Simon Innerbichler
4cfdc288a8
linting can be started by clicking Addins in RStudio, then "Lint current file". This commit also contains quick fixes for common linter messages like changing F to FALSE and T to TRUE.
1509 lines
59 KiB
R
1509 lines
59 KiB
R
################################################################################
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# All functions for plateflow
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################################################################################
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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(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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library(scales)
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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(
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#' 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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#' )
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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 = FALSE) {
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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 <- tryCatch(
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{
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gsl_nls(
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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, # 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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},
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warning = function(e) {
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mr <<- "In nlsModel singular gradient matrix"
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}
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)
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# Stop if singular gradient matrix
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if (is.character(mr)) {
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return(mr)
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}
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s_mr <- tryCatch(
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{
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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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)
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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(
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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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)
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s_mr <- summary(mrT)
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}
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if (!TransFlag) {
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startlistmu <- list(
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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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)
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tryCatch(
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{
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mu <- gsl_nls(
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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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},
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error = function(msg) {
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return(0)
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}
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)
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Sum_u <- tryCatch(
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{
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summary(mu)
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},
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error = function(msg) {
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return(0)
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}
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)
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} else {
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startlistmu <- list(
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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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)
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tryCatch(
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{
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muT <- gsl_nls(
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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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},
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error = function(msg) {
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return(0)
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}
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)
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Sum_u <- tryCatch(
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{
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summary(muT)
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},
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error = function(msg) {
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return(0)
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}
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)
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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 when the fitting has a singularity
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#'
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#' Returns the scatter plot of the data, with REF in blue and TEST in red.
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#' One plots are returned.
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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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#' @returns A grid object with 2 linearity plots, restricted and unrestricted model.
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#' @export
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#' @examples
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#' data.frame(
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#' R_dil1 = c(
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#' 10.0651024695491, 10.9844983291817, 10.7635586089293, 10.4597656321327, 10.3898668457823, 10.8171761349909,
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#' 10.319758021908, 10.1304854046653
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#' ),
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#' R_dil2 = c(
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#' 10.9649145494504, 10.0202868589385, 10.8424145955735, 10.9311360356894, 10.3284659026404,
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#' 10.6890147558796, 10.3014450252305, 10.9594838595181
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#' ),
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#' R_dil3 = c(
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#' 10.4630510824383, 10.4566715089363, 10.2350765290036, 10.3300581874798, 10.9648088137065,
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#' 10.286893755805, 10.4856643841389, 10.5275521552307
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#' ),
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#' T_dil1 = c(
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#' 12.732175566336, 12.7756403995095, 12.1672539684741, 12.7060603907892, 12.8000685682832,
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#' 12.8800092157515, 12.7160581291873, 12.6996878912416
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#' ),
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#' T_dil2 = c(
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#' 12.3923194313831, 12.0943488144175, 12.7955302154828, 12.4825917078735, 12.6856540203788,
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#' 12.7348548498556, 12.9222470610476, 12.1186618671252
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#' ),
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#' T_dil3 = c(
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#' 12.7899182255274, 12.9722600411128, 12.7078445380891, 12.4913523531941, 12.1718281909609,
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#' 12.5313873615133, 12.952802332772, 12.5960321394342
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#' ),
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#' log_dose = c(
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#' 0, -1.09861228866811, -2.19722457733622, -3.29583686600433, -4.39444915467244,
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#' -5.49306144334055, -6.59167373200866, -7.69028602067677
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#' )
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#' )
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#'
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#'
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#' p <- plotSingularity(dat)
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#' print(p)
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plotSingularity <- function(dat) { # sigmoid,det_sig,
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CORdat <- cor(dat[, 1], dat[, ncol(dat)])
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# browser()
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all_l <- melt(data.frame(dat), id.vars = "log_dose", variable.name = "replname", value.name = "readout")
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isRef <- rep(c(1, 0), 1, each = nrow(all_l) / 2)
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isSample <- rep(c(0, 1), 1, each = nrow(all_l) / 2)
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all_l2 <- cbind(all_l, isRef, isSample)
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# browser()
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log_dose <- unique(all_l$log_dose)
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seq_x <- seq(min(log_dose), max(log_dose), 0.1)
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# browser()
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# all_l2$readout[all_l2$readout < 0] <- 0.01
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all_l2$readouttrans <- log(all_l2$readout)
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# browser()
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pSing <- 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(
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title = paste("No 4pl fit possible"),
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color = "product"
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) +
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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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scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
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scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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# theme_bw() +
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theme(axis.text = element_text(size = 14))
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return(pSing)
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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(
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#' 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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#' )
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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 = FALSE) { # 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 = FALSE)
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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$coefficients["b", 1]
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c <- s_mr$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$log_dose)
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seq_x <- seq(min(log_dose), max(log_dose), 0.1)
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SAMPLE <- a + (d - a) / (1 + exp(b * ((c - r) - seq_x)))
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REF <- a + (d - a) / (1 + exp(b * (c - seq_x)))
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s_mu <- MODLS[[2]]
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as <- s_mu$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$readout)
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slopeEC50 <- b * (d - a) / 4
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Intercept <- a + (d - a) / 2 - b * (d - a) / 4 * c
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# browser()
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Xbendl3 <- c - (1.5434 / b)
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Xbendu3 <- c + (1.5434 / b)
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XbendlT <- c - r - (1.5434 / b)
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XbenduT <- c - r + (1.5434 / b)
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XasymplS <- c - (3 / b)
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XasympuS <- c + (3 / b)
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XasymplT <- c - r - (3 / b)
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XasympuT <- c - r + (3 / b)
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bendpoints <- c(
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bendREF_lower = round(Xbendl3, 3), bendREF_upper = round(Xbendu3, 3),
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bendSAMPLE_lower = round(XbendlT, 3), bendSAMPLE_upper = round(XbenduT, 3),
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asympREF_lower = round(XasymplS, 3), asympREF_upper = round(XasympuS, 3),
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asympSAMPLE_lower = round(XasymplT, 3), asympSAMPLE_upper = round(XasympuT, 3)
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)
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Dat$bendpoints <- bendpoints
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Dat$cfordils <- cs
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# browser()
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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(
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title = paste("restricted 4pl model"),
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color = "product"
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) +
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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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scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
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scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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# theme_bw() +
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theme(axis.text.x = element_text(size = 12, angle = 90), axis.text.y = element_text(size = 12))
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p2 <- p + geom_line(
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data = as.data.frame(pl_df), aes(x = seq_x, y = SAMPLE), color = "#C2173F",
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inherit.aes = FALSE
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) +
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geom_line(
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data = as.data.frame(pl_df), aes(x = seq_x, y = REF), color = "#4545BA",
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inherit.aes = FALSE
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) +
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geom_line(
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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 = FALSE
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) +
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geom_line(
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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 = FALSE
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) +
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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 = "#4545BABB", linetype = 3) +
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geom_vline(xintercept = c(XasymplT, XasympuT), col = "#C2173FBB", linetype = 3) +
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annotate("text", x = c, 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]
|
|
r_trans <- s_mrt$coefficients["r", 1]
|
|
|
|
XbendlTrans <- cs_trans - (1.5434 / b_trans)
|
|
XbenduTrans <- cs_trans + (1.5434 / b_trans)
|
|
XbendlTransT <- cs_trans - r_trans - (1.5434 / b_trans)
|
|
XbenduTransT <- cs_trans - r_trans + (1.5434 / b_trans)
|
|
bendpointsTRANS <- c(
|
|
bendREF_lower = round(XbendlTrans, 3), bendREF_upper = round(XbenduTrans, 3),
|
|
bendSAMPLE_lower = round(XbendlTransT, 3), bendSAMPLE_upper = round(XbenduTransT, 3)
|
|
)
|
|
Dat$bendpointsTRANS <- bendpointsTRANS
|
|
SAMPLEtrans <- a_trans + (d_trans - a_trans) / (1 + exp(b_trans * ((cs_trans - r_trans) - seq_x)))
|
|
REFtrans <- a_trans + (d_trans - a_trans) / (1 + exp(b_trans * ((cs_trans) - seq_x)))
|
|
|
|
pl_df_trans <- cbind(seq_x, SAMPLEtrans, REFtrans)
|
|
p_rt2 <- p_rt + geom_line(
|
|
data = as.data.frame(pl_df_trans), aes(x = seq_x, y = SAMPLEtrans), color = "#C2173F",
|
|
inherit.aes = FALSE
|
|
) +
|
|
geom_line(
|
|
data = as.data.frame(pl_df_trans), aes(x = seq_x, y = REFtrans), color = "#4545BA",
|
|
inherit.aes = FALSE
|
|
) +
|
|
geom_vline(xintercept = c(XbendlTrans, XbenduTrans), col = "#4545BA", linetype = 2) +
|
|
geom_vline(xintercept = c(XbendlTransT, XbenduTransT), col = "#C2173F", linetype = 2) +
|
|
theme(legend.position = "none", axis.text = element_text(size = 14))
|
|
|
|
# browser()
|
|
Sum_u <- MODLS[[2]]
|
|
# browser()
|
|
# if (is.null(det_sig)) {
|
|
ast <- Sum_u$coefficients["as", 1]
|
|
ate <- Sum_u$coefficients["at", 1]
|
|
bst <- Sum_u$coefficients["bs", 1]
|
|
bte <- Sum_u$coefficients["bt", 1]
|
|
cst <- Sum_u$coefficients["cs", 1]
|
|
cte <- Sum_u$coefficients["cs", 1] - Sum_u$coefficients["r", 1]
|
|
dst <- Sum_u$coefficients["ds", 1]
|
|
dte <- Sum_u$coefficients["dt", 1]
|
|
|
|
|
|
REFu <- ast + (dst - ast) / (1 + exp(bst * (cst - seq_x)))
|
|
SAMPLEu <- ate + (dte - ate) / (1 + exp(bte * (cte - seq_x)))
|
|
pl_df2 <- cbind(seq_x, SAMPLEu, REFu)
|
|
# browser()
|
|
pu <- ggplot(all_l2, aes(x = log_dose, y = readout, color = factor(isRef))) +
|
|
geom_point(size = 3) +
|
|
labs(title = "unrestricted 4pl model", color = "product") +
|
|
scale_color_manual(labels = c("test", "reference"), values = c("#C2173F88", "#4545BA88")) +
|
|
theme_bw()
|
|
pu2 <- pu + geom_line(
|
|
data = as.data.frame(pl_df2), aes(x = seq_x, y = SAMPLEu),
|
|
color = "#C2173F", inherit.aes = FALSE
|
|
) +
|
|
geom_line(
|
|
data = as.data.frame(pl_df2), aes(x = seq_x, y = REFu),
|
|
color = "#4545BA", inherit.aes = FALSE,
|
|
show.legend = FALSE
|
|
)
|
|
pu2_ <- pu2 +
|
|
theme(legend.position = "none", axis.text = element_text(size = 14))
|
|
|
|
putrans <- ggplot(all_l2, aes(x = log_dose, y = readouttrans, color = factor(isRef))) +
|
|
geom_point(size = 3) +
|
|
labs(title = "unrestricted transformed 4_pl-Model", color = "product") +
|
|
scale_color_manual(labels = c("test", "reference"), values = c("#C2173FAA", "#4545BAAA")) +
|
|
theme_bw()
|
|
|
|
Sum_ut <- MODLStrans[[2]]
|
|
|
|
ast_t <- Sum_ut$coefficients["as", 1]
|
|
ate_t <- Sum_ut$coefficients["at", 1]
|
|
bst_t <- Sum_ut$coefficients["bs", 1]
|
|
bte_t <- Sum_ut$coefficients["bt", 1]
|
|
cst_t <- Sum_ut$coefficients["cs", 1]
|
|
cte_t <- Sum_ut$coefficients["cs", 1] - Sum_ut$coefficients["r", 1]
|
|
dst_t <- Sum_ut$coefficients["ds", 1]
|
|
dte_t <- Sum_ut$coefficients["dt", 1]
|
|
|
|
REFu_trans <- ast_t + (dst_t - ast_t) / (1 + exp(bst_t * (cst_t - seq_x)))
|
|
SAMPLEu_trans <- ate_t + (dte_t - ate_t) / (1 + exp(bte_t * (cte_t - seq_x)))
|
|
pl_df2u_t <- cbind(seq_x, SAMPLEu_trans, REFu_trans)
|
|
|
|
pu2_t <- putrans + geom_line(
|
|
data = as.data.frame(pl_df2u_t), aes(x = seq_x, y = SAMPLEu_trans),
|
|
color = "#C2173F", inherit.aes = FALSE
|
|
) +
|
|
geom_line(
|
|
data = as.data.frame(pl_df2u_t), aes(x = seq_x, y = REFu_trans),
|
|
color = "#4545BA", inherit.aes = FALSE,
|
|
show.legend = FALSE
|
|
)
|
|
pu3_t <- pu2_t
|
|
if (TransFlag) grid.arrange(p_rt2, pu3_t, nrow = 1) else grid.arrange(p2, pu2_, nrow = 1)
|
|
}
|
|
|
|
#' Simulate a well-plate readout
|
|
#'
|
|
#' Returns a possible well-plate result based on the metadata provided and a variability.
|
|
#' Homo- and heteroscedasticity can be simulated.
|
|
#'
|
|
#' @param as, bs, cs, ds, at, bt, dt,r,ct, gt, gs are the parameter of the 5pl logistic regression.
|
|
#' @param sd_fac The standard deviation with units in % of upper-lower asymptote.
|
|
#' @param log_conc The natural log of the concentrations.
|
|
#' @param heteroNoise Boolean if heteroscedstic noise should be added.
|
|
#' @param noDilSeries A number >1 indicating how many dilution series per product should be simulated.
|
|
#' @param noDils A number >7 indicating how many dilutions steps per product should be simulated.
|
|
#' @returns A data-frame with readouts and natural log of concentrations.
|
|
#' @export
|
|
#' @examples
|
|
#' 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
|
|
#' lnConc <- c(1, 0, -1, -2, -3, -4, -5, -6)
|
|
#' heteroNoise <- FALSE
|
|
#' noDilS <- 3
|
|
#' noD <- 8
|
|
#' Calc_DilRes(as, bs, cs, ds, at, bt, dt, r, ct, sd_fac, gt, gs, log_conc = lnConc, heteroNoise, noDilS, noD)
|
|
Calc_DilRes <- function(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, log_conc,
|
|
heteroNoise = FALSE, noDilSeries, noDils) {
|
|
yAxfac <- (ds - as)
|
|
log_dose <- log_conc
|
|
isRef <- rep(c(1, 0), 1, each = length(log_conc) * noDilSeries)
|
|
isSample <- rep(c(0, 1), 1, each = length(log_conc) * noDilSeries)
|
|
# browser()
|
|
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)))
|
|
if (heteroNoise) {
|
|
# heterosc noise
|
|
ro_jit <- matrix(unlist(map(av, function(x) x + rnorm(1, 0, x * sd_fac / 100))), nrow = noDils, ncol = noDilSeries * 2)
|
|
} else {
|
|
# homosc noise
|
|
ro_jit <- matrix(unlist(map(av, function(x) x + rnorm(1, 0, sd_fac * yAxfac / 100))), nrow = noDils, ncol = noDilSeries * 2)
|
|
}
|
|
ro_jit <- abs(ro_jit)
|
|
|
|
ro_jit2 <- cbind(ro_jit, log_dose)
|
|
if (noDilSeries == 3) {
|
|
colnames(ro_jit2) <- c("R_dil1", "R_dil2", "R_dil3", "T_dil1", "T_dil2", "T_dil3", "log_dose")
|
|
} else {
|
|
colnames(ro_jit2) <- c("R_dil1", "R_dil2", "T_dil1", "T_dil2", "log_dose")
|
|
}
|
|
return(ro_jit2)
|
|
}
|
|
|
|
#' Calculate the potency of linear PLA
|
|
#'
|
|
#' The delta Method is used for calculating the potency using a restricted linear model.
|
|
#' Absolute and relative CIs are calculated. The model RMSE or the Pure Error can be used.
|
|
#'
|
|
#' @param circles Data frame with the 3 consecutive 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 potency estimate, absolute CIs, test result, relative CIs.
|
|
#' @export
|
|
#' @examples
|
|
#' CIRC <- data.frame(
|
|
#' log_dose = c(
|
|
#' -2.5, -2.5, -2.5, -3.2, -3.2, -3.2, -3.9, -3.9, -3.9,
|
|
#' -3.2, -3.2, -3.2, -3.9, -3.9, -3.9, -4.7, -4.7, -4.7
|
|
#' ),
|
|
#' replname = c(
|
|
#' "R_dil1", "R_dil1", "R_dil1", "R_dil2", "R_dil2", "R_dil2", "R_dil3", "R_dil3", "R_dil3",
|
|
#' "T_dil1", "T_dil1", "T_dil1", "T_dil2", "T_dil2", "T_dil2", "T_dil3", "T_dil3", "T_dil3"
|
|
#' ),
|
|
#' 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),
|
|
#' isRef = c(rep(1, 9), rep(0, 9)),
|
|
#' isSample = c(rep(0, 9), rep(1, 9))
|
|
#' )
|
|
#' Lim <- c(rep(0, 8), 70, 130) # only Lim 9 and 10 relevant
|
|
#' PureErrF <- TRUE
|
|
#'
|
|
#'
|
|
#' LinPotTab(circles = CIRC, Lim, PureErrF)
|
|
LinPotTab <- function(circles, Lim, PureErrFlag) {
|
|
circ_ABl <- circles
|
|
circ_Al <- circ_ABl[circ_ABl$isSample == 1, ]
|
|
circ_Al <- circ_ABl[circ_ABl$isSample == 0, ]
|
|
# restr CSSI model
|
|
modAB <- lm(readout ~ log_dose + isSample, circ_ABl)
|
|
coeffs <- modAB$coefficients
|
|
SU_modAB <- tryCatch(
|
|
{
|
|
SU_modAB <- summary(modAB)
|
|
},
|
|
error = function(msg) {
|
|
return(NA)
|
|
}
|
|
)
|
|
# Intercept diff/slope modAB
|
|
linPot <- exp(modAB$coefficients[3] / modAB$coefficients[2])
|
|
|
|
if (PureErrFlag) {
|
|
FitAnova <- anova(lm(readout ~ factor(log_dose) * isSample, circ_ABl))
|
|
meanPureErr <- FitAnova[4, 3]
|
|
DFsPure <- FitAnova[4, 1]
|
|
VCOV <- vcov(modAB)
|
|
V_V <- VCOV / SU_modAB$sigma^2
|
|
VCOVpure <- V_V * meanPureErr
|
|
SEsPure <- sqrt(diag(V_V) * meanPureErr)
|
|
}
|
|
|
|
log_pot_delta <- car::deltaMethod(modAB, "isSample/log_dose")
|
|
if (PureErrFlag) {
|
|
V_ <- log_pot_delta$SE^2 / SU_modAB$sigma^2
|
|
V_p <- V_ * meanPureErr
|
|
potDeltaPureSE <- sqrt(V_p)
|
|
CI_log_low <- log_pot_delta$Estimate - qt(0.975, DFsPure) * potDeltaPureSE
|
|
CI_log_up <- log_pot_delta$Estimate + qt(0.975, DFsPure) * potDeltaPureSE
|
|
} else {
|
|
CI_log_low <- log_pot_delta$Estimate - qt(0.975, df.residual(modAB)) * log_pot_delta$SE
|
|
CI_log_up <- log_pot_delta$Estimate + qt(0.975, df.residual(modAB)) * log_pot_delta$SE
|
|
}
|
|
# browser()
|
|
ExpLinPot <- exp(c(log_pot_delta$Estimate, CI_log_low, CI_log_up))
|
|
|
|
# Rel pot CI
|
|
relLinpotCI <- ExpLinPot / linPot * 100
|
|
if (relLinpotCI[2] > Lim[[9]] & relLinpotCI[3] < Lim[[10]]) test_potCI <- 0 else test_potCI <- 1
|
|
|
|
pottab <- cbind(
|
|
round(linPot * 100, 3), round(ExpLinPot[2] * 100, 3), round(ExpLinPot[3] * 100, 3),
|
|
round(test_potCI, 3), round(relLinpotCI[2], 3), round(relLinpotCI[3], 3)
|
|
)
|
|
colnames(pottab) <- c("Potency", "lower 95%CI", "upper 95%CI", "test_result", "lowerRel95%CI", "upperRel95%CI")
|
|
return(pottab)
|
|
}
|
|
|
|
|
|
#' Calculate the ANOVA of linear PLA and restricted and unrestr. model summaries
|
|
#'
|
|
#' The delta Method is used for calculating the potency using a restricted linear model.
|
|
#' Absolute and relative CIs are calculated. The model RMSE or the Pure Error can be used.
|
|
#'
|
|
#' @param circles Data frame with the 3 consecutive 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 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
|
|
#' dat <- 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)
|
|
#' )
|
|
#' 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),
|
|
#' 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"),
|
|
#' readout = c(31993, 31925, 49336, 49003, 68332, 68034, 33915, 34007, 52131, 52179, 70617, 69962),
|
|
#' isRef = c(rep(1, 6), rep(0, 6)),
|
|
#' isSample = c(rep(0, 6), rep(1, 6))
|
|
#' )
|
|
#' Lim <- c(rep(0, 8), 70, 130) # only Lim 9 and 10 relevant
|
|
#' PureErrF <- TRUE
|
|
#'
|
|
#'
|
|
#' ANOVAlintests(ro_new, circles, Lim, PureErrF)
|
|
ANOVAlintests <- function(ro_new, circles, 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$log_dose)
|
|
all_lA <- all_l[all_l$isSample == 1, ] # TEST
|
|
all_lB <- all_l[all_l$isSample == 0, ] # REF
|
|
# browser()
|
|
circ_ABl <- circles
|
|
circ_Al <- circ_ABl[circ_ABl$isSample == 1, ]
|
|
circ_Bl <- circ_ABl[circ_ABl$isSample == 0, ]
|
|
# restr CSSI model
|
|
modAB <- lm(readout ~ log_dose + isSample, circ_ABl)
|
|
# unrestr with interact term SSSI
|
|
modABu <- lm(readout ~ log_dose + isSample + log_dose * isSample, circ_ABl)
|
|
modA <- lm(readout ~ log_dose + isSample, circ_Al)
|
|
modB <- lm(readout ~ log_dose + isSample, circ_Bl)
|
|
|
|
log_pot_delta <- car::deltaMethod(modAB, "isSample/log_dose")
|
|
CI_log_low <- log_pot_delta$Estimate - qt(0.975, df.residual(modAB)) * log_pot_delta$SE
|
|
CI_log_up <- log_pot_delta$Estimate + qt(0.975, df.residual(modAB)) * log_pot_delta$SE
|
|
ExpLinPot <- exp(c(log_pot_delta$Estimate, CI_log_low, CI_log_up))
|
|
if (ExpLinPot[2] * 100 > Lim[9] & ExpLinPot[3] * 100 > Lim[10]) test_potCI <- 0 else test_potCI <- 1
|
|
|
|
su_mod <- summary(modAB)$coefficients
|
|
su_mod2 <- cbind(data.frame(parameter = c("intercept REF", "slope REF", "intercepts diff.")), su_mod)
|
|
su_modU <- summary(modABu)$coefficients
|
|
su_modU2 <- cbind(data.frame(parameter = c("intercept REF", "slope REF", "intercepts diff.", "slope difference")), su_modU)
|
|
|
|
uCI_SloDiff <- su_modU[4, 1] + qt(0.975, 8) * su_modU[4, 2]
|
|
lCI_SloDiff <- su_modU[4, 1] - qt(0.975, 8) * su_modU[4, 2]
|
|
SlopeDiffCI <- c(su_modU[4, 1], lCI_SloDiff, uCI_SloDiff)
|
|
|
|
lenCirc <- nrow(circ_ABl)
|
|
dfTreat <- 3
|
|
dfPrep <- 1
|
|
dfReg <- 1
|
|
dfnonP <- 1
|
|
dfRMSE <- c(lenCirc - 3 - 1)
|
|
dfTotal <- lenCirc - 1
|
|
dfPureE <- lenCirc - 6
|
|
dfNonLin <- dfRMSE - dfPureE
|
|
|
|
RSS <- sum(resid(lm(readout ~ log_dose * isSample, circ_ABl))^2)
|
|
MSE <- RSS / dfRMSE
|
|
SSE <- sum(resid(lm(readout ~ factor(log_dose) * isSample, circ_ABl))^2)
|
|
MSpure <- SSE / dfPureE
|
|
|
|
if (PureErrFlag) {
|
|
FitAnova <- anova(lm(readout ~ factor(log_dose) * isSample, circ_ABl))
|
|
meanPureErr <- FitAnova[4, 3]
|
|
SU_modAB <- tryCatch(
|
|
{
|
|
SU_modAB <- summary(modAB)
|
|
},
|
|
error = function(msg) {
|
|
return(NA)
|
|
}
|
|
)
|
|
if (length(SU_modAB) > 1) s_modABcoeffs <- summary(modAB)$coefficients
|
|
|
|
DFsPure <- FitAnova[4, 1]
|
|
VCOV <- vcov(modAB)
|
|
V_V <- VCOV / SU_modAB$sigma^2
|
|
VCOVpure <- V_V * meanPureErr
|
|
SEsPure <- sqrt(diag(V_V) * meanPureErr)
|
|
su_mod2[, 3] <- SEsPure
|
|
su_mod2[, 4] <- su_mod2[, 2] / su_mod2[, 3]
|
|
su_mod2[, 5] <- 2 * (1 - pt(abs(su_mod[, 4]), FitAnova[4, 1]))
|
|
|
|
s_mu <- summary(modABu)$coefficients
|
|
SU_modABu <- summary(modABu)
|
|
VCOVu <- vcov(modABu)
|
|
V_Vu <- VCOVu / SU_modABu$sigma^2
|
|
SEsPureU <- sqrt(diag(V_Vu) * meanPureErr)
|
|
|
|
su_modU2[, 3] <- SEsPureU
|
|
su_modU2[, 4] <- su_modU2[, 2] / su_modU2[, 3]
|
|
su_modU2[, 5] <- 2 * (1 - pt(abs(su_modU2[, 4]), FitAnova[4, 1]))
|
|
|
|
uCI_SloDiffP <- su_modU[4, 1] + qt(0.975, 8) * SEsPureU[4]
|
|
lCI_SloDiffP <- su_modU[4, 1] - qt(0.975, 8) * SEsPureU[4]
|
|
SlopeDiffCI <- c(su_modU[4, 1], lCI_SloDiffP, uCI_SloDiffP)
|
|
|
|
SSRes <- SSE
|
|
dfRes <- dfPureE
|
|
} else {
|
|
SSRes <- RSS
|
|
dfRes <- dfRMSE
|
|
}
|
|
|
|
# treatment
|
|
SStreat <- print(sum((predict(lm(readout ~ factor(log_dose) * isSample, circ_ABl)) - mean(circ_ABl$readout))^2))
|
|
F_treat <- (SStreat / dfTreat) / (SSRes / dfRes)
|
|
# Preparation
|
|
SSprep <- print(sum((predict(lm(readout ~ isSample, circ_ABl)) - mean(circ_ABl$readout))^2))
|
|
F_prep <- (SSprep / dfTreat) / (SSRes / dfRes)
|
|
# Regression
|
|
# ANOVA tape II SS of regression
|
|
SSreg <- Anova(lm(readout ~ log_dose + isSample, circ_ABl))[1, 1]
|
|
# Non-parallelism
|
|
# diff of RSS of restricted and unrestricted model
|
|
SSnonpar <- sum(resid(modAB)^2) - sum(resid(modABu)^2)
|
|
F_nonpar <- SSnonpar / (sum(resid(lm(readout ~ factor(log_dose) * isSample, circ_ABl))^2) / (lenCirc - 4))
|
|
|
|
# non-linearity
|
|
SSnonlin <- sum((predict(modABu) - predict(lm(readout ~ as.factor(log_dose) * isSample, circ_ABl)))^2)
|
|
# = RSS-SSE
|
|
# Total SS
|
|
SStot <- sum((circ_ABl$readout - mean(circ_ABl$readout))^2)
|
|
# Significance of R^2 F-ratio
|
|
# MSR/MSE
|
|
# sample A
|
|
F_R2_A <- sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Al)) - mean(predict(modA)))^2 - (predict(modA) - mean(circ_Al$readout))^2) /
|
|
(sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Al)) - circ_Al$readout)^2) / (nrow(circ_Al) - 3))
|
|
pFR2_A <- round(pf(F_R2_A, 1, 6), 4)
|
|
# sample B
|
|
F_R2_B <- sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Bl)) - mean(predict(modB)))^2 - (predict(modB) - mean(circ_Bl$readout))^2) /
|
|
(sum((predict(lm(readout ~ log_dose + I(log_dose^2), circ_Bl)) - circ_Bl$readout)^2) / (nrow(circ_Bl) - 3))
|
|
pFR2_B <- round(pf(F_R2_B, 1, 6), 4)
|
|
# sign of non-lin with pure error: MSSnonlin/MSSE
|
|
F_nonlin <- (SSnonlin / 2) / (SSE / dfPureE)
|
|
|
|
# sign of slope
|
|
F_slope_B <- sum((predict(modB) - mean(circ_Bl$readout))^2) / (sum((circ_Bl$readout - predict(modB))^2) / (nrow(circ_Bl) - 2))
|
|
F_slope_A <- sum((predict(modA) - mean(circ_Al$readout))^2) / (sum((circ_Al$readout - predict(modA))^2) / (nrow(circ_Al) - 2))
|
|
# F-test on regression: MSSreg/MSSE
|
|
if (is.na(F_nonlin)) F_nonlin <- 0
|
|
if (F_nonlin > 0) {
|
|
p_F_nonlin <- round(pf(F_nonlin, 2, dfPureE, lower.tail = FALSE), 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 = FALSE), 3)
|
|
p_F_treat <- round(pf(F_treat, 3, dfRes, lower.tail = FALSE), 3)
|
|
p_F_prep <- round(pf(F_prep, 1, dfRes, lower.tail = FALSE), 3)
|
|
p_F_slope_A <- round(pf(F_slope_A, 1, (nrow(circ_Al) - 2), lower.tail = FALSE), 3)
|
|
p_F_slope_B <- round(pf(F_slope_B, 1, (nrow(circ_Bl) - 2), lower.tail = FALSE), 3)
|
|
p_F_nonp <- round(pf(F_nonpar, 1, dfRes, lower.tail = FALSE), 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)
|
|
}
|
|
|
|
|
|
#' Plots linear regression on the scatterplot.
|
|
#'
|
|
#' Restricted and unrestricted model are plotted. The number of dilution steps used for the regression
|
|
#' are printed in the title. Reference is blue, red is the sample. The points used for regression
|
|
#' are highlighted with a circle.
|
|
#'
|
|
#' @param circle Data frame with the 3 consecutive concentrations and the respective readouts.
|
|
#' @param sigmoid Parameters of the unrestricted fit of the full dataset for drawing the curves.
|
|
#' @param all_l2 Long format data.frame of the readout data incl log(concentration) and isRef (0s and 1s).
|
|
#' @param pl_df Data.frame for plotting the regression lines.
|
|
#' @param indS Index of dilution, where regression starts for reference sample.
|
|
#' @param indT Index of dilution, where regression starts for test sample.
|
|
#' @returns A grid object of 2 plots of unrestricted and restricted linear PLA models.
|
|
#' @export
|
|
#' @examples
|
|
#' circle <- read.csv("~/plateflow/circle.csv")
|
|
#' all_l2 <- read.csv("~/plateflow/all_l2.csv")
|
|
#' sigmoid <- c(10.0, 10.0, 110.0, 110.0, 1.0, 1.0, -3.5, 0.0)
|
|
#' indS <- 3
|
|
#' indT <- 3
|
|
#' pl_df <- data.frame(
|
|
#' lnC = c(-1.203973, -1.897120, -2.590267, -3.283414, -3.976562, -4.669176, -5.362323, -6.053340),
|
|
#' plotS = c(113.772511, 97.668371, 81.564231, 65.460091, 49.355952, 33.264200, 17.160060, 1.105405),
|
|
#' plotT = c(114.213375, 97.588663, 80.963951, 64.339239, 47.714527, 31.102604, 14.477892, -2.095735)
|
|
#' )
|
|
#'
|
|
#'
|
|
#' PlotLinPLA_FUNC(circle, sigmoid, all_l2, pl_df, indS, indT)
|
|
PlotLinPLA_FUNC <- function(circle, sigmoid, all_l2, pl_df, indS, indT) {
|
|
# browser()
|
|
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$log_dose)
|
|
seq_x <- seq(min(log_dose), max(log_dose), 0.1)
|
|
if (!is.null(sigmoid)) {
|
|
SAMPLEtrue <- sigmoid[5] + (sigmoid[7] - sigmoid[5]) / (1 + exp(sigmoid[6] * ((sigmoid[4] - sigmoid[8] - seq_x))))
|
|
REFtrue <- sigmoid[1] + (sigmoid[3] - sigmoid[1]) / (1 + exp(sigmoid[2] * ((sigmoid[4] - seq_x))))
|
|
truePL_df <- cbind(seq_x, SAMPLEtrue, REFtrue)
|
|
} else {
|
|
SAMPLEtrue <- NULL
|
|
REFtrue <- NULL
|
|
truePL_df <- NULL
|
|
}
|
|
|
|
|
|
p <- ggplot(all_l2, aes(x = log_dose, y = readout, color = factor(isRef))) +
|
|
geom_point(size = 2) +
|
|
# labs(title=paste("linear regression model", indS,indT), color="product") +
|
|
scale_colour_manual(labels = c("test", "reference"), values = c("#C2173F", "#4545BA")) +
|
|
ylim(min(all_l2$readout), max(all_l2$readout)) +
|
|
scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
|
|
scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
|
|
theme_bw()
|
|
p2 <- p + geom_line(
|
|
data = pl_df, aes(x = lnC, y = plotS), color = "#4545BA",
|
|
inherit.aes = FALSE
|
|
) +
|
|
geom_line(
|
|
data = pl_df, aes(x = lnC, y = plotT), color = "#C2173F",
|
|
inherit.aes = FALSE
|
|
) +
|
|
{
|
|
if (!is.null(truePL_df)) {
|
|
geom_line(
|
|
data = data.frame(truePL_df), aes(x = seq_x, y = SAMPLEtrue), color = "#C2173F", linetype = 2, alpha = 0.4,
|
|
inherit.aes = FALSE
|
|
)
|
|
}
|
|
} +
|
|
{
|
|
if (!is.null(truePL_df)) {
|
|
geom_line(
|
|
data = data.frame(truePL_df), aes(x = seq_x, y = REFtrue), color = "#4545BA", linetype = 2, alpha = 0.4,
|
|
inherit.aes = FALSE
|
|
)
|
|
}
|
|
} +
|
|
labs(title = paste("unrestricted PLA model"), subtitle = paste("Regression starts for reference sample:", indS, "for test sample:", indT)) +
|
|
theme(legend.position = "none", axis.text = element_text(size = 14))
|
|
p3 <- p2 + geom_point(circle, mapping = aes(
|
|
x = log_dose, y = readout, shape = factor(isRef),
|
|
size = 5, alpha = 0.2
|
|
), col = c("black"), inherit.aes = FALSE) +
|
|
scale_shape_manual(labels = c("test", "reference"), values = c(21, 21))
|
|
# fit intercept for test and ref and common slope
|
|
|
|
|
|
pl_restS <- sum_mLin$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 = FALSE
|
|
) +
|
|
geom_line(
|
|
data = pl_rest, aes(x = lnC, y = plotT), color = "#C2173F",
|
|
inherit.aes = FALSE
|
|
) +
|
|
{
|
|
if (!is.null(truePL_df)) {
|
|
geom_line(
|
|
data = data.frame(truePL_df), aes(x = seq_x, y = SAMPLEtrue), color = "#C2173F", linetype = 2, alpha = 0.4,
|
|
inherit.aes = FALSE
|
|
)
|
|
}
|
|
} +
|
|
{
|
|
if (!is.null(truePL_df)) {
|
|
geom_line(
|
|
data = data.frame(truePL_df), aes(x = seq_x, y = REFtrue), color = "#4545BA", linetype = 2, alpha = 0.4,
|
|
inherit.aes = FALSE
|
|
)
|
|
}
|
|
} +
|
|
labs(
|
|
title = paste("restricted linear regression model"),
|
|
subtitle = paste("Regression on highlighted points")
|
|
) +
|
|
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
|
|
), col = c("black"), 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 = FALSE)
|
|
if (!PureErrFlag) {
|
|
pot_est <- FITs[[3]]
|
|
potU_est <- FITs[[4]]
|
|
} else {
|
|
FitAnova <- anova(lm(readout ~ factor(Conc) * isSample, all_l))
|
|
meanPureErr <- FitAnova[4, 3]
|
|
DFsPure <- FitAnova[4, 1]
|
|
# 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, DFsPure) * SEsPure["r"]),
|
|
exp(SU_mrCoeff["r", 1] + qt(0.975, DFsPure) * 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, DFsPure) * SEsPureU["r"]),
|
|
+exp(s_muCoeff["r", 1] + qt(0.975, DFsPure) * 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$readout < 0] <- 0.01
|
|
# browser()
|
|
FITs <- Fitting_FUNC(ro_new = ro_new, TransFlag = FALSE)
|
|
if (is.character(FITs)) {
|
|
return(FITs)
|
|
} # if singularity
|
|
|
|
POTr_CI <- FITs[[3]][2:3]
|
|
potAll2 <- FITs[[3]]
|
|
|
|
smr <- FITs[[1]]
|
|
smu <- FITs[[2]]
|
|
FitAnova <- anova(lm(readout ~ factor(Conc) * isSample, all_l))
|
|
# pure error
|
|
pureSS <- FitAnova[4, 2]
|
|
pureSS_df <- FitAnova[4, 1]
|
|
meanPureErr <- FitAnova[4, 3]
|
|
# vcovMU <- vcov(mu)
|
|
V_V <- smu$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 = FALSE), 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 = FALSE), 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 Lack-of-Fit*",
|
|
"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]
|
|
# Lack-of-Fit
|
|
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 = FALSE), 3)
|
|
p_SSprep <- round(pf((SSprep / AnovaDFs[2]) / MSE, AnovaDFs[2], RSS_df, lower.tail = FALSE), 3)
|
|
p_SSregr <- round(pf((SSregr / AnovaDFs[3]) / MSE, AnovaDFs[3], RSS_df, lower.tail = FALSE), 3)
|
|
p_SSnonp <- round(pf((SSnonparallel / AnovaDFs[4]) / MSE, AnovaDFs[3], RSS_df, lower.tail = FALSE), 3)
|
|
p_SSLoF <- round(pf((SSnonlin / LoF_df) / (SSE / SSE_df), LoF_df, SSE_df, lower.tail = FALSE), 5)
|
|
|
|
ANOVAtab <- data.frame(
|
|
Source = c(
|
|
"Treatment", "Preparation", "Regression",
|
|
"Non-Parallelism", "Residual Error", "Lack-of-Fit",
|
|
"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)
|
|
}
|