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CI file and PseudoRepFile added PLAscraper updated

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This commit is contained in:
Franz Innerbichler
2026-01-26 19:51:40 +01:00
parent edea03aee0
commit 41772c904b
3 changed files with 694 additions and 27 deletions
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ConfidenceIntervals.R Normal file
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#_______________________________________________________________________________________________________________________
library(tidyverse)
library(gslnls)
library(ggplot2)
library(reshape2)
library(drc)
all_data <- data.frame(Conc= c( 80000.000, 10666.667, 1422.2222, 189.62963, 25.28395, 3.37119, 0.44949, 0.05993),
ST_1 = c(414356, 402828, 405696, 510624 ,844864, 995120, 984952, 1076052),
ST_2 = c(407280, 398916, 410140, 530372, 851828, 997708, 1001036, 1069060),
ST_3 = c(371408, 424764, 397564, 498328, 808188, 910116, 995972, 974076),
REF_70_1 = c( 402432, 413852, 422292, 579012, 896164, 1029552, 1048636 ,994528),
REF_70_2 = c(401548, 406580, 429472, 560256, 895536, 976660, 945068, 1025772),
REF_70_3 = c(390632, 386176, 418596, 536832, 865956, 888640, 985340, 1053968))
# PLA GHZ250925nM2.docx
all_data <- data.frame(Conc= c( 80000.000, 10666.667, 1422.2222, 189.62963, 25.28395, 3.37119, 0.44949, 0.05993),
ST_1 = c(138743, 138450, 156121, 262387, 561683, 652307, 701813, 730525),
ST_2 = c(130197, 137202, 145935, 271407, 580252, 645236, 700294, 727128),
ST_3 = c(127476, 130984, 150794, 252283, 559926, 648084, 701467, 683004),
REF_70_1 = c(141259, 136659, 157568, 255747, 557256, 693976, 683018, 687692),
REF_70_2 = c(132591, 135163, 151050, 258229, 535896, 615267, 672638, 697947),
REF_70_3 = c(129988, 124818, 142684, 237172, 514545, 622616, 668689, 652239))
# PLA GHZ339_20251022_1MAK
all_data <- data.frame(Conc= c( 80000.000, 10666.667, 1422.2222, 189.62963, 25.28395, 3.37119, 0.44949, 0.05993),
GHZ339.01_REF_1 = c( 141179, 137438, 156331, 220605, 457358, 561383, 583467, 599980),
GHZ339.01_REF_2= c( 142449, 145681, 151674, 242837, 458059, 575359, 596014, 612299),
GHZ339.01_REF_3= c( 142608, 137862, 149732, 238484, 467783, 564427, 620924, 634909),
GHZ339DS_1 = c( 137179, 156595, 155272, 227545, 460416, 569905, 610124, 592936),
GHZ339DS_2= c( 139982, 144452, 161241, 243495, 458981, 564540, 613269, 605241),
GHZ339DS_3 = c( 153642, 145111, 158542, 229435, 458629, 556951, 612850, 614633))
# for information: 4-pl-model evaluated with drc-package
all_l <- melt(all_data, id.vars= "Conc", variable.name="replname", value.name="readout")
all_l$logreadout <- log(all_l$readout)
# add sample ID
all_l$sample <- c(rep(0,nrow(all_l)/2), rep(1,nrow(all_l)/2))
all_l$isRef <- c(rep(1,nrow(all_l)/2), rep(0,nrow(all_l)/2))
#all_l$sample <- c(rep("A",24), rep("B",24))
all_l$log_dose <- log(all_l$Conc)
# ' Achtung: logreadout'
pot <- drm(readout ~ Conc, sample, data = all_l, fct = LL.4(names = c("b", "d", "a", "c")),
pmodels = data.frame(1, 1, 1, sample))
potcomp <- drc::compParm(pot, "c", display = T)
confint(pot, display = T)
summary(pot)
supot <- summary(pot)$coefficients
var_log_s <- (supot[4,2]/supot[4,1])^2 # (se_b0 / b0)^2 # Approx variance of log(b)
var_log_t <- (supot[5,2]/supot[5,1])^2 #(se_b1 / b1)^2 # Approx variance of log(b)
#CoVarlog <- (CoSE/mean(c(b0,b1)))^2
# Simplified CI for log(b1/b2) (ignoring covariance for simplicity)
se_log_ratio <- sqrt(var_log_s + var_log_t) #-2*CoVarlog)
# log-pot
supot[4,1]/supot[5,1]
loguCI <- log(supot[4,1]) -log(supot[5,1])+qt(0.975,32)*se_log_ratio
loglCI <- log(supot[4,1]) -log(supot[5,1])-qt(0.975,32)*se_log_ratio
exp(c(loglCI,loguCI))
################################################################################
# calculation from GHZ PLA printout
# 1.00903 (0.90187 1.12894)
################################################################################
EDcomp(pot, percVec=c(50,50), interval="delta", display=T)
EDcomp(pot, percVec=c(50,50), interval="fieller", display=T)
#Estimate Lower Upper
#0/1:50/50 1.13150 0.99098 1.27201
#SE_ <- -(0.804-0.902186)/qt(0.975,44)
exp(potcomp[1]+qt(0.975, 43)*potcomp[2]) # = delta methode
#[1] 1.272015
# Delta method for ratios
deltaS <- 1/supot[5,1]^2*(vcov(pot)[4,4]-2*potcomp[1,1]*vcov(pot)[4,5]+potcomp[1,1]^2*vcov(pot)[5,5])
potcomp[1,1]+qt(0.975,48-5)*sqrt(deltaS)
potcomp[1,1]-qt(0.975,48-5)*sqrt(deltaS)
# Fieller method
EDcomp(pot, percVec=c(50,50), interval="fieller", display=T)
#Estimate Lower Upper
#0/1:50/50 1.13150 0.99848 1.28281
potcomp[1]+qnorm(0.975)*potcomp[2]
modelFit(pot)
# Lack-of-fit test
#
# ModelDf RSS Df F value p value
# ANOVA 32 0.050984
# DRC model 43 0.197391 11 8.3538 0.0000
startlist <- list(a = min(all_l$readout), b = -1.1, cs=mean(all_l$log_dose),
d = max(all_l$readout), r = 0)
mr <- gsl_nls(fn = readout ~ a + (d - a)/(1 + exp(b*(log_dose - (cs-r*sample)))),
data = all_l,
start = startlist, algorithm = "lmaccel",
control = gsl_nls_control(xtol = 1e-6, ftol = 1e-6, gtol = 1e-6))
s_mr <- summary(mr)$coefficients
(s_mr)
#sum(pot$predres[,2]^2)
# [1] 0.197391
RelPot_mr <- exp(s_mr[5,1]) # r exponentiated
print(paste("Potency restricted",RelPot_mr))
(POTr_CI <- exp(confint(mr, "r", method = "asymptotic")))
# mit profile Likelihood: (POTr_CI <- exp(confint(mr, "r", method = "profile")))
potcomp
potcomp[1]+qt(0.975,43)*potcomp[2]
potcomp[1]-qt(0.975,43)*potcomp[2]
print(POTr_CI)
# 2.5 % 97.5 %
# r 0.9994816 1.280957 # exact wie Stegmann
s_mr[5,1]+qt(0.975,43)*s_mr[5,2]
#[1] 0.2476073
exp(s_mr[5,1]+qt(0.975,43)*s_mr[5,2])
#[1] 1.280957 # exact wie Stegmann
exp(s_mr[5,1]-qt(0.975,43)*s_mr[5,2])
#[1] 0.9994816 # exact wie Stegmann
plot(all_l$log_dose, all_l$readout)
# weighting
w <- aggregate(all_l$readout, by=list(all_l$log_dose), FUN=mean)
splt <- split(all_l, f=c(all_l$log_dose))
Res <- sapply(1:8, function(x) splt[[x]]['readout']-w[,2][x])
SD <- aggregate(all_l$readout, by=list(all_l$log_dose), FUN=sd)
ResW <- sapply(1:8, function(x) Res[[x]] / SD[,2][x])
# PLA does not do weighting
# mr <- gsl_nls(fn = readout ~ a + (d - a)/(1 + exp(b*(log_dose - (cs-r*sample)))),
# data = all_l,
# start = startlist, weights=c(rep(SD$x/100,6)),
# control = gsl_nls_control(xtol = 1e-6, ftol = 1e-6, gtol = 1e-6))
# s_mr <- summary(mr)$coefficients
# (s_mr)
# exp(s_mr[5,1])
library(calculus)
jac <- calculus::jacobian(f="a+(d-a)/(1+exp(b*(c-r*sample-x)))" , var=c("d","b","c","a","r"))
resjac <- matrix(NA,nrow=48, ncol=5)
for (n in 1:nrow(all_l)) {
Vec <- c(a=s_mr[4,1], d=s_mr[1,1], c=s_mr[3,1], b=s_mr[2,1], r=s_mr[5,1], sample=all_l$sample[n], x=all_l$log_dose[n]) #
resjac[n,] <- evaluate(jac, var=Vec)
}
identical(resjac, mr$m$gradient()) # eigentlich gleich, aber col3 und 4 vertauscht;
resjac- mr$m$gradient()
#_______________________________________________________________________________
colnames(resjac) <- c("dd", "db", "ddc", "da", "dr")
V_V <- solve(t(resjac)%*%resjac)
RMSE <- sqrt(sum(mr$m$resid()^2)/43)
VCOV <- V_V*RMSE^2
SEs <- sqrt(diag(V_V)*RMSE^2) # stimmen mit summary(mr)$coefficients ueberein!!
SEs2 <- sqrt(diag(vcov(mr)))
#_______________________________________________________________________________
FitAnova <- anova(lm(readout ~ factor(Conc)*sample, all_l))
# Pure error
pureSS <- FitAnova[4,2]
pureSS_df <- FitAnova[4,1]
meanPureErr <- FitAnova[4,3]
SEsPure <- sqrt(diag(V_V)*meanPureErr)
# PLA software: 1.0252 - 1.4242
exp(s_mr[5,1]+qt(0.975,43)*SEsPure[5])
# dr
# 1.422001
exp(s_mr[5,1]-qt(0.975,43)*SEsPure[5])
# dr
# 1.027014
# Was PLA macht: 16 df wegen 8+8 Mittelwerte
exp(s_mr[5,1]+qt(0.975,48-16)*SEsPure[5])
exp(s_mr[5,1]-qt(0.975,48-16)*SEsPure[5])
# rel CIs
exp(s_mr[5,1]+qt(0.975,48-16)*SEsPure[5])/exp(s_mr[5,1])
exp(s_mr[5,1]-qt(0.975,48-16)*SEsPure[5])/exp(s_mr[5,1])
CORro <- cor(all_l$log_dose[1:8], all_l$readout[1:8])
if (CORro<0) B <- -1 else B <- 1
startlstmu <- list(as = min(all_l$readout), bs = B, cs = mean(all_l$log_dose), ds = max(all_l$readout),
at = min(all_l$readout), bt = B, ct = mean(all_l$log_dose), dt = max(all_l$readout))
mu <- tryCatch({
gsl_nls(readout ~ as*isRef + at*sample + (ds*isRef + dt*sample - as*isRef - at*sample)/
(1 + isRef*exp(bs*(cs -log_dose)) + sample*exp(bt*(ct -log_dose))),
data = all_l,
start = startlstmu,
control = gsl_nls_control(xtol = 1e-10, ftol = 1e-10, gtol = 1e-10))
}, error = function(msg){
return(0)
} )
if (length(mu)>1) {
smu_coef <- tryCatch({
summary(mu)$coefficients
}, error = function(msg){
return(rep(NA, 8))
} )
}
su <- summary(mu)
# Folgende CIs sind symmetrisch, also nicht im log berechnet
gslnls::confintd(mu, quote(dt/ds))
gslnls::confintd(mu, quote(bt/bs), level = 0.95)
# fit lwr upr
# bt/bs 1.009691 0.8629895 1.156392
potU <- drm(readout ~ Conc, sample, data = all_l, fct = LL.4(names = c("b", "d", "a", "c")),
pmodels = data.frame(sample, sample, sample, sample))
b0 <- coef(potU)[1]
b1 <- coef(potU)[2]
vcov(potU)
se_b0 <- sqrt(vcov(potU)[1, 1])
se_b1 <- sqrt(vcov(potU)[2, 2])
CoSE <- sqrt(abs(vcov(potU)[1, 2]))
# Calculate log(b) and its variance
log_b0 <- log(b0)
log_b1 <- log(b1)
var_log_b0 <- (se_b0 / b0)^2 # Approx variance of log(b)
var_log_b1 <- (se_b1 / b1)^2 # Approx variance of log(b)
CoVarlog <- (CoSE/mean(c(b0,b1)))^2
# Simplified CI for log(b1/b2) (ignoring covariance for simplicity)
se_log_ratio <- sqrt(var_log_b0 + var_log_b1 -2*CoVarlog)
z_score <- 1.96 # For 95% CI
lower_log_ratio <- log_b1 - log_b0 - z_score * se_log_ratio
upper_log_ratio <- log_b1 - log_b0 + z_score * se_log_ratio
# CI for the ratio itself
ci_ratio <- exp(c(lower_log_ratio, upper_log_ratio))
print(ci_ratio)
# b:1 b:1
# 0.8708895 1.1705629
# gsl_nls package
b0 <- coef(mu)[2]
b1 <- coef(mu)[6]
vcov(mu)
se_b0 <- sqrt(vcov(mu)[2, 2])
se_b1 <- sqrt(vcov(mu)[6, 6])
#CoSE <- sqrt(abs(vcov(mu)[6, 2]))
# Calculate log(b) and its variance
log_b0 <- log(abs(b0))
log_b1 <- log(abs(b1))
var_log_b0 <- (se_b0 / b0)^2 # Approx variance of log(b)
var_log_b1 <- (se_b1 / b1)^2 # Approx variance of log(b)
#CoVarlog <- (CoSE/mean(c(b0,b1)))^2
# Simplified CI for log(b1/b2) (ignoring covariance for simplicity)
se_log_ratio <- sqrt(var_log_b0 + var_log_b1) #-2*CoVarlog)
z_score <- 1.96 # For 95% CI
lower_log_ratio <- log_b1 - log_b0 - z_score * se_log_ratio
upper_log_ratio <- log_b1 - log_b0 + z_score * se_log_ratio
# CI for the ratio itself
ci_ratio <- exp(c(lower_log_ratio, upper_log_ratio))
print(ci_ratio)
# bt bt
# 0.876991 1.162469
# Pure error
FitAnova <- anova(lm(readout ~ factor(Conc)*sample, all_l))
# Pure error
pureSS <- FitAnova[4,2]
pureSS_df <- FitAnova[4,1]
meanPureErr <- FitAnova[4,3]
V_V <- vcov(mu)/su$sigma^2
SEsPure <- sqrt(diag(V_V)*meanPureErr)
VCOVpure <- V_V*meanPureErr
se_b0 <- sqrt(VCOVpure[2, 2])
se_b1 <- sqrt(VCOVpure[6, 6])
#CoSE <- sqrt(abs(vcov(mu)[6, 2]))
# Calculate log(b) and its variance
log_b0 <- log(abs(b0))
log_b1 <- log(abs(b1))
var_log_b0 <- (se_b0 / b0)^2 # Approx variance of log(b)
var_log_b1 <- (se_b1 / b1)^2 # Approx variance of log(b)
#CoVarlog <- (CoSE/mean(c(b0,b1)))^2
# Simplified CI for log(b1/b2) (ignoring covariance for simplicity)
se_log_ratio <- sqrt(var_log_b0 + var_log_b1) #-2*CoVarlog)
z_score <- 1.96 # For 95% CI
lower_log_ratio <- log_b1 - log_b0 - qt(0.975,32)* se_log_ratio
upper_log_ratio <- log_b1 - log_b0 + qt(0.975,32) * se_log_ratio
# ------------------------------------------------------------------------------
ci_ratio <- exp(c(lower_log_ratio, upper_log_ratio))
print(ci_ratio)
# BINGOOOO
# 1.009691
# bt bt
# 0.8937963 1.1406122
# im Vergleich: PLA printout
# 0.89354 - 1.14077, aber point est. 1.00977

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library(tidyverse)
library(gslnls)
library(ggplot2)
library(reshape2)
library(drc)
library(ggridges)
library(car)
# Pseudoreplication
# simulation of replication strategies
Calc_DilRes <- function(as = 10, bs = 1, cs = -6, ds = 110, at = 10, bt = 1, r=0.01,
ct = cs, dt = 110, sd_fac=0.1, gt=1, gs=1, log_concREF, log_concTEST, # -r
THEOconc ,
heteroNoise=FALSE, noDilSeries, noDils) {
# browser()
yAxfac <- (as-ds)
log_cREF_l <- melt(log_concREF)
log_doseREF <- log_cREF_l$value
log_cTEST_l <- melt(log_concTEST)
log_doseTEST <- log_cTEST_l$value
isRef <- rep(c(1,0),1,each=length(log_concREF))
isSample <- rep(c(0,1),1,each=length(log_concTEST))
#um with equal sample without
ro_jit <- as*isRef + at*isSample + (ds*isRef + dt*isSample - as*isRef - at*isSample)/
(1 + isRef*exp(bs*(cs - log_doseREF)) + isSample*exp(bt*(ct -log_doseTEST)))
ro_jit_ <- abs(ro_jit)
all_var <- rbind(log_cREF_l, log_cTEST_l)
ro_jit2 <- cbind(all_var, ro_jit_, rep(THEOconc, 6), isRef, isSample)
# if (noDilSeries==3) {
# colnames(ro_jit2) <- c("R_dil1","R_dil2","R_dil3","T_dil1","T_dil2","T_dil3", "log_dose")
# } else if (noDilSeries==2) {
# colnames(ro_jit2) <- c("R_dil1","R_dil2","T_dil1","T_dil2", "log_dose")
# }
colnames(ro_jit2) <- c("dil_no","repl","trueLogConc" ,"readout","log_dose","isRef","isSample")
return(ro_jit2)
}
RestrMF <- function(data, M) {
all_l <- data
#plot(all_l$log_dose, all_l$readout, col=all_l$sample)
CORro <- cor(all_l$log_dose[1:8], all_l$readout[1:8])
if (CORro<0) B <- -1 else B <- 1
# browser()
startlist <- list(a = min(all_l$readout), b = B, cs=mean(all_l$log_dose),
r=0.0001, d = max(all_l$readout))
mr <- tryCatch({
gsl_nls(fn = readout ~ a + (d - a)/(1 + exp(b*((cs-r*isSample)-log_dose))),
data = all_l,
start = startlist, #trace=T,
control = gsl_nls_control(xtol = 1e-6, ftol = 1e-6, gtol = 1e-6))
}, error = function(msg){
return(0)
} )
if (length(mr)>1) {
s_mr_coef <- summary(mr)$coefficients
s_mr <- summary(mr)
} else { s_mr_coef <- rep(0,4); s_mr <- 0 }
if (M == "R") {
return(s_mr_coef)
} else if (M =="CI") {
if (length(mr)==1) return(rep(0,4))
(RelPot_mr <- exp(s_mr_coef[4,1])) # r exponentiated
POTr_CI <- exp(confint(mr, "r", method = "asymptotic", level=0.95))
# Pure error
#browser()
suppressWarnings(FitAnova <- anova(lm(readout ~ factor(log_dose)*isSample, all_l)))
meanPureErr <- FitAnova[4,3]
SEsPure <- sqrt(diag(vcov(mr)/s_mr$sigma^2)*meanPureErr)
# PLA software: 1.0252 - 1.4242
lCIpure <- exp(s_mr_coef[4,1]-qt(0.975,nrow(all_l)-16)*SEsPure[4])
uCIpure <- exp(s_mr_coef[4,1]+qt(0.975,nrow(all_l)-16)*SEsPure[4])
if (is.infinite(uCIpure)) uCIpure <- NA
if (is.infinite(POTr_CI[2])) POTr_CI[2] <- NA
relCIpure <- c(lCIpure/RelPot_mr, uCIpure/RelPot_mr)
CIsDF <- c(RelPot_mr,POTr_CI, lCIpure, uCIpure)
names(CIsDF) <- c("potency","lCI","uCI","lpureCI","upureCI")
return(CIsDF)
} else if (M=="U") {
if (length(mr)==1) return(rep(0,4))
startlstmu <- list(as = min(all_l$readout), bs = B, cs = mean(all_l$log_dose), ds = max(all_l$readout),
at = min(all_l$readout), bt = B, ct = mean(all_l$log_dose), dt = max(all_l$readout))
mu <- tryCatch({
gsl_nls(readout ~ 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))),
data = all_l,
start = startlstmu, trace=T,
control = gsl_nls_control(xtol = 1e-10, ftol = 1e-10, gtol = 1e-10))
}, error = function(msg){
return(0)
} )
if (length(mu)>1) {
smu_coef <- tryCatch({
summary(mu)$coefficients
}, error = function(msg){
return(rep(NA, 8))
} )
#s_mu <- summary(mu)
return(smu_coef)
} else return(rep(0,8))
} else return()
}
Dil8F <- function(Cn1) {
CSer <- matrix(NA, nrow=8, ncol=3)
for (i in 1:8) {
Cn1 <- Cn1/dilfactor+rnorm(3,0,SD*Cn1)
CSer[i,] <- Cn1
}
return(CSer)
}
DilDirF <- function(OrigMenge) {
CDir <- matrix(NA, nrow=10, ncol=3)
for (DilExp in 1:10) {
C_d <- OrigMenge/dilfactor^DilExp+rnorm(3,0,SD*OrigMenge/dilfactor^DilExp)
CDir[DilExp,] <- C_d
}
return(CDir)
}
homhetCIF <- function(AssD_) {
# Confidence level
alpha <- 0.05
z <- qnorm(1 - alpha/2) # 1.96 for 95% CI
### 1. Homogeneously Weighted CI ###
mean_logRP <- mean(AssD_$logRP)
var_mean <- mean(AssD_$SE^2) / nrow(AssD_)
SE_mean <- sqrt(var_mean)
CI_homo <- c(mean_logRP - z * SE_mean, mean_logRP + z * SE_mean)
CI_homo_exp <- exp(CI_homo)
# cat("Homogeneous weighting:\n")
# cat(round(exp(mean_logRP),3), "95% CI =", round(CI_homo_exp[1], 3), "-", round(CI_homo_exp[2], 3), "\n\n")
### 2. Heterogeneously Weighted CI ###
weights <- 1 / (AssD_$SE^2)
weighted_mean <- sum(weights * AssD_$logRP) / sum(weights)
var_weighted <- 1 / sum(weights)
SE_weighted <- sqrt(var_weighted)
CI_hetero <- c(weighted_mean - z * SE_weighted, weighted_mean + z * SE_weighted)
CI_hetero_exp <- exp(CI_hetero)
res <- c(exp(mean_logRP) , CI_homo_exp, exp(weighted_mean), CI_hetero_exp )
return(res)
# cat("Heterogeneous weighting:\n")
# cat("Combined RP =", round(exp(weighted_mean), 3), "\n")
# cat("95% CI =", round(CI_hetero_exp[1], 6), "-", round(CI_hetero_exp[2], 6), "\n")
}
Conc <- c(1)
for (x in 1:10) Conc <- c(Conc, (1/3^x))
N <- 10000
OrigMenge <- 1
dilfactor <- 3
SD <- 0.02
rm(CIsM_all, DevTargC3_)
for (x in 1:3) {
if (x==1) { Repl1 <- x; Repl2 <- x } # Anzahl Replikate für C2 und C3
if (x==2) { Repl1 <- 1; Repl2 <- 3 }
if (x==3) { Repl1 <- x; Repl2 <- x }
Flags <- vector()
SDs1 <- SDs2 <- POT <- DevTargC3 <- hhCIV <- c()
for (nTrials in 1:N) {
C2 <- OrigMenge/dilfactor+rnorm(Repl1,0,SD*OrigMenge)
C3 <- C2/dilfactor+rnorm(Repl1,0,C2*SD)
CSer <- Dil8F(C3)
# "true" replication
C2_1 <- OrigMenge/dilfactor+rnorm(Repl2,0,SD*OrigMenge)
C3_1 <- C2_1/dilfactor+rnorm(Repl2,0,SD*C2_1)
CSer_3 <-Dil8F(C3_1)
# Flag <- sd(CSer)<sd(CSer_3)
# SDs1 <- c(SDs1, sd(CSer))
# SDs2 <- c(SDs2, sd(CSer_3))
#Flags <- c(Flags, Flag)
# all from stem solution directly
CDir <- DilDirF(OrigMenge)
CDir2 <- DilDirF(OrigMenge)
if (x==1) {
DevTargC3 <- c(DevTargC3, mean(C3), mean(CDir[2,]))
log_concREF <- log(CSer)
log_concTEST <- log(CDir[3:10,])
testC <- "pseudo"; refC <- "direct"
} # Anzahl Replikate für C2 und C3
if (x==2) {
DevTargC3 <- c(DevTargC3, mean(C3_1), mean(CDir[2,]))
log_concREF <- log(CSer_3)
log_concTEST <- log(CDir[3:10,])
testC <- "true"; refC <- "direct"
}
if (x==3) {
DevTargC3 <- c(DevTargC3, mean(CDir[2,]), mean(CDir2[2,]))
log_concREF <- log(CDir[3:10,])
log_concTEST <- log(CDir2[3:10,])
testC <- "true"; refC <- "true"
}
# DevTargC3 <- c(DevTargC3, mean(C3), mean(CDir[2,])) # c(DevTargC3, mean(CSer[4,]), mean(CSer_3[4,]))
ro_new <- Calc_DilRes( log_concREF=log_concREF,
log_concTEST=log_concTEST,THEOconc = log(Conc)[4:11])
CIs <- RestrMF(ro_new, M="CI")
RM <- RestrMF(ro_new, M="R")
POT <- c(POT, CIs)
CIsV <- c()
for (z in 0:2) {
CL <- RestrMF(ro_new[c((z*8+1):(8*(z+1)),(z*8+25):(8*(z+1)+24)),], M="CI")
#print(RestrMF(ro_new[c((z*8+1):(8*(z+1)),(z*8+25):(8*(z+1)+24)),], M="R"))
CIsV <- c(CIsV, CL[1:3])
}
AssD <- t(matrix(CIsV, nrow=3))
logAssD <- log(AssD)
AssD_ <- as.data.frame(AssD)
colnames(AssD_) <- c("RP", "lCI", "uCI")
AssD_$SE <- (logAssD[,3]-logAssD[,1])/qt(0.975,11)
AssD_$logRP <- logAssD[,1]
hhCIs <- homhetCIF(AssD_)
hhCIV <- c(hhCIV, hhCIs)
# p <- ggplot(ro_new, aes(x=trueLogConc, y=readout, col=as.factor(isRef))) +
# geom_point() +
# geom_line() +
# ggtitle(paste(nTrials, x)) +
# theme_bw()
# print(p)
#
# p2 <- ggplot(ro_new, aes(x=log_dose, y=readout, col=as.factor(isRef))) +
# geom_point(size=4) +
# ggtitle(paste(nTrials, x)) +
# geom_vline(xintercept = c(RM[3,1],(RM[3,1]-RM[4,1])), col=c("turquoise","red")) +
# theme_bw()
# print(p2)
}
CIsM <- t(matrix(POT, nrow=5))
CIsM <- cbind(CIsM, rep(x,nrow(CIsM)))
print(paste( testC, refC,sd(CIsM[,1])))
if (!exists("CIsM_all")) CIsM_all <- CIsM else CIsM_all <- rbind(CIsM_all, CIsM)
DevTargC3DF <- as.data.frame(t(matrix(DevTargC3, nrow=2)))
if (!exists("DevTargC3_")) DevTargC3_ <- DevTargC3DF else DevTargC3_ <- rbind(DevTargC3_, DevTargC3DF )
hhCIDF <- as.data.frame(t(matrix(hhCIV, nrow=6)))
if (!exists("hhCIDF_")) hhCIDF_ <- hhCIDF else hhCIDF_ <- rbind(hhCIDF_, hhCIDF )
}
CIsM_allDF <- as.data.frame(CIsM_all)
colnames(CIsM_allDF) <- c("potency","lCI","uCI","lpureCI","upureCI", "scheme")
CIsM_allDF$widthCI <- CIsM_allDF$uCI-CIsM_allDF$lCI
CIsM_allDF_ <- cbind(CIsM_allDF, DevTargC3_)
cn <- colnames(CIsM_allDF_)
cn[8:9] <- c("C3_REF","C3_TEST")
colnames(CIsM_allDF_) <- cn
CIsM_allDF_$C3_DIFF <- CIsM_allDF_$C3_TEST-CIsM_allDF_$C3_REF
cor(CIsM_allDF_$potency, CIsM_allDF_$C3_DIFF)
#cor(abs(log(CIsM_allDF_$potency)), (CIsM_allDF_[,9]-CIsM_allDF_[,8]))
plot( CIsM_allDF_$C3_DIFF,CIsM_allDF_$potency)
for (n in 0:2) {
te <- CIsM_allDF_[(n*N+1):((n+1)*N),]
print(paste("normal CI coverage", sum(log(te$lCI)<0 & log(te$uCI)>0) /N*100))
print(paste("pure CI coverage", sum(log(te$lpureCI)<0 & log(te$upureCI)>0) /N*100))
print(cor(te$potency, (te$C3_DIFF)))
te$logpot <- log(te$potency)
# plot(te$C3_DIFF,te$potency, pch=19, xlab="Difference at C3")
# abline(te$potency ~ te$C3_DIFF, col = "blue")
# lines(lowess(te$potency ~ te$C3_DIFF), col = "green")
scatterplot(logpot ~ C3_DIFF, data=te)
}
plot(te$C3_DIFF,te$logpot, pch=19, xlab="Difference at C3")
abline(te$logpot ~ te$C3_DIFF, col = "blue")
lines(lowess(te$logpot ~ te$C3_DIFF), col = "green")
scatterplot(logpot ~ C3_DIFF, data=te)
colnames(hhCIDF_) <- c("homomean", "lhomoCI", "uhomoCI","heteromean","lheteroCI","uheteroCI")
te <- cbind(CIsM_allDF, hhCIDF_)
p_histCI <- ggplot(CIsM_allDF, aes(x=widthCI, y=as.factor(scheme), fill=as.factor(scheme))) +
geom_density_ridges() +
theme_ridges()
p_histCI
p_histCI2 <- ggplot(CIsM_allDF, aes(x=widthCI, y=as.factor(scheme), fill=as.factor(scheme))) +
geom_density_ridges() +
theme_ridges()
p_histCI2
sum(Flags)/N*100
mean(SDs1)
mean(SDs2)
ro_new <- Calc_DilRes(sd_fac=0.1, log_concREF=log_concREF,
log_concTEST=log_concTEST,THEOconc = log(Conc)[4:11])

76
appPLAscraper.R
View File

@@ -11,7 +11,7 @@ library(pdftools)
library(stringr) library(stringr)
library(openxlsx) library(openxlsx)
library(evd) library(evd)
# library(fitdistrplus) library(fitdistrplus)
RestrMF <- function(data, M) { RestrMF <- function(data, M) {
all_l <- melt(data, id.vars="uniDoses", variable.name="replname", value.name="readout") all_l <- melt(data, id.vars="uniDoses", variable.name="replname", value.name="readout")
@@ -152,7 +152,7 @@ server <- function(input, output, session) {
filenames <- Dat$FNames filenames <- Dat$FNames
POTlist <- FINALresponses <- SampleNamesL <- list() POTlist <- FINALresponses <- SampleNamesL <- list()
DilSerVec <- vector() DilSerVec <- vector()
hide_spinner() #hide_spinner()
for (Nfile in 1:length(PDFs)) { for (Nfile in 1:length(PDFs)) {
tt = PDFs[[Nfile]] tt = PDFs[[Nfile]]
PageI <- strg <- POT <- vector(); resIND <- matrix(NA, ncol=2,nrow=3) PageI <- strg <- POT <- vector(); resIND <- matrix(NA, ncol=2,nrow=3)
@@ -160,6 +160,7 @@ server <- function(input, output, session) {
for (Seite in 1:length(tt)) { for (Seite in 1:length(tt)) {
Zeilen <- strsplit(tt[Seite], "\n") Zeilen <- strsplit(tt[Seite], "\n")
#Zeilen <- Zeilen[Zeilen != ""]
REGEXloc <- grep(pattern='Dose .alue', Zeilen[[1]]) REGEXloc <- grep(pattern='Dose .alue', Zeilen[[1]])
if (length(REGEXloc)>0) { if (length(REGEXloc)>0) {
#### get Standard ---- #### get Standard ----
@@ -227,16 +228,27 @@ server <- function(input, output, session) {
for (x in ProdsinPDF) { for (x in ProdsinPDF) {
ZeilenR <- strsplit(tt[resIND[x,1]], "\n")[[1]] ZeilenR <- strsplit(tt[resIND[x,1]], "\n")[[1]]
ZeilenREF <- ZeilenR[resIND[x,2]:length(ZeilenR)] ZeilenREF <- ZeilenR[resIND[x,2]:length(ZeilenR)]
#ZeilenREF <- ZeilenREF[ZeilenREF != ""]
grepDoses <- grep("Dose .alue", ZeilenREF) grepDoses <- grep("Dose .alue", ZeilenREF)
Doses <- unlist(regmatches(c(ZeilenREF[grepDoses[1]], ZeilenREF[grepDoses[2]]), Doses <- unlist(regmatches(c(ZeilenREF[grepDoses[1]], ZeilenREF[grepDoses[2]]),
gregexpr("([0-9]+)\\.?([0-9]+)", gregexpr("([0-9]+)\\.?([0-9]+)",
c(ZeilenREF[grepDoses[1]], ZeilenREF[grepDoses[2]])))) c(ZeilenREF[grepDoses[1]], ZeilenREF[grepDoses[2]]))))
uniDoses <- as.numeric(unique(Doses)) uniDoses <- as.numeric(unique(Doses))
Ndoses <- length(uniDoses) Ndoses <- length(uniDoses)
grepResponses <- grep(".esponse", ZeilenREF) grepResponses <- grep(".esponse", ZeilenREF)
grepMean <- grep(".ean", ZeilenREF) grepMean <- grep(".ean", ZeilenREF)
NoResp <- grepMean[1]-grepResponses[1] NoResp <- grepMean[1]-grepResponses[1]
# test for cancelled outlier
grepAUSS <- grep("T",ZeilenREF[grepMean[1]:grepResponses[1]])
if (length(grepAUSS)>0) {
AnzResp <- grepMean-grepResponses
AnzResp <- AnzResp[AnzResp > 0]
AnzResp2 <- c(AnzResp, DilSerVec)
AnzResp3 <- sort(table(AnzResp2),decreasing = T)[1]
NoResp <- as.numeric(names(AnzResp3))
}
IND <- c(grepDoses) IND <- c(grepDoses)
StepIX <- IND+1 StepIX <- IND+1
INDresp1 <- grepResponses INDresp1 <- grepResponses
@@ -250,7 +262,7 @@ server <- function(input, output, session) {
} }
Step <- unlist(regmatches(ZeilenREF[StepIX[1]], gregexpr("([0-9]+)", ZeilenREF[StepIX[1]]))) Step <- unlist(regmatches(ZeilenREF[StepIX[1]], gregexpr("([0-9]+)", ZeilenREF[StepIX[1]])))
maxStep <- max(as.numeric(Step)) maxStep <- max(as.numeric(Step))
browser()
resStep <- Ndoses-maxStep resStep <- Ndoses-maxStep
if (maxStep== (Ndoses-1)) { if (maxStep== (Ndoses-1)) {
ref_data <- data.frame(c(Responses[[1]][1:maxStep], Responses[[2]][1]), ref_data <- data.frame(c(Responses[[1]][1:maxStep], Responses[[2]][1]),
@@ -269,7 +281,7 @@ server <- function(input, output, session) {
if (x==1) colnames(ref_data) <- c(paste0(REF, "_1"), paste0(REF, "_2"), paste0(REF, "_3")) if (x==1) colnames(ref_data) <- c(paste0(REF, "_1"), paste0(REF, "_2"), paste0(REF, "_3"))
if (x==2) colnames(ref_data) <- c(paste0(TEST1, "_1"), paste0(TEST1, "_2"), paste0(TEST1, "_3")) if (x==2) colnames(ref_data) <- c(paste0(TEST1, "_1"), paste0(TEST1, "_2"), paste0(TEST1, "_3"))
if (x==3) colnames(ref_data) <- c(paste0(TEST2, "_1"), paste0(TEST2, "_2"), paste0(TEST2, "_3")) if (x==3) colnames(ref_data) <- c(paste0(TEST2, "_1"), paste0(TEST2, "_2"), paste0(TEST2, "_3"))
if (!exists("ALLE3")) { if (!exists("ALLE3")) {
ALLE3 <- ref_data ALLE3 <- ref_data
} else { ALLE3 <- cbind(ALLE3, ref_data) } } else { ALLE3 <- cbind(ALLE3, ref_data) }
@@ -326,7 +338,7 @@ server <- function(input, output, session) {
PotM1_ <- as.matrix(PotM1) PotM1_ <- as.matrix(PotM1)
colnames(PotM1_) <- names(PotM1) colnames(PotM1_) <- names(PotM1)
PotM1 <- cbind(rep(filenames[pdfInd], nrow(PotM1_)), PotM1_) PotM1_ <- cbind(rep(filenames[pdfInd], nrow(PotM1_)), PotM1_)
CalcPot[[pdfInd]] <- PotM1_ CalcPot[[pdfInd]] <- PotM1_
if (!exists("CalcPotDF")) CalcPotDF <- PotM1_ else CalcPotDF <- rbind(CalcPotDF, PotM1_) if (!exists("CalcPotDF")) CalcPotDF <- PotM1_ else CalcPotDF <- rbind(CalcPotDF, PotM1_)
@@ -367,7 +379,7 @@ server <- function(input, output, session) {
if (!exists("SIGtestDF")) { if (!exists("SIGtestDF")) {
if (exists("dfPlotsigTest2")) SIGtestDF <- rbind(dfPlotsigTest1, dfPlotsigTest2) else SIGtestDF <- dfPlotsigTest1 if (exists("dfPlotsigTest2")) SIGtestDF <- rbind(dfPlotsigTest1, dfPlotsigTest2) else SIGtestDF <- dfPlotsigTest1
} else { } else {
if (exists("dfPlotsigTest2")) SIGtestDF <- rbind(SIGtestDF,dfPlotsigTest1, dfPlotsigTest2) else SIGtestDF <- rbind(dfPlotsigTest1, dfPlotsigTest2) if (exists("dfPlotsigTest2")) SIGtestDF <- rbind(SIGtestDF,dfPlotsigTest1, dfPlotsigTest2) else SIGtestDF <- rbind(SIGtestDF, dfPlotsigTest1)
} }
} # pdfInd } # pdfInd
@@ -391,7 +403,7 @@ server <- function(input, output, session) {
x_UA <- max(X); x_LA <- min(X) x_UA <- max(X); x_LA <- min(X)
} else { x_UA <- min(X); x_LA <- max(X) } } else { x_UA <- min(X); x_LA <- max(X) }
p1 <- ggplot(SIGrefDF, aes(x_X, y=sigRef, col=as.factor(Prod))) + p1 <- ggplot(SIGrefDF, aes(x=X, y=sigRef, col=as.factor(Prod))) +
geom_line() + geom_line() +
annotate("text", label="x", x=x_UA, y=UasREF, alpha=0.2) + annotate("text", label="x", x=x_UA, y=UasREF, alpha=0.2) +
annotate("text", label="o", x=x_LA, y=LasREF, alpha=0.2) + annotate("text", label="o", x=x_LA, y=LasREF, alpha=0.2) +
@@ -406,7 +418,7 @@ server <- function(input, output, session) {
PLOTS$sigPlotREF <- p1 PLOTS$sigPlotREF <- p1
p2 <- ggplot(SIGtestDF, aes(x_X, y=sigTest, col=as.factor(Prod))) + p2 <- ggplot(SIGtestDF, aes(x=X, y=sigTest, col=as.factor(Prod))) +
geom_line() + geom_line() +
#annotate("text", label="x", x=x_UA, y=UasREF, alpha=0.2) + #annotate("text", label="x", x=x_UA, y=UasREF, alpha=0.2) +
#annotate("text", label="o", x=x_LA, y=LasREF, alpha=0.2) + #annotate("text", label="o", x=x_LA, y=LasREF, alpha=0.2) +
@@ -438,7 +450,7 @@ server <- function(input, output, session) {
#### Calculated Potencies table ---- #### Calculated Potencies table ----
colnames(CalcPotDF) <- c("pdf","rel. potency","lowerCI","upperCI","lower CI puerErr","upper CI pureErr") colnames(CalcPotDF) <- c("pdf","rel. potency","lowerCI","upperCI","lowerCI pureErr","upperCI pureErr")
SampleNamesL <- Dat$SampleNamesL SampleNamesL <- Dat$SampleNamesL
SampleNames <- unlist(SampleNamesL) SampleNames <- unlist(SampleNamesL)
if (length(SampleNames)/length(SampleNamesL) == 3) { if (length(SampleNames)/length(SampleNamesL) == 3) {
@@ -459,14 +471,14 @@ server <- function(input, output, session) {
output$CalcPotDF <- renderTable({ CalcPotDF2 }) output$CalcPotDF <- renderTable({ CalcPotDF2 })
#### CI plots ---- #### CI plots ----
CalcPotDF_ <- CalcPotDF[,c(1,7,8)] CalcPotDF_ <- CalcPotDF[,c(1,7,8)]
all_lPot <- melt(as.data.frame(CalcPotDF_, id.vars="pdf",variable.name="var", value.name="Climit")) all_lPot <- melt(as.data.frame(CalcPotDF_), id.vars="pdf",variable.name="var", value.name="Climit")
all_lPot[,3] <- as.numeric(all_lPot[,3]) all_lPot[,3] <- as.numeric(all_lPot[,3])
all_lPot[,3][all_lPot[,3] > 5] <- NA all_lPot[,3][all_lPot[,3] > 5] <- NA
all_lPot[,3][all_lPot[,3] < 0.1] <- NA all_lPot[,3][all_lPot[,3] < 0.1] <- NA
P_histCI <- ggplot(all_lPot, aes(x=Climit, fill=var)) + P_histCI <- ggplot(all_lPot, aes(x=Climit, fill=var)) +
geom_histogram(color="#e9ecef", alpha=0.6, position = "identity") + geom_histogram(color="#e9ecef", alpha=0.6, position = "identity") +
scale-fill-manual(values=c("darkgreen","darkblue","salmon2","tomato3")) + scale_fill_manual(values=c("darkgreen","darkblue","salmon2","tomato3")) +
ggtitle("Histogram of relative potencies, standard RMSEs") + ggtitle("Histogram of relative potencies, standard RMSEs") +
scale_x_continuous( scale_x_continuous(
breaks=seq(trunc(min(all_lPot$Climit, na.rm=T)*10)/10, max(all_lPot$Climit, na.rm=T)*1.1, by=0.2), breaks=seq(trunc(min(all_lPot$Climit, na.rm=T)*10)/10, max(all_lPot$Climit, na.rm=T)*1.1, by=0.2),
@@ -480,14 +492,14 @@ server <- function(input, output, session) {
CalcPotDF_Pure <- CalcPotDF[,c(1,9,10)] CalcPotDF_Pure <- CalcPotDF[,c(1,9,10)]
all_lPotPure <- melt(as.data.frame(CalcPotDF_Pure, id.vars="pdf",variable.name="var", value.name="Climit")) all_lPotPure <- melt(as.data.frame(CalcPotDF_Pure), id.vars="pdf",variable.name="var", value.name="Climit")
all_lPotPure[,3][all_lPotPure[,3] == 0] <- NA all_lPotPure[,3][all_lPotPure[,3] == 0] <- NA
all_lPotPure[,3][all_lPotPure[,3] > 5] <- NA all_lPotPure[,3][all_lPotPure[,3] > 5] <- NA
all_lPotPure[,3][all_lPotPure[,3] < 0.1] <- NA all_lPotPure[,3][all_lPotPure[,3] < 0.1] <- NA
P_histCIPure <- ggplot(all_lPotPure, aes(x=Climit, fill=var)) + P_histCIPure <- ggplot(all_lPotPure, aes(x=Climit, fill=var)) +
geom_histogram(color="#e9ecef", alpha=0.6, position = "identity") + geom_histogram(color="#e9ecef", alpha=0.6, position = "identity") +
scale-fill-manual(values=c("darkgreen","darkblue","salmon2","tomato3")) + scale_fill_manual(values=c("darkgreen","darkblue","salmon2","tomato3")) +
ggtitle("Histogram of relative potencies, pure error") + ggtitle("Histogram of relative potencies, pure error") +
scale_x_continuous( scale_x_continuous(
breaks=seq(trunc(min(all_lPotPure$Climit, na.rm=T)*10)/10, max(all_lPotPure$Climit, na.rm=T)*1.1, by=0.2), breaks=seq(trunc(min(all_lPotPure$Climit, na.rm=T)*10)/10, max(all_lPotPure$Climit, na.rm=T)*1.1, by=0.2),
@@ -514,23 +526,33 @@ server <- function(input, output, session) {
if (inherits(r, "try-error")) return(NA) if (inherits(r, "try-error")) return(NA)
return(r) return(r)
} }
uCIGumbel <- fitdistr(logCPDF_[,3], dg,start=list(loc=1,scale=1))
lCIGumbel <- fitdistr(logCPDF_[,2], dg,start=list(loc=1,scale=1))
Gumbel <- c(rev(dgumbel(seq(0,0.5,0.01), loc=uCIGumbel$extimate[1], scale=uCIGumbel$extimate[2])), dgumbel(seq(0,0.5,0.01), loc=uCIGumbel$extimate[1], scale=uCIGumbel$extimate[2]))
GumbelDF <- as.data.frame(cbind(X=seq(-0.5,0.5,0.01), Gumbel))
all_lPotPure <- melt(as.data.frame(logCPDF_), id.vars="pdf",variable.name = "var",value.name="Climit")
all_lPotPure[,3][all_lPotPure[,3] == 0] <- NA
uEAC099 <- exp(qgumbel(0.99, uCIGumbel$estimate[1],uCIGumbel$estimate[2]))*100
lEAC001 <- exp(-qgumbel(0.99, uCIGumbel$estimate[1],uCIGumbel$estimate[2]))*100
uEAC095 <- exp(qgumbel(0.95, uCIGumbel$estimate[1],uCIGumbel$estimate[2]))*100
lEAC005 <- exp(-qgumbel(0.95, uCIGumbel$estimate[1],uCIGumbel$estimate[2]))*100
uCIGumbel <- tryCatch({MASS::fitdistr(logCPDF_[,3], dg,start=list(loc=1,scale=1)) },
error = function(msg) {
return(-1)
})
if (length(uCIGumbel)>1) {
#lCIGumbel <- fitdistr(logCPDF_[,2], dg,start=list(loc=1,scale=1))
Gumbel <- c(rev(dgumbel(seq(0,0.5,0.01), loc=uCIGumbel$extimate[1], scale=uCIGumbel$extimate[2])), dgumbel(seq(0,0.5,0.01), loc=uCIGumbel$extimate[1], scale=uCIGumbel$extimate[2]))
GumbelDF <- as.data.frame(cbind(X=seq(-0.5,0.5,0.01), Gumbel))
all_lPotPure <- melt(as.data.frame(logCPDF_), id.vars="pdf",variable.name = "var",value.name="Climit")
all_lPotPure[,3][all_lPotPure[,3] == 0] <- NA
uEAC099 <- exp(qgumbel(0.99, uCIGumbel$estimate[1],uCIGumbel$estimate[2]))*100
lEAC001 <- exp(-qgumbel(0.99, uCIGumbel$estimate[1],uCIGumbel$estimate[2]))*100
uEAC095 <- exp(qgumbel(0.95, uCIGumbel$estimate[1],uCIGumbel$estimate[2]))*100
lEAC005 <- exp(-qgumbel(0.95, uCIGumbel$estimate[1],uCIGumbel$estimate[2]))*100
} else {
uEAC099 <- exp(quantile(0.99, logCPDF_[,3]))*100
lEAC001 <- exp(-quantile(0.99, logCPDF_[,3]))*100
uEAC095 <- exp(quantile(0.95, logCPDF_[,3]))*100
uEAC099 <- exp(-quantile(0.99, logCPDF_[,3]))*100
}
output$GumbelPlot <- renderPlot({ output$GumbelPlot <- renderPlot({
hist(all_lPotPure$Climit, breaks = 50, xlab="log(relative CLs)", hist(all_lPotPure$Climit, breaks = 50, xlab="log(relative CLs)",
main=paste("calculated EACs 99%:",round(lEAC001,0)), "-", round(uEAC099,0), " 95%", round(lEAC005,0), "-",round(uEAC095,0)) main=paste("calculated EACs 99%:",round(lEAC001,0)), "-", round(uEAC099,0), " 95%", round(lEAC005,0), "-",round(uEAC095,0))
lines(GumbelDF$X, GumbelDF$Gumbel) if (length(uCIGumbel)>1) { lines(GumbelDF$X, GumbelDF$Gumbel) }
}) })