XLSX with 300WSs added, INFO boxes added to OPTIMIZE
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@@ -1626,3 +1626,47 @@ bendFUN <- function(coeffsMR, coeffsMU) {
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)
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return(BPsMR_MU)
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}
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#' Fitting of Gumbel location and scale parameters, and plotting function
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#'
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#'
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#' @param H Histogram density results with the mid-points of the histograms.
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#' @returns A ggplot object and a table of Gumbel quantiles 0.95, 0.99, and 0.999.
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#' @export
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#' @examples
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#' suppressMessages(source("../../dev/setup.R"))
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#'
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#' H = structure(list(breaks = c(1.02, 1.04, 1.06, 1.08, 1.1, 1.12, 1.14),
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#' counts = c(2L, 12L, 11L, 4L, 0L, 1L),
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#' density = c(3.33333333333333, 20, 18.3333333333333, 6.66666666666666, 0, 1.66666666666667),
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#' mids = c(1.03, 1.05, 1.07, 1.09, 1.11, 1.13), xname = 'UppAsympt',
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#' equidist = TRUE), class = "histogram")
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#' Titel <- "test"
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#'
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#' GumbPlotFUN(H)
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GumbPlotFUN <- function(H, Titel="Maximum deviation of confidence limit") {
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H_df <- data.frame(Density = H[[3]], Mids=H[[4]])
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startlist <- list(mu = 1.1, b = 1)
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GuM <- gsl_nls(
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fn = Density ~ 1/b*exp(-((Mids-mu)/b+exp(-(Mids-mu)/b))),
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data = H_df,
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start = startlist, # race=T,
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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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SuGuM <- summary(GuM)$coefficients
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X_gu <- seq(min(H[["breaks"]]), max(H[["breaks"]]), by=0.001)
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DGu <- dgumbel(X_gu, loc=SuGuM[1,1], scale = SuGuM[2,1])
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DGuDF <- data.frame(X = X_gu, Y = DGu)
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Quantiles <- qgumbel(c(0.95,0.99,0.999),loc=SuGuM[1,1], scale = SuGuM[2,1] )
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INV_Quantiles <- 1/Quantiles
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p_gu <- ggplot(H_df) +
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geom_col(mapping=aes(x=Mids, y=Density)) +
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geom_line(DGuDF, mapping=aes(x=X, y=Y), inherit.aes = F) +
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geom_vline(xintercept = Quantiles, linetype=2, col="blue") +
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ggtitle(Titel) +
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theme_bw()
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return(list(p_gu, data.frame(Probabilities= c(0.95,0.99,0.999), INV_Quantiles,Quantiles)))
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}
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@@ -374,9 +374,10 @@ server <- function(input, output, session) {
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h4("Settings for report"),
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useShinyjs(),
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actionButton("btn", "Download 4PL PDF report", icon = icon("download")),
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downloadButton("downloadXLReport", style = "visibility: hidden;"),
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#downloadButton("downloadXLReport", label = "Download 4PL PDF report", class = "butt"),
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tags$style(type = "text/css", "#downloadXLReport {background-color: orange; color: black;font-family: Courier New}"),
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tags$style(type = "text/css", "#downloadXLReport {background-color: #4FCBD9; color: black;font-family: Courier New}"),
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downloadButton("downloadXLReportLin", label = "Download linear PLA PDF report", class = "butt"),
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tags$style(type = "text/css", "#downloadXLReportLin {background-color: #4FCBD9; color: black;font-family: Courier New}"),
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textInput("Author", "Author", value = ""),
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@@ -604,7 +605,7 @@ server <- function(input, output, session) {
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#### UI wizard ----
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output$wizard <- renderUI({
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navbarPage(
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title = "Dilution setting",
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title = "Optimizing wizard",
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tabPanel(
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"Plots",
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sidebarLayout(
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@@ -616,9 +617,12 @@ server <- function(input, output, session) {
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title = "Upload multiple worksheets", status = "warning", solidHeader = TRUE, width = 12, "Please upload your EXCEL file here",
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fileInput("MiFile", "", accept = ".xlsx")
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),
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conditionalPanel(condition="input.tabs3 == 'Dilution slider'",
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sliderInput("dilslider", "Adjust the dilutions(+-change in %)", min = -100,max=100, value=0, step=1, round=0),
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#checkboxInput("fixupper","Fix highest concentration (if unticked, the center is fixed)",FALSE),
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sliderInput("dilmover", "Move the dilutions(+-move in log-units)", min = -3,max=3, value=0, step=0.1, round=1),
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),
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numericInput("TolConf","confidence", value=0.95, step=0.01),
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numericInput("TolPop","population", value=0.9, step=0.01)
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)
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@@ -626,7 +630,7 @@ server <- function(input, output, session) {
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mainPanel(
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tabsetPanel(
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id = "tabs3",
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tabPanel("4pl",
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tabPanel("Sigmoid fits",
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column(12,
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@@ -634,15 +638,33 @@ server <- function(input, output, session) {
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# tableOutput("CIs"),
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# "The confidence interval table is interactive for changes in: variability slider (%SD), potency of test-slider,
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# and 'Adjust the dilutions'-slider",
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# tableOutput("optimalDils"),
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plotOutput("sigPlotREF"),
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plotOutput("sigPlotTEST"),
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plotOutput("sigPlotTEST")),
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box(title = "INFO for sigmiod plots", status = "info", solidHeader = TRUE, collapsible = TRUE, width = 12,collapsed=T,
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uiOutput('ExplTextOPTsigs'),
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),
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column(8,
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plotOutput("GuPlotUA")),
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column(4, tableOutput("GuTabUA")),
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column(8, plotOutput("GuPlotSLO")),
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column(4,
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tableOutput("GuTabSLO")),
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box(title = "INFO for histograms", status = "info", solidHeader = TRUE, collapsible = TRUE, width = 12,collapsed=T,
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uiOutput('ExplTextOPTGumbel'),
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br()
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)
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#selectInput(inputId = "scenario", label = "Select an 'optimal' scenario:", choices = c("scenario 2", "scenario 3", "scenario 6", "steep slope"))
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)),
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),
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tabPanel("Dilution slider",
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h4("Adjust the dilutions"),
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plotOutput("plotfordilutions"),
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box(title = "INFO for sigmiod plots", status = "info", solidHeader = TRUE, collapsible = TRUE, width = 12,collapsed=T,
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uiOutput('ExplTextOPTdils'),
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),
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h5("Dilution factors"),
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box(
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title = "Adjusted dilutions", status = "primary", solidHeader = TRUE, width = 12, collapsible = TRUE,
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@@ -655,10 +677,12 @@ server <- function(input, output, session) {
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tabPanel("Plots",
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h4("Histograms of parameters"),
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plotOutput("linerangeCIs"),
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column(6,
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plotOutput("ratioSlope"),
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plotOutput("ratioLas"),
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plotOutput("ratioLas")),
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column(6,
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plotOutput("ratioUas"),
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plotOutput("widthCIs"),
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plotOutput("widthCIs")),
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column(6,
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plotOutput("histEC50REF"),
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plotOutput("histLasREF"),
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@@ -672,8 +696,7 @@ server <- function(input, output, session) {
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),
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tabPanel(
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"Report",
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h4("Settings for report"),
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"Download",
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#useShinyjs(),
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#actionButton("btn2", "Download PDF report", icon = icon("download")),
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downloadButton("downloadWizardData", label = "Download model and plots", class = "butt"),
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@@ -748,19 +771,32 @@ server <- function(input, output, session) {
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#### input Wizard XL file ----
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observe({
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if (!is.null(input$MiFile)) {
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withProgress(message = "You clicked this button.... Working...", value =0 , {
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MinFile <- input$MiFile
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ext <- tools::file_ext(MinFile$name)
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file.rename(MinFile$datapath, paste(MinFile$datapath, ".xlsx", sep = ""))
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t.filelocation <- gsub("\\\\", "/", paste(MinFile$datapath, ext, sep = "."))
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sheets <- openxlsx::getSheetNames(t.filelocation)
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incProgress(.25,message = "Reading from EXCEL.... Working...")
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dat <- lapply(sheets, openxlsx::read.xlsx, xlsxFile = t.filelocation)
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incProgress(.35, message = "All WS read.... Working...")
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names(dat) <- sheets
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Dat$Mws <- dat
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names(Dat$Mws) <- sheets
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Dat$Msheets <- sheets
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Dat$MFileName <- input$MiFile[["name"]]
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#output$downloadData
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incProgress(1, message = "Calculating...COMPLETE!")
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# Sys.sleep to show message 1 sec
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Sys.sleep(1)
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})
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}
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})
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observeEvent(input$MiFile, {showSpinner("sigPlotREF")})
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#### process XLSX file ----
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observe({
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@@ -2251,7 +2287,7 @@ server <- function(input, output, session) {
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})
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#### Meta plots all XL ----
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#### Wizard plots all XL ----
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observe({
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if (!is.null(Dat$Mws)) {
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@@ -2259,7 +2295,11 @@ server <- function(input, output, session) {
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AllXL <- Dat$Mws
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AllSheets <- Dat$Msheets
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URMcoefsL <- RMcoefsL <- potEstL <- list()
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URMcoefsL <- RMcoefsL <- potEstL <- UAciL <- SLOciL <- list()
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for (N_WS in 1:length(AllXL)) {
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@@ -2283,7 +2323,7 @@ server <- function(input, output, session) {
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CORro <- COR_FUNC(datWS[, logI], datWS[, 3])
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}
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Dat$datWS2 <- datWS2
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#browser()
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FITs <- Fitting_FUNC(datWS2, TransFlag = F, nameWS = nameWS)
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if (!is.character(FITs)) {
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pot_est <- FITs[[3]]
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@@ -2295,6 +2335,10 @@ server <- function(input, output, session) {
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URMcoefs <- t(matrix(unlist(URMcoefs1[,1])))
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URMcoefs_ <- cbind(AllSheets[[N_WS]], URMcoefs)
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URMcoefsL[[N_WS]] <- URMcoefs_
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URMV_V <- SU_mu$cov.unscaled
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SEsRMSE <- sqrt(diag(URMV_V) * SU_mu$sigma^2)
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UAciL[[N_WS]] <- ParamCI_F(URMcoefs[1,7], URMcoefs[1,3], SEsRMSE[7],SEsRMSE[3], CoVar=0, SU_mu$df[2], Conf = 0.975)
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SLOciL[[N_WS]] <- ParamCI_F(URMcoefs[1,6], URMcoefs[1,2], SEsRMSE[6],SEsRMSE[2], CoVar=0, SU_mu$df[2], Conf = 0.975)
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SU_mr <- FITs[[1]]
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RMcoefs1 <- SU_mr$coefficients
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@@ -2316,6 +2360,12 @@ server <- function(input, output, session) {
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}
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} #for N_WS
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#browser()
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URMcoefsDF <- t(matrix(unlist(URMcoefsL),nrow=9))
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colnames(URMcoefsDF) <- c("WS name", "lowerAs REF","slope REF","upperAs REF","EC50 REF", "lowerAs TEST","slope TEST","upperAs TEST","EC50 Difference")
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@@ -2337,6 +2387,31 @@ server <- function(input, output, session) {
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UasRatio <- UasTEST/UasREF
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ratioDF <- data.frame(WS_name = URMcoefsDF[,1], slopeRatio = slopeRatio, LasRatio = LasRatio, UasRatio = UasRatio)
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UAciM <- t(matrix(unlist(UAciL),nrow=2))
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SLOciM <- t(matrix(unlist(SLOciL),nrow=2))
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colnames(UAciM) <- c("lowerCL_upperA", "upperCL_upperA")
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colnames(SLOciM) <- c("lowerCL_Hslope","upperCL_Hslope")
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UAciDF <- data.frame("lowerCL_upperA" =UAciM[,1], "upperCL_upperA" = UAciM[,2],"reciLowerCI"= 1/UAciM[,1])
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UAciDF$MaxDeviation <- apply(UAciDF, 1,max)
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SLOciDF <- data.frame("lowerCL_upperA" =SLOciM[,1], "upperCL_upperA" = SLOciM[,2],"reciLowerCI"= 1/SLOciM[,1])
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SLOciDF$MaxDeviation <- apply(SLOciDF, 1,max)
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#browser()
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H <- hist(UAciDF[,4])
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GumbelL <- GumbPlotFUN(H, Titel="Histogram of max. deviation of upper asymptote ratio confidence limits")
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p_gu <- GumbelL[[1]]
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Gu_tab <- GumbelL[[2]]
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output$GuPlotUA <- renderPlot({ p_gu })
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output$GuTabUA <- renderTable({ Gu_tab }, digits=3)
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H_s <- hist(SLOciDF[,4])
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GumbelL_s <- GumbPlotFUN(H_s, Titel="Histogram of max. deviation of Hill's slope ratio confidence limits")
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p_gu_s <- GumbelL_s[[1]]
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Gu_tab_s <- GumbelL_s[[2]]
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output$GuPlotSLO <- renderPlot({ p_gu_s })
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output$GuTabSLO <- renderTable({ Gu_tab_s }, digits = 3)
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RMcoefsDF <- t(matrix(unlist(RMcoefsL),nrow=6))
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colnames(RMcoefsDF) <- c("WS name", "lower asymptote","Hill's slope","upper asymptote","log(EC50 ref)","logEC50 difference")
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Dat$URMcoefsDF <- URMcoefsDF
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@@ -2355,7 +2430,7 @@ server <- function(input, output, session) {
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x_UA <- max(X); x_LA <- min(X)
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} else { x_UA <- min(X); x_LA <- max(X) }
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#browser()
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#browser()
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BoxDF <- data.frame(EC50REF = EC50REF, EC50TEST = EC50TEST, LasREF = LasREF, UasREF = UasREF)
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UasParTolREF <- normtol.int(x = UasREF, alpha = 1-input$TolConf, P = input$TolPop, side = 2)
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LasParTolREF <- normtol.int(x = LasREF, alpha = 1-input$TolConf, P = input$TolPop, side = 2)
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@@ -2376,10 +2451,12 @@ server <- function(input, output, session) {
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xlab("dilutions") +
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ggtitle("REF sample 4PL-fits (unrestricted model, gray vertical lines: EC50)") +
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theme_bw() +
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{ if (nrow(SIGrefDF)>30) theme(legend.position="none") } +
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theme(axis.text = element_text(face = "bold", size = 15),
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plot.title = element_text(size = 15, face = "bold"),
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plot.margin = unit(c(0.2, 0.2, 0.5, 0.5), "lines"))
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output$sigPlotREF <- renderPlot({ p1 })
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Dat$sigPlotREF <- p1
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@@ -2396,6 +2473,7 @@ server <- function(input, output, session) {
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xlab("dilutions") +
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ggtitle("TEST sample 4PL-fits (unrestricted model, gray vertical lines: EC50)") +
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theme_bw() +
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{ if (nrow(SIGtestDF)>30) theme(legend.position="none") } +
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theme(axis.text = element_text(face = "bold", size = 15),
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plot.title = element_text(size = 15, face = "bold"))
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@@ -2403,7 +2481,10 @@ server <- function(input, output, session) {
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Dat$sigPlotTEST <- p2
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#### histograms right panel ----
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#### Wizard histograms ----
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@@ -2424,23 +2505,26 @@ server <- function(input, output, session) {
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# scale_x_continuous(
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# breaks=seq(trunc(min(all_lPot$Potency_and_CI, na.rm=T)*10)/10, max(all_lPot$Potency_and_CI, na.rm=T)*1.1, by=0.4),
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# ) +
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xlab("Index of worksheets") +
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theme_bw() +
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theme(axis.text = element_text(face="bold", size=15),
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axis.text.x = element_text(angle=90),
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plot.title= element_text(size=15, face="bold"))
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plot.title= element_text(size=15, face="bold")) +
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theme(legend.position="none")
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#P_linerangeCI
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output$linerangeCIs <- renderPlot({ P_linerangeCI })
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P_widthCIs <- ggplot(CalcPotDF, aes(x=width_CI, fill="blue")) +
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geom_histogram(color="#e9ecef", alpha=0.6, position = "identity") +
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geom_density(alpha = 0.1) +
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P_widthCIs <- ggplot(CalcPotDF, aes(x=width_CI, fill="red"), ) +
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geom_histogram(aes(y = after_stat(density)), bins = 10, position = "identity", alpha=0.6) +
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geom_density(alpha = 0.5) +
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#scale_fill_manual(values=c("darkgreen","darkblue","salmon2","tomato3")) +
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labs(title = "Histogram of width of CLs", subtitle = paste("with upper",input$TolConf,input$TolPop, "tolerance interval")) +
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geom_vline(xintercept = widthCLTol[[5]]) +
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geom_vline(xintercept = widthCLTol[[5]], linetype=2) +
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theme_bw() +
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theme(axis.text = element_text(face="bold", size=15),
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axis.text.x = element_text(angle=90),
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plot.title= element_text(size=15, face="bold"))
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plot.title= element_text(size=15, face="bold")) +
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theme(legend.position="none")
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output$widthCIs <- renderPlot({ P_widthCIs })
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@@ -2449,41 +2533,44 @@ server <- function(input, output, session) {
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UasTol <- normtol.int(x = UasRatio, alpha = 1-input$TolConf, P = input$TolPop, side = 2)
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P_ratioSlope <- ggplot(ratioDF, aes(x=slopeRatio, fill="turquoise")) +
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geom_histogram(color="#e9ecef", alpha=0.6, position = "identity") +
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geom_histogram(aes(y = after_stat(density)),color="#e9ecef", alpha=0.6, position = "identity",bins = 10) +
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geom_density(alpha = 0.1) +
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#scale_fill_manual(values=c("darkgreen","darkblue","salmon2","tomato3")) +
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labs(title = "Histogram of Hill's slope ratios ", subtitle = paste("with",input$TolConf,input$TolPop, "tolerance interval")) +
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geom_vline(xintercept = c(SlopeTol[[4]], SlopeTol[[5]]), linetype=2, col="grey") +
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geom_vline(xintercept = c(SlopeTol[[4]], SlopeTol[[5]]), linetype=2) +
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theme_bw() +
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theme(axis.text = element_text(face="bold", size=15),
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axis.text.x = element_text(angle=90),
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plot.title= element_text(size=15, face="bold"))
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plot.title= element_text(size=15, face="bold")) +
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theme(legend.position="none")
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output$ratioSlope <- renderPlot({ P_ratioSlope })
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P_ratioLas <- ggplot(ratioDF, aes(x=LasRatio, fill="turquoise")) +
|
||||
geom_histogram(color="#e9ecef", alpha=0.6, position = "identity") +
|
||||
geom_histogram(aes(y = after_stat(density)),color="#e9ecef", alpha=0.6, position = "identity", bins = 10) +
|
||||
geom_density(alpha = 0.1) +
|
||||
#scale_fill_manual(values=c("darkgreen","darkblue","salmon2","tomato3")) +
|
||||
labs(title = "Histogram of lower asymptote ratios ", subtitle = paste("with",input$TolConf,input$TolPop, "tolerance interval")) +
|
||||
geom_vline(xintercept = c(LasTol[[4]], LasTol[[5]]), linetype=2, col="grey") +
|
||||
geom_vline(xintercept = c(LasTol[[4]], LasTol[[5]]), linetype=2) +
|
||||
theme_bw() +
|
||||
theme(axis.text = element_text(face="bold", size=15),
|
||||
axis.text.x = element_text(angle=90),
|
||||
plot.title= element_text(size=15, face="bold"))
|
||||
plot.title= element_text(size=15, face="bold")) +
|
||||
theme(legend.position="none")
|
||||
|
||||
output$ratioLas <- renderPlot({ P_ratioLas })
|
||||
|
||||
P_ratioUas <- ggplot(ratioDF, aes(x=UasRatio, fill="turquoise")) +
|
||||
geom_histogram(color="#e9ecef", alpha=0.6, position = "identity") +
|
||||
geom_histogram(aes(y = after_stat(density)),color="#e9ecef", alpha=0.6, position = "identity", bins = 10) +
|
||||
geom_density(alpha = 0.1) +
|
||||
#scale_fill_manual(values=c("darkgreen","darkblue","salmon2","tomato3")) +
|
||||
labs(title = "Histogram of upper asymptote ratios ", subtitle = paste("with",input$TolConf,input$TolPop, "tolerance interval")) +
|
||||
geom_vline(xintercept = c(UasTol[[4]], UasTol[[5]]), linetype=2, col="grey") +
|
||||
geom_vline(xintercept = c(UasTol[[4]], UasTol[[5]]), linetype=2) +
|
||||
theme_bw() +
|
||||
theme(axis.text = element_text(face="bold", size=15),
|
||||
axis.text.x = element_text(angle=90),
|
||||
plot.title= element_text(size=15, face="bold"))
|
||||
plot.title= element_text(size=15, face="bold")) +
|
||||
theme(legend.position="none")
|
||||
|
||||
output$ratioUas <- renderPlot({ P_ratioUas })
|
||||
|
||||
@@ -2537,41 +2624,7 @@ server <- function(input, output, session) {
|
||||
Dat$newDils <- dils2
|
||||
|
||||
|
||||
#sigmoid <- sigmoid()
|
||||
|
||||
# BPs <- Dat$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 {
|
||||
# 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)
|
||||
#
|
||||
|
||||
output$adjlogdil <- renderTable({
|
||||
adjlogdilfactors <- round(dilfactors, 3)
|
||||
@@ -2620,6 +2673,8 @@ server <- function(input, output, session) {
|
||||
} # if (!is.null(p2))
|
||||
} # if !is.null Dat$Mws
|
||||
|
||||
|
||||
|
||||
})
|
||||
|
||||
#### Dilutions CI table ----
|
||||
@@ -3057,6 +3112,28 @@ server <- function(input, output, session) {
|
||||
|
||||
}, contentType = "application/zip"
|
||||
)
|
||||
|
||||
output$ExplTextOPTsigs = renderUI({
|
||||
HTML("<b>Note</b>: The plots above contain the sigmiod fits of all uploaded worksheets, separated for reference and test, as long as a fit was possible.
|
||||
The estimated upper and lower asymptotes are shown with a grey 'x' or 'o' at each side of the plots.
|
||||
The horizontal grey dashed lines indicate the tolerance interval of the asymptotes.
|
||||
Please note that the asymptotes are often not normally distributed, or contain different groups, when the assay setup was changed.
|
||||
Therefore the tolerance interval should be seen as a gross surrogate.")
|
||||
})
|
||||
output$ExplTextOPTGumbel = renderUI({
|
||||
HTML("<b>Note</b>: The histograms show the 'maximum deviation' of the confidence interval.
|
||||
The 'maximum deviation' is either the upper confidence limit, or 1/lower confidence limit, whichever is greater.
|
||||
The blue dashed lines are the 0.95, 0.99 and 0.999 quantiles of the distribution.
|
||||
The tables on the right list the 0.95, 0.99 and 0.999 quantiles, and the inverses thereof, i.e. 1/quantiles.
|
||||
Thus, the number pairs are symmetric on a log-scale. Any of the number-pairs can be used to set the equivalence limits.")
|
||||
})
|
||||
output$ExplTextOPTdils = renderUI({
|
||||
HTML("<b>Note</b>: All sigmoids of the test samples are shown, because the test samples contain a wider variability, with stability and forced degradation samples.
|
||||
To adjust the width of the dilution range, draw the slider to the left to shrink or to the right to increase the range.
|
||||
Once the width is set, move the dilution range to the right or to the left, using the 2nd slider.
|
||||
The dilutions and the steps inbetween are annotated in red on the plot.
|
||||
The table below shows again the dilution concentrations and the steps.")
|
||||
})
|
||||
}
|
||||
|
||||
shinyApp(ui, server)
|
||||
Reference in new issue
Block a user