R/Samples-methods.R
In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs
Defines functions
DLTLikelihood
Documented in
DLTLikelihood
# nolint start
#####################################################################################
## Author: Daniel Sabanes Bove [sabanesd *a*t* roche *.* com],
## Wai Yin Yeung [w *.* yeung1 *a*t* lancaster *.* ac *.* uk]
## Project: Object-oriented implementation of CRM designs
##
## Time-stamp: <[Samples-methods.R] by DSB Mon 11/05/2015 17:46>
##
## Description:
## Methods for processing the MCMC samples.
##
## History:
## 25/03/2014 file creation
## 10/07/2015 Adding more methods for pseudo models
#####################################################################################
##' @include McmcOptions-methods.R
##' @include Model-methods.R
##' @include fromQuantiles.R
{}
## --------------------------------------------------
## Extract certain parameter from "Samples" object to produce
## plots with "ggmcmc" package
## --------------------------------------------------
##' Get specific parameter samples and produce a data.frame
##'
##' Here you have to specify with \code{pos} which
##' parameter you would like to extract from the \code{\linkS4class{Samples}}
##' object
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param pos the name of the parameter
##' @param envir for vectorial parameters, you can give the indices of the
##' elements you would like to extract. If \code{NULL}, the whole vector samples
##' will be returned
##' @param mode not used
##' @param inherits not used
##'
##' @return the data frame suitable for use with \code{\link[ggmcmc]{ggmcmc}}
##'
##' @example examples/Sample-methods-get.R
##' @export
##' @keywords methods
setMethod("get",
signature=
signature(x="Samples",
pos="character",
envir="ANY",
mode="ANY",
inherits="ANY"),
def=
function(x,
pos,
envir=NULL,
mode=NULL,
inherits=NULL){
## check the parameter name
stopifnot(is.scalar(pos),
pos %in% names(x@data))
## get the samples for this parameter
d <- x@data[[pos]]
## this can be either a vector or a matrix
## how many parameters do we have?
nPars <- NCOL(d)
## what are the names of all parameter
## elements?
elements <-
if(nPars == 1L)
pos
else
paste(pos,
"[", seq_len(nPars), "]",
sep="")
## in case we have a vector parameter
if(nPars > 1L)
{
## what are the indices to be returned?
indices <-
if(is.null(envir))
{
seq_along(elements)
} else {
stopifnot(is.numeric(envir),
all(envir %in% seq_along(elements)))
as.integer(envir)
}
## subset the data matrix and par names appropriately
d <- d[, indices, drop=FALSE]
elements <- elements[indices]
## and also reduce the number of parameters
nPars <- length(indices)
}
## now we can build
ret <- data.frame(Iteration=seq_len(NROW(d)),
Chain=1L,
Parameter=
factor(rep(elements, each=NROW(d)),
levels=elements),
value=as.numeric(d))
## add the attributes
ret <- structure(ret,
nChains=1L,
nParameters=nPars,
nIterations=x@options@iterations,
nBurnin=x@options@burnin,
nThin=x@options@step,
description=elements,
parallel=FALSE)
return(ret)
})
## --------------------------------------------------
## Get fitted curves from Samples
## --------------------------------------------------
##' Fit method for the Samples class
##'
##' Note this new generic function is necessary because the \code{\link{fitted}}
##' function only allows the first argument \code{object} to appear in the
##' signature. But we need also other arguments in the signature.
##'
##' @param object the \code{\linkS4class{Samples}} object
##' @param model the \code{\linkS4class{Model}} object
##' @param data the \code{\linkS4class{Data}} object
##' @param \dots unused
##' @return the data frame with required information (see method details)
##'
##' @export
##' @keywords methods
setGeneric("fit",
def=
function(object,
model,
data,
...){
## there should be no default method,
## therefore just forward to next method!
standardGeneric("fit")},
valueClass="data.frame")
## --------------------------------------------------
## Get fitted dose-tox curve from Samples
## --------------------------------------------------
##' @param points at which dose levels is the fit requested? default is the dose
##' grid
##' @param quantiles the quantiles to be calculated (default: 0.025 and
##' 0.975)
##' @param middle the function for computing the middle point. Default:
##' \code{\link{mean}}
##'
##' @describeIn fit This method returns a data frame with dose, middle, lower
##' and upper quantiles for the dose-toxicity curve
##' @example examples/Sample-methods-fit.R
##'
setMethod("fit",
signature=
signature(object="Samples",
model="Model",
data="Data"),
def=
function(object,
model,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the dose-tox
## curve at the dose grid points.
probSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
## evaluate the probs, for all samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
probSamples[, i] <- prob(dose=points[i],
model,
object)
}
## extract middle curve
middleCurve <- apply(probSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(probSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## --------------------------------------------------
## Get fitted dose-tox and dose-biomarker curves from Samples
## --------------------------------------------------
##' @describeIn fit This method returns a data frame with dose, and middle,
##' lower and upper quantiles, for both the dose-tox and dose-biomarker (suffix
##' "Biomarker") curves, for all grid points (Note that currently only the grid
##' points can be used, because the DualEndpointRW models only allow that)
##'
##' @example examples/Sample-methods-fit-DualEndpoint.R
setMethod("fit",
signature=
signature(object="Samples",
model="DualEndpoint",
data="DataDual"),
def=
function(object,
model,
data,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles))
## first obtain the dose-tox curve results from the parent method
start <- callNextMethod(object=object,
model=model,
data=data,
points=data@doseGrid,
quantiles=quantiles,
middle=middle,
...)
## now obtain the dose-biomarker results
## get the biomarker level samples
## at the dose grid points.
biomLevelSamples <- matrix(nrow=sampleSize(object@options),
ncol=data@nGrid)
## evaluate the biomLevels, for all samples.
for(i in seq_len(data@nGrid))
{
## Now we want to evaluate for the
## following dose:
biomLevelSamples[, i] <- biomLevel(dose=data@doseGrid[i],
xLevel=i,
model,
object)
}
## extract middle curve
middleCurve <- apply(biomLevelSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(biomLevelSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
biomResults <- data.frame(middleBiomarker=middleCurve,
lowerBiomarker=quantCurve[1, ],
upperBiomarker=quantCurve[2, ])
## return both, pasted together
return(cbind(start, biomResults))
})
## --------------------------------------------------
## Approximate posterior with (log) normal distribution
## --------------------------------------------------
##' Approximate posterior with (log) normal distribution
##'
##' It is recommended to use \code{\link{set.seed}} before, in order
##' to be able to reproduce the resulting approximating model exactly.
##'
##' @param object the \code{\linkS4class{Samples}} object
##' @param model the \code{\linkS4class{Model}} object
##' @param data the \code{\linkS4class{Data}} object
##' @param \dots additional arguments (see methods)
##' @return the approximation model
##'
##' @export
##' @keywords methods
setGeneric("approximate",
def=
function(object, model, data, ...){
## there should be no default method,
## therefore just forward to next method!
standardGeneric("approximate")},
valueClass="Model")
##' @param points optional parameter, which gives the dose values at which
##' the approximation should rely on (default: 5 values equally spaced from
##' minimum to maximum of the dose grid)
##' @param refDose the reference dose to be used (default: median of
##' \code{points})
##' @param logNormal use the log-normal prior? (not default) otherwise, the
##' normal prior for the logistic regression coefficients is used
##' @param verbose be verbose (progress statements and plot)? (default)
##'
##' @describeIn approximate Here the \dots argument can transport additional arguments for
##' \code{\link{Quantiles2LogisticNormal}}, e.g. in order to control the
##' approximation quality, etc.
##'
##' @example examples/Sample-methods-approximate.R
setMethod("approximate",
signature=
signature(object="Samples"),
def=
function(object,
model,
data,
points=
seq(from=min(data@doseGrid),
to=max(data@doseGrid),
length=5L),
refDose=median(points),
logNormal=FALSE,
verbose=TRUE,
...){
## get the required quantiles at these dose levels:
quants <- fit(object,
model,
data,
points=points,
quantiles=c(0.025, 0.975),
middle=median)
## get better starting values if it is already a logistic normal
## model
if(is(model, "LogisticNormal") && (! logNormal))
{
means <- sapply(object@data,
mean)
cov <- cov(as.data.frame(object@data))
parstart <- c(means[1], means[2],
sqrt(cov[1, 1]), sqrt(cov[2, 2]),
cov2cor(cov)[1, 2])
} else if(is(model, "LogisticLogNormal") && logNormal) {
datTrafo <- with(object@data,
cbind(alpha0,
log(alpha1)))
means <- colMeans(datTrafo)
cov <- cov(datTrafo)
parstart <- c(means[1], means[2],
sqrt(cov[1, 1]), sqrt(cov[2, 2]),
cov2cor(cov)[1, 2])
} else {
parstart <- NULL
}
## run the approx function
quantRes <- Quantiles2LogisticNormal(dosegrid=quants$dose,
refDose=refDose,
lower=quants$lower,
upper=quants$upper,
median=quants$middle,
verbose=verbose,
parstart=parstart,
logNormal=logNormal,
...)
if(verbose)
{
matplot(x=points,
quantRes$required,
type="l", col="blue", lty=1)
matlines(x=points,
quantRes$quantiles,
col="red", lty=1)
legend("bottomright",
legend=c("original", "approximation"),
col=c("blue", "red"),
lty=1,
bty="n")
}
## return the model
return(quantRes$model)
})
## --------------------------------------------------
## Plot dose-tox fit from a model
## --------------------------------------------------
##' Plotting dose-toxicity model fits
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{Model}} object
##' @param data the \code{\linkS4class{Data}} object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-toxicity model fit
##'
##' @example examples/Sample-methods-plot.R
##' @export
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="Samples",
y="Model"),
def=
function(x, y, data, ...,
xlab="Dose level",
ylab="Probability of DLT [%]",
showLegend=TRUE){
## check args
stopifnot(is.bool(showLegend))
## get the fit
plotData <- fit(x,
model=y,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
## make the plot
gdata <-
with(plotData,
data.frame(x=rep(dose, 3),
y=c(middle, lower, upper) * 100,
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotData)),
Type=
factor(c(rep("Estimate",
nrow(plotData)),
rep("95% Credible Interval",
nrow(plotData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
ret <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("red"),
geom="line",
xlab=xlab,
ylab=ylab,
ylim=c(0, 100))
ret <- ret +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2), guide=ifelse(showLegend,
"legend", FALSE))
return(ret)
})
## --------------------------------------------------
## Special method for dual endpoint model
## --------------------------------------------------
##' Plotting dose-toxicity and dose-biomarker model fits
##'
##' When we have the dual endpoint model,
##' also the dose-biomarker fit is shown in the plot
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{DualEndpoint}} object
##' @param data the \code{\linkS4class{DataDual}} object
##' @param extrapolate should the biomarker fit be extrapolated to the whole
##' dose grid? (default)
##' @param showLegend should the legend be shown? (not default)
##' @param \dots additional arguments for the parent method
##' \code{\link{plot,Samples,Model-method}}
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object with the dose-toxicity and dose-biomarker model fits
##'
##' @example examples/Sample-methods-plot-DualEndpoint.R
##' @export
setMethod("plot",
signature=
signature(x="Samples",
y="DualEndpoint"),
def=
function(x, y, data, extrapolate=TRUE, showLegend=FALSE, ...){
stopifnot(is.bool(extrapolate))
## call the superclass method, to get the toxicity plot
plot1 <- callNextMethod(x, y, data, showLegend=showLegend, ...)
## only look at these dose levels for the plot:
xLevels <-
if(extrapolate)
seq_along(data@doseGrid)
else
1:max(data@xLevel)
## get the plot data for the biomarker plot
functionSamples <- matrix(nrow=sampleSize(x@options),
ncol=length(xLevels))
## evaluate the biomLevels, for all samples.
for(i in seq_along(xLevels))
{
## Now we want to evaluate for the
## following dose:
functionSamples[, i] <-
biomLevel(dose=data@doseGrid[xLevels[i]],
xLevel=xLevels[i],
model=y,
samples=x)
}
## extract mean curve
meanCurve <- colMeans(functionSamples)
## extract quantiles
quantiles <- c(0.025, 0.975)
quantCurve <- apply(functionSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
plotData <- data.frame(dose=data@doseGrid[xLevels],
mean=meanCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## make the second plot
gdata <-
with(plotData,
data.frame(x=rep(dose, 3),
y=c(mean, lower, upper),
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotData)),
Type=
factor(c(rep("Estimate",
nrow(plotData)),
rep("95% Credible Interval",
nrow(plotData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
plot2 <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("blue"),
geom="line",
xlab="Dose level",
ylab="Biomarker level")
plot2 <- plot2 +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2),
guide=ifelse(showLegend,
"legend", FALSE))
## arrange both plots side by side
ret <- gridExtra::arrangeGrob(plot1, plot2, ncol=2)
return(ret)
})
## -------------------------------------------------------------------------------------
## Get fitted dose-tox curve from Samples for 'LogisticIndepBeta' model class
## ------------------------------------------------------------------------------------
##' @describeIn fit This method return a data frame with dose, middle lower and upper quantiles
##' for the dose-DLE curve using DLE samples for \dQuote{LogisticIndepBeta} model class
##' @example examples/Samples-method-fitDLE.R
setMethod("fit",
signature=
signature(object="Samples",
model="LogisticIndepBeta",
data="Data"),
def=
function(object,
model,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the dose-tox
## curve at the dose grid points.
probSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
## evaluate the probs, for all samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
probSamples[, i] <- prob(dose=points[i],
model,
object)
}
## extract middle curve
middleCurve <- apply(probSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(probSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## -------------------------------------------------------------------------------------
## Get fitted dose-efficacy curve from Samples for 'Effloglog' model class
## ------------------------------------------------------------------------------------
##' @describeIn fit This method returns a data frame with dose, middle, lower, upper quantiles for
##' the dose-efficacy curve using efficacy samples for \dQuote{Effloglog} model class
##' @example examples/Samples-method-fitEff.R
setMethod("fit",
signature=
signature(object="Samples",
model="Effloglog",
data="DataDual"),
def=
function(object,
model,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the dose-tox
## curve at the dose grid points.
ExpEffSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
## evaluate the probs, for all samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
ExpEffSamples[, i] <- ExpEff(dose=points[i],
model,
object)
}
## extract middle curve
middleCurve <- apply(ExpEffSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(ExpEffSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## ==========================================================================================
## --------------------------------------------------------------------
## Get fitted dose-efficacy based on the Efficacy Flexible model
## -------------------------------------------------------------
##' @describeIn fit This method returns a data frame with dose, middle, lower and upper
##' quantiles for the dose-efficacy curve using efficacy samples for \dQuote{EffFlexi}
##' model class
##' @example examples/Samples-method-fitEffFlexi.R
setMethod("fit",
signature=
signature(object="Samples",
model="EffFlexi",
data="DataDual"),
def=
function(object,
model,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the dose-tox
## curve at the dose grid points.
ExpEffSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
## evaluate the probs, for all samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
ExpEffSamples[, i] <- ExpEff(dose=points[i],
model,
object)
}
## extract middle curve
middleCurve <- apply(ExpEffSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(ExpEffSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## ==============================================================
## ----------------------------------------------------------------
## Get fitted values at all dose levels from gain samples
## -----------------------------------------------------------------
##' Get the fitted values for the gain values at all dose levels based on
##' a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample
##' and data. This method returns a data frame with dose, middle, lower and upper quantiles
##' of the gain value samples
##'
##' @param DLEmodel the DLE pseudo model of \code{\linkS4class{ModelTox}} class object
##' @param DLEsamples the DLE samples of \code{\linkS4class{Samples}} class object
##' @param Effmodel the efficacy pseudo model of \code{\linkS4class{ModelEff}} class object
##' @param Effsamples the efficacy samples of \code{\linkS4class{Samples}} class object
##' @param data the data input of \code{\linkS4class{DataDual}} class object
##' @param \dots additional arguments for methods
##'
##' @export
##' @keywords methods
setGeneric("fitGain",
def=
function(DLEmodel,
DLEsamples,
Effmodel,
Effsamples,
data,
...){
## there should be no default method,
## therefore just forward to next method!
standardGeneric("fitGain")},
valueClass="data.frame")
##' @describeIn fitGain This method returns a data frame with dose, middle, lower, upper quantiles for
##' the gain values obtained given the DLE and the efficacy samples
##' @param points at which dose levels is the fit requested? default is the dose
##' grid
##' @param quantiles the quantiles to be calculated (default: 0.025 and
##' 0.975)
##' @param middle the function for computing the middle point. Default:
##' \code{\link{mean}}
##' @example examples/Samples-method-fitGain.R
setMethod("fitGain",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="Samples",
Effmodel="ModelEff",
Effsamples="Samples",
data="DataDual"),
def=
function(DLEmodel,
DLEsamples,
Effmodel,
Effsamples,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the gain
## at the dose grid points.
GainSamples <- matrix(nrow=sampleSize(DLEsamples@options),
ncol=length(points))
## evaluate the probs, for all gain samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
GainSamples[, i] <- gain(dose=points[i],
DLEmodel=DLEmodel,
DLEsamples=DLEsamples,
Effmodel=Effmodel,
Effsamples=Effsamples)
}
## extract middle curve
middleCurve <- apply(GainSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(GainSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## ---------------------------------------------------------------------------------
## Plot the fitted dose-DLE curve with pseudo DLE model with samples
## -------------------------------------------------------------------------------
##' Plot the fitted dose-DLE curve using a \code{\linkS4class{ModelTox}} class model with samples
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{ModelTox}} model class object
##' @param data the \code{\linkS4class{Data}} object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-DLE model fit
##'
##' @example examples/Samples-method-plotModelTox.R
##' @export
##' @keywords methods
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="Samples",
y="ModelTox"),
def=
function(x, y, data, ...,
xlab="Dose level",
ylab="Probability of DLT [%]",
showLegend=TRUE){
## check args
stopifnot(is.bool(showLegend))
## get the fit
plotData <- fit(x,
model=y,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
## make the plot
gdata <-
with(plotData,
data.frame(x=rep(dose, 3),
y=c(middle, lower, upper) * 100,
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotData)),
Type=
factor(c(rep("Estimate",
nrow(plotData)),
rep("95% Credible Interval",
nrow(plotData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
ret <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("red"),
geom="line",
xlab=xlab,
ylab=ylab,
ylim=c(0, 100))
ret <- ret +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2), guide=ifelse(showLegend,
"legend", FALSE))
return(ret)
})
## --------------------------------------------------------------------------------------------
## Plot the fitted dose-efficacy curve using a pseudo efficacy model with samples
## -------------------------------------------------------------------------------------------
##' Plot the fitted dose-efficacy curve using a model from \code{\linkS4class{ModelEff}} class
##' with samples
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{ModelEff}} model class object
##' @param data the \code{\linkS4class{Data}} object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-efficacy model fit
##'
##' @example examples/Samples-method-plotModelEff.R
##' @export
##' @keywords methods
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="Samples",
y="ModelEff"),
def=
function(x, y, data, ...,
xlab="Dose level",
ylab="Expected Efficacy",
showLegend=TRUE){
## check args
stopifnot(is.bool(showLegend))
## get the fit
plotData <- fit(x,
model=y,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
## make the plot
gdata <-
with(plotData,
data.frame(x=rep(dose, 3),
y=c(middle, lower, upper) ,
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotData)),
Type=
factor(c(rep("Estimate",
nrow(plotData)),
rep("95% Credible Interval",
nrow(plotData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
ret <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("blue"),
geom="line",
xlab=xlab,
ylab=ylab,
xlim=c(0,max(data@doseGrid)))
ret <- ret +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2), guide=ifelse(showLegend,
"legend", FALSE))
return(ret)
})
## ----------------------------------------------------------------------------------------
## Plot of fitted dose-DLE curve based on a pseudo DLE model without sample
##-------------------------------------------------------------------------------------
##' Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples
##'
##' @param x the data of \code{\linkS4class{Data}} class object
##' @param y the model of the \code{\linkS4class{ModelTox}} class object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-DLE model plot
##'
##' @example examples/Samples-method-plotModelToxNoSamples.R
##' @export
##' @keywords methods
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="Data",
y="ModelTox"),
def=
function(x,y,
xlab="Dose level",
ylab="Probability of DLE",
showLegend=TRUE,...){
##check args
stopifnot(is.bool(showLegend))
##Make sure the right model estimates are use with the given data
y <- update(object=y,data=x)
##create data frame
plotData <- data.frame(dose=x@doseGrid,
probDLE=prob(dose=x@doseGrid,
model=y))
##Look for TD30 and TD35
TD30 <-dose(prob=0.30,
model=y)
TD35 <-dose(prob=0.35,
model=y)
##make the plot
gdata <- with(plotData,
data.frame(x=dose,
y=probDLE,
group=rep("Estimated DLE",each=nrow(plotData)),
Type=factor(rep("Estimated DLE",nrow(plotData)),levels="Estimated DLE")))
plot1 <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("red"),
geom="line",
xlab=xlab,
ylab=ylab,
ylim=c(0,1))
plot1 <- plot1 + ggplot2::scale_linetype_manual(breaks="Estimated DLE",
values=c(1,2),
guide=ifelse(showLegend,"legend",FALSE))
plot1 <- plot1 +
geom_line(size=1.5,colour="red")
return(plot1)
})
## ---------------------------------------------------------------------------------------------
## Plot the fitted dose-efficacy curve given a pseudo efficacy model without samples
## ----------------------------------------------------------------------------------
##' Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples
##'
##' @param x the data of \code{\linkS4class{DataDual}} class object
##' @param y the model of the \code{\linkS4class{ModelEff}} class object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-efficacy model plot
##'
##' @example examples/Samples-method-plotModelEffNoSamples.R
##' @export
##' @keywords methods
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="DataDual",
y="ModelEff"),
def=
function(x,y,...,
xlab="Dose level",
ylab="Expected Efficacy",
showLegend=TRUE){
##check args
stopifnot(is.bool(showLegend))
y <- update(object=y,data=x)
##create data frame
plotEffData<- data.frame(dose=x@doseGrid,
ExpEff=ExpEff(dose=x@doseGrid,
model=y))
##make the second plot
ggdata<-with(plotEffData,
data.frame(x=dose,
y=ExpEff,
group=rep("Estimated Expected Efficacy",each=nrow(plotEffData)),
Type=factor(rep("Estimated Expected Efficacy",nrow(plotEffData)),levels="Estimated Expected Efficacy")))
##Get efficacy plot
plot2 <- ggplot(data=ggdata, aes(x=x,y=y), group=group) +
xlab("Dose Levels")+
ylab(paste("Estimated Expected Efficacy")) + xlim(c(0,max(x@doseGrid))) +
geom_line(colour=I("blue"), size=1.5)
plot2 <- plot2 +
geom_line(size=1.5,colour="blue")
return(plot2)
})
## ----------------------------------------------------------------------------------------------------------
## Plot the gain curve using a pseudo DLE and a pseudo Efficacy model with samples
## ----------------------------------------------------------------------------------------------------
##' Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model,
##' a DLE sample, a given efficacy pseudo model and an efficacy sample
##'
##' @param DLEmodel the dose-DLE model of \code{\linkS4class{ModelTox}} class object
##' @param DLEsamples the DLE sample of \code{\linkS4class{Samples}} class object
##' @param Effmodel the dose-efficacy model of \code{\linkS4class{ModelEff}} class object
##' @param Effsamples the efficacy sample of of \code{\linkS4class{Samples}} class object
##' @param data the data input of \code{\linkS4class{DataDual}} class object
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the plot
##'
##' @example examples/Samples-method-plotGain.R
##' @export
##' @keywords methods
setGeneric("plotGain",
def=
function(DLEmodel,
DLEsamples,
Effmodel,
Effsamples,
data,...){
standardGeneric("plotGain")})
##' @describeIn plotGain Standard method
setMethod("plotGain",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="Samples",
Effmodel="ModelEff",
Effsamples="Samples"),
def=
function(DLEmodel,DLEsamples,Effmodel,Effsamples,data,...){
##Get fitted values for probabilities of DLE at all dose levels
plotDLEData <- fit(DLEsamples,
model=DLEmodel,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
##Get fitted values for mean efficacy values at all dose levels
plotEffData <- fit(Effsamples,
model=Effmodel,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
##Get fitted values for gain values at all dose levels
plotGainData <- fitGain(DLEmodel=DLEmodel,
DLEsamples=DLEsamples,
Effmodel=Effmodel,
Effsamples=Effsamples,
data=data)
##For each of the dose levels, take the mean for the probabilties of DLE, mean efiicacy values
## and gain values. Hence combine them into a data frame
plotData<-data.frame(dose=rep(data@doseGrid,3),
values=c(plotDLEData$middle,
plotEffData$middle,
plotGainData$middle))
## only the line plots for the mean value of the DLE, efficacy and gain samples
##at all dose levels
gdata<-with(plotData,
data.frame(x=dose,
y=values,
group=c(rep("p(DLE)",length(data@doseGrid)),
rep("Mean Expected Efficacy",length(data@doseGrid)),
rep("Gain",length(data@doseGrid))),
Type=factor("Estimate",levels="Estimate")
))
plot1 <- ggplot(data=gdata, aes(x=x,y=y))+geom_line(aes(group=group,color=group),size=1.5)+
ggplot2::scale_colour_manual(name="curves",values=c("green3","blue","red"))+
xlab("Dose Level")+ xlim(c(0,max(data@doseGrid)))+
ylab(paste("Values")) + ylim(c(min(gdata$y),max(gdata$y)))
return(plot1)
})
##----------------------------------------------------------------------------------------------------
## Plot the gain curve using a pseudo DLE and a pseudo Efficacy model without samples
## ----------------------------------------------------------------------------------------------------
##' Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model,
##' and a given efficacy pseudo model
##'
##' @describeIn plotGain Standard method
##'
##' @example examples/Samples-method-plotGainNoSamples.R
##' @export
##' @keywords methods
setMethod("plotGain",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="missing",
Effmodel="ModelEff",
Effsamples="missing"),
def=
function(DLEmodel,Effmodel,data,...){
##Make sure the model estimates are corresponds to the input data
DLEmodel <- update(object=DLEmodel,data=data)
Effmodel <- update(object=Effmodel,data=data)
plotData<-data.frame(dose=rep(data@doseGrid,3),
values=c(prob(dose=data@doseGrid,
model=DLEmodel),
ExpEff(dose=data@doseGrid,
model=Effmodel),
gain(dose=data@doseGrid,
DLEmodel=DLEmodel,
Effmodel=Effmodel)))
gdata<-with(plotData,
data.frame(x=dose,
y=values,
group=c(rep("p(DLE)",length(data@doseGrid)),
rep("Expected Efficacy",length(data@doseGrid)),
rep("Gain",length(data@doseGrid))),
Type=factor("Estimate",levels="Estimate")
))
##plot1 <- ggplot(data=gdata, aes(x=x,y=y))+geom_line(aes(group=group,color=group),size=1.5)
plot1 <- ggplot(data=gdata, aes(x=x,y=y))+geom_line(aes(group=group,color=group),size=1.5)+
ggplot2::scale_colour_manual(name="curves",values=c("blue","green3","red"))+
xlab("Dose Level")+ xlim(c(0,max(data@doseGrid)))+
ylab(paste("Values")) + ylim(c(min(gdata$y),max(gdata$y)))
TD30 <- dose(prob=0.3,model=DLEmodel)
Gainfun<-function(DOSE){
-gain(DOSE,DLEmodel=DLEmodel,Effmodel=Effmodel)
}
Gstar<-(optim(min(data@doseGrid),Gainfun,method = "L-BFGS-B",lower=min(data@doseGrid),upper=max(data@doseGrid))$par)
MaxGain<--(optim(min(data@doseGrid),Gainfun,method = "L-BFGS-B",lower=min(data@doseGrid),upper=max(data@doseGrid))$value)
if ((TD30 < min(data@doseGrid))|(TD30 > max(data@doseGrid))) {
plot1<-plot1
print(paste("TD30",paste(TD30," not within dose Grid")))} else {plot1 <-plot1 + geom_point(data=data.frame(x=TD30,y=0.3),aes(x=x,y=y),colour="violet", shape=16, size=8) +
annotate("text",label="p(DLE=0.3)",x=TD30+1,y=0.2,size=5,colour="violet")}
if ((Gstar < min(data@doseGrid))|(Gstar > max(data@doseGrid))) {
plot1<-plot1
print(paste("Gstar=",paste(Gstar," not within dose Grid")))} else {plot1 <- plot1 + geom_point(data=data.frame(x=Gstar,y=MaxGain),aes(x=x,y=y),colour="green3", shape=17, size=8) +
annotate("text",label="Max Gain",x=Gstar,y=MaxGain-0.1,size=5,colour="green3")}
return(plot1)
})
##==========================================================================================
## -------------------------------------------------------------------------------
## Plot of the DLE and efficacy curve sides by side with samples
## -----------------------------------------------------------------------------
##' Plot of the DLE and efficacy curve side by side given a DLE pseudo model,
##' a DLE sample, an efficacy pseudo model and a given efficacy sample
##'
##' @param DLEmodel the pseudo DLE model of \code{\linkS4class{ModelTox}} class object
##' @param DLEsamples the DLE samples of \code{\linkS4class{Samples}} class object
##' @param Effmodel the pseudo efficacy model of \code{\linkS4class{ModelEff}} class object
##' @param Effsamples the Efficacy samples of \code{\linkS4class{Samples}} class object
##' @param data the data input of \code{\linkS4class{DataDual}} class object
##' @param extrapolate should the biomarker fit be extrapolated to the whole
##' dose grid? (default)
##' @param showLegend should the legend be shown? (not default)
##' @param \dots additional arguments for the parent method
##' \code{\link{plot,Samples,Model-method}}
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object with the dose-toxicity and dose-efficacy model fits
##'
##' @example examples/Samples-method-plotDualResponses.R
##'
##' @export
##' @keywords methods
setGeneric("plotDualResponses",
def=
function(DLEmodel,
DLEsamples,
Effmodel,
Effsamples,
data,...){
standardGeneric("plotDualResponses")})
##' @describeIn plotDualResponses function still to be documented
setMethod("plotDualResponses",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="Samples",
Effmodel="ModelEff",
Effsamples="Samples"),
def=
function(DLEmodel, DLEsamples, Effmodel,Effsamples,data, extrapolate=TRUE, showLegend=FALSE,...){
stopifnot(is.bool(extrapolate))
## Get Toxicity plot
## get the fit
plotDLEData <- fit(DLEsamples,
model=DLEmodel,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
## make the plot
gdata <-
with(plotDLEData,
data.frame(x=rep(dose, 3),
y=c(middle, lower, upper) * 100,
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotDLEData)),
Type=
factor(c(rep("Estimate",
nrow(plotDLEData)),
rep("95% Credible Interval",
nrow(plotDLEData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
ret1 <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("red"),
geom="line",
xlab="Dose Levels",
ylab="Probability of DLE [%]",
ylim=c(0, 100))
ret1 <- ret1 + ggplot2::scale_linetype_manual(breaks=c("Estimate",
"95% Credible Interval"),
values=c(1,2), guide=ifelse(showLegend,
"legend", FALSE))
##only look at these dose levels for the plot:
xLevels<- if (extrapolate) {
seq_along(data@doseGrid)} else {1:max(data@xLevel)}
##get the plot data for the efficacy
functionSamples <- matrix(nrow=sampleSize(Effsamples@options),
ncol=length(xLevels))
##evaluate the efficacy for all samples
for (i in seq_along(xLevels))
{
##Now we want to evaluate for the following dose
functionSamples[,i] <- ExpEff(dose=data@doseGrid[xLevels[i]],
model=Effmodel,
samples=Effsamples)
}
##extract mean curve
meanCurve <- colMeans(functionSamples)
##extract quantiles
quantiles=c(0.025, 0.975)
quantCurve <- apply(functionSamples, 2L, quantile, prob=quantiles)
##now create the data frame
plotEffData <-data.frame(dose=data@doseGrid[xLevels],
mean=meanCurve,
lower=quantCurve[1,],
upper=quantCurve[2,])
##make the second plot
ggdata<-with(plotEffData, data.frame(x=rep(dose,3),
y=c(mean,lower,upper),
group=
rep(c("mean","lower","upper"),
each=nrow(plotEffData)),
Type=
factor(c(rep("Estimate",
nrow(plotEffData)),
rep("95% Credible Interval",
nrow(plotEffData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
plot2 <- ggplot2::qplot(x=x,
y=y,
data=ggdata,
group=group,
linetype=Type,
colour=I("blue"),
geom="line",
xlab="Dose level",
ylab="Expected Efficacy")
plot2 <- plot2 +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2),
guide=ifelse(showLegend,
"legend", FALSE))
## arrange both plots side by side
ret <- gridExtra::arrangeGrob(ret1, plot2, ncol=2)
return(ret)
})
##------------------------------------------------------------------------------
## Plot of the DLE and efficacy curve sides by side without samples
## -----------------------------------------------------------------------------
##' Plot of the dose-DLE and dose-efficacy curve side by side given a DLE pseudo model
##' and a given pseudo efficacy model without DLE and efficacy samples
##'
##' @describeIn plotDualResponses Plot the DLE and efficacy curve side by side given a DLE model
##' and an efficacy model without any samples
##'
##' @example examples/Samples-method-plotDualResponsesNoSamples.R
##'
##' @export
##' @keywords methods
setMethod("plotDualResponses",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="missing",
Effmodel="ModelEff",
Effsamples="missing"),
def=
function(DLEmodel,Effmodel,data,...){
## Get Toxicity plot
## get the fit
##Make sure the model estimates are corresponds to the input data
DLEmodel <- update(object=DLEmodel,data=data)
Effmodel <- update(object=Effmodel,data=data)
plotDLEData <- data.frame(dose=data@doseGrid,
probDLE=prob(dose=data@doseGrid,
model=DLEmodel))
## make the plot
gdata <- with(plotDLEData,
data.frame(x=dose,
y=probDLE,
group=rep("Estimated DLE",each=nrow(plotDLEData)),
Type=factor(rep("Estimated DLE",nrow(plotDLEData)),levels="Estimated DLE")))
plot1 <- ggplot(data=gdata, aes(x=x,y=y), group=group) +
xlab("Dose Levels")+
ylab(paste("Probability of DLE")) + ylim(c(0,1)) + xlim(c(0,max(data@doseGrid))) +
geom_line(colour=I("red"), size=1.5)
plot1 <- plot1 +
geom_line(size=1.5,colour="red")
##only look at these dose levels for the plot:
##get the plot data for the efficacy
plotEffData<- data.frame(dose=data@doseGrid,
ExpEff=ExpEff(dose=data@doseGrid,
model=Effmodel))
##make the second plot
ggdata<-with(plotEffData,
data.frame(x=dose,
y=ExpEff,
group=rep("Estimated Expected Efficacy",each=nrow(plotEffData)),
Type=factor(rep("Estimated Expected Efficacy",nrow(plotEffData)),levels="Estimated Expected Efficacy")))
##Get efficacy plot
plot2 <- ggplot(data=ggdata, aes(x=x,y=y), group=group) +
xlab("Dose Levels")+
ylab(paste("Estimatimated Expected Efficacy")) + xlim(c(0,max(data@doseGrid))) +
geom_line(colour=I("blue"), size=1.5)
plot2 <- plot2 +
geom_line(size=1.5,colour="blue")
## arrange both plots side by side
ret <- gridExtra::arrangeGrob(plot1, plot2, ncol=2)
return(ret)
})
## =======================================================================================================
## ----------------------------------------------------------------
## Get fitted DLT free survival (piecewise exponential model) based on
## the DA-CRM model
## -----------------------------------------------------------------
##' Get the fitted DLT free survival (piecewise exponential model).
##' This function returns a data frame with dose, middle, lower and upper
##' quantiles for the `PEM` curve. If hazard=TRUE,
##' @param object mcmc samples
##' @param model the mDA-CRM model
##' @param data the data input, a \code{\linkS4class{DataDA}} class object
##' @param quantiles the quantiles to be calculated (default: 0.025 and
##' 0.975)
##' @param middle the function for computing the middle point. Default:
##' \code{\link{mean}}
##' @param hazard should the the hazard over time be plotted based on the `PEM`? (not default)
##' Otherwise ...
##' @param \dots additional arguments for methods
##'
##' @export
##' @keywords methods
setGeneric("fitPEM",
def=
function(object,
model,
data,
quantiles=c(0.025, 0.975),
middle=mean,
hazard=FALSE,
...){
## there should be no default method,
## therefore just forward to next method!
standardGeneric("fitPEM")},
valueClass="data.frame")
#' Likelihood of DLTs in each interval
#'
#' This is a helper function for the `fitPEM` methods below.
#'
#' @param lambda the vector of piecewise hazards
#' @param Tmax the end of the time interval for DLTs
#' @return vector with the probabilities for DLTs within the intervals.
#'
#' @keywords internal
DLTLikelihood <- function(lambda,
Tmax)
{
npiece <-length(lambda)
h <- seq(from=0L, to=Tmax, length=npiece + 1)
#Length of each time interval;
sT<-rep(0,npiece)
for(i in 1:npiece){
sT[i]<-h[i+1]-h[i]
}
#calculate the exponential part of the distribution:
s_ij <- function(t, j)
{
if(t > h[j])
{
min(t-h[j],h[j+1]-h[j])
} else {
0
}
}
#The cumulative hazard function
expNmu <- function(t)
{
ret <- 1
for(j in 1:npiece)
{
ret <-ret * exp(- lambda[j] * s_ij(t, j))
}
return(ret)
}
#CDF of the piecewise exponential
piece_exp.cdf<-function(x){
1-expNmu(x)
}
DLTFreeS<-function(x)
{
(expNmu(x)-expNmu(Tmax))/piece_exp.cdf(Tmax)
}
pDLT<-rep(0,npiece+1)
for(i in 1:(npiece)){
pDLT[i] <- DLTFreeS(h[i]) - DLTFreeS(h[i+1])
}
return(pDLT)
}
## --------------------------------------------------------------------
## Get fitted DLT free survival (piecewise exponential model) based on
## the DA-CRM model
## -------------------------------------------------------------
##' @describeIn fitPEM This method works for the \code{\linkS4class{DALogisticLogNormal}}
##' model class.
##' @example examples/Samples-method-fitPEM-DALogisticLogNormal.R
setMethod("fitPEM",
signature=
signature(object="Samples",
model="DALogisticLogNormal",
data="DataDA"),
def=
function(object,
model,
data,
quantiles=c(0.025, 0.975),
middle=mean,
hazard=FALSE,
...){
## some checks
stopifnot(is.probRange(quantiles))
##Plot points
points<-seq(0, data@Tmax, length=model@npiece+1)
## first we have to get samples from the PEM
## at intercept points and 2 middel points between
## intercepts.
PEMSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
i_max<-max(seq_along(points))
## evaluate the probs, for all samples.
#The PEM
if(hazard==FALSE){
PEMSamples<-t(apply(object@data$lambda,1,function(x){
fit<-DLTLikelihood(x, data@Tmax)
return(fit)
}))
}else if(hazard==TRUE){
for(i in seq_along(points))
{
if(i==i_max){
PEMSamples[,i_max]<-object@data$lambda[, model@npiece]}else{
PEMSamples[,i]<-object@data$lambda[,i]}
}
}
## extract middle curve
middleCurve <- apply(PEMSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(PEMSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(time=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)})
## =======================================================================================================
## --------------------------------------------------
## Plot survival curve fit over time
## --------------------------------------------------
## todo: add example file
##' Plotting dose-toxicity model fits
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{DALogisticLogNormal}} object
##' @param data the \code{\linkS4class{DataDA}} object
##' @param hazard see \code{\link{fitPEM}} for the explanation
##' @param \dots not used
##' @param showLegend should the legend be shown? (default)
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-toxicity model fit
##'
##' @export
##' @importFrom ggplot2 qplot scale_linetype_manual
##' @importFrom gridExtra arrangeGrob
setMethod("plot",
signature=
signature(x="Samples",
y="DALogisticLogNormal"
),
def=
function(x, y, data, hazard=FALSE, ...,
showLegend=TRUE){
## check args
stopifnot(is.bool(showLegend))
## call the superclass method, to get the toxicity plot
plot1 <- callNextMethod(x, y, data, showLegend=showLegend, ...)
## get the fit
fitData <- fitPEM(x,
model=y,
data=data,
quantiles=c(0.025,0.975),
middle=mean,
hazard=hazard)
## make the plot
Tpoints<-seq(0,data@Tmax,length=y@npiece+1)
plotData <-
with(fitData,
data.frame(x=rep(Tpoints, 3),
y=c(middle, lower, upper) * 100,
group=
rep(c("mean", "lower", "upper"),
each=nrow(fitData)),
Type=
factor(c(rep("Estimate",
nrow(fitData)),
rep("95% Credible Interval",
nrow(fitData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
plot2 <- qplot(x=x,
y=y,
data=plotData,
group=group,
linetype=Type,
colour=I("blue"),
geom="step",
xlab="Time",
ylab=if(hazard) "Hazard rate*100" else "Probability of DLT [%]",
ylim=if(hazard) range(plotData$y) else c(0, 100))
ret <- gridExtra::arrangeGrob(plot1, plot2, ncol=2)
return(ret)
})
## =======================================================================================================
# nolint end
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Defines functions DLTLikelihood
Documented in DLTLikelihood
# nolint start
#####################################################################################
## Author: Daniel Sabanes Bove [sabanesd *a*t* roche *.* com],
## Wai Yin Yeung [w *.* yeung1 *a*t* lancaster *.* ac *.* uk]
## Project: Object-oriented implementation of CRM designs
##
## Time-stamp: <[Samples-methods.R] by DSB Mon 11/05/2015 17:46>
##
## Description:
## Methods for processing the MCMC samples.
##
## History:
## 25/03/2014 file creation
## 10/07/2015 Adding more methods for pseudo models
#####################################################################################
##' @include McmcOptions-methods.R
##' @include Model-methods.R
##' @include fromQuantiles.R
{}
## --------------------------------------------------
## Extract certain parameter from "Samples" object to produce
## plots with "ggmcmc" package
## --------------------------------------------------
##' Get specific parameter samples and produce a data.frame
##'
##' Here you have to specify with \code{pos} which
##' parameter you would like to extract from the \code{\linkS4class{Samples}}
##' object
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param pos the name of the parameter
##' @param envir for vectorial parameters, you can give the indices of the
##' elements you would like to extract. If \code{NULL}, the whole vector samples
##' will be returned
##' @param mode not used
##' @param inherits not used
##'
##' @return the data frame suitable for use with \code{\link[ggmcmc]{ggmcmc}}
##'
##' @example examples/Sample-methods-get.R
##' @export
##' @keywords methods
setMethod("get",
signature=
signature(x="Samples",
pos="character",
envir="ANY",
mode="ANY",
inherits="ANY"),
def=
function(x,
pos,
envir=NULL,
mode=NULL,
inherits=NULL){
## check the parameter name
stopifnot(is.scalar(pos),
pos %in% names(x@data))
## get the samples for this parameter
d <- x@data[[pos]]
## this can be either a vector or a matrix
## how many parameters do we have?
nPars <- NCOL(d)
## what are the names of all parameter
## elements?
elements <-
if(nPars == 1L)
pos
else
paste(pos,
"[", seq_len(nPars), "]",
sep="")
## in case we have a vector parameter
if(nPars > 1L)
{
## what are the indices to be returned?
indices <-
if(is.null(envir))
{
seq_along(elements)
} else {
stopifnot(is.numeric(envir),
all(envir %in% seq_along(elements)))
as.integer(envir)
}
## subset the data matrix and par names appropriately
d <- d[, indices, drop=FALSE]
elements <- elements[indices]
## and also reduce the number of parameters
nPars <- length(indices)
}
## now we can build
ret <- data.frame(Iteration=seq_len(NROW(d)),
Chain=1L,
Parameter=
factor(rep(elements, each=NROW(d)),
levels=elements),
value=as.numeric(d))
## add the attributes
ret <- structure(ret,
nChains=1L,
nParameters=nPars,
nIterations=x@options@iterations,
nBurnin=x@options@burnin,
nThin=x@options@step,
description=elements,
parallel=FALSE)
return(ret)
})
## --------------------------------------------------
## Get fitted curves from Samples
## --------------------------------------------------
##' Fit method for the Samples class
##'
##' Note this new generic function is necessary because the \code{\link{fitted}}
##' function only allows the first argument \code{object} to appear in the
##' signature. But we need also other arguments in the signature.
##'
##' @param object the \code{\linkS4class{Samples}} object
##' @param model the \code{\linkS4class{Model}} object
##' @param data the \code{\linkS4class{Data}} object
##' @param \dots unused
##' @return the data frame with required information (see method details)
##'
##' @export
##' @keywords methods
setGeneric("fit",
def=
function(object,
model,
data,
...){
## there should be no default method,
## therefore just forward to next method!
standardGeneric("fit")},
valueClass="data.frame")
## --------------------------------------------------
## Get fitted dose-tox curve from Samples
## --------------------------------------------------
##' @param points at which dose levels is the fit requested? default is the dose
##' grid
##' @param quantiles the quantiles to be calculated (default: 0.025 and
##' 0.975)
##' @param middle the function for computing the middle point. Default:
##' \code{\link{mean}}
##'
##' @describeIn fit This method returns a data frame with dose, middle, lower
##' and upper quantiles for the dose-toxicity curve
##' @example examples/Sample-methods-fit.R
##'
setMethod("fit",
signature=
signature(object="Samples",
model="Model",
data="Data"),
def=
function(object,
model,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the dose-tox
## curve at the dose grid points.
probSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
## evaluate the probs, for all samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
probSamples[, i] <- prob(dose=points[i],
model,
object)
}
## extract middle curve
middleCurve <- apply(probSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(probSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## --------------------------------------------------
## Get fitted dose-tox and dose-biomarker curves from Samples
## --------------------------------------------------
##' @describeIn fit This method returns a data frame with dose, and middle,
##' lower and upper quantiles, for both the dose-tox and dose-biomarker (suffix
##' "Biomarker") curves, for all grid points (Note that currently only the grid
##' points can be used, because the DualEndpointRW models only allow that)
##'
##' @example examples/Sample-methods-fit-DualEndpoint.R
setMethod("fit",
signature=
signature(object="Samples",
model="DualEndpoint",
data="DataDual"),
def=
function(object,
model,
data,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles))
## first obtain the dose-tox curve results from the parent method
start <- callNextMethod(object=object,
model=model,
data=data,
points=data@doseGrid,
quantiles=quantiles,
middle=middle,
...)
## now obtain the dose-biomarker results
## get the biomarker level samples
## at the dose grid points.
biomLevelSamples <- matrix(nrow=sampleSize(object@options),
ncol=data@nGrid)
## evaluate the biomLevels, for all samples.
for(i in seq_len(data@nGrid))
{
## Now we want to evaluate for the
## following dose:
biomLevelSamples[, i] <- biomLevel(dose=data@doseGrid[i],
xLevel=i,
model,
object)
}
## extract middle curve
middleCurve <- apply(biomLevelSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(biomLevelSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
biomResults <- data.frame(middleBiomarker=middleCurve,
lowerBiomarker=quantCurve[1, ],
upperBiomarker=quantCurve[2, ])
## return both, pasted together
return(cbind(start, biomResults))
})
## --------------------------------------------------
## Approximate posterior with (log) normal distribution
## --------------------------------------------------
##' Approximate posterior with (log) normal distribution
##'
##' It is recommended to use \code{\link{set.seed}} before, in order
##' to be able to reproduce the resulting approximating model exactly.
##'
##' @param object the \code{\linkS4class{Samples}} object
##' @param model the \code{\linkS4class{Model}} object
##' @param data the \code{\linkS4class{Data}} object
##' @param \dots additional arguments (see methods)
##' @return the approximation model
##'
##' @export
##' @keywords methods
setGeneric("approximate",
def=
function(object, model, data, ...){
## there should be no default method,
## therefore just forward to next method!
standardGeneric("approximate")},
valueClass="Model")
##' @param points optional parameter, which gives the dose values at which
##' the approximation should rely on (default: 5 values equally spaced from
##' minimum to maximum of the dose grid)
##' @param refDose the reference dose to be used (default: median of
##' \code{points})
##' @param logNormal use the log-normal prior? (not default) otherwise, the
##' normal prior for the logistic regression coefficients is used
##' @param verbose be verbose (progress statements and plot)? (default)
##'
##' @describeIn approximate Here the \dots argument can transport additional arguments for
##' \code{\link{Quantiles2LogisticNormal}}, e.g. in order to control the
##' approximation quality, etc.
##'
##' @example examples/Sample-methods-approximate.R
setMethod("approximate",
signature=
signature(object="Samples"),
def=
function(object,
model,
data,
points=
seq(from=min(data@doseGrid),
to=max(data@doseGrid),
length=5L),
refDose=median(points),
logNormal=FALSE,
verbose=TRUE,
...){
## get the required quantiles at these dose levels:
quants <- fit(object,
model,
data,
points=points,
quantiles=c(0.025, 0.975),
middle=median)
## get better starting values if it is already a logistic normal
## model
if(is(model, "LogisticNormal") && (! logNormal))
{
means <- sapply(object@data,
mean)
cov <- cov(as.data.frame(object@data))
parstart <- c(means[1], means[2],
sqrt(cov[1, 1]), sqrt(cov[2, 2]),
cov2cor(cov)[1, 2])
} else if(is(model, "LogisticLogNormal") && logNormal) {
datTrafo <- with(object@data,
cbind(alpha0,
log(alpha1)))
means <- colMeans(datTrafo)
cov <- cov(datTrafo)
parstart <- c(means[1], means[2],
sqrt(cov[1, 1]), sqrt(cov[2, 2]),
cov2cor(cov)[1, 2])
} else {
parstart <- NULL
}
## run the approx function
quantRes <- Quantiles2LogisticNormal(dosegrid=quants$dose,
refDose=refDose,
lower=quants$lower,
upper=quants$upper,
median=quants$middle,
verbose=verbose,
parstart=parstart,
logNormal=logNormal,
...)
if(verbose)
{
matplot(x=points,
quantRes$required,
type="l", col="blue", lty=1)
matlines(x=points,
quantRes$quantiles,
col="red", lty=1)
legend("bottomright",
legend=c("original", "approximation"),
col=c("blue", "red"),
lty=1,
bty="n")
}
## return the model
return(quantRes$model)
})
## --------------------------------------------------
## Plot dose-tox fit from a model
## --------------------------------------------------
##' Plotting dose-toxicity model fits
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{Model}} object
##' @param data the \code{\linkS4class{Data}} object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-toxicity model fit
##'
##' @example examples/Sample-methods-plot.R
##' @export
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="Samples",
y="Model"),
def=
function(x, y, data, ...,
xlab="Dose level",
ylab="Probability of DLT [%]",
showLegend=TRUE){
## check args
stopifnot(is.bool(showLegend))
## get the fit
plotData <- fit(x,
model=y,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
## make the plot
gdata <-
with(plotData,
data.frame(x=rep(dose, 3),
y=c(middle, lower, upper) * 100,
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotData)),
Type=
factor(c(rep("Estimate",
nrow(plotData)),
rep("95% Credible Interval",
nrow(plotData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
ret <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("red"),
geom="line",
xlab=xlab,
ylab=ylab,
ylim=c(0, 100))
ret <- ret +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2), guide=ifelse(showLegend,
"legend", FALSE))
return(ret)
})
## --------------------------------------------------
## Special method for dual endpoint model
## --------------------------------------------------
##' Plotting dose-toxicity and dose-biomarker model fits
##'
##' When we have the dual endpoint model,
##' also the dose-biomarker fit is shown in the plot
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{DualEndpoint}} object
##' @param data the \code{\linkS4class{DataDual}} object
##' @param extrapolate should the biomarker fit be extrapolated to the whole
##' dose grid? (default)
##' @param showLegend should the legend be shown? (not default)
##' @param \dots additional arguments for the parent method
##' \code{\link{plot,Samples,Model-method}}
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object with the dose-toxicity and dose-biomarker model fits
##'
##' @example examples/Sample-methods-plot-DualEndpoint.R
##' @export
setMethod("plot",
signature=
signature(x="Samples",
y="DualEndpoint"),
def=
function(x, y, data, extrapolate=TRUE, showLegend=FALSE, ...){
stopifnot(is.bool(extrapolate))
## call the superclass method, to get the toxicity plot
plot1 <- callNextMethod(x, y, data, showLegend=showLegend, ...)
## only look at these dose levels for the plot:
xLevels <-
if(extrapolate)
seq_along(data@doseGrid)
else
1:max(data@xLevel)
## get the plot data for the biomarker plot
functionSamples <- matrix(nrow=sampleSize(x@options),
ncol=length(xLevels))
## evaluate the biomLevels, for all samples.
for(i in seq_along(xLevels))
{
## Now we want to evaluate for the
## following dose:
functionSamples[, i] <-
biomLevel(dose=data@doseGrid[xLevels[i]],
xLevel=xLevels[i],
model=y,
samples=x)
}
## extract mean curve
meanCurve <- colMeans(functionSamples)
## extract quantiles
quantiles <- c(0.025, 0.975)
quantCurve <- apply(functionSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
plotData <- data.frame(dose=data@doseGrid[xLevels],
mean=meanCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## make the second plot
gdata <-
with(plotData,
data.frame(x=rep(dose, 3),
y=c(mean, lower, upper),
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotData)),
Type=
factor(c(rep("Estimate",
nrow(plotData)),
rep("95% Credible Interval",
nrow(plotData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
plot2 <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("blue"),
geom="line",
xlab="Dose level",
ylab="Biomarker level")
plot2 <- plot2 +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2),
guide=ifelse(showLegend,
"legend", FALSE))
## arrange both plots side by side
ret <- gridExtra::arrangeGrob(plot1, plot2, ncol=2)
return(ret)
})
## -------------------------------------------------------------------------------------
## Get fitted dose-tox curve from Samples for 'LogisticIndepBeta' model class
## ------------------------------------------------------------------------------------
##' @describeIn fit This method return a data frame with dose, middle lower and upper quantiles
##' for the dose-DLE curve using DLE samples for \dQuote{LogisticIndepBeta} model class
##' @example examples/Samples-method-fitDLE.R
setMethod("fit",
signature=
signature(object="Samples",
model="LogisticIndepBeta",
data="Data"),
def=
function(object,
model,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the dose-tox
## curve at the dose grid points.
probSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
## evaluate the probs, for all samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
probSamples[, i] <- prob(dose=points[i],
model,
object)
}
## extract middle curve
middleCurve <- apply(probSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(probSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## -------------------------------------------------------------------------------------
## Get fitted dose-efficacy curve from Samples for 'Effloglog' model class
## ------------------------------------------------------------------------------------
##' @describeIn fit This method returns a data frame with dose, middle, lower, upper quantiles for
##' the dose-efficacy curve using efficacy samples for \dQuote{Effloglog} model class
##' @example examples/Samples-method-fitEff.R
setMethod("fit",
signature=
signature(object="Samples",
model="Effloglog",
data="DataDual"),
def=
function(object,
model,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the dose-tox
## curve at the dose grid points.
ExpEffSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
## evaluate the probs, for all samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
ExpEffSamples[, i] <- ExpEff(dose=points[i],
model,
object)
}
## extract middle curve
middleCurve <- apply(ExpEffSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(ExpEffSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## ==========================================================================================
## --------------------------------------------------------------------
## Get fitted dose-efficacy based on the Efficacy Flexible model
## -------------------------------------------------------------
##' @describeIn fit This method returns a data frame with dose, middle, lower and upper
##' quantiles for the dose-efficacy curve using efficacy samples for \dQuote{EffFlexi}
##' model class
##' @example examples/Samples-method-fitEffFlexi.R
setMethod("fit",
signature=
signature(object="Samples",
model="EffFlexi",
data="DataDual"),
def=
function(object,
model,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the dose-tox
## curve at the dose grid points.
ExpEffSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
## evaluate the probs, for all samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
ExpEffSamples[, i] <- ExpEff(dose=points[i],
model,
object)
}
## extract middle curve
middleCurve <- apply(ExpEffSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(ExpEffSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## ==============================================================
## ----------------------------------------------------------------
## Get fitted values at all dose levels from gain samples
## -----------------------------------------------------------------
##' Get the fitted values for the gain values at all dose levels based on
##' a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample
##' and data. This method returns a data frame with dose, middle, lower and upper quantiles
##' of the gain value samples
##'
##' @param DLEmodel the DLE pseudo model of \code{\linkS4class{ModelTox}} class object
##' @param DLEsamples the DLE samples of \code{\linkS4class{Samples}} class object
##' @param Effmodel the efficacy pseudo model of \code{\linkS4class{ModelEff}} class object
##' @param Effsamples the efficacy samples of \code{\linkS4class{Samples}} class object
##' @param data the data input of \code{\linkS4class{DataDual}} class object
##' @param \dots additional arguments for methods
##'
##' @export
##' @keywords methods
setGeneric("fitGain",
def=
function(DLEmodel,
DLEsamples,
Effmodel,
Effsamples,
data,
...){
## there should be no default method,
## therefore just forward to next method!
standardGeneric("fitGain")},
valueClass="data.frame")
##' @describeIn fitGain This method returns a data frame with dose, middle, lower, upper quantiles for
##' the gain values obtained given the DLE and the efficacy samples
##' @param points at which dose levels is the fit requested? default is the dose
##' grid
##' @param quantiles the quantiles to be calculated (default: 0.025 and
##' 0.975)
##' @param middle the function for computing the middle point. Default:
##' \code{\link{mean}}
##' @example examples/Samples-method-fitGain.R
setMethod("fitGain",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="Samples",
Effmodel="ModelEff",
Effsamples="Samples",
data="DataDual"),
def=
function(DLEmodel,
DLEsamples,
Effmodel,
Effsamples,
data,
points=data@doseGrid,
quantiles=c(0.025, 0.975),
middle=mean,
...){
## some checks
stopifnot(is.probRange(quantiles),
is.numeric(points))
## first we have to get samples from the gain
## at the dose grid points.
GainSamples <- matrix(nrow=sampleSize(DLEsamples@options),
ncol=length(points))
## evaluate the probs, for all gain samples.
for(i in seq_along(points))
{
## Now we want to evaluate for the
## following dose:
GainSamples[, i] <- gain(dose=points[i],
DLEmodel=DLEmodel,
DLEsamples=DLEsamples,
Effmodel=Effmodel,
Effsamples=Effsamples)
}
## extract middle curve
middleCurve <- apply(GainSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(GainSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(dose=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)
})
## ---------------------------------------------------------------------------------
## Plot the fitted dose-DLE curve with pseudo DLE model with samples
## -------------------------------------------------------------------------------
##' Plot the fitted dose-DLE curve using a \code{\linkS4class{ModelTox}} class model with samples
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{ModelTox}} model class object
##' @param data the \code{\linkS4class{Data}} object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-DLE model fit
##'
##' @example examples/Samples-method-plotModelTox.R
##' @export
##' @keywords methods
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="Samples",
y="ModelTox"),
def=
function(x, y, data, ...,
xlab="Dose level",
ylab="Probability of DLT [%]",
showLegend=TRUE){
## check args
stopifnot(is.bool(showLegend))
## get the fit
plotData <- fit(x,
model=y,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
## make the plot
gdata <-
with(plotData,
data.frame(x=rep(dose, 3),
y=c(middle, lower, upper) * 100,
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotData)),
Type=
factor(c(rep("Estimate",
nrow(plotData)),
rep("95% Credible Interval",
nrow(plotData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
ret <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("red"),
geom="line",
xlab=xlab,
ylab=ylab,
ylim=c(0, 100))
ret <- ret +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2), guide=ifelse(showLegend,
"legend", FALSE))
return(ret)
})
## --------------------------------------------------------------------------------------------
## Plot the fitted dose-efficacy curve using a pseudo efficacy model with samples
## -------------------------------------------------------------------------------------------
##' Plot the fitted dose-efficacy curve using a model from \code{\linkS4class{ModelEff}} class
##' with samples
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{ModelEff}} model class object
##' @param data the \code{\linkS4class{Data}} object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-efficacy model fit
##'
##' @example examples/Samples-method-plotModelEff.R
##' @export
##' @keywords methods
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="Samples",
y="ModelEff"),
def=
function(x, y, data, ...,
xlab="Dose level",
ylab="Expected Efficacy",
showLegend=TRUE){
## check args
stopifnot(is.bool(showLegend))
## get the fit
plotData <- fit(x,
model=y,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
## make the plot
gdata <-
with(plotData,
data.frame(x=rep(dose, 3),
y=c(middle, lower, upper) ,
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotData)),
Type=
factor(c(rep("Estimate",
nrow(plotData)),
rep("95% Credible Interval",
nrow(plotData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
ret <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("blue"),
geom="line",
xlab=xlab,
ylab=ylab,
xlim=c(0,max(data@doseGrid)))
ret <- ret +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2), guide=ifelse(showLegend,
"legend", FALSE))
return(ret)
})
## ----------------------------------------------------------------------------------------
## Plot of fitted dose-DLE curve based on a pseudo DLE model without sample
##-------------------------------------------------------------------------------------
##' Plot of the fitted dose-tox based with a given pseudo DLE model and data without samples
##'
##' @param x the data of \code{\linkS4class{Data}} class object
##' @param y the model of the \code{\linkS4class{ModelTox}} class object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-DLE model plot
##'
##' @example examples/Samples-method-plotModelToxNoSamples.R
##' @export
##' @keywords methods
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="Data",
y="ModelTox"),
def=
function(x,y,
xlab="Dose level",
ylab="Probability of DLE",
showLegend=TRUE,...){
##check args
stopifnot(is.bool(showLegend))
##Make sure the right model estimates are use with the given data
y <- update(object=y,data=x)
##create data frame
plotData <- data.frame(dose=x@doseGrid,
probDLE=prob(dose=x@doseGrid,
model=y))
##Look for TD30 and TD35
TD30 <-dose(prob=0.30,
model=y)
TD35 <-dose(prob=0.35,
model=y)
##make the plot
gdata <- with(plotData,
data.frame(x=dose,
y=probDLE,
group=rep("Estimated DLE",each=nrow(plotData)),
Type=factor(rep("Estimated DLE",nrow(plotData)),levels="Estimated DLE")))
plot1 <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("red"),
geom="line",
xlab=xlab,
ylab=ylab,
ylim=c(0,1))
plot1 <- plot1 + ggplot2::scale_linetype_manual(breaks="Estimated DLE",
values=c(1,2),
guide=ifelse(showLegend,"legend",FALSE))
plot1 <- plot1 +
geom_line(size=1.5,colour="red")
return(plot1)
})
## ---------------------------------------------------------------------------------------------
## Plot the fitted dose-efficacy curve given a pseudo efficacy model without samples
## ----------------------------------------------------------------------------------
##' Plot of the fitted dose-efficacy based with a given pseudo efficacy model and data without samples
##'
##' @param x the data of \code{\linkS4class{DataDual}} class object
##' @param y the model of the \code{\linkS4class{ModelEff}} class object
##' @param xlab the x axis label
##' @param ylab the y axis label
##' @param showLegend should the legend be shown? (default)
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-efficacy model plot
##'
##' @example examples/Samples-method-plotModelEffNoSamples.R
##' @export
##' @keywords methods
##' @importFrom ggplot2 qplot scale_linetype_manual
setMethod("plot",
signature=
signature(x="DataDual",
y="ModelEff"),
def=
function(x,y,...,
xlab="Dose level",
ylab="Expected Efficacy",
showLegend=TRUE){
##check args
stopifnot(is.bool(showLegend))
y <- update(object=y,data=x)
##create data frame
plotEffData<- data.frame(dose=x@doseGrid,
ExpEff=ExpEff(dose=x@doseGrid,
model=y))
##make the second plot
ggdata<-with(plotEffData,
data.frame(x=dose,
y=ExpEff,
group=rep("Estimated Expected Efficacy",each=nrow(plotEffData)),
Type=factor(rep("Estimated Expected Efficacy",nrow(plotEffData)),levels="Estimated Expected Efficacy")))
##Get efficacy plot
plot2 <- ggplot(data=ggdata, aes(x=x,y=y), group=group) +
xlab("Dose Levels")+
ylab(paste("Estimated Expected Efficacy")) + xlim(c(0,max(x@doseGrid))) +
geom_line(colour=I("blue"), size=1.5)
plot2 <- plot2 +
geom_line(size=1.5,colour="blue")
return(plot2)
})
## ----------------------------------------------------------------------------------------------------------
## Plot the gain curve using a pseudo DLE and a pseudo Efficacy model with samples
## ----------------------------------------------------------------------------------------------------
##' Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model,
##' a DLE sample, a given efficacy pseudo model and an efficacy sample
##'
##' @param DLEmodel the dose-DLE model of \code{\linkS4class{ModelTox}} class object
##' @param DLEsamples the DLE sample of \code{\linkS4class{Samples}} class object
##' @param Effmodel the dose-efficacy model of \code{\linkS4class{ModelEff}} class object
##' @param Effsamples the efficacy sample of of \code{\linkS4class{Samples}} class object
##' @param data the data input of \code{\linkS4class{DataDual}} class object
##' @param \dots not used
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the plot
##'
##' @example examples/Samples-method-plotGain.R
##' @export
##' @keywords methods
setGeneric("plotGain",
def=
function(DLEmodel,
DLEsamples,
Effmodel,
Effsamples,
data,...){
standardGeneric("plotGain")})
##' @describeIn plotGain Standard method
setMethod("plotGain",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="Samples",
Effmodel="ModelEff",
Effsamples="Samples"),
def=
function(DLEmodel,DLEsamples,Effmodel,Effsamples,data,...){
##Get fitted values for probabilities of DLE at all dose levels
plotDLEData <- fit(DLEsamples,
model=DLEmodel,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
##Get fitted values for mean efficacy values at all dose levels
plotEffData <- fit(Effsamples,
model=Effmodel,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
##Get fitted values for gain values at all dose levels
plotGainData <- fitGain(DLEmodel=DLEmodel,
DLEsamples=DLEsamples,
Effmodel=Effmodel,
Effsamples=Effsamples,
data=data)
##For each of the dose levels, take the mean for the probabilties of DLE, mean efiicacy values
## and gain values. Hence combine them into a data frame
plotData<-data.frame(dose=rep(data@doseGrid,3),
values=c(plotDLEData$middle,
plotEffData$middle,
plotGainData$middle))
## only the line plots for the mean value of the DLE, efficacy and gain samples
##at all dose levels
gdata<-with(plotData,
data.frame(x=dose,
y=values,
group=c(rep("p(DLE)",length(data@doseGrid)),
rep("Mean Expected Efficacy",length(data@doseGrid)),
rep("Gain",length(data@doseGrid))),
Type=factor("Estimate",levels="Estimate")
))
plot1 <- ggplot(data=gdata, aes(x=x,y=y))+geom_line(aes(group=group,color=group),size=1.5)+
ggplot2::scale_colour_manual(name="curves",values=c("green3","blue","red"))+
xlab("Dose Level")+ xlim(c(0,max(data@doseGrid)))+
ylab(paste("Values")) + ylim(c(min(gdata$y),max(gdata$y)))
return(plot1)
})
##----------------------------------------------------------------------------------------------------
## Plot the gain curve using a pseudo DLE and a pseudo Efficacy model without samples
## ----------------------------------------------------------------------------------------------------
##' Plot the gain curve in addition with the dose-DLE and dose-efficacy curve using a given DLE pseudo model,
##' and a given efficacy pseudo model
##'
##' @describeIn plotGain Standard method
##'
##' @example examples/Samples-method-plotGainNoSamples.R
##' @export
##' @keywords methods
setMethod("plotGain",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="missing",
Effmodel="ModelEff",
Effsamples="missing"),
def=
function(DLEmodel,Effmodel,data,...){
##Make sure the model estimates are corresponds to the input data
DLEmodel <- update(object=DLEmodel,data=data)
Effmodel <- update(object=Effmodel,data=data)
plotData<-data.frame(dose=rep(data@doseGrid,3),
values=c(prob(dose=data@doseGrid,
model=DLEmodel),
ExpEff(dose=data@doseGrid,
model=Effmodel),
gain(dose=data@doseGrid,
DLEmodel=DLEmodel,
Effmodel=Effmodel)))
gdata<-with(plotData,
data.frame(x=dose,
y=values,
group=c(rep("p(DLE)",length(data@doseGrid)),
rep("Expected Efficacy",length(data@doseGrid)),
rep("Gain",length(data@doseGrid))),
Type=factor("Estimate",levels="Estimate")
))
##plot1 <- ggplot(data=gdata, aes(x=x,y=y))+geom_line(aes(group=group,color=group),size=1.5)
plot1 <- ggplot(data=gdata, aes(x=x,y=y))+geom_line(aes(group=group,color=group),size=1.5)+
ggplot2::scale_colour_manual(name="curves",values=c("blue","green3","red"))+
xlab("Dose Level")+ xlim(c(0,max(data@doseGrid)))+
ylab(paste("Values")) + ylim(c(min(gdata$y),max(gdata$y)))
TD30 <- dose(prob=0.3,model=DLEmodel)
Gainfun<-function(DOSE){
-gain(DOSE,DLEmodel=DLEmodel,Effmodel=Effmodel)
}
Gstar<-(optim(min(data@doseGrid),Gainfun,method = "L-BFGS-B",lower=min(data@doseGrid),upper=max(data@doseGrid))$par)
MaxGain<--(optim(min(data@doseGrid),Gainfun,method = "L-BFGS-B",lower=min(data@doseGrid),upper=max(data@doseGrid))$value)
if ((TD30 < min(data@doseGrid))|(TD30 > max(data@doseGrid))) {
plot1<-plot1
print(paste("TD30",paste(TD30," not within dose Grid")))} else {plot1 <-plot1 + geom_point(data=data.frame(x=TD30,y=0.3),aes(x=x,y=y),colour="violet", shape=16, size=8) +
annotate("text",label="p(DLE=0.3)",x=TD30+1,y=0.2,size=5,colour="violet")}
if ((Gstar < min(data@doseGrid))|(Gstar > max(data@doseGrid))) {
plot1<-plot1
print(paste("Gstar=",paste(Gstar," not within dose Grid")))} else {plot1 <- plot1 + geom_point(data=data.frame(x=Gstar,y=MaxGain),aes(x=x,y=y),colour="green3", shape=17, size=8) +
annotate("text",label="Max Gain",x=Gstar,y=MaxGain-0.1,size=5,colour="green3")}
return(plot1)
})
##==========================================================================================
## -------------------------------------------------------------------------------
## Plot of the DLE and efficacy curve sides by side with samples
## -----------------------------------------------------------------------------
##' Plot of the DLE and efficacy curve side by side given a DLE pseudo model,
##' a DLE sample, an efficacy pseudo model and a given efficacy sample
##'
##' @param DLEmodel the pseudo DLE model of \code{\linkS4class{ModelTox}} class object
##' @param DLEsamples the DLE samples of \code{\linkS4class{Samples}} class object
##' @param Effmodel the pseudo efficacy model of \code{\linkS4class{ModelEff}} class object
##' @param Effsamples the Efficacy samples of \code{\linkS4class{Samples}} class object
##' @param data the data input of \code{\linkS4class{DataDual}} class object
##' @param extrapolate should the biomarker fit be extrapolated to the whole
##' dose grid? (default)
##' @param showLegend should the legend be shown? (not default)
##' @param \dots additional arguments for the parent method
##' \code{\link{plot,Samples,Model-method}}
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object with the dose-toxicity and dose-efficacy model fits
##'
##' @example examples/Samples-method-plotDualResponses.R
##'
##' @export
##' @keywords methods
setGeneric("plotDualResponses",
def=
function(DLEmodel,
DLEsamples,
Effmodel,
Effsamples,
data,...){
standardGeneric("plotDualResponses")})
##' @describeIn plotDualResponses function still to be documented
setMethod("plotDualResponses",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="Samples",
Effmodel="ModelEff",
Effsamples="Samples"),
def=
function(DLEmodel, DLEsamples, Effmodel,Effsamples,data, extrapolate=TRUE, showLegend=FALSE,...){
stopifnot(is.bool(extrapolate))
## Get Toxicity plot
## get the fit
plotDLEData <- fit(DLEsamples,
model=DLEmodel,
data=data,
quantiles=c(0.025, 0.975),
middle=mean)
## make the plot
gdata <-
with(plotDLEData,
data.frame(x=rep(dose, 3),
y=c(middle, lower, upper) * 100,
group=
rep(c("mean", "lower", "upper"),
each=nrow(plotDLEData)),
Type=
factor(c(rep("Estimate",
nrow(plotDLEData)),
rep("95% Credible Interval",
nrow(plotDLEData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
ret1 <- ggplot2::qplot(x=x,
y=y,
data=gdata,
group=group,
linetype=Type,
colour=I("red"),
geom="line",
xlab="Dose Levels",
ylab="Probability of DLE [%]",
ylim=c(0, 100))
ret1 <- ret1 + ggplot2::scale_linetype_manual(breaks=c("Estimate",
"95% Credible Interval"),
values=c(1,2), guide=ifelse(showLegend,
"legend", FALSE))
##only look at these dose levels for the plot:
xLevels<- if (extrapolate) {
seq_along(data@doseGrid)} else {1:max(data@xLevel)}
##get the plot data for the efficacy
functionSamples <- matrix(nrow=sampleSize(Effsamples@options),
ncol=length(xLevels))
##evaluate the efficacy for all samples
for (i in seq_along(xLevels))
{
##Now we want to evaluate for the following dose
functionSamples[,i] <- ExpEff(dose=data@doseGrid[xLevels[i]],
model=Effmodel,
samples=Effsamples)
}
##extract mean curve
meanCurve <- colMeans(functionSamples)
##extract quantiles
quantiles=c(0.025, 0.975)
quantCurve <- apply(functionSamples, 2L, quantile, prob=quantiles)
##now create the data frame
plotEffData <-data.frame(dose=data@doseGrid[xLevels],
mean=meanCurve,
lower=quantCurve[1,],
upper=quantCurve[2,])
##make the second plot
ggdata<-with(plotEffData, data.frame(x=rep(dose,3),
y=c(mean,lower,upper),
group=
rep(c("mean","lower","upper"),
each=nrow(plotEffData)),
Type=
factor(c(rep("Estimate",
nrow(plotEffData)),
rep("95% Credible Interval",
nrow(plotEffData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
plot2 <- ggplot2::qplot(x=x,
y=y,
data=ggdata,
group=group,
linetype=Type,
colour=I("blue"),
geom="line",
xlab="Dose level",
ylab="Expected Efficacy")
plot2 <- plot2 +
ggplot2::scale_linetype_manual(breaks=
c("Estimate",
"95% Credible Interval"),
values=c(1,2),
guide=ifelse(showLegend,
"legend", FALSE))
## arrange both plots side by side
ret <- gridExtra::arrangeGrob(ret1, plot2, ncol=2)
return(ret)
})
##------------------------------------------------------------------------------
## Plot of the DLE and efficacy curve sides by side without samples
## -----------------------------------------------------------------------------
##' Plot of the dose-DLE and dose-efficacy curve side by side given a DLE pseudo model
##' and a given pseudo efficacy model without DLE and efficacy samples
##'
##' @describeIn plotDualResponses Plot the DLE and efficacy curve side by side given a DLE model
##' and an efficacy model without any samples
##'
##' @example examples/Samples-method-plotDualResponsesNoSamples.R
##'
##' @export
##' @keywords methods
setMethod("plotDualResponses",
signature=
signature(DLEmodel="ModelTox",
DLEsamples="missing",
Effmodel="ModelEff",
Effsamples="missing"),
def=
function(DLEmodel,Effmodel,data,...){
## Get Toxicity plot
## get the fit
##Make sure the model estimates are corresponds to the input data
DLEmodel <- update(object=DLEmodel,data=data)
Effmodel <- update(object=Effmodel,data=data)
plotDLEData <- data.frame(dose=data@doseGrid,
probDLE=prob(dose=data@doseGrid,
model=DLEmodel))
## make the plot
gdata <- with(plotDLEData,
data.frame(x=dose,
y=probDLE,
group=rep("Estimated DLE",each=nrow(plotDLEData)),
Type=factor(rep("Estimated DLE",nrow(plotDLEData)),levels="Estimated DLE")))
plot1 <- ggplot(data=gdata, aes(x=x,y=y), group=group) +
xlab("Dose Levels")+
ylab(paste("Probability of DLE")) + ylim(c(0,1)) + xlim(c(0,max(data@doseGrid))) +
geom_line(colour=I("red"), size=1.5)
plot1 <- plot1 +
geom_line(size=1.5,colour="red")
##only look at these dose levels for the plot:
##get the plot data for the efficacy
plotEffData<- data.frame(dose=data@doseGrid,
ExpEff=ExpEff(dose=data@doseGrid,
model=Effmodel))
##make the second plot
ggdata<-with(plotEffData,
data.frame(x=dose,
y=ExpEff,
group=rep("Estimated Expected Efficacy",each=nrow(plotEffData)),
Type=factor(rep("Estimated Expected Efficacy",nrow(plotEffData)),levels="Estimated Expected Efficacy")))
##Get efficacy plot
plot2 <- ggplot(data=ggdata, aes(x=x,y=y), group=group) +
xlab("Dose Levels")+
ylab(paste("Estimatimated Expected Efficacy")) + xlim(c(0,max(data@doseGrid))) +
geom_line(colour=I("blue"), size=1.5)
plot2 <- plot2 +
geom_line(size=1.5,colour="blue")
## arrange both plots side by side
ret <- gridExtra::arrangeGrob(plot1, plot2, ncol=2)
return(ret)
})
## =======================================================================================================
## ----------------------------------------------------------------
## Get fitted DLT free survival (piecewise exponential model) based on
## the DA-CRM model
## -----------------------------------------------------------------
##' Get the fitted DLT free survival (piecewise exponential model).
##' This function returns a data frame with dose, middle, lower and upper
##' quantiles for the `PEM` curve. If hazard=TRUE,
##' @param object mcmc samples
##' @param model the mDA-CRM model
##' @param data the data input, a \code{\linkS4class{DataDA}} class object
##' @param quantiles the quantiles to be calculated (default: 0.025 and
##' 0.975)
##' @param middle the function for computing the middle point. Default:
##' \code{\link{mean}}
##' @param hazard should the the hazard over time be plotted based on the `PEM`? (not default)
##' Otherwise ...
##' @param \dots additional arguments for methods
##'
##' @export
##' @keywords methods
setGeneric("fitPEM",
def=
function(object,
model,
data,
quantiles=c(0.025, 0.975),
middle=mean,
hazard=FALSE,
...){
## there should be no default method,
## therefore just forward to next method!
standardGeneric("fitPEM")},
valueClass="data.frame")
#' Likelihood of DLTs in each interval
#'
#' This is a helper function for the `fitPEM` methods below.
#'
#' @param lambda the vector of piecewise hazards
#' @param Tmax the end of the time interval for DLTs
#' @return vector with the probabilities for DLTs within the intervals.
#'
#' @keywords internal
DLTLikelihood <- function(lambda,
Tmax)
{
npiece <-length(lambda)
h <- seq(from=0L, to=Tmax, length=npiece + 1)
#Length of each time interval;
sT<-rep(0,npiece)
for(i in 1:npiece){
sT[i]<-h[i+1]-h[i]
}
#calculate the exponential part of the distribution:
s_ij <- function(t, j)
{
if(t > h[j])
{
min(t-h[j],h[j+1]-h[j])
} else {
0
}
}
#The cumulative hazard function
expNmu <- function(t)
{
ret <- 1
for(j in 1:npiece)
{
ret <-ret * exp(- lambda[j] * s_ij(t, j))
}
return(ret)
}
#CDF of the piecewise exponential
piece_exp.cdf<-function(x){
1-expNmu(x)
}
DLTFreeS<-function(x)
{
(expNmu(x)-expNmu(Tmax))/piece_exp.cdf(Tmax)
}
pDLT<-rep(0,npiece+1)
for(i in 1:(npiece)){
pDLT[i] <- DLTFreeS(h[i]) - DLTFreeS(h[i+1])
}
return(pDLT)
}
## --------------------------------------------------------------------
## Get fitted DLT free survival (piecewise exponential model) based on
## the DA-CRM model
## -------------------------------------------------------------
##' @describeIn fitPEM This method works for the \code{\linkS4class{DALogisticLogNormal}}
##' model class.
##' @example examples/Samples-method-fitPEM-DALogisticLogNormal.R
setMethod("fitPEM",
signature=
signature(object="Samples",
model="DALogisticLogNormal",
data="DataDA"),
def=
function(object,
model,
data,
quantiles=c(0.025, 0.975),
middle=mean,
hazard=FALSE,
...){
## some checks
stopifnot(is.probRange(quantiles))
##Plot points
points<-seq(0, data@Tmax, length=model@npiece+1)
## first we have to get samples from the PEM
## at intercept points and 2 middel points between
## intercepts.
PEMSamples <- matrix(nrow=sampleSize(object@options),
ncol=length(points))
i_max<-max(seq_along(points))
## evaluate the probs, for all samples.
#The PEM
if(hazard==FALSE){
PEMSamples<-t(apply(object@data$lambda,1,function(x){
fit<-DLTLikelihood(x, data@Tmax)
return(fit)
}))
}else if(hazard==TRUE){
for(i in seq_along(points))
{
if(i==i_max){
PEMSamples[,i_max]<-object@data$lambda[, model@npiece]}else{
PEMSamples[,i]<-object@data$lambda[,i]}
}
}
## extract middle curve
middleCurve <- apply(PEMSamples, 2L, FUN=middle)
## extract quantiles
quantCurve <- apply(PEMSamples, 2L, quantile,
prob=quantiles)
## now create the data frame
ret <- data.frame(time=points,
middle=middleCurve,
lower=quantCurve[1, ],
upper=quantCurve[2, ])
## return it
return(ret)})
## =======================================================================================================
## --------------------------------------------------
## Plot survival curve fit over time
## --------------------------------------------------
## todo: add example file
##' Plotting dose-toxicity model fits
##'
##' @param x the \code{\linkS4class{Samples}} object
##' @param y the \code{\linkS4class{DALogisticLogNormal}} object
##' @param data the \code{\linkS4class{DataDA}} object
##' @param hazard see \code{\link{fitPEM}} for the explanation
##' @param \dots not used
##' @param showLegend should the legend be shown? (default)
##' @return This returns the \code{\link[ggplot2]{ggplot}}
##' object for the dose-toxicity model fit
##'
##' @export
##' @importFrom ggplot2 qplot scale_linetype_manual
##' @importFrom gridExtra arrangeGrob
setMethod("plot",
signature=
signature(x="Samples",
y="DALogisticLogNormal"
),
def=
function(x, y, data, hazard=FALSE, ...,
showLegend=TRUE){
## check args
stopifnot(is.bool(showLegend))
## call the superclass method, to get the toxicity plot
plot1 <- callNextMethod(x, y, data, showLegend=showLegend, ...)
## get the fit
fitData <- fitPEM(x,
model=y,
data=data,
quantiles=c(0.025,0.975),
middle=mean,
hazard=hazard)
## make the plot
Tpoints<-seq(0,data@Tmax,length=y@npiece+1)
plotData <-
with(fitData,
data.frame(x=rep(Tpoints, 3),
y=c(middle, lower, upper) * 100,
group=
rep(c("mean", "lower", "upper"),
each=nrow(fitData)),
Type=
factor(c(rep("Estimate",
nrow(fitData)),
rep("95% Credible Interval",
nrow(fitData) * 2)),
levels=
c("Estimate",
"95% Credible Interval"))))
plot2 <- qplot(x=x,
y=y,
data=plotData,
group=group,
linetype=Type,
colour=I("blue"),
geom="step",
xlab="Time",
ylab=if(hazard) "Hazard rate*100" else "Probability of DLT [%]",
ylim=if(hazard) range(plotData$y) else c(0, 100))
ret <- gridExtra::arrangeGrob(plot1, plot2, ncol=2)
return(ret)
})
## =======================================================================================================
# nolint end
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