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if(getRversion() >= '2.15.1') utils::globalVariables(c("ok", "dose"))
drfit <- function(data, startlogED50 = NA, chooseone=TRUE,
probit = TRUE, logit = FALSE, weibull = FALSE,
linlogit = FALSE, level = 0.95,
linlogitWrong = NA, allWrong = NA,
ps0 = 1, ls0 = 0.5, ws0 = 0.5,
b0 = 2, f0 = 0,
showED50 = FALSE,
EDx = NULL, EDx.tolerance = 1e-4)
{
if(!is.null(data$ok)) data <- subset(data,ok!="no fit") # Don't use data
# with ok set to
# "no fit"
substances <- levels(data$substance)
ri <- rix <- 0 # ri is the index over the result rows
# rix is used later to check if any
# model result was appended
rsubstance <- array() # the substance names in the results
rndl <- vector() # number of dose levels
rn <- vector() # mean number of replicates
# in each dose level
runit <- vector() # vector of units for each result row
rlhd <- rlld <- vector() # highest and lowest doses tested
mtype <- array() # the modeltypes
sigma <- array() # the standard deviation of the residuals
logED50 <- vector()
logED50low <- logED50high <- vector()
a <- b <- c <- vector()
models <- list() # a list containing the dose-response models
splitted <- split(data,data$substance)
for (i in substances) {
tmp <- splitted[[i]]
fit <- FALSE
if (length(tmp) != 0) {
unit <- levels(as.factor(as.vector(tmp$unit)))
message("\n",i,": Fitting data...\n")
} else {
unit <- ""
message("\n",i,": No data\n")
}
if (length(unit) == 0) {
unit <- NA
}
if (length(unit) > 1) {
message("More than one unit for substance ",i,", halting\n\n")
break
}
if (length(tmp$response) == 0) {
nodata = TRUE
} else {
nodata = FALSE
}
rix <- ri
if (nodata) {
n <- ndl <- 0
} else {
ndl <- length(levels(factor(tmp$dose)))
n <- length(tmp$response)
if (is.na(startlogED50[i])){
w <- 1/abs(tmp$response - 0.3)
startlogED50[[i]] <- sum(w * log10(tmp$dose))/sum(w)
}
highestdose <- max(tmp$dose)
lowestdose <- min(tmp$dose)
lhd <- log10(highestdose)
lld <- log10(lowestdose)
responseathighestdose <- mean(subset(tmp,dose==highestdose)$response)
responseatlowestdose <- mean(subset(tmp,dose==lowestdose)$response)
if (responseathighestdose < 0.5) {
inactive <- FALSE
if (responseatlowestdose < 0.5) {
active <- TRUE
} else {
active <- FALSE
if (linlogit &&
length(subset(linlogitWrong,linlogitWrong == i))==0 &&
length(subset(allWrong,allWrong == i))==0) {
m <- try(nls(response ~ linlogitf(dose,1,f,logED50,b),
data=tmp, algorithm="port",
start=list(f=f0,logED50=startlogED50[[i]],b=b0)),
silent = TRUE)
if (!inherits(m, "try-error")) {
fit <- TRUE
ri <- ri + 1
models[[ri]] <- m
s <- summary(m)
sigma[[ri]] <- s$sigma
rsubstance[[ri]] <- i
rndl[[ri]] <- ndl
rn[[ri]] <- n
runit[[ri]] <- unit
rlld[[ri]] <- log10(lowestdose)
rlhd[[ri]] <- log10(highestdose)
logED50[[ri]] <- coef(m)[["logED50"]]
if (logED50[[ri]] > rlhd[[ri]]) {
mtype[[ri]] <- "no fit"
logED50[[ri]] <- NA
logED50low[[ri]] <- NA
logED50high[[ri]] <- NA
a[[ri]] <- NA
b[[ri]] <- NA
c[[ri]] <- NA
} else {
mtype[[ri]] <- "linlogit"
logED50conf <- try(confint(m,"logED50",level=level),
silent = TRUE)
if (!inherits(logED50conf, "try-error")) {
logED50low[[ri]] <- logED50conf[[1]]
logED50high[[ri]] <- logED50conf[[2]]
} else {
logED50low[[ri]] <- NA
logED50high[[ri]] <- NA
}
a[[ri]] <- coef(m)[["logED50"]]
b[[ri]] <- coef(m)[["b"]]
c[[ri]] <- coef(m)[["f"]]
}
}
}
if (probit &&
length(subset(allWrong,allWrong == i))==0) {
m <- try(nls(response ~ pnorm(-log10(dose),-logED50,scale),
data=tmp, algorithm="port",
start=list(logED50=startlogED50[[i]],scale=ps0)),
silent = TRUE)
if (chooseone==FALSE || fit==FALSE) {
if (!inherits(m, "try-error")) {
fit <- TRUE
ri <- ri + 1
models[[ri]] <- m
s <- summary(m)
sigma[[ri]] <- s$sigma
rsubstance[[ri]] <- i
rndl[[ri]] <- ndl
rn[[ri]] <- n
runit[[ri]] <- unit
rlld[[ri]] <- log10(lowestdose)
rlhd[[ri]] <- log10(highestdose)
logED50[[ri]] <- coef(m)[["logED50"]]
c[[ri]] <- NA
if (logED50[[ri]] > rlhd[[ri]]) {
mtype[[ri]] <- "no fit"
logED50[[ri]] <- NA
logED50low[[ri]] <- NA
logED50high[[ri]] <- NA
a[[ri]] <- NA
b[[ri]] <- NA
} else {
mtype[[ri]] <- "probit"
logED50conf <- try(confint(m,"logED50",level=level),
silent = TRUE)
if (!inherits(logED50conf, "try-error")) {
logED50low[[ri]] <- logED50conf[[1]]
logED50high[[ri]] <- logED50conf[[2]]
} else {
logED50low[[ri]] <- NA
logED50high[[ri]] <- NA
}
a[[ri]] <- coef(m)[["logED50"]]
b[[ri]] <- coef(m)[["scale"]]
}
}
}
}
if (logit &&
length(subset(allWrong,allWrong == i))==0) {
m <- try(nls(response ~ plogis(-log10(dose),-logED50,scale),
data=tmp, algorithm="port",
start=list(logED50=startlogED50[[i]],scale=ls0)),
silent = TRUE)
if (chooseone==FALSE || fit==FALSE) {
if (!inherits(m, "try-error")) {
fit <- TRUE
ri <- ri + 1
models[[ri]] <- m
s <- summary(m)
sigma[[ri]] <- s$sigma
rsubstance[[ri]] <- i
rndl[[ri]] <- ndl
rn[[ri]] <- n
runit[[ri]] <- unit
rlld[[ri]] <- log10(lowestdose)
rlhd[[ri]] <- log10(highestdose)
logED50[[ri]] <- a[[ri]] <- coef(m)[["logED50"]]
b[[ri]] <- coef(m)[["scale"]]
c[[ri]] <- NA
if (logED50[[ri]] > rlhd[[ri]]) {
mtype[[ri]] <- "no fit"
logED50[[ri]] <- NA
logED50low[[ri]] <- NA
logED50high[[ri]] <- NA
a[[ri]] <- NA
b[[ri]] <- NA
} else {
mtype[[ri]] <- "logit"
logED50conf <- try(confint(m,"logED50",level=level),
silent = TRUE)
if (!inherits(logED50conf, "try-error")) {
logED50low[[ri]] <- logED50conf[[1]]
logED50high[[ri]] <- logED50conf[[2]]
} else {
logED50low[[ri]] <- NA
logED50high[[ri]] <- NA
}
}
}
}
}
if (weibull &&
length(subset(allWrong,allWrong == i))==0) {
m <- try(nls(response ~ pweibull(-log10(dose)+location,shape),
data=tmp, algorithm="port",
start=list(location=startlogED50[[i]],shape=ws0)),
silent = TRUE)
if (chooseone==FALSE || fit==FALSE) {
if (!inherits(m, "try-error")) {
ri <- ri + 1
models[[ri]] <- m
a[[ri]] <- coef(m)[["location"]]
b[[ri]] <- coef(m)[["shape"]]
sqrdev <- function(logdose) {
(0.5 - pweibull( - logdose + a[[ri]], b[[ri]]))^2
}
logED50[[ri]] <- nlm(sqrdev,startlogED50[[i]])$estimate
if (sqrdev(logED50[[ri]]) > 0.1) {
message("\nCan't find ED50 for fitted weibull model of ",i,
" data\nwith startlogED50 ", startlogED50[[i]],"\n")
ri <- ri - 1
length(a) <- length(b) <- ri
length(logED50) <- ri
} else {
c[[ri]] <- NA
fit <- TRUE
s <- summary(m)
sigma[[ri]] <- s$sigma
rsubstance[[ri]] <- i
rndl[[ri]] <- ndl
rn[[ri]] <- n
runit[[ri]] <- unit
rlld[[ri]] <- log10(lowestdose)
rlhd[[ri]] <- log10(highestdose)
logED50low[[ri]] <- NA
logED50high[[ri]] <- NA
if (logED50[[ri]] > rlhd[[ri]]) {
mtype[[ri]] <- "no fit"
logED50[[ri]] <- NA
a[[ri]] <- NA
b[[ri]] <- NA
} else {
mtype[[ri]] <- "weibull"
}
}
}
}
}
}
} else {
inactive <- TRUE
}
}
if (ri == rix) { # if no entry was appended for this substance
ri <- ri + 1
rsubstance[[ri]] <- i
rndl[[ri]] <- ndl
rn[[ri]] <- n
if (nodata) {
rlld[[ri]] <- rlhd[[i]] <- NA
mtype[[ri]] <- "no data"
runit[[ri]] <- NA
} else {
rlld[[ri]] <- log10(lowestdose)
rlhd[[i]] <- log10(highestdose)
runit[[ri]] <- unit
if (inactive) {
mtype[[ri]] <- "inactive"
} else {
if (active) {
mtype[[ri]] <- "active"
} else {
mtype[[ri]] <- "no fit"
}
}
}
sigma[[ri]] <- NA
logED50[[ri]] <- NA
logED50low[[ri]] <- NA
logED50high[[ri]] <- NA
a[[ri]] <- NA
b[[ri]] <- NA
c[[ri]] <- NA
}
}
results <- data.frame(rsubstance, rndl, rn, rlld, rlhd, mtype,
logED50, logED50low, logED50high, runit, sigma, a, b)
lower_level_percent = paste(100 * (1 - level)/2, "%", sep = "")
upper_level_percent = paste(100 * (1 + level)/2, "%", sep = "")
names(results) <- c("Substance","ndl","n","lld","lhd","mtype","logED50",
lower_level_percent, upper_level_percent,
"unit","sigma","a","b")
if (linlogit) {
results$c <- c
}
if (showED50) {
results[c("ED50", paste("ED50", c(lower_level_percent, upper_level_percent)))] <-
10^results[7:9]
}
if (!is.null(EDx)) {
for (row.i in 1:ri) {
if (mtype[[row.i]] %in% c("probit", "logit", "weibull", "linlogit")) {
for (ED in EDx) {
of <- function(x) {
abs(predict(models[[row.i]], data.frame(dose = 10^x)) -
(1 - (ED/100)))
}
# Search over interval starting an order of magnitude below
# the lowest dose up to one order of magnitude above the
# highest dose
o = optimize(of,
results[row.i, c("lld", "lhd")] + c(-1, 1))
# Only keep results within the tolerance
if ((o$objective) < EDx.tolerance) {
logdose.ED = o$minimum
results[row.i, paste0("EDx", ED)] <- 10^logdose.ED
}
}
}
}
}
rownames(results) <- 1:ri
attr(results, "models") <- models
return(results)
}
# vim: set ts=4 sw=4 expandtab:
|
