R/plot.trtsel.R
In mdbrown/TreatmentSelection: Evaluate Treatment Selection Biomarkers
#' plot risk curves, treatment effect curves or cdf of risk for a trtsel
#' object.
#'
#' Plots a treatment selection marker. Estimated "risk curves" and "treatment
#' effect curves" are plotted. An object of class "trtsel" must first be
#' created using the function "trtsel" by supplying a data.frame containing
#' marker, treatment, and event status information.
#'
#'
#' @aliases plot.trtsel plot
#' @param x An object of class "trtsel", created by using the function
#' "trtsel."
#' @param bootstraps Number of bootstrap replicates for creating confidence
#' intervals and bands. Default value is 500. Set bootstraps=0 if no confidence
#' intervals or bands are desired.
#' @param plot.type Type of plot to produce. Options are "risk" (default) for
#' risk curves which show risk of adverse event as a function of marker and
#' treatment, "treatment effect" for the treatment effect curve which shows the
#' difference in marker-specific risk of adverse event with vs. without
#' treatment, "cdf" for the cumulative distribution function of the treatment
#' effect, or "selection impact" to show a plot of the estimated event rate if
#' different proportions of the population are treated. Cdf plots cannot be
#' generated for a discrete marker.
#' @param ci Indication of whether horizontal or vertical confidence intervals
#' are desired. Character string of either "horizontal" (default) or
#' "vertical." For a discrete marker only vertical confidence bands can be
#' produced. See note below for more information.
#' @param alpha (1-alpha)*100\% confidence intervals are calculated. Default
#' value is alpha = 0.05 which yields 95\% CI's.
#' @param fixed.values A numeric vector indicating fixed values on the x- or
#' y-axes at which bootstrap confidence intervals are provided. If
#' "fixed.values" are provided, point-wise confidence intervals will be printed
#' (i.e. conf.bands will be taken as FALSE). Only applies to a continuous
#' marker.
#' @param offset If confidence intervals are to be plotted for specified
#' fixed.values, offset is the amount of distance to offset confidence
#' intervals so that they do not overlap on the plot. The default value is
#' 0.01. Only applies to a continuous marker.
#' @param conf.bands Indication of whether pointwise confidence intervals are
#' shown for the curve(s). TRUE (default) or FALSE. If "fixed.values" are
#' input, this option is ignored and no confidence bands are produced.
#' @param conf.bandsN If conf.bands = TRUE, the number of points along the x-
#' or y-axis at which to calculate the pointwise confidence intervals. The
#' default is 100.
#' @param trt.names A vector of length 2 indicating the names for the two
#' treatment options, T= 1 and T = 0, respectively, for the plot legend. This
#' option is only used when plot.type="risk". The default value is
#' c("Treatment", "No Treatment").
#' @param xlab A label for the x-axis. Default values depend on plot.type.
#' @param ylab A label for the y-axis. Default values depend on plot.type.
#' @param xlim The limits for the x-axisof the plot, in the form c(x1,x2)
#' @param ylim The limits for the y-axis of the plot, in the form c(y1,y2)
#' @param main The main title for the plot.
#' @param show.marker.axis For risk curve plots with a continuous marker,
#' should an axis showing marker values be displayed? default is TRUE.
#' @param \dots N/A
#' @return
#'
#' Generates a plot. In addition, if the function call is assigned to a
#' variable (as in "myplot <- plot(mytrtsel)"), plot.trtsel returns a list
#' composed of: \item{plot}{ ggplot object containing the printed plot. }
#' \item{ci.bounds}{ A data.frame containing the bounds of the bootstrap-based
#' confidence intervals, along with the fixed.values they are centered around,
#' if applicable. }
#' @note For a discrete marker, vertical confidence intervals are generated by
#' fixing the marker value and generating a bootstrap distribution for risk
#' given marker. Bands are then generated based on the percentiles of the
#' empirical bootstrap distribution. For a continuous marker, vertical
#' confidence intervals are generated by fixing the empirical marker quantiles,
#' and generating the empirical distribution of risk given marker percentile.
#' @seealso \code{\link{trtsel}} for creating trtsel objects,
#' \code{\link{evaluate.trtsel}} for evaluating marker performance,
#' \code{\link{calibrate.trtsel}} for assessing model calibration, and
#' \code{\link{compare.trtsel}} to compare two trtsel object.
#' @examples
#'
#'
#' data(tsdata)
#'
#' ###########################
#' ## Create trtsel objects
#' ###########################
#'
#' trtsel.Y1 <- trtsel(event ~ Y1*trt,
#' treatment.name = "trt",
#' data = tsdata,
#' study.design = "RCT",
#' default.trt = "trt all")
#'
#'
#'
#'
#' ##########################
#' ## Use the plot function
#' ##########################
#'
#' # Plot risk curves
#' plot(trtsel.Y1, main = "Marker Y1",
#' plot.type = "risk", bootstraps = 10, #set higher in practice
#' trt.names=c("trt","no trt"))
#'
#' # Now with confidence intervals around fixed.values
#' plot(trtsel.Y1, main = "Marker Y1",
#' plot.type = "risk", ci = "horizontal",
#' fixed.values = c(.2, .5),
#' offset = .01, bootstraps = 10,
#' trt.names=c("trt","no trt"))
#'
#' # Plot treatment effect curves
#' #with confidence intervals around fixed.values
#' plot(trtsel.Y1, main = "Marker Y1",
#' plot.type = "treatment effect",
#' ci = "vertical", conf.bands = FALSE,
#' fixed.values = c(10, 20), bootstraps = 10)
#'
#' @import ggplot2
#' @import grid
#' @import graphics
#' @method plot trtsel
#' @export
plot.trtsel <-
function(x, bootstraps = 500,
plot.type = c( "treatment effect","risk", "cdf", "selection impact"),
ci = c("default", "horizontal", "vertical", "none"),
alpha = .05,
fixed.values = NULL,
offset = 0.01,
conf.bands = TRUE,
conf.bandsN = 100,
trt.names = c("Treatment", " No \nTreatment"),
xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, main = NULL, show.marker.axis = TRUE,
...)
{
if(!is.trtsel(x)) stop("x must be an object of class 'trtsel' created by using the function 'trtsel' see ?trtsel for more help")
plot.type <- match.arg(plot.type)
if(!is.element(plot.type, c("risk", "treatment effect", "cdf", "selection impact"))){
stop("plot.type must be one of \"treatment effect\", \"risk\", \"cdf\" or \"selection impact\"")
}
stopifnot(length(plot.type) ==1)
ci <- match.arg(ci)
if(!is.element(ci, c("default", "horizontal", "vertical", "none"))){
stop("ci must be one of \"default\", \"horizontal\", or \"vertical\" or \"none\" ")
}
if(alpha<0 | alpha > 1) stop("Error: alpha should be between 0 and 1")
if(bootstraps < 2) warning("Number of bootstraps must be greater than 1, bootstrap confidence intervals will not be computed")
if(ci == "none") bootstraps = 0;
# theta curves can only be plotted for cohort data and with vertical ci's
if(substr(plot.type, 1, 3)=="sel"){
# if(substr(x$model.fit$study.design, 1, 3) != "ran") stop("theta curves cannot be created for subcohort designs")
if(substr(ci, 1, 1) =="h") {
warning("horizontal ci's are not available for selection impact curves, vertical ci's will be calculated")
ci = "vertical"
}
}
#set default ci's
if(ci =="default"){
#continuous marker
if(is.null(x$model.fit$disc.rec.no.trt)){
if(substr(plot.type, 1, 3) =="ris") ci = "horizontal"
if(substr(plot.type, 1, 3) =="tre") ci = "horizontal"
if(substr(plot.type, 1, 3) =="cdf") ci = "vertical"
if(substr(plot.type, 1, 3) =="sel") ci = "vertical"
}else{
if(substr(plot.type, 1, 3) =="ris") ci = "vertical"
if(substr(plot.type, 1, 3) =="tre") ci = "vertical"
if(substr(plot.type, 1, 3) =="cdf") ci = "horizontal"
}
print(paste(ci, "confidence intervals will be plotted."))
}
#no cdf plots for binary marker
if(!is.null(x$model.fit$disc.rec.no.trt)){
if(is.element(substr(plot.type, 1, 3), c("cdf", "sel"))) stop("cdf or selection impact plots cannot be created for a binary marker. Please choose plot.type to be \"risk\" or \"treatment effect\" ")
}
#save the current plot parameters
#old.par <- par(no.readonly = TRUE)
#extract the needed data from x, which is our TrtSel object
rho <- x$model.fit$cohort.attributes
if(length(rho) == 4) rho = c(rho, -9999, -9999, -9999) #accomodate the nested case-control design
study.design <- x$model.fit$study.design
link <- x$model.fit$family$family
boot.sample <- x$functions$boot.sample
get.F <- x$functions$get.F
delta <- x$derived.data$trt.effect
if(link == "risks_provided") {
marker = NULL
show.marker.axis = FALSE
}
if(is.null(x$model.fit$disc.rec.no.trt)){ #continuous marker
plot.functions <- list( predcurvePLOT_gg, trteffectPLOT_gg, CDFdeltaPLOT_gg, SelectionImpactPLOT_gg)
if(length(fixed.values)!=0) conf.bands = FALSE
if(conf.bands & length(fixed.values)==0 ){
#create fixed values
if(substr(ci, 1,1 )=="v"){
if(is.element(substr(plot.type, 1,3), c("tre", "ris", "sel"))) fixed.values = seq(from = 1, to = 100, length.out = conf.bandsN)
else if(substr(plot.type, 1,3)=="cdf") fixed.values = seq(from = min(delta), to = max(delta), length.out = conf.bandsN)
}else{
if(is.element(substr(plot.type, 1,3), c("cdf"))) fixed.values = seq(from = 1, to = 100, length.out = conf.bandsN)
else if(substr(plot.type, 1,3)=="tre") fixed.values = seq(from = min(delta), to = max(delta), length.out = conf.bandsN)
else if(substr(plot.type, 1,3)=="ris"){
allrisks <- c(x$derived.data$fittedrisk.t0,
x$derived.data$fittedrisk.t1)
fixed.values = seq(from = min(allrisks), to = max(allrisks), length.out = conf.bandsN)
}
## add case for theta curve
}
offset = 0
}
##Bootstrap for confidence intervals...
#bootstrapping done by fixing response and strapping marker
if((conf.bands & bootstraps>1) | (length(fixed.values)>0 & bootstraps > 1)){
ci.bounds <- get.plot.ci(x,
plot.type,
ci, bootstraps,
fixed.values =fixed.values,
alpha = alpha)
}else{
ci.bounds <- NULL
conf.bands = FALSE
}
## end Bootstrap
}else{
#discrete marker...ignore fixed values and ci bands, we only need ci's around the observed points
plot.functions <- list( predcurvePLOT_gg_disc, trteffectPLOT_gg_disc, CDFdeltaPLOT_gg_disc)
##Bootstrap for confidence intervals...
#much simpler for a discrete marker
if(( conf.bands & bootstraps>1) ){
ci.bounds <- get.plot.ci_disc(x, plot.type,
ci, bootstraps,
alpha)
ci = ci.bounds$newci
ci.bounds = ci.bounds$myconf.ints
}else{
ci.bounds <- NULL
conf.bands = FALSE
}
## end Bootstrap
}
tmp.plotfun <- plot.functions[[match(plot.type, c("risk", "treatment effect", "cdf", "selection impact"))]]
if(is.null(x$model.fit$disc.rec.no.trt)){ # continous marker
if(substring(plot.type, 1, 4) == "risk"){
curves <- tmp.plotfun(x, ci, ci.bounds, get.F, fixed.values, conf.bands, rho, trt.names, xlab, ylab, xlim, ylim, main, show.marker.axis, offset = offset)
if(!is.null(ci.bounds)){
ci.bounds <- data.frame(t(ci.bounds))
ci.bounds <- cbind(fixed.values, ci.bounds)
names(ci.bounds) <- c("fixed.values", "trt0.lower", "trt0.upper", "trt1.lower", "trt1.upper")
}
}else{
curves <- tmp.plotfun(x, ci, ci.bounds, get.F, fixed.values, conf.bands, rho, xlab, ylab, xlim, ylim, main)
if(!is.null(ci.bounds)){
ci.bounds <- data.frame(t(ci.bounds))
ci.bounds <- cbind(fixed.values, ci.bounds)
names(ci.bounds) <- c("fixed.values", "lower", "upper")
}
}
}else{ # discrete marker
curves <- tmp.plotfun(x=x, ci = ci, ci.bounds = ci.bounds, get.F = get.F,
xlab = xlab, ylab=ylab,
xlim = xlim, ylim=ylim,
main=main, trt.names = trt.names)
if(!is.null(ci.bounds)){
ci.bounds <- data.frame(curves[[2]])
}
}
#par(old.par)
invisible(list("plot" = curves[[1]], "ci.bounds" = ci.bounds))
}
mdbrown/TreatmentSelection documentation built on May 22, 2019, 3:23 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' plot risk curves, treatment effect curves or cdf of risk for a trtsel
#' object.
#'
#' Plots a treatment selection marker. Estimated "risk curves" and "treatment
#' effect curves" are plotted. An object of class "trtsel" must first be
#' created using the function "trtsel" by supplying a data.frame containing
#' marker, treatment, and event status information.
#'
#'
#' @aliases plot.trtsel plot
#' @param x An object of class "trtsel", created by using the function
#' "trtsel."
#' @param bootstraps Number of bootstrap replicates for creating confidence
#' intervals and bands. Default value is 500. Set bootstraps=0 if no confidence
#' intervals or bands are desired.
#' @param plot.type Type of plot to produce. Options are "risk" (default) for
#' risk curves which show risk of adverse event as a function of marker and
#' treatment, "treatment effect" for the treatment effect curve which shows the
#' difference in marker-specific risk of adverse event with vs. without
#' treatment, "cdf" for the cumulative distribution function of the treatment
#' effect, or "selection impact" to show a plot of the estimated event rate if
#' different proportions of the population are treated. Cdf plots cannot be
#' generated for a discrete marker.
#' @param ci Indication of whether horizontal or vertical confidence intervals
#' are desired. Character string of either "horizontal" (default) or
#' "vertical." For a discrete marker only vertical confidence bands can be
#' produced. See note below for more information.
#' @param alpha (1-alpha)*100\% confidence intervals are calculated. Default
#' value is alpha = 0.05 which yields 95\% CI's.
#' @param fixed.values A numeric vector indicating fixed values on the x- or
#' y-axes at which bootstrap confidence intervals are provided. If
#' "fixed.values" are provided, point-wise confidence intervals will be printed
#' (i.e. conf.bands will be taken as FALSE). Only applies to a continuous
#' marker.
#' @param offset If confidence intervals are to be plotted for specified
#' fixed.values, offset is the amount of distance to offset confidence
#' intervals so that they do not overlap on the plot. The default value is
#' 0.01. Only applies to a continuous marker.
#' @param conf.bands Indication of whether pointwise confidence intervals are
#' shown for the curve(s). TRUE (default) or FALSE. If "fixed.values" are
#' input, this option is ignored and no confidence bands are produced.
#' @param conf.bandsN If conf.bands = TRUE, the number of points along the x-
#' or y-axis at which to calculate the pointwise confidence intervals. The
#' default is 100.
#' @param trt.names A vector of length 2 indicating the names for the two
#' treatment options, T= 1 and T = 0, respectively, for the plot legend. This
#' option is only used when plot.type="risk". The default value is
#' c("Treatment", "No Treatment").
#' @param xlab A label for the x-axis. Default values depend on plot.type.
#' @param ylab A label for the y-axis. Default values depend on plot.type.
#' @param xlim The limits for the x-axisof the plot, in the form c(x1,x2)
#' @param ylim The limits for the y-axis of the plot, in the form c(y1,y2)
#' @param main The main title for the plot.
#' @param show.marker.axis For risk curve plots with a continuous marker,
#' should an axis showing marker values be displayed? default is TRUE.
#' @param \dots N/A
#' @return
#'
#' Generates a plot. In addition, if the function call is assigned to a
#' variable (as in "myplot <- plot(mytrtsel)"), plot.trtsel returns a list
#' composed of: \item{plot}{ ggplot object containing the printed plot. }
#' \item{ci.bounds}{ A data.frame containing the bounds of the bootstrap-based
#' confidence intervals, along with the fixed.values they are centered around,
#' if applicable. }
#' @note For a discrete marker, vertical confidence intervals are generated by
#' fixing the marker value and generating a bootstrap distribution for risk
#' given marker. Bands are then generated based on the percentiles of the
#' empirical bootstrap distribution. For a continuous marker, vertical
#' confidence intervals are generated by fixing the empirical marker quantiles,
#' and generating the empirical distribution of risk given marker percentile.
#' @seealso \code{\link{trtsel}} for creating trtsel objects,
#' \code{\link{evaluate.trtsel}} for evaluating marker performance,
#' \code{\link{calibrate.trtsel}} for assessing model calibration, and
#' \code{\link{compare.trtsel}} to compare two trtsel object.
#' @examples
#'
#'
#' data(tsdata)
#'
#' ###########################
#' ## Create trtsel objects
#' ###########################
#'
#' trtsel.Y1 <- trtsel(event ~ Y1*trt,
#' treatment.name = "trt",
#' data = tsdata,
#' study.design = "RCT",
#' default.trt = "trt all")
#'
#'
#'
#'
#' ##########################
#' ## Use the plot function
#' ##########################
#'
#' # Plot risk curves
#' plot(trtsel.Y1, main = "Marker Y1",
#' plot.type = "risk", bootstraps = 10, #set higher in practice
#' trt.names=c("trt","no trt"))
#'
#' # Now with confidence intervals around fixed.values
#' plot(trtsel.Y1, main = "Marker Y1",
#' plot.type = "risk", ci = "horizontal",
#' fixed.values = c(.2, .5),
#' offset = .01, bootstraps = 10,
#' trt.names=c("trt","no trt"))
#'
#' # Plot treatment effect curves
#' #with confidence intervals around fixed.values
#' plot(trtsel.Y1, main = "Marker Y1",
#' plot.type = "treatment effect",
#' ci = "vertical", conf.bands = FALSE,
#' fixed.values = c(10, 20), bootstraps = 10)
#'
#' @import ggplot2
#' @import grid
#' @import graphics
#' @method plot trtsel
#' @export
plot.trtsel <-
function(x, bootstraps = 500,
plot.type = c( "treatment effect","risk", "cdf", "selection impact"),
ci = c("default", "horizontal", "vertical", "none"),
alpha = .05,
fixed.values = NULL,
offset = 0.01,
conf.bands = TRUE,
conf.bandsN = 100,
trt.names = c("Treatment", " No \nTreatment"),
xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, main = NULL, show.marker.axis = TRUE,
...)
{
if(!is.trtsel(x)) stop("x must be an object of class 'trtsel' created by using the function 'trtsel' see ?trtsel for more help")
plot.type <- match.arg(plot.type)
if(!is.element(plot.type, c("risk", "treatment effect", "cdf", "selection impact"))){
stop("plot.type must be one of \"treatment effect\", \"risk\", \"cdf\" or \"selection impact\"")
}
stopifnot(length(plot.type) ==1)
ci <- match.arg(ci)
if(!is.element(ci, c("default", "horizontal", "vertical", "none"))){
stop("ci must be one of \"default\", \"horizontal\", or \"vertical\" or \"none\" ")
}
if(alpha<0 | alpha > 1) stop("Error: alpha should be between 0 and 1")
if(bootstraps < 2) warning("Number of bootstraps must be greater than 1, bootstrap confidence intervals will not be computed")
if(ci == "none") bootstraps = 0;
# theta curves can only be plotted for cohort data and with vertical ci's
if(substr(plot.type, 1, 3)=="sel"){
# if(substr(x$model.fit$study.design, 1, 3) != "ran") stop("theta curves cannot be created for subcohort designs")
if(substr(ci, 1, 1) =="h") {
warning("horizontal ci's are not available for selection impact curves, vertical ci's will be calculated")
ci = "vertical"
}
}
#set default ci's
if(ci =="default"){
#continuous marker
if(is.null(x$model.fit$disc.rec.no.trt)){
if(substr(plot.type, 1, 3) =="ris") ci = "horizontal"
if(substr(plot.type, 1, 3) =="tre") ci = "horizontal"
if(substr(plot.type, 1, 3) =="cdf") ci = "vertical"
if(substr(plot.type, 1, 3) =="sel") ci = "vertical"
}else{
if(substr(plot.type, 1, 3) =="ris") ci = "vertical"
if(substr(plot.type, 1, 3) =="tre") ci = "vertical"
if(substr(plot.type, 1, 3) =="cdf") ci = "horizontal"
}
print(paste(ci, "confidence intervals will be plotted."))
}
#no cdf plots for binary marker
if(!is.null(x$model.fit$disc.rec.no.trt)){
if(is.element(substr(plot.type, 1, 3), c("cdf", "sel"))) stop("cdf or selection impact plots cannot be created for a binary marker. Please choose plot.type to be \"risk\" or \"treatment effect\" ")
}
#save the current plot parameters
#old.par <- par(no.readonly = TRUE)
#extract the needed data from x, which is our TrtSel object
rho <- x$model.fit$cohort.attributes
if(length(rho) == 4) rho = c(rho, -9999, -9999, -9999) #accomodate the nested case-control design
study.design <- x$model.fit$study.design
link <- x$model.fit$family$family
boot.sample <- x$functions$boot.sample
get.F <- x$functions$get.F
delta <- x$derived.data$trt.effect
if(link == "risks_provided") {
marker = NULL
show.marker.axis = FALSE
}
if(is.null(x$model.fit$disc.rec.no.trt)){ #continuous marker
plot.functions <- list( predcurvePLOT_gg, trteffectPLOT_gg, CDFdeltaPLOT_gg, SelectionImpactPLOT_gg)
if(length(fixed.values)!=0) conf.bands = FALSE
if(conf.bands & length(fixed.values)==0 ){
#create fixed values
if(substr(ci, 1,1 )=="v"){
if(is.element(substr(plot.type, 1,3), c("tre", "ris", "sel"))) fixed.values = seq(from = 1, to = 100, length.out = conf.bandsN)
else if(substr(plot.type, 1,3)=="cdf") fixed.values = seq(from = min(delta), to = max(delta), length.out = conf.bandsN)
}else{
if(is.element(substr(plot.type, 1,3), c("cdf"))) fixed.values = seq(from = 1, to = 100, length.out = conf.bandsN)
else if(substr(plot.type, 1,3)=="tre") fixed.values = seq(from = min(delta), to = max(delta), length.out = conf.bandsN)
else if(substr(plot.type, 1,3)=="ris"){
allrisks <- c(x$derived.data$fittedrisk.t0,
x$derived.data$fittedrisk.t1)
fixed.values = seq(from = min(allrisks), to = max(allrisks), length.out = conf.bandsN)
}
## add case for theta curve
}
offset = 0
}
##Bootstrap for confidence intervals...
#bootstrapping done by fixing response and strapping marker
if((conf.bands & bootstraps>1) | (length(fixed.values)>0 & bootstraps > 1)){
ci.bounds <- get.plot.ci(x,
plot.type,
ci, bootstraps,
fixed.values =fixed.values,
alpha = alpha)
}else{
ci.bounds <- NULL
conf.bands = FALSE
}
## end Bootstrap
}else{
#discrete marker...ignore fixed values and ci bands, we only need ci's around the observed points
plot.functions <- list( predcurvePLOT_gg_disc, trteffectPLOT_gg_disc, CDFdeltaPLOT_gg_disc)
##Bootstrap for confidence intervals...
#much simpler for a discrete marker
if(( conf.bands & bootstraps>1) ){
ci.bounds <- get.plot.ci_disc(x, plot.type,
ci, bootstraps,
alpha)
ci = ci.bounds$newci
ci.bounds = ci.bounds$myconf.ints
}else{
ci.bounds <- NULL
conf.bands = FALSE
}
## end Bootstrap
}
tmp.plotfun <- plot.functions[[match(plot.type, c("risk", "treatment effect", "cdf", "selection impact"))]]
if(is.null(x$model.fit$disc.rec.no.trt)){ # continous marker
if(substring(plot.type, 1, 4) == "risk"){
curves <- tmp.plotfun(x, ci, ci.bounds, get.F, fixed.values, conf.bands, rho, trt.names, xlab, ylab, xlim, ylim, main, show.marker.axis, offset = offset)
if(!is.null(ci.bounds)){
ci.bounds <- data.frame(t(ci.bounds))
ci.bounds <- cbind(fixed.values, ci.bounds)
names(ci.bounds) <- c("fixed.values", "trt0.lower", "trt0.upper", "trt1.lower", "trt1.upper")
}
}else{
curves <- tmp.plotfun(x, ci, ci.bounds, get.F, fixed.values, conf.bands, rho, xlab, ylab, xlim, ylim, main)
if(!is.null(ci.bounds)){
ci.bounds <- data.frame(t(ci.bounds))
ci.bounds <- cbind(fixed.values, ci.bounds)
names(ci.bounds) <- c("fixed.values", "lower", "upper")
}
}
}else{ # discrete marker
curves <- tmp.plotfun(x=x, ci = ci, ci.bounds = ci.bounds, get.F = get.F,
xlab = xlab, ylab=ylab,
xlim = xlim, ylim=ylim,
main=main, trt.names = trt.names)
if(!is.null(ci.bounds)){
ci.bounds <- data.frame(curves[[2]])
}
}
#par(old.par)
invisible(list("plot" = curves[[1]], "ci.bounds" = ci.bounds))
}
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