames(potU_est_log) <- c("estimate","lowerCI2","upperCI")
|
|
#browser()
|
|
su_mr_log <- FITstrans[[1]] #summary(mr_log)
|
|
Dat$RMSE_Rlog <- su_mr_log$sigma
|
|
su_mu_log <- FITstrans[[2]] # summary(mu_log)
|
|
Dat$RMSE_Ulog <- su_mu_log$sigma
|
|
Dat$up_lowAslog <- su_mu_log$coefficients[1,1] - su_mu_log$coefficients[4,1]
|
|
potALL <- rbind(round(pot_est,6), round(potU_est,6), round(pot_est_log,6), round(potU_est_log,6))
|
|
|
|
potALL2 <- cbind(c("restricted","unrestricted","ln-transformed restr","ln-transformed unrestr"), potALL)
|
|
return(potALL2)
|
|
}
|
|
|
|
#' Calculates logarithmized CIs for ratios
|
|
#'
|
|
#' Ratio CIs can be unbound, and not calulatable. Therefore the ratios are logarithmized.
|
|
#' The Covariance is not used, as it is 0 for comparisons between products.
|
|
#'
|
|
#' @param xs The estimate of the reference sample.
|
|
#' @param xt The estimate of the test sample.
|
|
#' @param se_xs The standard error of the estimate of the reference sample.
|
|
#' @param se_xt The standard error of the estimate of the test sample.
|
|
#' @param CoVar The covariance between test and reference estimate (set to 0).
|
|
#' @param DFs Degrees of freedom, either from pure error term (no of readouts - no of concentrations*2), or RMSE term (no of readouts - 8 parameters).
|
|
#' @param Conf The confidence of the Confidence Interval (default 95% which requires 0.975).
|
|
#' @returns A data-frame with the lower and upper CI in anti-log form.
|
|
#' @export
|
|
#' @examples
|
|
#' xs=2; xt=3.2; se_xt=0.34;se_xs=0.23; DFs=32-16
|
|
#'
|
|
#'
|
|
#' ParamCI_F(xt,xs,se_xt, se_xs, CoVar,DFs, Conf=0.975)
|
|
|
|
|
|
|
|
ParamCI_F <- function(xt,xs,se_xt, se_xs, CoVar,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(xs)
|
|
var_log_xt <- (se_xt/xt)^2
|
|
se_log_ratio <- sqrt(var_log_xs + var_log_xt) #-2*CoVar/(xs*xt)
|
|
|
|
lower_log_ratio <- log_xt-log_xs - qt(Conf,DFs)*se_log_ratio
|
|
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)
|
|
}
|
|
|
|
|
|
#' Calculates EQ tests and F-Tests for 4P
|
|
#'
|
|
#' CIs of asmptote ratios,slope ratios, asympt-difference ratio, and lower asympt-diff. calculated.
|
|
#' In addition the F-test on significance of regression and non-linearity are calculated.
|
|
#'
|
|
#' @param ro_new Data frame with the concentrations and the respective readouts.
|
|
#' @param Lim The limits to check if the relative CI is within the limits.
|
|
#' @param PureErrFlag Boolean if Pure Error should be used.
|
|
#' @returns A data-frame with the lower and upper CI in anti-log form.
|
|
#' @export
|
|
#' @examples
|
|
#'
|
|
#' dat <- data.frame(REF1=c(1547, 1620, 1644, 2504, 3426, 3512, 3401, 3787), REF2=c(1492, 1536, 1384, 2286, 3046, 3479, 3516, 3497),
|
|
#' REF3=c(1468, 1827, 1558, 2252, 3002, 3349, 2945, 3665),
|
|
#' TEST1=c(1405, 1523, 1502, 1474, 2383, 3221, 3589, 3445), TEST2=c(1420, 1516, 1544, 1512, 2226, 3219, 3327, 3591),
|
|
#' TEST3=c(1399, 1376, 1588, 1475, 2148, 3083, 2942, 3466), log_dose=c(5.01,3.401,2.708,2.015,1.32176,0.62861,-0.0645385,-1.6739764))
|
|
#' Lim <- c(-1, 1, 0.005 , 2, 0.5, 2, 0.5,2,75,133, 75, 133)
|
|
#' PureErrF <- FALSE
|
|
#'
|
|
#' tests_FUNC(ro_new, Lim,PureErrF)
|
|
|
|
|
|
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
|
|
#browser()
|
|
FITs <- Fitting_FUNC(ro_new = ro_new, TransFlag = FALSE)
|
|
POTr_CI <- FITs[[3]][2:3]
|
|
potAll2 <- FITs[[3]]
|
|
|
|
smr <- FITs[[1]]
|
|
smu <- FITs[[2]]
|
|
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 <- smu$cov.unscaled
|
|
SEsPure <- sqrt(diag(V_V)*meanPureErr)
|
|
VCOVpure <- V_V*meanPureErr
|
|
DFsPure <- FitAnova[4,1]
|
|
|
|
|
|
|
|
testPOTr <- logical()
|
|
if (POTr_CI[1]*100>Lim[[9]] & POTr_CI[2]*100<Lim[[10]] ) testPOTr <- 0 else testPOTr <- 1
|
|
|
|
potAllU2 <- FITs[[4]]
|
|
test_c <- logical()
|
|
if((potAllU2[2] >Lim[[9]]/100 & potAllU2[3] <Lim[[10]]/100)) test_c <- 0 else test_c <- 1
|
|
predPot <- FITs[[5]]
|
|
predPotU <- FITs[[6]]
|
|
|
|
vcovMU <- V_V*smu$sigma^2
|
|
|
|
#### ANOVA ----
|
|
noConc <- length(unique(all_l$Conc))
|
|
nofitted <- noConc
|
|
AnovaDFs <- c(nofitted-1,1,3,nofitted-4-1,nrow(all_l)-nofitted, nofitted,nrow(all_l)-2*nofitted,nrow(all_l)-1)
|
|
SStreat <- round(sum((predPotU-mean(all_l$readout))^2),5)
|
|
SSregr <- round(sum((predPot-mean(all_l$readout))^2),5)
|
|
# non-parallelism
|
|
SSnonparall <- round(sum(smr$residuals^2)-sum(smu$residuals^2),5)
|
|
SSprep <- round(sum((predict(lm(readout ~ isSample, all_l))-mean(all_l$readout))^2),5)
|
|
|
|
RSS <- round(sum(smu$residuals^2),5)
|
|
RSS_df <- AnovaDFs[5]
|
|
MSEunr <- RSS/RSS_df
|
|
RMSEunr <- sqrt(RSS/RSS_df)
|
|
# Pure Err
|
|
FitAnova <- anova(lm(readout ~ factor(Conc)*isSample, all_l))
|
|
SSE <- sum(resid(lm(readout ~ factor(Conc)*isSample, all_l))^2) # =FitAnova[4,2]
|
|
SSE_df <- FitAnova[4,1]
|
|
PureMSE <- SSE/SSE_df
|
|
RMSE_pure <- sqrt(PureMSE)
|
|
## non-lin = LoF
|
|
if (PureErrFlag) { ERR <- PureMSE; ERR_df <- SSE_df } else { ERR <- MSEunr; ERR_df <- RSS_df }
|
|
SSnonlin <- sum((predict(lm(readout ~ factor(Conc)*isSample, all_l))-predPotU)^2)
|
|
LoF_df <- FitAnova[1,1]+FitAnova[2,1]
|
|
F_regr <- (SSregr/AnovaDFs[3])/ERR
|
|
p_F_regr <- round(pf(F_regr, AnovaDFs[3], ERR_df, lower.tail = F),5)
|
|
if (ncol(ro_new)<4) F_nonlin <- 0 else F_nonlin <- (SSnonlin/AnovaDFs[6])/ERR
|
|
if (F_nonlin > 0) {
|
|
p_F_nonlin <- round(pf(F_nonlin, AnovaDFs[6], ERR_df, lower.tail = F),5)
|
|
} else { p_F_nonlin <- "SSnonlin neg or single dilutions" }
|
|
|
|
test_a <- test_b <- test_d <- test_ad <- logical()
|
|
|
|
RSS_r <- round(sum(smr$residuals^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$RMSE_unr <- round(RMSEunr,6)
|
|
|
|
coeffs <- smu$coefficients[,1]
|
|
#browser()
|
|
## EQ test on lower As diff
|
|
as <- coeffs["as"]
|
|
at <- coeffs["at"]
|
|
lAs_diff <- (at-as)
|
|
uCI_laDiff <- lAs_diff+qt(0.975,smu$df[2])*sqrt(smu$coefficients["ds",2]^2+smu$coefficients["dt",2]^2)
|
|
lCI_laDiff <- lAs_diff-qt(0.975,smu$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
|
|
|
|
#### 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)-8
|
|
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(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 <- slopeCI2
|
|
|
|
#### 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$lAsCI <- lAsCI2
|
|
|
|
#### EQtest on ratio of As difference ----
|
|
AsDiffRatio <- (dt-at)/(ds-as)
|
|
|
|
dt_at <- (dt-at)
|
|
ds_as <- (ds-as)
|
|
se_ds_asPure <- sqrt(VCOVpure["as","as"]+VCOVpure["ds","ds"]-2*VCOVpure["as","ds"])
|
|
se_dt_atPure <- sqrt(VCOVpure["at","at"]+VCOVpure["dt","dt"]-2*VCOVpure["at","dt"])
|
|
se_ds_asRMSE <- sqrt(vcovMU["as","as"]+vcovMU["ds","ds"]-2*vcovMU["as","ds"])
|
|
se_dt_atRMSE <- sqrt(vcovMU["at","at"]+vcovMU["dt","dt"]-2*vcovMU["at","dt"])
|
|
if (PureErrFlag) se_ds_as <- se_ds_asPure else se_ds_as <- se_ds_asRMSE
|
|
if (PureErrFlag) se_dt_at <- se_dt_atPure else se_dt_at <- se_dt_atRMSE
|
|
|
|
AsDiffCI2 <- ParamCI_F(dt_at,ds_as,se_dt_at, se_ds_as,CoVar=0, DFs, Conf=0.975)
|
|
if (AsDiffCI2[1] > Lim[[11]] & AsDiffCI2[2] < Lim[[12]]) test_ad <- 0 else test_ad <- 1
|
|
estLowA <- round(at/as,5)
|
|
|
|
Dat$up_lowAs <- abs(ds-as)
|
|
|
|
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(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]]),
|
|
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)
|
|
}
|
|
|
|
#' ANOVA unrestricted model
|
|
#'
|
|
#' ANOVA with unrestricted model.
|
|
#'
|
|
#'
|
|
#' @param ro_new Data frame with the concentrations and the respective readouts.
|
|
#' @returns A data-frame with the analysis of variance.
|
|
#' @export
|
|
#' @examples
|
|
#'
|
|
#' ro_new <- data.frame(REF1=c(1547, 1620, 1644, 2504, 3426, 3512, 3401, 3787), REF2=c(1492, 1536, 1384, 2286, 3046, 3479, 3516, 3497),
|
|
#' REF3=c(1468, 1827, 1558, 2252, 3002, 3349, 2945, 3665),
|
|
#' TEST1=c(1405, 1523, 1502, 1474, 2383, 3221, 3589, 3445), TEST2=c(1420, 1516, 1544, 1512, 2226, 3219, 3327, 3591),
|
|
#' TEST3=c(1399, 1376, 1588, 1475, 2148, 3083, 2942, 3466), log_dose=c(5.01,3.401,2.708,2.015,1.32176,0.62861,-0.0645385,-1.6739764))
|
|
#'
|
|
#'
|
|
#' ANOVA4plUnresfunc(ro_new)
|
|
|
|
|
|
ANOVA4plUnresfunc <- function(ro_new) {
|
|
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
|
|
|
|
FITs <- Fitting_FUNC(ro_new = ro_new, TransFlag = FALSE)
|
|
smr <- FITs[[1]]
|
|
smu <- FITs[[2]]
|
|
smrPREDs <- FITs[[5]]
|
|
smuPREDs <- FITs[[6]]
|
|
# pot <- drm(readout ~ Conc, isSample, data=all_l, 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((smuPREDs - mean(all_l$readout))^2),5)
|
|
SStreat_df <- length(unique(all_l$log_dose))-1
|
|
SSregr <- round(sum((smrPREDs-mean(all_l$readout))^2),5)
|
|
## Non-parallel
|
|
SSnonparallel <- round(sum(smr$residuals^2) - sum(smu$residuals^2),5)
|
|
## Preparation
|
|
SSprep <- round(sum((predict(lm(readout ~ isSample, all_l)) - mean(all_l$readout))^2),5)
|
|
## Resid Err
|
|
RSS <- round(sum(smu$residuals^2),5)
|
|
RSS_df <- nrow(all_l)-SStreat_df-1
|
|
FitAnova <- anova(lm(readout ~ factor(Conc)*isSample, all_l))
|
|
# PureErr
|
|
SSE <- round(FitAnova[4,3],5)
|
|
SSE_df <- FitAnova[4,1]
|
|
# Non-Linearity
|
|
SSnonlin <- round(sum((predict(lm(readout ~ factor(Conc)*isSample, all_l)) - smuPREDs)^2),4)
|
|
LoF_df <- FitAnova[1,1]+FitAnova[2,1]
|
|
## Total
|
|
SStot <- round(sum((all_l$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)
|
|
}
|
|
|
|
#' Calculates descriptive statistics per concentration
|
|
#'
|
|
#' Mean, SD, and CV% per concentration and product..
|
|
#'
|
|
#'
|
|
#' @param ro_new Data frame with the concentrations and the respective readouts.
|
|
#' @returns A data-frame with the concentrations and metadata.
|
|
#' @export
|
|
#' @examples
|
|
#'
|
|
#' ro_new <- data.frame(REF1=c(1547, 1620, 1644, 2504, 3426, 3512, 3401, 3787), REF2=c(1492, 1536, 1384, 2286, 3046, 3479, 3516, 3497),
|
|
#' REF3=c(1468, 1827, 1558, 2252, 3002, 3349, 2945, 3665),
|
|
#' TEST1=c(1405, 1523, 1502, 1474, 2383, 3221, 3589, 3445), TEST2=c(1420, 1516, 1544, 1512, 2226, 3219, 3327, 3591),
|
|
#' TEST3=c(1399, 1376, 1588, 1475, 2148, 3083, 2942, 3466), log_dose=c(5.01,3.401,2.708,2.015,1.32176,0.62861,-0.0645385,-1.6739764))
|
|
#'
|
|
#'
|
|
#' perConcTab(ro_new, noDilSeries=3)
|
|
|
|
|
|
|
|
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)
|
|
|
|
}
|
|
|
|
#' Calculates dilution series.
|
|
#'
|
|
#' Recursive function to calcuate a geometric dilution series.
|
|
#'
|
|
#' @param x Starting concentration.
|
|
#' @param Div Divisor to get next concentration.
|
|
#' @param N Number of dilution step, increases.
|
|
#' @param res Vector of results.
|
|
#' @param noDil Final number of concentrations -1 (the initial one).
|
|
#' @returns A data-frame with the concentrations and metadata.
|
|
#' @export
|
|
#' @examples
|
|
#'
|
|
#' x <- 1; Div <- 3;N <- 0; res <- c(); noDil <- 7
|
|
#'
|
|
#' divFUN(x,Div,N,res,noDil)
|
|
|
|
|
|
|
|
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)
|
|
}
|
|
|
|
|
|
|
|
#### ui ----
|
|
|
|
ui <- dashboardPage(
|
|
dashboardHeader(title = "Plateflow"),
|
|
dashboardSidebar(
|
|
sidebarMenu(
|
|
img(src="logo.png", width=230),
|
|
menuItem("Home", tabName="home", icon=icon("home")),
|
|
menuItem("Data template", tabName = "template", icon=icon("table"),
|
|
menuSubItem( tags$li(a("EXCEL File", target="self",href="TestFile.xlsx")))
|
|
),
|
|
# menuItem("User Manual /Validation", tabName = "manual", icon=icon("book"), # tabName here and in dashboard body need to be identical
|
|
# menuSubItem(icon = NULL, tags$li(a("Document", target="self",href="UserManual.pdf")))
|
|
# ),
|
|
menuItem("EXCEL upload", tabName="Dataupload", icon=icon("magnet", lib="glyphicon")),
|
|
menuItem("4PL metadata + report", tabName="fourPL", icon=icon("chart-line", lib="font-awesome")),
|
|
#menuItem("XLSX diagnostics", tabName="XLdiagn", icon=icon("chart-bar", lib="font-awesome")),
|
|
# menuItem("Linear regression + report", tabName="pla", icon=icon("pencil", lib="glyphicon")),
|
|
menuItem("Wizard", tabName="wizard", icon=icon("chart-column", lib="font-awesome")),
|
|
menuItem("Documentation", tabName="documentation", icon=icon("chart-area", lib="font-awesome"))
|
|
),
|
|
tags$footer(HTML(paste(tags$strong(tags$u("InnerAnalytics")), paste(rep(" ",9), collapse=""),
|
|
"Developer:", paste(rep(" ",9), collapse=""),
|
|
"Host on:", paste(rep(" ",9), collapse=""),
|
|
"Version:")),
|
|
align="left", style=
|
|
"position:fixed; bottom:0;width=100%; background: #FFC337BB; font-family: Times New Roman; font-size:100%;
|
|
padding: 5px; color:#4545BA; box-sizing: border-box; z-index: 1000;")),
|
|
dashboardBody(
|
|
fluidPage(
|
|
tabItems(
|
|
tabItem(tabName = "home", htmlOutput("homePage")),
|
|
tabItem(tabName = "Dataupload", uiOutput("Dataupload")),
|
|
tabItem(tabName = "fourPL", uiOutput("fourPL")),
|
|
#tabItem(tabName = "XLdiagn", uiOutput("XLdiagn")),
|
|
tabItem(tabName = "pla", uiOutput("pla")),
|
|
tabItem(tabName = "wizard", uiOutput("wizard")),
|
|
tabItem(tabName = "documentation", uiOutput("docu"))
|
|
)
|
|
|
|
)
|
|
), skin="blue"
|
|
)
|
|
|
|
|
|
#### server ----
|
|
server <- function(input, output, session) {
|
|
ReportParS <- reactiveValues()
|
|
IPReportParS <- reactiveValues()
|
|
|
|
#### renderUIs ----
|
|
output$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(),
|
|
column(4,
|
|
tags$table(id="dose-table",
|
|
numericInput("lEACdiffla","lower EAC for diff. of LA", -0.175, step=0.001),
|
|
numericInput("uEACdiffla","upper EAC for diff. of LA", 0.189, step=0.001),
|
|
numericInput("lEACratiola","lower EAC ratio of LAs", 0.005, step=0.001),
|
|
numericInput("uEACratiola","upper EAC for ratio of LAs", 100, step=1),
|
|
|
|
numericInput("lEACratioSlope","lower EAC for ratio of slopes", 0.55, step=0.01),
|
|
numericInput("uEACratioSlope","upper EAC for ratio of slopes", 1.84, step=0.1),
|
|
numericInput("lEACratioua","lower EAC for ratio of UAs", 0.75, step=0.1),
|
|
numericInput("uEACratioua","upper EAC for ratio of UAs", 1.33, step=0.1),
|
|
numericInput("lowerPot","lower EAC for potency", 75, step=1),
|
|
numericInput("upperPot","upper EAC for potency", 133, step=1),
|
|
numericInput("lEACratioAdiff","lower EAC of ratio of asymptote differences", 0.75, step=0.01),
|
|
numericInput("uEACratioAdiff","upper EAC of ratio of asymptote differences", 1.33, step=0.01)
|
|
))
|
|
|
|
),
|
|
tabPanel("Documentation",
|
|
h4("Introduction "),
|
|
h4("Information on dilution setting"),
|
|
"(for details see:", a(href="ADONIS.pdf","Whitepaper", download=NA, target="_blank"),")",tags$br(),
|
|
"Bend points are calculated according to following formula:",
|
|
withMathJax(" $$bp_{1/2} = \\pm\\frac{1.31696}{Hill's slope}$$")),
|
|
tabPanel("Configuration",
|
|
verbatimTextOutput("sessioninfo"))
|
|
)
|
|
})
|
|
##### UI XL ----
|
|
output$Dataupload <- renderUI({
|
|
navbarPage(title="Information",
|
|
tabPanel(title = "Real data",
|
|
tabsetPanel(
|
|
tabPanel("Data input",
|
|
|
|
|
|
column(3,
|
|
#img(src="Screenshot.png", width=200),
|
|
box(title = "Upload", status="warning",solidHeader = T, width=12, "Please upload your EXCEL file here",
|
|
fileInput("iFile",'',accept=".xlsx")),
|
|
uiOutput(outputId = "sheetName"),
|
|
"For data format in the EXCEL file see Data template",
|
|
"If no data are uploaded, the settings to the right are used for calculations.",
|
|
|
|
tags$head(tags$style(HTML("label {font-size:80%;margin-bottom: 3px;margin-top: 3px;}"))),
|
|
|
|
div(checkboxInput("PureErr", "Should pure error be used for calculation of CIs?", FALSE),
|
|
style = "font-size: 24px !important;color: #C2173F"),
|
|
|
|
#actionLink("selectall","SelectAll"),
|
|
h5("\n\n\n Author: Franz Innerbichler, InnerAnalytics")),
|
|
column(6,
|
|
h4("Suitability tests for 4-parametric logistic regression"),
|
|
checkboxGroupInput("selectedSSTs", "Which suitability tests to be used?", choices= c("F-test on Regr."="1",
|
|
"EQ-test on lower asymptote difference"= "2",
|
|
"EQ-test on ratio of lower asymptote"= "3","EQ-test on ratio of Hill slopes"= "4",
|
|
"EQ-test on ratio of upper asymptote"= "5", "F-test on non-linearity"="6",
|
|
"EQ-test on ratio of asymptote differences"= "7"),
|
|
selected= c("1","2","3","4","5","6","7")),
|
|
h4("Suitability tests for Parallel Line Assay"),
|
|
checkboxGroupInput("selectedSSTsLinear", "Which suitability tests to be used?",
|
|
choices= c("F-test on Regr."="1",
|
|
"F-test on non-linearity"= "2",
|
|
"F-test on R^2 A"= "3","F-test on R^2 B"= "4",
|
|
"F-test on slope A"= "5", "F-test on slope B"="6",
|
|
"F-test on non-parallelism"= "7", "F-test on preparation"="8"),
|
|
selected= c("1","2","3","4","5","6","7","8"))
|
|
)
|
|
|
|
),
|
|
tabPanel("4pl-Analysis",
|
|
tags$style(HTML("pre { color: black; background-color: #FFE1FF;
|
|
font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif;font-size: 12px;} ")),
|
|
|
|
wellPanel(
|
|
fluidRow(
|
|
column(4,
|
|
#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"),
|
|
tableOutput("relpotTestTab"),
|
|
plotOutput("relpotTestPlot", width="300px", height="150px"), # Pot CI plot
|
|
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(8,
|
|
plotOutput("XLplot"),
|
|
DTOutput("pottab4plXL"),
|
|
plotOutput("diagnplot"),
|
|
DTOutput("EQtests"),
|
|
|
|
DTOutput("pottab4plTransXL"),
|
|
tableOutput("ANOVAXLS")
|
|
)
|
|
|
|
))),
|
|
tabPanel("linear Analysis",
|
|
sidebarLayout(
|
|
sidebarPanel(
|
|
width=2,
|
|
fluidRow(
|
|
column(12,
|
|
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("linear PLA",
|
|
column(12,
|
|
htmlOutput("PureErrW3"),
|
|
tags$head(tags$style("#PureErrW3{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"),
|
|
|
|
h3("Tests for linear PLA):"),
|
|
box(title="Suitability tests", status="primary",solidHeader = T, width=12,
|
|
DTOutput("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"),
|
|
|
|
h3("ANOVA for parallel line assay"),
|
|
DTOutput("ANOVAlin"))
|
|
),
|
|
tabPanel("Report",
|
|
h4("Settings for report")
|
|
))
|
|
)
|
|
)
|
|
),
|
|
tabPanel("parameter estimates",
|
|
column(3,style = "background: #4FCBD922",
|
|
br(),
|
|
h4("Regression results restricted"),
|
|
tableOutput("coeffs_r"),
|
|
"Bend points restricted",
|
|
tableOutput("bends_r2")),
|
|
column(3,style = "background: #B5C74022",
|
|
br(),
|
|
h4("Regression results unrestricted"),
|
|
tableOutput("coeffs_unr")),
|
|
column(3,style = "background: #F9545422",
|
|
h4("Regression results (ln-transformed)"),
|
|
tableOutput("logcoeffs_r"),
|
|
tableOutput("bends_unr2"),
|
|
|
|
tableOutput("logcoeffs_unr"))
|
|
),
|
|
tabPanel("Report",
|
|
h4("Settings for report"),
|
|
downloadButton("downloadXLReport", label="Download PDF report", class="butt"),
|
|
tags$style(type="text/css","#downloadXLReport {background-color: orange; color: black;font-family: COurier New}"),
|
|
)
|
|
)
|
|
)
|
|
)
|
|
})
|
|
|
|
|
|
|
|
##### UI Meta ----
|
|
output$fourPL <- renderUI({
|
|
navbarPage(title="4PL+linear reg",
|
|
tabPanel("Analysis and Plots",
|
|
#sidebarLayout(
|
|
# sidebarPanel(
|
|
# width=4,
|
|
# fluidRow(
|
|
# )
|
|
# ),
|
|
mainPanel(width=12,
|
|
tabsetPanel(id="tabs",
|
|
tabPanel("Settings",
|
|
h4("Settings of 4PL regression"),
|
|
div(checkboxInput("PureErrMeta", "Should pure error be used for calculation of CIs?", FALSE),
|
|
style = "font-size: 24px !important;color: #C2173F"),
|
|
|
|
h4("User help"),
|
|
h5("If new dilutions are entered, please adjust EC50 to avoid calculation errors"),
|
|
# 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 ...")),
|
|
#h5("Diagnostics only shown if EXCEL is uploaded"),
|
|
column(2,style = "background: #7FAEFF",
|
|
#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(2,style = "background: #7FAEFF",
|
|
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(2,style = "background: #627ADD",
|
|
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(2,style = "background: #627ADD",
|
|
|
|
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)
|
|
),
|
|
column(2,style = "background: #4FCBD9",
|
|
h4("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),
|
|
verbatimTextOutput("dilutions")
|
|
),
|
|
|
|
column(2,
|
|
h3("Settings"),
|
|
helpText("Vary the slider to see the effect of special cause"),
|
|
sliderInput("sdfac","Variability as % of lower to upper asymptote", max=10, value=3, min=0.1, step=0.1),
|
|
checkboxInput("heterosked","heteroskedastic noise", FALSE),
|
|
sliderInput("potencydiff","potency of test (%)", max=200, min=50, value=100, step=1),
|
|
sliderInput("outlL","outlier in lower asymptote", min=0, max=1.5, value=0, step=0.1),
|
|
sliderInput("outlM","outlier in mid part", min=0, max=1.5,value=0, step=0.1),
|
|
sliderInput("outlU","outlier in upper asymptote", min=0, max=1.5,value=0, step=0.1)
|
|
),
|
|
h4("log-dilutions from settings above"),
|
|
verbatimTextOutput("logdil"),
|
|
column(8,
|
|
box(title = "Simulated data per log-concentration", status="warning",solidHeader = T, width=12, "incl. mean, sd and CV%",
|
|
DT::dataTableOutput("ConctabMeta")))
|
|
|
|
#)
|
|
),
|
|
#### 4pl fits ----
|
|
tabPanel("4pl-fit",
|
|
tags$head(tags$style("#PureErrW2{color: red;
|
|
font-size: 10px;
|
|
font_style: italic;}")),
|
|
wellPanel(
|
|
fluidRow(
|
|
column(10,
|
|
tags$style(span(htmlOutput("PureErrW3"), style="color: red")),
|
|
htmlOutput("PureErrW3"),
|
|
|
|
plotOutput("plot4plMeta", width = "80%"),
|
|
DTOutput("pottab4pl"),
|
|
"Footnote: test performed on relative CIs.",
|
|
|
|
DTOutput("EQtests4pl"), # SSTs
|
|
h5("*...The estimate for F-test on regression and on non-linearity is the p-value"),
|
|
h5("F-test on regression passes if F-value > F-crit and thus p < 0.05"),
|
|
h5("F-test on non-linearity passes if F-value < F-crit and thus p > 0.05"),
|
|
h5("Test results outcome: 0 ... test passed (for EQ tests: CI within limits);
|
|
1 ... test failed (for EQ tests CI not within limits);
|
|
-1 ... calculations unbound/denominator too close to 0"),
|
|
#plotOutput("CIplot, height=50%")
|
|
),
|
|
column(8,
|
|
"4 PL ANOVA unrestricted",
|
|
box(title = "ANOVA unrestricted", status="warning",solidHeader = T, width=12, "",
|
|
DT::dataTableOutput("ANOVA")),
|
|
h5("Please note that for the CIs of rel. potency the RSS of the restricted model is used"),
|
|
h5("RSS ... 'Residual error' in the SumSquares column"),
|
|
h5("MSE ... 'Residual error' in the MeanSquaress column"),
|
|
h5("SSE ... 'Pure error' in the SumSquares column"),
|
|
h5("RMSE ... Square root of the 'Residual Error' in the MeanSquares column"),
|
|
verbatimTextOutput("RMSE")
|
|
)
|
|
))
|
|
),
|
|
|
|
tabPanel("ln-transformed y",
|
|
h4("ln-transformed y-axis plots"),
|
|
plotOutput("plot4plTransMeta", width = "80%"),
|
|
DT::dataTableOutput("pottab4plTrans"),
|
|
),
|
|
tabPanel("linear regression",
|
|
h4("Evaluations from meta-data"),
|
|
htmlOutput("PureErrW3"),
|
|
tags$head(tags$style("#PureErrW3{color: red;
|
|
font-size: 16px;
|
|
font_style: italic;}")),
|
|
column(12,
|
|
|
|
plotOutput("plotLinMeta"),
|
|
"Delta method is used for potency CIs",
|
|
DTOutput("pottabMeta")
|
|
),
|
|
column(5,
|
|
h3("Tests for linear PLA:"),
|
|
box(title="Suitability tests", status="primary",solidHeader = T,collapsible=T, width=12,
|
|
DTOutput("TESTSlinMeta")),
|
|
|
|
h5("The estimate is the p-value of the test"),
|
|
h5("F-tests on regression, significance of slopes, and preparation need to have a p-value <0.05 to pass"),
|
|
h5("All other tests pass if p-value > 0.05"),
|
|
box(title="Unrestricted linear model (SSSI):", status="primary",solidHeader = T,collapsible=T, width=12,
|
|
tableOutput("SummaryModABuMeta")),
|
|
h4("Restricted linear model (CSSI):"),
|
|
box(title="Restricted linear model (CSSI):", status="primary",solidHeader = T,collapsible=T, width=12,
|
|
tableOutput("SummaryModABMeta"))
|
|
),
|
|
|
|
column(6,
|
|
|
|
h3("ANOVA for parallel line assay"),
|
|
box(title="ANOVA for simultated data", status="primary",solidHeader = T, collapsible=T, width=12,
|
|
DTOutput("ANOVAlinMeta")),
|
|
" CI for difference of slopes:",
|
|
tableOutput("SlopeDiffCIMeta"),
|
|
)
|
|
),
|
|
tabPanel("Report",
|
|
h4("Settings for report"),
|
|
downloadButton("downloadXLReport", label="Download PDF report", class="butt"),
|
|
tags$style(type="text/css","#downloadXLReport {background-color: orange; color: black;font-family: COurier New}"),
|
|
)
|
|
)
|
|
)
|
|
#)
|
|
)
|
|
)
|
|
})
|
|
|
|
|
|
output$pla <- renderUI({
|
|
navbarPage(title="pla",
|
|
tabPanel("Analysis and Plots",
|
|
|
|
)
|
|
)
|
|
})
|
|
|
|
output$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")
|
|
))
|
|
)
|
|
)))
|
|
})
|
|
|
|
|
|
|
|
v <- reactiveValues(num_dose=0, next.dose.t=0)
|
|
|
|
sigmoid <- reactive({
|
|
sig <- c(input$lowAsymptREF, input$lowAsymptTEST,input$uppAsymptREF,input$uppAsymptREF,
|
|
input$slopeREF,input$slopeTEST,input$EC50,input$potDiff)
|
|
sig
|
|
})
|
|
|
|
CONC <- reactive({
|
|
Konz_ <- c(input$CONC1,input$CONC2,input$CONC3,input$CONC4,
|
|
input$CONC5,input$CONC6,input$CONC7,input$CONC8,
|
|
input$CONC9,input$CONC10,input$CONC11,input$CONC12)
|
|
if (any(na.omit(Konz_)==0)) Konz_[Konz_ ==0] <- 0.0000001
|
|
Konz <- na.omit(Konz_)
|
|
})
|
|
|
|
Dils <- reactive({
|
|
Dilutions <- c(input$ConcStart,input$dilutionFac,input$NoDil,input$NoDilSer)
|
|
})
|
|
|
|
#### input EXCEL file ----
|
|
|
|
observe({
|
|
if (!is.null(input$iFile)) {
|
|
inFile <- input$iFile
|
|
ext <- tools::file_ext(inFile$name)
|
|
file.rename(inFile$datapath, paste(inFile$datapath, ".xlsx",sep=""))
|
|
t.filelocation <- gsub('\\\\','/',paste(inFile$datapath, ext,sep="."))
|
|
sheets <- openxlsx::getSheetNames(t.filelocation)
|
|
dat <- lapply(sheets, openxlsx::read.xlsx, xlsxFile = t.filelocation)
|
|
names(dat) <- sheets
|
|
Dat$wb <- dat
|
|
names(Dat$wb) <- sheets
|
|
Dat$sheets <- sheets
|
|
Dat$FileName <- input$iFile[["name"]]
|
|
|
|
}
|
|
})
|
|
output$sheetName <- renderUI({
|
|
if (!is.null(Dat$wb)) {
|
|
#browser()
|
|
cnSheets <- Dat$sheets
|
|
cnSheets2 <- c("please choose", cnSheets)
|
|
selectInput(inputId = "sheet", label="Select a sheet:",choices = cnSheets)
|
|
}
|
|
})
|
|
observeEvent(input$sign_out, {
|
|
unlink(input$iFile$datapath)
|
|
reset(id = "") # from shinyjs package
|
|
})
|
|
|
|
|
|
#### process XLSX file ----
|
|
observe({
|
|
if (!is.null(input$iFile)) {
|
|
if (!is.null(input$sheet)) {
|
|
if (input$sheet != "please choose") {
|
|
#browser()
|
|
|
|
XLdat <- Dat$wb[input$sheet][[1]]
|
|
if (is.null(XLdat)) XLdat <- Dat$wb[Dat$sheets[1]][[1]]
|
|
cn <- colnames(XLdat)
|
|
logI <- grep("log", cn)
|
|
logDoseI <- grep("log_dose", cn)
|
|
if (length(logI)>0 & length(logDoseI)==0) {
|
|
XLdat$log_dose <- XLdat[,logI]
|
|
XLdat2 <- XLdat[,-logI]
|
|
CORro <- cor(XLdat$log_dose, XLdat[,3])
|
|
} else if (length(logI)==0 & length(logDoseI)==0) {
|
|
Ind <- grep("dilu|dose|Dose|Conc|conc",cn)
|
|
XLdat$log_dose <- log(XLdat[,Ind])
|
|
CORro <- cor(XLdat[,Ind], XLdat[,3])
|
|
XLdat2 <- XLdat[,-Ind]
|
|
} else if (length(logI)>0 & length(logDoseI)>0) {
|
|
XLdat2 <- XLdat
|
|
CORro <- cor(XLdat[,logI], XLdat[,3])
|
|
}
|
|
Dat$EXCEL <- XLdat2
|
|
PureErrFlag <- input$PureErr
|
|
warning_text2 <- reactive({
|
|
ifelse(PureErrFlag, 'Pure Error is selected', '')
|
|
})
|
|
output$PureErrW2 <- renderText(warning_text2())
|
|
|
|
noDilSeries <-(ncol(XLdat2)-1)/2
|
|
noDils <- nrow(XLdat2)
|
|
Dat$noDilSeriesXL <- noDilSeries
|
|
|
|
all_l <- melt(data.frame(XLdat2), id.vars="log_dose",variable.name = "replname", value.name = "readout")
|
|
isRef <- rep(c(1,0),1,each=nrow(XLdat2)*noDilSeries)
|
|
isSample <- rep(c(0,1),1,each=nrow(XLdat2)*noDilSeries)
|
|
all_l$isRef <- isRef
|
|
all_l$isSample <- isSample
|
|
all_l$Conc <- exp(all_l$log_dose)
|
|
# all_l$readout[all_l$readout < 0] <- 0.01
|
|
REP$all_l <- all_l
|
|
|
|
#### XLSX eval ----
|
|
if (CORro<0) SLOPE <- -1 else SLOPE <- 1
|
|
FITs <- Fitting_FUNC(XLdat2, TransFlag = FALSE)
|
|
|
|
|
|
Smr <- FITs[[1]] # summary(mr)
|
|
Smu <- FITs[[2]] # summary(mu)
|
|
coeffsMR <- Smr$coefficients[,1]
|
|
coeffsMU <- Smu$coefficients[,1]
|
|
Dat$coeffsMRes <- coeffsMR
|
|
Dat$coeffsMUnr <- coeffsMU
|
|
Dat$coeffs_UN <- coeffsMU
|
|
names(coeffsMU) <- c("lowAsym REF", "slope REF","upperAsym REF","EC50 REF","lowAsym TEST","slope TEST","upperAsym TEST","r")
|
|
|
|
if (!PureErrFlag) {
|
|
pot_est <- FITs[[3]]
|
|
potU_est <- FITs[[4]]
|
|
colnames(pot_est) <- c("estimate","lowerCI","upperCI")
|
|
colnames(potU_est) <- c("estimate","lowerCI","upperCI")
|
|
} else {
|
|
FitAnova <- anova(lm(readout ~ factor(log_dose)*isSample, all_l))
|
|
meanPureErr <- FitAnova[4,3]
|
|
DFsPure <- FitAnova[4,1]
|
|
#VCOV <- vcov(mr)
|
|
V_V <- Smr$cov.unscaled #VCOV/Smr$sigma^2
|
|
VCOVpure <- V_V*meanPureErr
|
|
SEsPure <- sqrt(diag(V_V)*meanPureErr)
|
|
pot_est <- data.frame(estimate=exp(coeffsMR[5]), lowerCI = exp(coeffsMR[5]-qt(0.975,DFsPure)*SEsPure[5]),
|
|
upperCI = exp(coeffsMR[5]+qt(0.975,DFsPure)*SEsPure[5]))
|
|
#VCOVu <- vcov(mu)
|
|
V_Vu <- Smu$cov.unscaled
|
|
#VCOVpure <- V_Vu*meanPureErr
|
|
SEsPureU <- sqrt(diag(V_Vu)*meanPureErr)
|
|
potU_est <- data.frame(estimate=exp(coeffsMU[7]), lowerCI = exp(coeffsMU[7]-qt(0.975,DFsPure)*SEsPureU[7]),
|
|
upperCI = exp(coeffsMU[7]+qt(0.975,DFsPure)*SEsPureU[7]))
|
|
|
|
}
|
|
|
|
|
|
#browser()
|
|
Dat$potDiffXL <- potU_est[1]*100
|
|
RMSE_unr_diagn <- Smu$sigma # sqrt(SU$resVar)
|
|
RMSE_res_diagn <- Smr$sigma #sqrt(SR$resVar)
|
|
up_lowDiffDiagn <- Smu$coefficients["ds",1]-Smu$coefficients["as",1]
|
|
ProzSD_diagn <- RMSE_unr_diagn*100/up_lowDiffDiagn
|
|
Dat$ProzSD_XL <- ProzSD_diagn
|
|
|
|
observe({
|
|
pot_est3 <- data.frame(pot_est*100)
|
|
MaxPl <- max(input$upperPot, pot_est3$upperCI)
|
|
MinPl <- min(input$lowerPot, pot_est3$lowerCI)
|
|
MaxPl_ <- MaxPl*1.2
|
|
MinPl_ <- MinPl*0.8
|
|
#browser()
|
|
p_relCI <- ggplot(data=pot_est3, aes(xmin=lowerCI, xmax=upperCI, y=1)) +
|
|
geom_linerange(size=4, col="darkseagreen",alpha=0.5) +
|
|
geom_point(aes(x=estimate, y=1), col="grey15", shape=13, size=10) +
|
|
geom_vline(xintercept = c(input$lowerPot, input$upperPot), col="indianred") +
|
|
annotate("text", x=input$lowerPot-13, y=1.040, label=paste("lower EAC:", input$lowerPot), col="indianred") +
|
|
annotate("text", x=input$upperPot+13, y=1.040, label=paste("upper EAC:", input$upperPot), col="indianred") +
|
|
annotate("text", x=pot_est3$lowerCI-10, y=1.020, label=paste("lower CL:", round(pot_est3$lowerCI,1)), col="darkgreen") +
|
|
annotate("text", x=pot_est3$upperCI+10, y=1.020, label=paste("upper CL:", round(pot_est3$upperCI,1)), col="darkgreen") +
|
|
annotate("text", x=pot_est3$estimate, y=0.98, label=paste("rel. potency:", round(pot_est3$estimate,1)), col="black") +
|
|
ylim(c(0.95, 1.05)) +
|
|
xlim(c(MinPl_,MaxPl_)) +
|
|
xlab("relative potency + confidence interval") +
|
|
theme_bw() +
|
|
theme(axis.title.y=element_blank(),
|
|
axis.text.y=element_blank(),
|
|
axis.ticks.y=element_blank())
|
|
output$relpotTestPlot <- renderPlot({
|
|
p_relCI
|
|
})
|
|
REP$relpotTestPlot <- p_relCI
|
|
|
|
output$relpotTestTab <- renderTable({ pot_est3 })
|
|
})
|
|
|
|
|
|
ANOVAtab2 <- ANOVA4plUnresfunc(ro_new = XLdat2)
|
|
output$ANOVAXLS <- renderTable({ ANOVAtab2 })
|
|
|
|
REP$ANOVAXLS <- ANOVAtab2
|
|
|
|
#browser()
|
|
FITsTrans <- Fitting_FUNC(XLdat2, TransFlag = TRUE)
|
|
#
|
|
|
|
SUlog <- FITsTrans[[2]]
|
|
SRlog <- FITsTrans[[1]]
|
|
RMSE_unrlog_diagn <- SUlog$sigma
|
|
RMSE_reslog_diagn <- SRlog$sigma
|
|
|
|
up_lowDifflogDiagn <- SUlog$coefficients["ds",1]-SUlog$coefficients["as",1]
|
|
ProzSDlog_diagn <- RMSE_unrlog_diagn * 100 / up_lowDifflogDiagn
|
|
|
|
#### Diagnostic RMSE table ####
|
|
DiagnTable <- data.frame(parameter = c("RMSE unrestricted", "RMSE_restr.", "Diff_upper-lowerAsymp", "%SD (unrestricted)",
|
|
"RMSE log_unrestricted", "RMSE log_restr", "diff_up-lowAsymp_log", "%SD (log unrestricted)"),
|
|
result = c(round(RMSE_unr_diagn, 4), round(RMSE_res_diagn, 4),
|
|
round(up_lowDiffDiagn, 4), round(ProzSD_diagn, 4),
|
|
round(RMSE_unrlog_diagn, 4), round(RMSE_reslog_diagn, 4),
|
|
round(up_lowDifflogDiagn, 4), round(ProzSDlog_diagn, 4)))
|
|
|
|
Dat$DiagnTable <- DiagnTable
|
|
REP$DiagnTable <- DiagnTable
|
|
|
|
logpotest <- FITsTrans[[3]] #exp(confintd(mrlog, "r", method = "asymptotic")) # compParm(logpot, "c")
|
|
logpotUest <- FITsTrans[[4]] # exp(confintd(mulog, "r", method = "asymptotic")) # compParm(logpotu, "c")
|
|
|
|
# Berechnung der Konfidenzintervalle (CI)
|
|
# logpotCI <- c(exp(Smrlog[5,1] - qt(0.975, nrow(all_1)-5) * Smrlog[5,2]), exp(Smrlog[5,1]), exp(Smrlog[5,1] + qt(0.975, nrow(all_1)-5) * Smrlog[5,2]))
|
|
colnames(logpotest) <- c("estimate", "lowerCI", "upperCI")
|
|
|
|
colnames(logpotUest) <- c("estimate", "lowerCI", "upperCI")
|
|
#browser()
|
|
cnXL <- colnames(XLdat2)
|
|
Filesample <- data.frame(Test = c("File name", "samples"), Test2=c(Dat$FileName, paste(cnXL[1], " vs ", cnXL[4])))
|
|
colnames(Filesample) <- c("", "")
|
|
output$Filesampl <- renderTable({ Filesample }, rownames = F)
|
|
|
|
UnRPLAausw <- data.frame(Information = c("model", "lower asymptote Ref", "Hill's slope Ref", "upper asymptote Ref","EC50 Ref",
|
|
"lower asymptote Test", "Hill's slope Test",
|
|
"upper asymptote Test","EC50 Difference",
|
|
"relative potency", "lower CI", "upper CI"),
|
|
Results = unlist(c("UNRESTRICTED", round(coeffsMU, 3), round(potU_est*100, 3)))) # von psl_nls
|
|
|
|
# "log relative potency", "log lower CI", "log upper CI", round(logpotest, 3), round(compParm(potu, "c", display = F), 3)
|
|
|
|
output$coeffs_unr <- renderTable({
|
|
UnRPLAausw
|
|
})
|
|
|
|
#browser()
|
|
UnRPLAausw2 <- data.frame(Dat$bendpointsTRANS)
|
|
if (length(UnRPLAausw2) > 0) {
|
|
colnames(UnRPLAausw2) <- c("bendpoints log")
|
|
UnrBendLog <- data.frame(Bendpoint = c("REF_lower","REF_upper",
|
|
"TEST_lower","REF_lower"),
|
|
bendpoints_logscale = UnRPLAausw2)
|
|
|
|
output$bends_unr2 <- renderTable({
|
|
UnrBendLog
|
|
})
|
|
}
|
|
REP$UnRPLAausw <- UnRPLAausw
|
|
REP$UnRPLAausw2 <- UnRPLAausw2
|
|
|
|
# browser()
|
|
coeffs_R <- coeffsMR # pot$coefficients
|
|
coeffs_R[5] <- coeffs_R[4] - coeffs_R[5]
|
|
names(coeffs_R) <- c("lower A", "slope", "upper A", "EC50 REF", "EC50 TEST")
|
|
|
|
# --- Ergebnistabelle: RESTRICTED ---
|
|
PLAAusw <- data.frame(
|
|
Information = c("model", "lower asymptote", "Hill's slope", "upper asymptote","EC50 Ref",
|
|
"EC50 Test", "relative potency",
|
|
"lower CI", "upper CI"),
|
|
Results = unlist(c("RESTRICTED", round(coeffs_R, 3),
|
|
round(pot_est[1, ] * 100, 3)))) # von gs1_nls
|
|
output$coeffs_r <- renderTable({ PLAAusw })
|
|
|
|
PLAAusw2 <- data.frame(Dat$bendpoints)
|
|
output$bends_r2 <- renderTable({ PLAAusw2 }, digits = 3, rownames = T)
|
|
|
|
REP$PLAausw <- PLAAusw
|
|
REP$PLBend <- PLAAusw2
|
|
|
|
#### Koeffizienten-Extraktion ----
|
|
logcoeffs_R <- SRlog$coefficients[, 1] # logpot$coefficients
|
|
names(logcoeffs_R) <- c("lower A", "Hill's slope", "upper A", "EC50 REF","EC50 DIFF")
|
|
|
|
# --- Ergebnistabelle: LOG RESTRICTED ---
|
|
|
|
LogPLAAusw <- data.frame(
|
|
Information = c("model", "lower asymptote", "Hill's slope", "upper asymptote","EC50 Ref",
|
|
"EC50 difference", "log relative potency",
|
|
"log lower CI", "log upper CI"),
|
|
Results = unlist(c("LOG RESTRICTED", round(logcoeffs_R, 3),
|
|
round(logpotest * 100, 3)))) # von gsl_nls
|
|
|
|
output$logcoeffs_r <- renderTable({ LogPLAAusw })
|
|
REP$LogPLAausw <- LogPLAAusw
|
|
|
|
logcoeffs_UNR <- SUlog$coefficients[,1]
|
|
names(logcoeffs_UNR) <- c("lower asymptote Ref", "Hill's slope Ref", "upper asymptote Ref","EC50 Ref",
|
|
"lower asymptote Test", "Hill's slope Test", "upper asymptote Test","EC50 Diff"
|
|
)
|
|
|
|
# --- Ergebnistabelle: LOG UNRESTRICTED ---
|
|
|
|
LogUnrPLAAusw <- data.frame(
|
|
Information = c("model", "lower asymptote Ref", "Hill's slope Ref", "upper asymptote Ref","EC50 Ref",
|
|
"lower asymptote Test", "Hill's slope Test", "upper asymptote Test","EC50 Diff" ,
|
|
"relative potency", "lower CI", "upper CI"),
|
|
|
|
Results = unlist(c("LOG UNRESTRICTED", round(logcoeffs_UNR, 3),
|
|
round(logpotUest * 100, 3)))) # von gsl_nls
|
|
|
|
output$logcoeffs_unr <- renderTable({
|
|
LogUnrPLAAusw
|
|
})
|
|
REP$LogUnrPLAausw <- LogUnrPLAAusw
|
|
#browser()
|
|
|
|
|
|
|
|
if (exists("Ind")) {
|
|
Dat$dilution <- XLdat[,Ind]
|
|
} else Dat$dilution <- exp(XLdat[,logI])
|
|
# --- Plot-Ausgabe ---
|
|
output$XLplot <- renderPlot({
|
|
plot_f(XLdat2, TransFlag=F)
|
|
})
|
|
|
|
REP$XLdat2 <- XLdat2
|
|
|
|
# --- Diagnose-Plots (Residualanalyse) ---
|
|
output$diagnplot <- renderPlot({
|
|
op <- par(mfrow = c(2, 2), mar = c(3.2, 3.2, 2, .5), mgp = c(2, .7, 0))
|
|
PREDs <- FITs[[5]]
|
|
PREDsU <- FITs[[6]]
|
|
# 1. Residuals vs Fitted
|
|
plot(Smr$residuals ~ PREDs, main = "Residuals restricted")
|
|
abline(h = 0)
|
|
qqnorm(Smr$residuals)
|
|
qqline(Smr$residuals)
|
|
plot(Smu$residuals ~ PREDsU, main = "Residuals unrestricted")
|
|
abline(h = 0)
|
|
|
|
qqnorm(Smu$residuals)
|
|
qqline(Smu$residuals)
|
|
|
|
par(op) # Parameter zurücksetzen
|
|
})
|
|
|
|
|
|
pot <- drm(readout ~ Conc, isSample, data=all_l, 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))
|
|
output$AIC <- renderTable({
|
|
AIC <- AIC(pot, potU)
|
|
})
|
|
|
|
output$VarDiagn <- renderTable({
|
|
DiagnTable
|
|
}, digits=4)
|
|
|
|
output$relpotplot <- renderPlot({
|
|
relpot(potU, intervall="fieller", bty="l",
|
|
main="Quality of rel. potency over response")
|
|
})
|
|
|
|
|
|
|
|
} # !please choose
|
|
} # input$sheet
|
|
} # input$iFile
|
|
})
|
|
|
|
#### make geomDils reactive ----
|
|
observe({
|
|
#browser()
|
|
if (is.null(input$ConcStart)) return(NULL)
|
|
if (!is.na(input$ConcStart) & !is.na(input$dilutionFac) &!is.na(input$NoDil) &!is.na(input$NoDilSer)) {
|
|
upR <- input$ConcStart
|
|
noDil <- input$NoDil
|
|
noDilSer <- input$NoDilSer
|
|
Div <- input$dilutionFac
|
|
res <- c()
|
|
N_ <- 1
|
|
Conc <- c(upR, divFUN(upR,Div,N=N_,res,noDil))
|
|
|
|
Dat$MetaConc <- Conc
|
|
}
|
|
})
|
|
|
|
#### updateSlider on XLSX ----
|
|
# observe({
|
|
# if (!is.null(Dat$potDiffXL)) {
|
|
# updateSliderInput(session, "potencydiff",
|
|
# value=round(as.numeric(Dat$potDiffXL[[1]]),5))
|
|
# }
|
|
# })
|
|
# observeEvent(input$potencydiff, {
|
|
# if (!is.null(Dat$potDiffXL)) {
|
|
# updateSliderInput(session, "potencydiff",
|
|
# value=round(as.numeric(input$potencydiff),5))
|
|
# }
|
|
# })
|
|
# observe({
|
|
# if (!is.null(Dat$ProzSD_XL)) {
|
|
# updateSliderInput(session, "sdfac",
|
|
# value=round(as.numeric(Dat$ProzSD_XL[[1]]),5))
|
|
# }
|
|
# })
|
|
# observeEvent(input$sdfac, {
|
|
# if (!is.null(Dat$ProzSD_XL)) {
|
|
# updateSliderInput(session, "sdfac",
|
|
# value=round(as.numeric(Dat$ProzSD_XL[[1]]),5))
|
|
# }
|
|
# })
|
|
|
|
#### updaterNumeric Input ----
|
|
# observe({
|
|
# if(!is.null(Dat$coeffs_UN)) {
|
|
# updateNumericInput(session, "lowAsymptREF",
|
|
# value=round(as.numeric(Dat$coeffs_UN[3]),5), min=0)
|
|
# updateNumericInput(session, "lowAsymptTEST",
|
|
# value=round(as.numeric(Dat$coeffs_UN[4]),5), min=0)
|
|
# updateNumericInput(session, "uppAsymptREF",
|
|
# value=round(as.numeric(Dat$coeffs_UN[5]),5), min=0)
|
|
# updateNumericInput(session, "uppAsymptTEST",
|
|
# value=round(as.numeric(Dat$coeffs_UN[6]),5), min=0)
|
|
# updateNumericInput(session, "slopeREF",
|
|
# value=round(as.numeric(Dat$coeffs_UN[1]),5))
|
|
# updateNumericInput(session, "slopeTEST",
|
|
# value=round(as.numeric(Dat$coeffs_UN[2]),5))
|
|
# updateNumericInput(session, "EC50",
|
|
# value=round(as.numeric(Dat$coeffs_UN[7]),5))
|
|
# updateNumericInput(session, "potDiff",
|
|
# value=round(as.numeric(Dat$coeffs_UN[7])- as.numeric(Dat$coeffs_UN[8]),5))
|
|
# }
|
|
# })
|
|
#
|
|
# observe({
|
|
# if(!is.null(Dat$dilution)) {
|
|
# updateNumericInput(session, "CONC1",
|
|
# value=as.numeric(Dat$dilution[1]))
|
|
# updateNumericInput(session, "CONC2",
|
|
# value=as.numeric(Dat$dilution[2]))
|
|
# updateNumericInput(session, "CONC3",
|
|
# value=as.numeric(Dat$dilution[3]))
|
|
# updateNumericInput(session, "CONC4",
|
|
# value=as.numeric(Dat$dilution[4]))
|
|
# updateNumericInput(session, "CONC5",
|
|
# value=as.numeric(Dat$dilution[5]))
|
|
# updateNumericInput(session, "CONC6",
|
|
# value=as.numeric(Dat$dilution[6]))
|
|
# updateNumericInput(session, "CONC7",
|
|
# value=as.numeric(Dat$dilution[7]))
|
|
# updateNumericInput(session, "CONC8",
|
|
# value=as.numeric(Dat$dilution[8]))
|
|
# updateNumericInput(session, "CONC9",
|
|
# value=as.numeric(Dat$dilution[9]))
|
|
# updateNumericInput(session, "CONC10",
|
|
# value=as.numeric(Dat$dilution[10]))
|
|
# updateNumericInput(session, "CONC11",
|
|
# value=as.numeric(Dat$dilution[11]))
|
|
# updateNumericInput(session, "CONC12",
|
|
# value=as.numeric(Dat$dilution[12]))
|
|
#
|
|
# }
|
|
# })
|
|
|
|
observe({
|
|
if(!is.null(Dat$MetaConc)) {
|
|
updateNumericInput(session, "CONC1",
|
|
value=as.numeric(Dat$MetaConc[1]))
|
|
updateNumericInput(session, "CONC2",
|
|
value=as.numeric(Dat$MetaConc[2]))
|
|
updateNumericInput(session, "CONC3",
|
|
value=as.numeric(Dat$MetaConc[3]))
|
|
updateNumericInput(session, "CONC4",
|
|
value=as.numeric(Dat$MetaConc[4]))
|
|
updateNumericInput(session, "CONC5",
|
|
value=as.numeric(Dat$MetaConc[5]))
|
|
updateNumericInput(session, "CONC6",
|
|
value=as.numeric(Dat$MetaConc[6]))
|
|
updateNumericInput(session, "CONC7",
|
|
value=as.numeric(Dat$MetaConc[7]))
|
|
updateNumericInput(session, "CONC8",
|
|
value=as.numeric(Dat$MetaConc[8]))
|
|
updateNumericInput(session, "CONC9",
|
|
value=as.numeric(Dat$MetaConc[9]))
|
|
updateNumericInput(session, "CONC10",
|
|
value=as.numeric(Dat$MetaConc[10]))
|
|
updateNumericInput(session, "CONC11",
|
|
value=as.numeric(Dat$MetaConc[11]))
|
|
updateNumericInput(session, "CONC12",
|
|
value=as.numeric(Dat$MetaConc[12]))
|
|
|
|
}
|
|
})
|
|
|
|
#### render logDilsText ----
|
|
output$logdil <- renderText({
|
|
if (!is.null(Dat$MetaConc)) {
|
|
Conc <- Dat$MetaConc
|
|
} else Conc <- CONC()
|
|
logdilu <-log(Conc)
|
|
logdilu
|
|
})
|
|
|
|
|
|
#### reactive dataset sim ----
|
|
|
|
sim <- reactive({
|
|
#browser()
|
|
if(is.null(sigmoid())) return(NULL)
|
|
sd_fac_ <- as.numeric(input$sdfac)
|
|
r_ <- log(as.numeric(input$potencydiff)/100)
|
|
as = sigmoid()[1]; bs = sigmoid()[5];cs = sigmoid()[7];ds = sigmoid()[3];at = sigmoid()[2];
|
|
bt = sigmoid()[6];r = sigmoid()[8]; ct = cs-r_; dt = sigmoid()[4];
|
|
if (!is.null(Dat$MetaConc)) Conc <- Dat$MetaConc else Conc <- CONC()
|
|
log_conc <- log(Conc)
|
|
av_test <- as + (ds-as)/(1+exp(bs*(cs - log_conc)))
|
|
av_ref <- at + (dt-at)/(1+exp(bt*(ct - log_conc)))
|
|
#browser()
|
|
if (!is.na(input$NoDilSer)) {
|
|
noDilSer <- input$NoDilSer
|
|
} else if (!is.null(Dat$noDilSeriesXL)) noDilSer <- Dat$noDilSeriesXL else noDilSer <- 3
|
|
if (!is.na(input$NoDil)) noDil <- input$NoDil else noDil <- length(log_conc)
|
|
isRef <- rep(c(1,0), 1,each=noDilSer*noDil)
|
|
isSample <- rep(c(0,1), 1,each=noDilSer*noDil)
|
|
|
|
#if (is.null(Dat$EXCEL)) {
|
|
ro_new <- Calc_DilRes(as=as,at=at,ds=ds,dt=dt,cs=cs,ct=ct,r=r_,bt=bt,bs=bs, log_conc = log_conc,
|
|
sd_fac=sd_fac_,
|
|
# auslenkU=outlierU,
|
|
# auslenkM=outlierM,
|
|
# auslenkL=outlierL,
|
|
heteroNoise = input$heterosked, noDilSeries = noDilSer, noDils = noDil)
|
|
#} else ro_new <- Dat$EXCEL
|
|
})
|
|
# })
|
|
|
|
####sim2 ----
|
|
sim2 <- reactive({
|
|
tab <- sim()
|
|
#if (is.null(Dat$EXCEL)) return(tab) else return(Dat$EXCEL)
|
|
})
|
|
|
|
|
|
#### Plot 4pl ----
|
|
output$plot4plMeta <- renderPlot({
|
|
#browser()
|
|
sigmoid <- sigmoid()
|
|
det_sig=NULL
|
|
plot_f(sim2(), TransFlag = F)
|
|
})
|
|
|
|
#### Plot 4pl Transformed ----
|
|
output$plot4plTransMeta <- renderPlot({
|
|
#browser()
|
|
sigmoid <- sigmoid()
|
|
det_sig=NULL
|
|
plot_f(sim2(), TransFlag = T)
|
|
})
|
|
|
|
|
|
#### Testergebnisse für 4PL ----
|
|
observe({
|
|
if (is.null(sim2())) return(NULL)
|
|
if (is.null(input$PureErrMeta)) return(NULL)
|
|
#observeEvent(input$StartCalc,{
|
|
PureErrFlag <- input$PureErrMeta
|
|
warning_text3 <- reactive({
|
|
ifelse(PureErrFlag, 'Pure error selected','')
|
|
})
|
|
|
|
#browser()
|
|
output$PureErrW3 <- renderText(warning_text3())
|
|
|
|
Limite <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla),
|
|
as.numeric(input$lEACratiola), as.numeric(input$uEACratiola),
|
|
as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope),
|
|
as.numeric(input$lEACratioua), as.numeric(input$uEACratioua),
|
|
as.numeric(input$lowerPot), as.numeric(input$upperPot),
|
|
as.numeric(input$lEACratioAdiff), as.numeric(input$uEACratioAdiff))
|
|
Dat$limite <- Limite
|
|
#browser()
|
|
|
|
tab <- tests_FUNC(sim2(), Limite, PureErrFlag = PureErrFlag)
|
|
if (length(tab)>1) {
|
|
tab[1,6:7] <- c("-","-")
|
|
Dat$tests_FUNC <- tab
|
|
REP$testsTab <- tab
|
|
tab2 <- tab[1:7,]
|
|
|
|
dat <- datatable(tab2,options = list(
|
|
paging=TRUE,
|
|
dom="t",
|
|
rownames=FALSE
|
|
)) %>% formatStyle("test_results",
|
|
target='row',
|
|
backgroundColor = styleEqual(c(-1,0,1),
|
|
c("pink",'lightgreen','lightgrey')))
|
|
} else { dat <- datatable(data.frame(test_results = "Convergeance failed for the uploaded dataset")) }
|
|
#browser()
|
|
output$EQtests4pl <- renderDT({ dat})
|
|
|
|
}) # observe
|
|
|
|
#### Testergebnisse für XLSX ----
|
|
observe({
|
|
if (is.null(Dat$EXCEL)) return(NULL)
|
|
if (is.null(input$PureErr)) return(NULL)
|
|
#observeEvent(input$StartCalc,{
|
|
PureErrFlag <- input$PureErr
|
|
warning_text3 <- reactive({
|
|
ifelse(PureErrFlag, 'Pure error selected','')
|
|
})
|
|
|
|
|
|
output$PureErrW3 <- renderText(warning_text3())
|
|
|
|
Limite <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla),
|
|
as.numeric(input$lEACratiola), as.numeric(input$uEACratiola),
|
|
as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope),
|
|
as.numeric(input$lEACratioua), as.numeric(input$uEACratioua),
|
|
as.numeric(input$lowerPot), as.numeric(input$upperPot),
|
|
as.numeric(input$lEACratioAdiff), as.numeric(input$uEACratioAdiff))
|
|
Dat$limite <- Limite
|
|
#browser()
|
|
SelTests <- as.numeric(input$selectedSSTs)
|
|
tab <- tests_FUNC(Dat$EXCEL, Limite, PureErrFlag = PureErrFlag)
|
|
|
|
tab[1,6:7] <- c("-","-")
|
|
Dat$tests_FUNC <- tab
|
|
REP$testsTab <- tab
|
|
tab2 <- tab[SelTests,]
|
|
|
|
dat <- datatable(tab2,options = list(
|
|
paging=TRUE,
|
|
dom="t",
|
|
rownames=FALSE
|
|
)) %>% formatStyle("test_results",
|
|
target='row',
|
|
backgroundColor = styleEqual(c(-1,0,1),
|
|
c("pink",'lightgreen','lightgrey')))
|
|
|
|
output$EQtests <- renderDT({ dat })
|
|
|
|
}) # observe
|
|
|
|
####plot CIs ----
|
|
observe({
|
|
tab <- Dat$tests_FUNC
|
|
if (is.null(tab)) return(NULL)
|
|
|
|
tab2 <- tab[-c(1,2,3,6),]
|
|
tab2[,3:7] <- apply(tab2[,3:7],2,as.numeric)
|
|
tab2[4:5,3:7] <- tab2[4:5,3:7]/100
|
|
|
|
p_CIs <- ggplot(tab2,aes(x=test,y=estimate, color=test,group=test)) +
|
|
geom_point() +
|
|
geom_errorbar(aes(ymin=lower_CI, ymax=upper_CI), width=0.4) +
|
|
geom_crossbar(aes(ymin=lower_limit, ymax=upper_limit), size=0.8) +
|
|
coord_flip() +
|
|
theme_bw() +
|
|
theme(legend.position = "none",text = element_text(size=20))
|
|
|
|
output$CIplot <- renderPlot({ p_CIs}, height=200)
|
|
|
|
REP$CIplot <- p_CIs
|
|
})
|
|
|
|
|
|
output$simdat <- DT::renderDataTable({
|
|
tab <- sim2()
|
|
if (is.character(tab)) stop(tab)
|
|
|
|
tab2 <- round(tab, 5)
|
|
colnames(tab2) <- c(paste("T", seq(1,(ncol(tab2)-1)/2)),
|
|
paste("R", seq(1,(ncol(tab2)-1)/2)), "log_conc" )
|
|
dat <- datatable(tab2, options=list(
|
|
paging=T,
|
|
pageLength=20,
|
|
dom="t"
|
|
))
|
|
})
|
|
##### Concentrationtab Meta ----
|
|
output$ConctabMeta <- DT::renderDataTable({
|
|
if (!is.na(Dils()[1]) & is.na(Dils()[4])) return(NULL)
|
|
tab <- sim2()
|
|
if (is.character(tab)) stop(tab)
|
|
if (!is.na(Dils()[4])) {
|
|
noDilSer <- Dils()[4]
|
|
} else if (!is.null(Dat$noDilSeriesXL)) {
|
|
noDilSer <- Dat$noDilSeriesXL
|
|
} else { noDilSer <- 3 }
|
|
|
|
Conc <- CONC()
|
|
Conctab <- perConcTab(tab, noDilSeries = noDilSer)
|
|
Dat$Conctab <- Conctab
|
|
|
|
dat <- datatable(Conctab, options=list(
|
|
paging=T,
|
|
pageLength=12,
|
|
dom="t"
|
|
)) %>% formatStyle(0,
|
|
target='row',
|
|
backgroundColor = styleEqual(c("avs","sds","cv", "avs_test","sds_test","cv_test"),
|
|
c('lightgrey','lightgreen','pink','lightgrey','lightgreen','pink'))
|
|
) %>% formatRound(columns=colnames(Conctab), digits=3)
|
|
})
|
|
|
|
output$Conctab <- DT::renderDataTable({
|
|
if (!is.na(Dils()[1]) & is.na(Dils()[4])) return(NULL)
|
|
tab <- sim2()
|
|
if (is.character(tab)) stop(tab)
|
|
if (!is.na(Dils()[4])) {
|
|
noDilSer <- Dils()[4]
|
|
} else if (!is.null(Dat$noDilSeriesXL)) {
|
|
noDilSer <- Dat$noDilSeriesXL
|
|
} else { noDilSer <- 3 }
|
|
|
|
Conc <- CONC()
|
|
Conctab <- perConcTab(tab, noDilSeries = noDilSer)
|
|
Dat$Conctab <- Conctab
|
|
|
|
dat <- datatable(Conctab, options=list(
|
|
paging=T,
|
|
pageLength=12,
|
|
dom="t"
|
|
)) %>% formatStyle(0,
|
|
target='row',
|
|
backgroundColor = styleEqual(c("avs","sds","cv", "avs_test","sds_test","cv_test"),
|
|
c('lightgrey','lightgreen','pink','lightgrey','lightgreen','pink'))
|
|
) %>% formatRound(columns=colnames(Conctab), digits=3)
|
|
})
|
|
|
|
#### process XL linearly, Plot output ----
|
|
output$plotLin <- renderPlot({
|
|
|
|
if (is.null(Dat$EXCEL)) return(NULL)
|
|
tab <- Dat$EXCEL
|
|
|
|
# tab <- sim2()
|
|
if (is.character(tab)) stop(tab)
|
|
#browser()
|
|
log_conc <- tab$log_dose
|
|
noDilSer = (ncol(tab)-1)/2
|
|
noDil <- nrow(tab)
|
|
Conctab <- perConcTab(tab, noDilSer)
|
|
# if (!is.na(Dils()[3])) noDil <- Dils()[3] else noDil = length(Conc)
|
|
#
|
|
slopeSt <- slopeTe <- matrix(NA, nrow=noDil-2,ncol=2)
|
|
for (i in 1:(noDil-2)) {
|
|
avs <- Conctab[noDilSer+1,]
|
|
threes <- data.frame(lnC=log_conc[i:(i+2)], resp=avs[i:(i+2)])
|
|
lm3St <- lm(resp ~ lnC, data=threes)
|
|
slopeSt[i,] <- lm3St$coefficients
|
|
avt <- Conctab[noDilSer*2+4,]
|
|
threet <- data.frame(lnC=log_conc[i:(i+2)], resp=avt[i:(i+2)])
|
|
lm3Te <- lm(resp ~ lnC, data=threet)
|
|
slopeTe[i,] <- lm3Te$coefficients
|
|
}
|
|
|
|
indS <- which(abs(slopeSt[,2]) == max(abs(slopeSt[,2])))
|
|
indT <- which(abs(slopeTe[,2]) == max(abs(slopeTe[,2])))
|
|
|
|
pl_ <- slopeSt[indS,1]+slopeSt[indS,2]*log_conc
|
|
pl_T <- slopeTe[indT,1]+slopeTe[indT,2]*log_conc
|
|
pl_df <- data.frame(lnC=log_conc, plotS=pl_, plotT=pl_T)
|
|
|
|
all_l <- melt(data.frame(tab), 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_l2 <- cbind(all_l,isRef, isSample)
|
|
all_l2S <- all_l2[all_l2$isRef == 1,]
|
|
all_l2T <- all_l2[all_l2$isRef == 0,]
|
|
all_mS <- all_l2S[order(all_l2S$log_dose, decreasing=TRUE),]
|
|
all_mT <- all_l2T[order(all_l2T$log_dose, decreasing=TRUE),]
|
|
|
|
circleS <- all_mS[(indS*noDilSer-(noDilSer-1)):((indS+2)*noDilSer),]
|
|
circleT <- all_mT[(indT*noDilSer-(noDilSer-1)):((indT+2)*noDilSer),]
|
|
circle <- rbind(circleS,circleT)
|
|
|
|
Dat$circles <- circle
|
|
#browser()
|
|
sigmoid <- Dat$coeffsMUnr
|
|
|
|
pLin <- PlotLinPLA_FUNC(circle, sigmoid = sigmoid, all_l2, pl_df, indS, indT)
|
|
|
|
|
|
pLin
|
|
})
|
|
|
|
#### process metadata, Plot output ----
|
|
output$plotLinMeta <- renderPlot({
|
|
tab <- sim2()
|
|
|
|
if(is.null(tab)) return(NULL)
|
|
if (is.character(tab)) stop(tab)
|
|
#browser()
|
|
if (!is.na(Dils()[4])) noDilSer <- Dils()[4] else noDilSer = (ncol(tab)-1)/2
|
|
Conc <- CONC()
|
|
log_conc <- log(Conc)
|
|
Conctab <- perConcTab(tab, noDilSer)
|
|
if (!is.na(Dils()[3])) noDil <- Dils()[3] else noDil = length(Conc)
|
|
|
|
slopeSt <- slopeTe <- matrix(NA, nrow=noDil-2,ncol=2)
|
|
for (i in 1:(noDil-2)) {
|
|
avs <- Conctab[noDilSer+1,]
|
|
threes <- data.frame(lnC=log(Conc[i:(i+2)]), resp=avs[i:(i+2)])
|
|
lm3St <- lm(resp ~ lnC, data=threes)
|
|
slopeSt[i,] <- lm3St$coefficients
|
|
avt <- Conctab[noDilSer*2+4,]
|
|
threet <- data.frame(lnC=log(Conc[i:(i+2)]), resp=avt[i:(i+2)])
|
|
lm3Te <- lm(resp ~ lnC, data=threet)
|
|
slopeTe[i,] <- lm3Te$coefficients
|
|
}
|
|
|
|
indS <- which(abs(slopeSt[,2]) == max(abs(slopeSt[,2])))
|
|
indT <- which(abs(slopeTe[,2]) == max(abs(slopeTe[,2])))
|
|
|
|
pl_ <- slopeSt[indS,1]+slopeSt[indS,2]*log_conc
|
|
pl_T <- slopeTe[indT,1]+slopeTe[indT,2]*log_conc
|
|
pl_df <- data.frame(lnC=log_conc, plotS=pl_, plotT=pl_T)
|
|
|
|
all_l <- melt(data.frame(tab), id.vars="log_dose",variable.name="replname",value.name="readout")
|
|
isRef <- rep(c(1,0), 1,each=nrow(all_l)/2)
|
|
isSample <- rep(c(0,1), 1,each=nrow(all_l)/2)
|
|
all_l2 <- cbind(all_l,isRef, isSample)
|
|
all_l2S <- all_l2[all_l2$isRef == 1,]
|
|
all_l2T <- all_l2[all_l2$isRef == 0,]
|
|
all_mS <- all_l2S[order(all_l2S$log_dose, decreasing=TRUE),]
|
|
all_mT <- all_l2T[order(all_l2T$log_dose, decreasing=TRUE),]
|
|
|
|
circleS <- all_mS[(indS*noDilSer-(noDilSer-1)):((indS+2)*noDilSer),]
|
|
circleT <- all_mT[(indT*noDilSer-(noDilSer-1)):((indT+2)*noDilSer),]
|
|
circle <- rbind(circleS,circleT)
|
|
|
|
Dat$circlesMeta <- circle
|
|
sigmoid <- sigmoid()
|
|
#browser()
|
|
pLin2 <- PlotLinPLA_FUNC(circle, sigmoid = sigmoid, all_l2, pl_df,indS, indT)
|
|
pLin2
|
|
})
|
|
|
|
#### linear PLA tests Metadata ----
|
|
output$TESTSlinMeta <- DT::renderDataTable({
|
|
tab <- sim2()
|
|
if (is.null(tab)) return(NULL)
|
|
Conc <- CONC()
|
|
Limite <- Dat$limite
|
|
|
|
circlesMeta <- Dat$circlesMeta
|
|
PureErrFlag <- input$PureErrMeta
|
|
warning_text <- reactive({
|
|
ifelse(PureErrFlag, 'Pure error is selected','')
|
|
})
|
|
output$PureErrW <- renderText(warning_text())
|
|
browser()
|
|
LIN <- ANOVAlintests(tab,circlesMeta,Limite,PureErrFlag=PureErrFlag)
|
|
df <- LIN[[1]]
|
|
su_modU <- LIN[[2]]
|
|
su_mod2 <- LIN[[4]]
|
|
|
|
output$SummaryModABuMeta <- renderTable({ su_modU }, digits=5)
|
|
output$SummaryModABMeta <- renderTable({ su_mod2 }, digits=5)
|
|
|
|
slopeDiffCI <- t(data.frame(LIN[[3]]))
|
|
colnames(slopeDiffCI) <- c("slope difference","lower CI","upper CI")
|
|
output$SlopeDiffCIMeta <- renderTable({ slopeDiffCI },digits=5)
|
|
|
|
SelTestsL <- as.numeric(input$selectedSSTsLinear)
|
|
df2 <- df
|
|
|
|
Dat$ANOVAMeta <- df[,4:length(df)]
|
|
dat <- datatable(df2[,1:3],
|
|
options=list(
|
|
paging=T, dom="t",rownames=F
|
|
)) %>% formatStyle("test_results", target="row",backgroundColor = styleEqual(c(-1,0,1),
|
|
c("pink","lightgreen","lightgrey")))
|
|
|
|
})
|
|
|
|
#### linear PLA tests XLinput ----
|
|
output$TESTSlin <- DT::renderDataTable({
|
|
tab <- Dat$EXCEL
|
|
if (is.character(tab)) stop(tab)
|
|
Conc <- exp(tab$log_dose)
|
|
Limite <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla),
|
|
as.numeric(input$lEACratiola), as.numeric(input$uEACratiola),
|
|
as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope),
|
|
as.numeric(input$lEACratioua), as.numeric(input$uEACratioua),
|
|
as.numeric(input$lowerPot), as.numeric(input$upperPot),
|
|
as.numeric(input$lEACratioAdiff), as.numeric(input$uEACratioAdiff))
|
|
|
|
noDil <- nrow(tab)
|
|
noDilSer <- Dat$noDilSeriesXL
|
|
Conctab <- perConcTab(tab, noDilSeries = noDilSer)
|
|
#browser()
|
|
slopeSt <- slopeTe <- matrix(NA, nrow=noDil-2,ncol=2)
|
|
for (i in 1:(noDil-2)) {
|
|
avs <- Conctab[noDilSer+1,]
|
|
threes <- data.frame(lnC=log(Conc[i:(i+2)]), resp=avs[i:(i+2)])
|
|
lm3St <- lm(resp ~ lnC, data=threes)
|
|
slopeSt[i,] <- lm3St$coefficients
|
|
avt <- Conctab[noDilSer*2+4,]
|
|
threet <- data.frame(lnC=log(Conc[i:(i+2)]), resp=avt[i:(i+2)])
|
|
lm3Te <- lm(resp ~ lnC, data=threet)
|
|
slopeTe[i,] <- lm3Te$coefficients
|
|
}
|
|
|
|
indS <- which(abs(slopeSt[,2]) == max(abs(slopeSt[,2])))
|
|
indT <- which(abs(slopeTe[,2]) == max(abs(slopeTe[,2])))
|
|
|
|
# pl_ <- slopeSt[indS,1]+slopeSt[indS,2]*log_conc
|
|
# pl_T <- slopeTe[indT,1]+slopeTe[indT,2]*log_conc
|
|
# pl_df <- data.frame(lnC=log_conc, plotS=pl_, plotT=pl_T)
|
|
|
|
all_l <- melt(data.frame(tab), 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_l2 <- cbind(all_l,isRef, isSample)
|
|
all_l2S <- all_l2[all_l2$isRef == 1,]
|
|
all_l2T <- all_l2[all_l2$isRef == 0,]
|
|
all_mS <- all_l2S[order(all_l2S$log_dose, decreasing=TRUE),]
|
|
all_mT <- all_l2T[order(all_l2T$log_dose, decreasing=TRUE),]
|
|
|
|
circleS <- all_mS[(indS*noDilSer-(noDilSer-1)):((indS+2)*noDilSer),]
|
|
circleT <- all_mT[(indT*noDilSer-(noDilSer-1)):((indT+2)*noDilSer),]
|
|
circle <- rbind(circleS,circleT)
|
|
|
|
PureErrFlag <- input$PureErr
|
|
warning_text <- reactive({
|
|
ifelse(PureErrFlag, 'Pure error is selected','')
|
|
})
|
|
output$PureErrW3 <- renderText(warning_text())
|
|
#browser()
|
|
LIN <- ANOVAlintests(tab,circle,Limite,PureErrFlag=PureErrFlag)
|
|
df <- LIN[[1]]
|
|
su_modU <- LIN[[2]]
|
|
su_mod2 <- LIN[[4]]
|
|
|
|
output$SummaryModABu <- renderTable({ su_modU }, digits=5)
|
|
output$SummaryModAB <- renderTable({ su_mod2 }, digits=5)
|
|
|
|
slopeDiffCI <- t(data.frame(LIN[[3]]))
|
|
colnames(slopeDiffCI) <- c("slope difference","lower CI","upper CI")
|
|
output$SlopeDiffCI <- renderTable({ slopeDiffCI },digits=5)
|
|
|
|
SelTestsL <- as.numeric(input$selectedSSTsLinear)
|
|
df2 <- df[SelTestsL,]
|
|
|
|
Dat$ANOVA <- df[,4:length(df)]
|
|
dat <- datatable(df2[,1:3],
|
|
options=list(
|
|
paging=T, dom="t",rownames=F
|
|
)) %>% formatStyle("test_results", target="row",backgroundColor = styleEqual(c(-1,0,1),
|
|
c("pink","lightgreen","lightgrey")))
|
|
|
|
})
|
|
|
|
#### output 4PL ANOVA tests Meta ----
|
|
output$ANOVA <- DT::renderDataTable({
|
|
sigmoid <- sigmoid()
|
|
tab <- ANOVA4plUnresfunc(sim2()) # ,sigmoid
|
|
dat <- datatable(tab,
|
|
options=list(
|
|
dom="t",rownames=F
|
|
)) %>% formatStyle("p_value", target="row",
|
|
backgroundColor = styleEqual(c("p_value"),
|
|
c("lightgrey")))
|
|
})
|
|
|
|
#### output 4PL ANOVA tests XL ----
|
|
# not needed
|
|
# output$ANOVA_XL <- DT::renderDataTable({
|
|
# tab <- Dat$EXCEL
|
|
# tab <- ANOVA4plUnresfunc(sim2()) # ,sigmoid
|
|
# dat <- datatable(tab,
|
|
# options=list(
|
|
# dom="t",rownames=F
|
|
# )) %>% formatStyle("p_value", target="row",
|
|
# backgroundColor = styleEqual(c("p_value"),
|
|
# c("lightgrey")))
|
|
# })
|
|
|
|
|
|
#### output RMSEs ----
|
|
output$RMSE <- renderText({
|
|
paste("RMSE (unrestricted model):", Dat$RMSE_unr, "(Use it to compare against RMSE restr. model for non-parallelism)\n",
|
|
"RMSE (restricted model):", Dat$RMSE_r, "\n",
|
|
"Pure RMSE (unrestricted model):", Dat$RMSE_pure, "\n",
|
|
"%SD (unr. model): ", Dat$RMSE_unr*100/Dat$up_lowAs, "(calculated as: RMSE/(upper-lower Asymptote)*100\n",
|
|
"RMSE (log restr. model): ", Dat$RMSE_Rlog, "\n",
|
|
"RMSE (log unrestr. model): ", Dat$RMSE_Ulog, "\n",
|
|
"%SDlog (unr model): ", Dat$RMSE_Ulog*100/Dat$up_lowAslog )
|
|
})
|
|
|
|
output$ANOVAlin <- DT::renderDataTable({
|
|
if (is.null(Dat$ANOVA)) return(NULL)
|
|
ANOVAlin <- Dat$ANOVA
|
|
dat <- datatable(ANOVAlin,
|
|
options=list(
|
|
dom="t",rownames=F
|
|
)) %>% formatStyle("p.value", target='cell',
|
|
backgroundColor = styleEqual(c("p.value"),
|
|
c("lightgrey")))
|
|
})
|
|
|
|
output$ANOVAlinMeta <- DT::renderDataTable({
|
|
ANOVAlin <- Dat$ANOVAMeta
|
|
dat <- datatable(ANOVAlin,
|
|
options=list(
|
|
dom="t",rownames=F
|
|
)) %>% formatStyle("p.value", target='cell',
|
|
backgroundColor = styleEqual(c("p.value"),
|
|
c("lightgrey")))
|
|
})
|
|
#### output Lin pot tab XL ----
|
|
output$pottab <- DT::renderDataTable({
|
|
if (is.null(Dat$circles)) return(NULL)
|
|
Lim <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla),
|
|
as.numeric(input$lEACratiola), as.numeric(input$uEACratiola),
|
|
as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope),
|
|
as.numeric(input$lEACratioua), as.numeric(input$uEACratioua),
|
|
as.numeric(input$lowerPot), as.numeric(input$upperPot))
|
|
|
|
circles <- Dat$circles
|
|
PureErrFlag <- input$PureErr
|
|
pottab <- LinPotTab(circles,Lim,PureErrFlag = PureErrFlag)
|
|
#browser()
|
|
dat <- datatable(pottab,
|
|
options=list(
|
|
dom="t",rownames=F
|
|
)) %>% formatStyle("test_result", target='row',
|
|
backgroundColor = styleEqual(c(0,1), c("lightgrey","#F9545488")))
|
|
})
|
|
|
|
|
|
### output pot tab Meta ----
|
|
output$pottabMeta <- DT::renderDataTable({
|
|
|
|
Lim <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla),
|
|
as.numeric(input$lEACratiola), as.numeric(input$uEACratiola),
|
|
as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope),
|
|
as.numeric(input$lEACratioua), as.numeric(input$uEACratioua),
|
|
as.numeric(input$lowerPot), as.numeric(input$upperPot))
|
|
|
|
circles <- Dat$circlesMeta
|
|
PureErrFlag <- input$PureErrMeta
|
|
pottab <- LinPotTab(circles,Lim,PureErrFlag = PureErrFlag)
|
|
#browser()
|
|
dat <- datatable(pottab,
|
|
options=list(
|
|
dom="t",rownames=F
|
|
)) %>% formatStyle("test_result", target='row',
|
|
backgroundColor = styleEqual(c(0,1), c("lightgrey","#F9545488")))
|
|
})
|
|
|
|
#### 4pl potency table Meta ----
|
|
observe({
|
|
#browser()
|
|
if (is.null(sim2()) | is.null(Dils())) return(NULL)
|
|
ro_new <- sim2()
|
|
Dils_ <- Dils()
|
|
if (!is.na(Dils()[4])) noDilSer <- Dils()[4] else noDilSer <- 3
|
|
PureErrFl <- input$PureErrMeta
|
|
pottab4 <- pot4plFUNC(ro_new = ro_new, PureErrFlag = PureErrFl)
|
|
#browser()
|
|
Lim <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla),
|
|
as.numeric(input$lEACratiola), as.numeric(input$uEACratiola),
|
|
as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope),
|
|
as.numeric(input$lEACratioua), as.numeric(input$uEACratioua),
|
|
as.numeric(input$lowerPot), as.numeric(input$upperPot))
|
|
|
|
|
|
pottab4_ <- data.frame(pottab4)
|
|
pottab4_$potency <- round(as.numeric(pottab4[,2])*100,1)
|
|
pottab4_$`lower95%CI` <- round(as.numeric(pottab4[,3])*100,2)
|
|
pottab4_$`upper95%CI` <- round(as.numeric(pottab4[,4])*100,2)
|
|
pottab4_$relative_lowerCL <- round(pottab4_[,6]/pottab4_[,5]*100,2)
|
|
pottab4_$relative_upperCL <- round(pottab4_[,7]/pottab4_[,5]*100,2)
|
|
|
|
if (as.numeric(pottab4_$relative_lowerCL[1]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[1]) < Lim[[10]] ) {
|
|
test_potCI <- 0
|
|
} else {test_potCI <- 1 }
|
|
if (as.numeric(pottab4_$relative_lowerCL[2]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[2]) < Lim[[10]] ) {
|
|
test_potUCI <- 0
|
|
} else {test_potUCI <- 1 }
|
|
if (as.numeric(pottab4_$relative_lowerCL[3]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[3]) < Lim[[10]] ) {
|
|
test_potCI_t <- 0
|
|
} else {test_potCI_t <- 1 }
|
|
if (as.numeric(pottab4_$relative_lowerCL[4]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[4]) < Lim[[10]] ) {
|
|
test_potUCI_t <- 0
|
|
} else {test_potUCI_t <- 1 }
|
|
pottab4_ <- cbind(pottab4_[,-(2:4)], data.frame(tests=c(test_potCI, test_potUCI,test_potCI_t,test_potUCI_t)))
|
|
colnames(pottab4_) <- c("model","potency","lower95%CI","upper95%CI","relative_lower95%CI","relative_upper95%CI","test_result")
|
|
|
|
output$pottab4pl <- DT::renderDataTable({
|
|
dat <- datatable(pottab4_[1:2,],
|
|
options=list(digits=3,
|
|
paging=T, dom="t",rownames=F
|
|
)) %>% formatStyle("test_result", target="row",backgroundColor = styleEqual(c(0,1),
|
|
c("lightgreen","pink")))
|
|
})
|
|
output$pottab4plTrans <- DT::renderDataTable({
|
|
dat <- datatable(pottab4_[3:4,],
|
|
options=list(digits=3,
|
|
paging=T, dom="t",rownames=F
|
|
)) %>% formatStyle("test_result", target="row",backgroundColor = styleEqual(c(0,1),
|
|
c("lightgreen","pink")))
|
|
})
|
|
})
|
|
|
|
#### 4pl potency table XL ----
|
|
observe({
|
|
#browser()
|
|
if (is.null(Dat$EXCEL)) return(NULL)
|
|
ro_new <- Dat$EXCEL
|
|
|
|
noDilSer <- Dat$noDilSeriesXL
|
|
PureErrFl <- input$PureErr
|
|
pottab4 <- pot4plFUNC(ro_new = ro_new, PureErrFlag = PureErrFl)
|
|
#browser()
|
|
Lim <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla),
|
|
as.numeric(input$lEACratiola), as.numeric(input$uEACratiola),
|
|
as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope),
|
|
as.numeric(input$lEACratioua), as.numeric(input$uEACratioua),
|
|
as.numeric(input$lowerPot), as.numeric(input$upperPot))
|
|
|
|
|
|
pottab4_ <- data.frame(pottab4)
|
|
pottab4_$potency <- round(as.numeric(pottab4[,2])*100,1)
|
|
pottab4_$`lower95%CI` <- round(as.numeric(pottab4[,3])*100,2)
|
|
pottab4_$`upper95%CI` <- round(as.numeric(pottab4[,4])*100,2)
|
|
pottab4_$relative_lowerCL <- round(pottab4_[,6]/pottab4_[,5]*100,2)
|
|
pottab4_$relative_upperCL <- round(pottab4_[,7]/pottab4_[,5]*100,2)
|
|
|
|
if (as.numeric(pottab4_$relative_lowerCL[1]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[1]) < Lim[[10]] ) {
|
|
test_potCI <- 0
|
|
} else {test_potCI <- 1 }
|
|
if (as.numeric(pottab4_$relative_lowerCL[2]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[2]) < Lim[[10]] ) {
|
|
test_potUCI <- 0
|
|
} else {test_potUCI <- 1 }
|
|
if (as.numeric(pottab4_$relative_lowerCL[3]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[3]) < Lim[[10]] ) {
|
|
test_potCI_t <- 0
|
|
} else {test_potCI_t <- 1 }
|
|
if (as.numeric(pottab4_$relative_lowerCL[4]) > Lim[[9]] & as.numeric(pottab4_$relative_upperCL[4]) < Lim[[10]] ) {
|
|
test_potUCI_t <- 0
|
|
} else {test_potUCI_t <- 1 }
|
|
pottab4_ <- cbind(pottab4_[,-(2:4)], data.frame(tests=c(test_potCI, test_potUCI,test_potCI_t,test_potUCI_t)))
|
|
colnames(pottab4_) <- c("model","potency","lower95%CI","upper95%CI","relative_lower95%CI","relative_upper95%CI","test_result")
|
|
|
|
output$pottab4plXL <- DT::renderDataTable({
|
|
dat <- datatable(pottab4_[1:2,],
|
|
options=list(digits=3,
|
|
paging=T, dom="t",rownames=F
|
|
)) %>% formatStyle("test_result", target="row",backgroundColor = styleEqual(c(0,1),
|
|
c("#B5C74055","#F9545455")))
|
|
})
|
|
output$pottab4plTransXL <- DT::renderDataTable({
|
|
dat <- datatable(pottab4_[3:4,],
|
|
options=list(digits=3,
|
|
paging=T, dom="t",rownames=F
|
|
)) %>% formatStyle("test_result", target="row",backgroundColor = styleEqual(c(0,1),
|
|
c("#B5C74055","#F9545455")))
|
|
})
|
|
})
|
|
|
|
|
|
#### Dilutions Simulator ----
|
|
output$plotfordilutions <- renderPlot({
|
|
tab <- sim2()
|
|
#browser()
|
|
tab <- as.data.frame(tab)
|
|
dils <- tab$log_dose
|
|
min_y <- min(tab[,1:3])
|
|
max_y <- max(tab[,1:3])
|
|
|
|
if (input$fixupper) {
|
|
dils_av <- dils-max(dils)
|
|
dils_av_ <- dils_av*(input$dilslider/100+1)
|
|
dils2 <- round(dils_av_ + max(dils),4)
|
|
dilfactors <- 1/exp(dils2-lag(dils2))
|
|
} else {
|
|
if (!is.null(Dat$cfordils)) {
|
|
av <- Dat$cfordils
|
|
} else { av <- (min(dils) + max(dils))/2 }
|
|
dils_av <- dils-av
|
|
dils_avsc <- dils_av*(input$dilslider/100+1)
|
|
dils2 <- dils_avsc+av
|
|
dilfactors <- 1/exp(dils2-lag(dils2))
|
|
}
|
|
|
|
Dat$newDils <- dils2
|
|
|
|
sigmoid <- sigmoid()
|
|
|
|
#browser()
|
|
BPs <- Dat$bendpoints
|
|
EC50REF <- (BPs[2]+BPs[1])/2
|
|
Einh <- abs((BPs[2]-BPs[1])/5)
|
|
asyml <- EC50REF-2*(EC50REF-BPs[1])
|
|
asymu <- EC50REF+2*(EC50REF-BPs[1])
|
|
|
|
det_sig <- Dat$coeffs_UN
|
|
|
|
if (is.null(Dat$coeffs_UN)) {
|
|
SAMPLE50 <- sigmoid[1] + (sigmoid[3] - sigmoid[1])/(1+exp(sigmoid[5]*( (sigmoid[7]+0.693147)- dils2)))
|
|
SAMPLE200 <- sigmoid[1] + (sigmoid[3] - sigmoid[1])/(1+exp(sigmoid[5]*( (sigmoid[7]-0.693147)-dils2)))
|
|
Xbend50l <- sigmoid[7] + 0.693147-1.5434/sigmoid[5]
|
|
Xbend200l <- sigmoid[7] - 0.693147-1.5434/sigmoid[5]
|
|
Xbend50u <- sigmoid[7] + 0.693147+1.5434/sigmoid[5]
|
|
Xbend200u <- sigmoid[7] - 0.693147+1.5434/sigmoid[5]
|
|
Xbend50 <- max(Xbend50l, Xbend50u)
|
|
Xbend200 <- min(Xbend200l, Xbend200u)
|
|
dummy <- plot_f(tab)
|
|
} else {
|
|
|
|
#browser()
|
|
SAMPLE50 <- det_sig[3] + (det_sig[5] - det_sig[3])/(1+exp(det_sig[1]*(det_sig[7]+0.693147-dils2)))
|
|
SAMPLE200 <- det_sig[3] + (det_sig[5] - det_sig[3])/(1+exp(det_sig[1]*(det_sig[7]-0.693147-dils2)))
|
|
Xbend50l <- det_sig[7] + 0.693147-1.5434/det_sig[1]
|
|
Xbend200l <- det_sig[7] - 0.693147-1.5434/det_sig[1]
|
|
Xbend50u <- det_sig[7] + 0.693147+1.5434/det_sig[1]
|
|
Xbend200u <- det_sig[7] - 0.693147+1.5434/det_sig[1]
|
|
Xbend50 <- max(Xbend50l, Xbend50u)
|
|
Xbend200 <- min(Xbend200l, Xbend200u)
|
|
dummy <- plot_f(tab)
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
pl_df <- cbind(dils2, SAMPLE50, SAMPLE200)
|
|
|
|
#browser()
|
|
# scenario2
|
|
eqSpac <- abs((BPs[1]-BPs[2])/5)
|
|
optdils <- c((asyml+BPs[1])/2, BPs[1], BPs[1]+1*eqSpac, BPs[1]+2*eqSpac,BPs[1]+3*eqSpac,BPs[1]+4*eqSpac,BPs[2], (asymu+BPs[2])/2)
|
|
# scenario 3
|
|
eqSpac_3 <- abs((BPs[1]-BPs[2])/3)
|
|
optdils_3 <- c(BPs[1]-2*eqSpac_3, BPs[1]-eqSpac_3, BPs[1], BPs[1]+1*eqSpac_3, BPs[1]+2*eqSpac_3,BPs[2], BPs[2]+eqSpac_3, BPs[2]+2*eqSpac_3)
|
|
# scenario 6
|
|
Einh2 <- abs(((BPs[2]-BPs[1])*0.7)/5)
|
|
eqSpac2 <- (2*0.7/Einh)/3
|
|
optdils2 <- c((asyml+BPs[1])/2, BPs[1], EC50REF-1.5*Einh2, EC50REF-0.5*Einh2,EC50REF+0.5*Einh2,EC50REF+1.5*Einh2, BPs[2], (asymu+BPs[2])/2)
|
|
# steep slope
|
|
eqSpac3 <- (abs(Xbend200-Xbend50))/5
|
|
optdils3 <- c(Xbend200-eqSpac3,Xbend200, Xbend200+1*eqSpac3, Xbend200+2*eqSpac3,Xbend200+3*eqSpac3,Xbend200+4*eqSpac3,Xbend50, Xbend50+eqSpac3)
|
|
|
|
output$extremebps <- renderTable({
|
|
ExtremeBPs <- c(Xbend50,Xbend200)
|
|
DF2 <- data.frame(sample=c("50% sample (right)", "200% sample (left)"), Extreme_BPs=ExtremeBPs)
|
|
DF2
|
|
})
|
|
|
|
optD <- data.frame(cbind(optdils, optdils_3,optdils2, optdils3))
|
|
colnames(optD) <- c("scenario2","scenario3","scenario6","steep slope")
|
|
|
|
output$optimalDils <- renderTable({ optD })
|
|
|
|
output$adjlogdil <- renderTable({
|
|
adjlogdilfactors <- round(dilfactors,3)
|
|
adjlogdils <- round(dils2,3)
|
|
adjdils <- round(exp(dils2),3)
|
|
DilsTable <- data.frame('adjusted ln(dilutions)' = adjlogdils,
|
|
'adjusted ln_dilution_factors' = adjlogdilfactors,
|
|
'adjusted dilutions' = adjdils)
|
|
DilsTable
|
|
})
|
|
|
|
if (!is.null(Dat$p2)) {
|
|
p2 <- Dat$p2
|
|
p_dil <- p2 +
|
|
annotate("pointrange",x=dils2,y=rep(min_y, length(dils2)), xmin=min(dils2), xmax=max(dils2)) +
|
|
annotate("text", x=dils2,y=rep(min_y+(max_y-min_y)*0.05, length(dils2)), label=as.character(round(dils2,3))) +
|
|
annotate("text", x=dils2[-1]+(max(dils2)-min(dils2))*0.05,
|
|
y=rep(min_y+(max_y-min_y)*0.1, length(dils2[-1])),
|
|
label=as.character(round(dilfactors[-1],3))) +
|
|
geom_line(data=as.data.frame(pl_df),aes(x=dils2,y=SAMPLE50), color="grey15", linetype=2,
|
|
inherit.aes = F) +
|
|
geom_line(data=as.data.frame(pl_df),aes(x=dils2,y=SAMPLE200), color="grey15", linetype=2,
|
|
inherit.aes = F) +
|
|
geom_vline(xintercept=c(Xbend50,Xbend200), col="grey15", linetype=2) +
|
|
{if (input$scenario =="scenario 6") annotate("pointrange",x=optdils2,y=rep(min_y+(max_y-min_y)*0.2, length(optdils2)),
|
|
xmin=min(optdils2), xmax=max(optdils2), color="seagreen")} +
|
|
{if (input$scenario =="scenario 6") annotate("text",x=optdils2,y=rep(min_y+(max_y-min_y)*0.25, length(optdils2)),
|
|
label=as.character(round(optdils2,3)), color="seagreen")} +
|
|
{if (input$scenario =="scenario 2") annotate("pointrange",x=optdils,y=rep(min_y+(max_y-min_y)*0.2, length(optdils)),
|
|
xmin=min(optdils), xmax=max(optdils), color="seagreen")} +
|
|
{if (input$scenario =="scenario 2") annotate("text",x=optdils,y=rep(min_y+(max_y-min_y)*0.25, length(optdils)),
|
|
label=as.character(round(optdils,3)), color="seagreen")} +
|
|
{if (input$scenario =="scenario 3") annotate("pointrange",x=optdils_3,y=rep(min_y+(max_y-min_y)*0.2, length(optdils_3)),
|
|
xmin=min(optdils_3), xmax=max(optdils_3), color="seagreen")} +
|
|
{if (input$scenario =="scenario 3") annotate("text",x=optdils_3,y=rep(min_y+(max_y-min_y)*0.25, length(optdils_3)),
|
|
label=as.character(round(optdils_3,3)), color="seagreen")} +
|
|
{if (input$scenario =="steep slope") annotate("pointrange",x=optdils3,y=rep(min_y+(max_y-min_y)*0.2, length(optdils3)),
|
|
xmin=min(optdils3), xmax=max(optdils3), color="seagreen")} +
|
|
{if (input$scenario =="steep slope") annotate("text",x=optdils3,y=rep(min_y+(max_y-min_y)*0.25, length(optdils3)),
|
|
label=as.character(round(optdils3,3)), color="seagreen")} +
|
|
annotate("text",x=optdils[1],y=(max_y+min_y)*0.5,
|
|
label=paste("in green: optimal \n dilutions acc. to Whitepaper\n", input$scenario), color="seagreen",
|
|
size=14/.pt,fontface="bold")
|
|
}
|
|
print(p_dil)
|
|
|
|
})
|
|
|
|
#### Dilutions CI table ----
|
|
observe({
|
|
if (is.null(input$potencydiff)) return(NULL)
|
|
output$CIs <- renderTable({
|
|
PureErrFlag <- input$PureErr
|
|
if (is.null(Dat$coeffs_UN)) {
|
|
# checks if an EXCEL was uploaded
|
|
sigmoid <- sigmoid()
|
|
det_sig=NULL
|
|
|
|
ast = sigmoid()[1];bst = sigmoid()[5];cst = sigmoid()[7];dst = sigmoid()[3];ate = sigmoid()[2];
|
|
bte = sigmoid()[6];r_ = sigmoid()[8];
|
|
cte = cst-r_;dte = sigmoid()[4];
|
|
} else {
|
|
sigmoid <- NULL
|
|
det_sig <- Dat$coeffs_UN
|
|
ast <- det_sig[3]
|
|
ate <- det_sig[4]
|
|
bst <- det_sig[1]
|
|
bte <- det_sig[2]
|
|
cst <- det_sig[7]
|
|
cte <- det_sig[7] -log(input$potencydiff/100)
|
|
dst <- det_sig[5]
|
|
dte <- det_sig[6]
|
|
r_ <- log(input$potencydiff/100)
|
|
|
|
}
|
|
if (!is.na(input$NoDilSer)) {
|
|
noDilSer <- input$NoDilSer
|
|
} else if (!is.null(Dat$NoDilSeriesXL)) noDilSer <- Dat$noDilSeriesXL else noDilSer <- 3
|
|
if (!is.na(input$NoDil)) noDil <- input$NoDil else noDil <- length(Dat$newDils)
|
|
#browser()
|
|
tab <- Calc_DilRes(as=ast,at=ate,ds=dst,dt=dte,cs=cst,ct=cte,r=r_,bt=bte,bs=bst,
|
|
sd_fac=input$sdfac,log_conc=Dat$newDils,
|
|
# auslenkU=outlierU,
|
|
# auslenkM=outlierM,
|
|
# auslenkL=outlierL,
|
|
heteroNoise = FALSE, noDilSeries = noDilSer, noDils = noDil)
|
|
Limite <- list(as.numeric(input$lEACdiffla), as.numeric(input$uEACdiffla),
|
|
as.numeric(input$lEACratiola), as.numeric(input$uEACratiola),
|
|
as.numeric(input$lEACratioSlope), as.numeric(input$uEACratioSlope),
|
|
as.numeric(input$lEACratioua), as.numeric(input$uEACratioua),
|
|
as.numeric(input$lowerPot), as.numeric(input$upperPot),
|
|
as.numeric(input$lEACratioAdiff), as.numeric(input$uEACratioAdiff))
|
|
|
|
CItable <- tests_FUNC(tab,Limite,PureErrFlag=PureErrFlag)
|
|
|
|
CItable_ <- CItable[-c(1,2,6,8,9),-c(2,4,5)]
|
|
potAll <- pot4plFUNC(tab, input$PureErr)
|
|
restrPot <- potAll[1,1:4]
|
|
restrPot[2:4] <- round(as.numeric(restrPot[2:4]),5)
|
|
potAll_ <- rbind(CItable_, restrPot)
|
|
potAll_$CIwidth <- as.numeric(potAll_[,4])-as.numeric(potAll_[,3])
|
|
potAll_[,1] <- c("ratio of lower asymptotes","ratio of slopes","ratio of upper asymptotes", "ratio of asympt. differences","restricted potency")
|
|
|
|
output$bps <- renderTable({
|
|
DF <- data.frame(sample=names(Dat$bendpoints),BPs=Dat$bendpoints)
|
|
DF
|
|
})
|
|
return(potAll_)
|
|
})
|
|
})
|
|
|
|
#### simulations ----
|
|
observe({
|
|
observeEvent(input$goSim,{
|
|
sd_fac_ <- as.numeric(input$sdfac)
|
|
|
|
r_ <- log(as.numeric(input$potencydiff)/100)
|
|
Conc <- Dat$MetaConc
|
|
as = sigmoid()[1]; bs = sigmoid()[5];cs = sigmoid()[7];ds = sigmoid()[3];at = sigmoid()[2];
|
|
bt = sigmoid()[6];r = sigmoid()[8]; ct = cs-r_; dt = sigmoid()[4]
|
|
|
|
if (!is.null(Dat$MetaConc)) {
|
|
Conc <- Dat$MetaConc
|
|
} else {
|
|
Conc <- CONC()
|
|
}
|
|
log_dose <- log(Conc)
|
|
yAxfac <- (ds-as)
|
|
|
|
if (!is.na(input$NoDilSer)) {
|
|
noDilSer <- input$NoDilSer
|
|
} else if (!is.null(Dat$NoDilSeriesXL)) noDilSer <- Dat$noDilSeriesXL else noDilSer <- 3
|
|
if (!is.na(input$NoDil)) noDil <- input$NoDil else noDil <- length(Conc)
|
|
isRef <- rep(c(1,0),1,each=noDilSer*noDil)
|
|
isSample <- rep(c(0,1),1,each=noDilSer*noDil)
|
|
N <- as.numeric(input$simN)
|
|
|
|
av <- as*isRef + at*isSample + (ds*isRef + dt*isSample - as*isRef - at*isSample)/
|
|
(1+isRef*exp(bs*(cs - log_dose)) + isSample*exp(bt*(ct-log_dose)))
|
|
|
|
resHist <- matrix(NA,nrow=N, ncol=13)
|
|
residualsList <- list()
|
|
start.time2 <- Sys.time()
|
|
withProgress(message = 'Making plot', value=0, {
|
|
for (i in 1:N) {
|
|
if (input$heterosked) {
|
|
# heterosc noise
|
|
ro_jit <- matrix(unlist(map(av, function(x) x+rnorm(1,0,x*sd_fac_/100))), nrow=noDil, ncol=noDilSer*2)
|
|
} else {
|
|
# homosc noise
|
|
ro_jit <- matrix(unlist(map(av, function(x) x+rnorm(1,0,sd_fac_*yAxfac/100))), nrow=noDil, ncol=noDilSer*2)
|
|
}
|
|
# browser()
|
|
ro_jit <- abs(ro_jit)
|
|
ro_new <- cbind(ro_jit, log_dose)
|
|
all_l <- melt(data.frame(ro_new), id.vars="log_dose", variable.name = "replname", value.name = "readout")
|
|
all_l$isRef <- isRef
|
|
all_l$isSample <- isSample
|
|
all_l$Conc <- exp(all_l$log_dose)
|
|
pot <- drm(readout ~ Conc, isSample, data=all_l, fct=LL.4(names=c("b","d","a","c")),
|
|
pmodels=data.frame(1,1,1,isSample))
|
|
potAll <- EDcomp(pot, percVec=c(50,50), interval="delta", display=FALSE)
|
|
potAll2 <- potAll[1:3]
|
|
RSS <- sum(pot$predres[,2]^2)
|
|
dfreed <- nrow(all_l)-5
|
|
MSE <- RSS/dfreed
|
|
|
|
potU <- drm(readout ~ Conc, isSample, data=all_l, fct=LL.4(names=c("b","d","a","c")),
|
|
pmodels=data.frame(isSample, isSample,isSample,isSample))
|
|
DF_U <- nrow(all_l)-8
|
|
|
|
uAsratio <- compParm(potU, "a",display=F)
|
|
uCIuAs <- uAsratio[1]+qt(0.975,DF_U)*uAsratio[2]
|
|
lCIuAs <- uAsratio[1]-qt(0.975,DF_U)*uAsratio[2]
|
|
lAsratio <- compParm(potU, "d",display=F)
|
|
uCIlAs <- lAsratio[1]+qt(0.975,DF_U)*lAsratio[2]
|
|
lCIlAs <- lAsratio[1]-qt(0.975,DF_U)*lAsratio[2]
|
|
Sloperatio <- compParm(potU, "b",display=F)
|
|
uCISlo <- Sloperatio[1]+qt(0.975,DF_U)*Sloperatio[2]
|
|
lCISlo <- Sloperatio[1]-qt(0.975,DF_U)*Sloperatio[2]
|
|
su <- summary(potU)
|
|
|
|
v <- vcov(potU)[c(5,6),c(5,6)]
|
|
Vd <- vcov(potU)[c(3,4),c(3,4)]
|
|
Va_d <- v+Vd
|
|
|
|
A_DTEST <- su$coefficients[6,1]-su$coefficients[4,1]
|
|
A_DREF <- su$coefficients[5,1]-su$coefficients[3,1]
|
|
if (abs(at/(sqrt(Va_d[2,2]/3))) > qt(0.95,2)) {
|
|
try(Fie_ad <- round(FiellerRatio(A_DREF,A_DTEST, Va_d),5))
|
|
}
|
|
if (!exists("Fie_ad")) Fie_ad <- NA
|
|
|
|
resHist[i,] <- c(potAll2, sqrt(MSE),Sloperatio[1],lCISlo, uCISlo,
|
|
uAsratio[1], lCIuAs, uCIuAs, Fie_ad[1],Fie_ad[2],Fie_ad[3])
|
|
colnames(resHist) <- c("pot4pl","lCI4pl","uCI4pl","RMSE","estSlope_ratio",
|
|
"lCISlope_ratio","uCISlope_ratio","estuAs_ratio",
|
|
"lCIuAs_ratio","uCIuAs_ratio","estAsyDiff_ratio",
|
|
"lCIAsyDiff_ratio", "uCIAsyDiff_ratio")
|
|
|
|
incProgress(1/N, detail=paste("Doing simulations",i))
|
|
} # withProgress
|
|
|
|
})
|
|
end.time2 <- Sys.time()
|
|
|
|
Dat$resHist <- resHist
|
|
})
|
|
})
|
|
|
|
|
|
#### simulation Histograms output ----
|
|
|
|
output$plotHistuAs <- renderPlot({
|
|
if (!is.null(Dat$resHist)) {
|
|
|
|
resHist <- Dat$resHist
|
|
#browser()
|
|
resHistuAs <- as.data.frame(resHist[,8:10])
|
|
resHistuAs_l <- melt(data.frame(resHistuAs), variable.name="ratio_CIs", value.name = "readout")
|
|
#browser()
|
|
lowquant_uAs <- quantile(resHistuAs[,2], probs=as.numeric(input$lowQuant)/100)
|
|
upquant_uAs <- quantile(resHistuAs[,3], probs=as.numeric(input$uppQuant)/100)
|
|
|
|
p_uAs <- ggplot(resHistuAs_l) +
|
|
geom_histogram(aes(readout, fill=ratio_CIs),alpha=0.5,position="identity") +
|
|
labs(title = paste("upper asymptote ratio EACs:", round(lowquant_uAs,3), " to ", round(upquant_uAs,3))) +
|
|
geom_vline(xintercept = c(lowquant_uAs, upquant_uAs), color="black", linetype="dashed", linewidth=1) +
|
|
geom_vline(xintercept = c(input$lEACratioua , input$uEACratioua), color="red", linetype="dashed", linewidth=1) +
|
|
theme_bw()
|
|
|
|
# asympt diff ratio
|
|
resHistAsDiff <- as.data.frame(resHist[,11:13])
|
|
resHistAsDiff_l <- melt(data.frame(resHistAsDiff), variable.name="ratio_CIs", value.name = "readout")
|
|
|
|
lowquant_AsDiff <- quantile(resHistAsDiff[,2], probs=as.numeric(input$lowQuant)/100)
|
|
upquant_AsDiff <- quantile(resHistAsDiff[,3], probs=as.numeric(input$uppQuant)/100)
|
|
|
|
p_AsDiff <- ggplot(resHistAsDiff_l, aes(readout, fill=ratio_CIs)) +
|
|
geom_histogram(alpha=0.5,position="identity") +
|
|
labs(title = paste("asymptote diff. ratio EACs:", round(lowquant_AsDiff,3), " to ", round(upquant_AsDiff,3))) +
|
|
geom_vline(xintercept = c(lowquant_AsDiff, upquant_AsDiff), color="black", linetype="dashed", linewidth=1) +
|
|
geom_vline(xintercept = c(input$lEACratioAdiff , input$uEACratioAdiff), color="red", linetype="dashed", linewidth=1) +
|
|
theme_bw()
|
|
|
|
# Slope ratio
|
|
resHistSlo <- as.data.frame(resHist[,5:7])
|
|
resHistSlo_l <- melt(data.frame(resHistSlo), variable.name="ratio_CIs", value.name = "readout")
|
|
|
|
lowquant_Slo <- quantile(resHistSlo[,2], probs=as.numeric(input$lowQuant)/100)
|
|
upquant_Slo <- quantile(resHistSlo[,3], probs=as.numeric(input$uppQuant)/100)
|
|
|
|
p_Slo <- ggplot(resHistSlo_l, aes(readout, fill=ratio_CIs)) +
|
|
geom_histogram(alpha=0.5,position="identity") +
|
|
labs(title = paste("Slope ratio EACs:", round(lowquant_Slo,3), " to ", round(upquant_Slo,3))) +
|
|
geom_vline(xintercept = c(lowquant_Slo, upquant_Slo), color="black", linetype="dashed", linewidth=1) +
|
|
geom_vline(xintercept = c(input$lEACratioSlope , input$uEACratioSlope), color="red", linetype="dashed", linewidth=1) +
|
|
theme_bw()
|
|
|
|
# poency ratio
|
|
resHistPot <- as.data.frame(resHist[,1:3])
|
|
resHistPot_l <- melt(data.frame(resHistPot), variable.name="ratio_CIs", value.name = "readout")
|
|
|
|
lowquant_Pot <- quantile(resHistPot[,2], probs=as.numeric(input$lowQuant)/100)
|
|
upquant_Pot <- quantile(resHistPot[,3], probs=as.numeric(input$uppQuant)/100)
|
|
#browser()
|
|
p_Pot <- ggplot(resHistPot_l, aes(readout, fill=ratio_CIs)) +
|
|
geom_histogram(alpha=0.5,position="identity") +
|
|
labs(title = paste("Poency ratio EACs:", round(lowquant_Pot,3), " to ", round(upquant_Pot,3))) +
|
|
geom_vline(xintercept = c(lowquant_Pot, upquant_Pot), color="black", linetype="dashed", linewidth=1) +
|
|
geom_vline(xintercept = c(input$lowerPot/100, input$upperPot/100), color="red", linetype="dashed", linewidth=1) +
|
|
theme_bw()
|
|
|
|
grid.arrange(p_Slo, p_AsDiff, p_uAs, p_Pot, nrow=1)
|
|
|
|
}
|
|
})
|
|
|
|
#### download XL report----
|
|
|
|
output$downloadXLReport <- downloadHandler(
|
|
filename= paste0("Report_4PLEvaluation", Dat$FileName,".pdf"),
|
|
|
|
content = function(file) {
|
|
tpdr <- tempdir()
|
|
tempReport <- file.path(tpdr,"Doc_BioassayReport.Rmd")
|
|
file.copy("Doc_BioassayReport.Rmd", tempReport, overwrite = T)
|
|
|
|
tempReportc <- file.path(tpdr,"logo.png")
|
|
file.copy("logo.png", tempReportc, overwrite = T)
|
|
|
|
rmarkdown::render(tempReport, output_file = file,
|
|
params = list(FileName = Dat$FileName,
|
|
author = Dat$author,
|
|
REP = REP,
|
|
coeffs = Dat$coeffs_UN),
|
|
envir = new.env(parent = globalenv()))
|
|
|
|
}
|
|
)
|
|
|
|
|
|
}
|
|
|
|
shinyApp(ui, server)
|