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#' calculate percentiles of original data using distance-related weight
#' percentiles of simulated data with corresponding confidence interval
#'
#' @param orig.data the original data for model fitting
#' @param sim.data the simulated data from NONMEM
#' @param n.timebin the number of bin in X axis
#' @param n.sim the number of simulation in the simulated data
#' @param n.hist the number of shifted
#' @param q.list numeric vector of probabilities with values in [0,1]
#' @param conf.level confidence level of the interval
#' @param X.name the name of X variable in the original scatter plot
#' @param Y.name the name of Y variable in the original scatter plot
#' @param opt.DV.point option to put data point in the plot
#' @param weight.flag option to use weight in average shifted calculation
#' @param Y.min minimum of Y range in the plot
#' @param Y.max maximum of Y range in the plot
#' @param only.med option to use only median
#' @param plot.flag TRUE: drawing plot / FALSE: generate data for drawing plot
#' @return plot or the values to draw plot
#' @export
#' @seealso \code{\link{asVPC.binW}}
#' @references new paper...
#' @author Eun-Kyung Lee \email{lee.eunk@@gmail.com}
#' @examples
#' data(origdata)
#' data(simdata)
#' asVPC.distanceW(origdata,simdata,n.timebin=10, n.sim=100,n.hist=3)
asVPC.distanceW<-function(orig.data,sim.data,n.timebin,n.sim,n.hist,
q.list=c(0.05,0.5,0.95),
conf.level=0.95,
X.name="TIME",Y.name="DV",
opt.DV.point=FALSE,
weight.flag=FALSE,
Y.min=NULL,
Y.max=NULL,
only.med=FALSE,
plot.flag=TRUE){
SIM.CIarea.1<-NULL
SIM.CIarea.2<-NULL
SIM.CIarea.3<-NULL
DV.point<-NULL
DV.quant<-NULL
SIM.quant<-NULL
ID<-NULL;G<-NULL
bintot.N<-n.timebin*n.hist
time.bin<-makeCOVbin(orig.data[,X.name],N.covbin=bintot.N)
alpha<-1-conf.level
Q.CI<-vector("list",3)
orig.Q<-NULL
bintot.N<-nrow(time.bin$COV.bin.summary)
for(i in 1:bintot.N){
if(i<n.hist){
sel.id<-which(as.numeric(time.bin$COV.bin)<=i+n.hist-1)
sel.id1<-which(as.numeric(time.bin$COV.bin)==i)
mid.point<-median(orig.data[sel.id1,X.name])
low.point<-time.bin$COV.bin.summary$lower.COV[i]
upper.point<-time.bin$COV.bin.summary$upper.COV[i]
} else if(i >(bintot.N-n.hist+1)){
sel.id<-which(as.numeric(time.bin$COV.bin)>=i-(n.hist-1))
sel.id1<-which(as.numeric(time.bin$COV.bin)==i)
mid.point<-median(orig.data[sel.id1,X.name])
low.point<-time.bin$COV.bin.summary$lower.COV[i]
upper.point<-time.bin$COV.bin.summary$upper.COV[i]
} else{
sel.id<-which(as.numeric(time.bin$COV.bin)>i-n.hist &
as.numeric(time.bin$COV.bin)<i+n.hist)
sel.id1<-which(as.numeric(time.bin$COV.bin)==i)
mid.point<-median(orig.data[sel.id1,X.name])
low.point<-time.bin$COV.bin.summary$lower.COV[i]
upper.point<-time.bin$COV.bin.summary$upper.COV[i]
}
if(bintot.N<length(table(orig.data$TIME))){
dist.temp<-abs(orig.data$TIME[sel.id]-mid.point)
temp.weight<-(max(dist.temp)-dist.temp)/diff(range(dist.temp))
} else{
A<-as.numeric(time.bin$COV.bin[sel.id])
temp<-abs(A-median(range(A)))
temp.weight<-(max(temp)+1)-temp
temp.weight<-temp.weight/max(temp.weight)
}
if(weight.flag){
temp.quantile<-t(apply(sim.data[sel.id,],2,function(x)
Hmisc::wtd.quantile(x,weight=temp.weight,
prob=q.list,na.rm=TRUE)))
temp.orig.q<-Hmisc::wtd.quantile(orig.data[,Y.name][sel.id],
weight=temp.weight,prob=q.list,na.rm=TRUE)
} else{
temp.quantile<-t(apply(sim.data[sel.id,],2,function(x)
quantile(x,prob=q.list,na.rm=TRUE)))
temp.orig.q<-quantile(orig.data[,Y.name][sel.id],
prob=q.list,na.rm=TRUE)
}
orig.Q<-rbind(orig.Q,c(mid.point,temp.orig.q))
temp<-t(apply(temp.quantile,2,function(x)
quantile(x,prob=c(alpha/2,0.5,1-alpha/2),na.rm=TRUE)))
for(j in 1:length(q.list))
Q.CI[[j]]<-rbind(Q.CI[[j]],c(mid.point,low.point,upper.point,temp[j,]))
}
keep.name<-NULL
for(j in 1:length(q.list)){
keep.name<-c(keep.name,paste("Q",round(q.list[j]*100),"th",sep=""))
colnames(Q.CI[[j]])<-c("mid","Lower","upper",colnames(Q.CI[[j]])[4:6])
}
names(Q.CI)<-keep.name
colnames(orig.Q)<-c("mid","Y1","Y2","Y3")
orig.Q<-data.frame(orig.Q)
plot.data<-data.frame(orig.data,X=orig.data[,X.name],Y=orig.data[,Y.name])
if(is.null(Y.min)) Y.min<-min(c(plot.data$Y,Q.CI[[1]][,4]),na.rm=T)
if(is.null(Y.max)) Y.max<-max(c(plot.data$Y,Q.CI[[length(Q.CI)]][,6]),na.rm=T)
P.temp<-ggplot(plot.data,aes(x=X,y=Y))+ylim(Y.min,Y.max)+
labs(x=X.name,y=Y.name)+theme_bw()+
theme(panel.grid.major=element_line(colour="white"))+
theme(panel.grid.minor=element_line(colour="white"))
test.LU<-Q.CI[[1]][,2:3]
test.data.tot<-Q.CI
X.temp<-c(test.LU[,1],test.LU[nrow(test.LU),2])
n.temp<-nrow(test.LU)
X<-c(test.LU[1,1],rep(test.LU[2:n.temp,1],each=2),test.LU[n.temp,2])
X<-c(X,X[length(X):1])
if(!only.med){
test.data<-test.data.tot[[1]]
Y<-c(rep(test.data[,4],each=2),rep(test.data[(n.temp:1),6],each=2))
SIM.CIarea.1<-data.frame(X=X,Y=Y,ID=1)
P.temp<-P.temp+geom_polygon(data= SIM.CIarea.1,
aes(x=X,y=Y,group=ID,fill=ID),
fill="gray80",colour="gray80")
test.data<-test.data.tot[[3]]
Y<-c(rep(test.data[,4],each=2),rep(test.data[(n.temp:1),6],each=2))
SIM.CIarea.3<-data.frame(X=X,Y=Y,ID=1)
P.temp<-P.temp+geom_polygon(data= SIM.CIarea.3,
aes(x=X,y=Y,group=ID,fill=ID),
fill="gray80",colour="gray80")
}
test.data<-test.data.tot[[2]]
Y<-c(rep(test.data[,4],each=2),rep(test.data[(n.temp:1),6],each=2))
SIM.CIarea.2<-data.frame(X=X,Y=Y,ID=1)
P.temp<-P.temp+geom_polygon(data= SIM.CIarea.2,
aes(x=X,y=Y,group=ID,fill=ID),
fill="gray50",colour="gray50")
if(opt.DV.point==TRUE){
P.temp<-P.temp+geom_point(,color="grey30",size=2,alpha=0.5)
DV.point<-data.frame(X=orig.data[,X.name],Y=orig.data[,Y.name])
}
DV.quant<-data.frame(X=rep(orig.Q$mid,length(q.list)),
G=factor(rep(paste("Q",round(q.list*100),"th",sep=""),
each=nrow(orig.Q))),
Y=unlist(orig.Q[,-1]))
P.temp<-P.temp+geom_line(data=DV.quant[DV.quant$G!="Q50th",],
aes(x=X,y=Y,group=G),linetype=2,
size=1,color="black")+
geom_line(data=DV.quant[DV.quant$G=="Q50th",],
aes(x=X,y=Y,group=G),linetype=1,
size=1,color="black")
colnames(orig.Q)<-c("X.mid",paste("Q",round(q.list*100),"th",sep=""))
if(plot.flag){
P.temp
} else{
return(list(SIM.CIarea.1=SIM.CIarea.1,SIM.CIarea.2=SIM.CIarea.2,
SIM.CIarea.3=SIM.CIarea.3,DV.point=DV.point,
DV.quant=DV.quant,SIM.quant=SIM.quant))
}
}
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