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rm(list=ls())
library("QREM")
boot.QREM <- function(func, linmod, dframe0, qn, n, sampleFrom=NULL,
B=100, err=10, maxit=1000, tol=0.001,
maxInvLambda=300, seedno=71371, showEst=FALSE) {
t0 <- Sys.time()
set.seed(seedno)
bs_set <- sample(n, replace = TRUE)
if (! is.null(sampleFrom))
colNum <- which(colnames(dframe0) == sampleFrom)
else
colNum <- 0
if (! is.null(sampleFrom))
dframe <- dframe0[which(dframe0[,colNum] %in% bs_set),]
else
dframe <- dframe0[bs_set,]
qremFit0 <- QREM(func, linmod, dframe, qn, err=err, maxit=maxit,
tol=tol, maxInvLambda=maxInvLambda)
if (B == 1)
return(qremFit0$coef$beta)
oneIt <- as.numeric(difftime(Sys.time(), t0, units = "secs"))
useCores <- detectCores() - 1
if (showEst){
cat("One iteration ", ceiling(oneIt), "seconds\n")
cat("Estimated completion time, using", useCores, " cores >",
ceiling(oneIt*ceiling(B/useCores))," seconds\n")
}
t0 <- Sys.time()
n_coefs <- length(qremFit0$coef$beta)
# Initiate cluster
cl <- makeCluster(useCores)
clusterExport(cl,varlist=c("func","linmod", "dframe0", "qn","n","QREM",
"lm","lmer", "gam",
"getME","colNum","fixef","ranef","seedno",
"err", "maxit","tol","maxInvLambda","seedno"),
envir=environment())
QREMpar=parLapply(cl, 1:(B-1),
function(repnum) {
set.seed(seedno + 19 * repnum)
bs_set <- sample(n, replace = TRUE)
if (! is.null(sampleFrom))
dframe <- dframe0[which(dframe0[,colNum] %in% bs_set),]
else
dframe <- dframe0[bs_set,]
qremFit <- QREM(func, linmod, dframe, qn, err=err, maxit=maxit,
tol=tol, maxInvLambda=maxInvLambda)
qremFit$coef$beta
}
)
stopCluster(cl)
if (showEst)
cat("Actual completion time =", ceiling(as.numeric(difftime(Sys.time(), t0, units = "secs")))," seconds\n")
rbind(qremFit0$coef$beta, matrix(unlist(QREMpar), ncol = n_coefs, byrow = TRUE))
}
zalpha <- qnorm(0.025)
# data from
# ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS
load("ERdat2006.RData")
# pre-process the data and create the variables and the data frame
# show some diagnostics plots and tables
#
y <- log(ERdat$LOV+1,base=60)
hist(y,breaks=20)
Month <- as.factor(ERdat$month)
barplot(table(Month))
DOW <- as.factor(ERdat$dow)
barplot(table(DOW))
Sex <- as.factor(ERdat$sex)
levels(Sex) <- c("F","M")
pie(table(Sex))
plot(y~Month)
plot(y~DOW)
Race <- as.factor(ERdat$race)
barplot(table(Race))
Race2 <- Race
Race2[which(as.numeric(as.character(Race)) %in% c(-9,3,4,5,6))] <- 3
Race2 <- factor(Race2)
levels(Race2) = c("W","B","O")
barplot(table(Race2))
Age <- ERdat$age/100
hist(Age)
PayType <- as.factor(ERdat$paytype)
# 0 = Blank, 1 = Private insurance, 2 = Medicare, 3 = Medicaid/SCHIP
# 4 = Worker's Compensation, 5 = Self-pay, 6 = No charge/charity
# 7 = Other, 8 = Unknown
barplot(table(PayType))
# combine 0,7,8,
PayType2 <- PayType
PayType2[which(as.numeric(as.character(PayType)) %in% c(2,3,4))] <- 2
PayType2[which(as.numeric(as.character(PayType)) %in% c(5))] <- 3
PayType2[which(as.numeric(as.character(PayType)) %in% c(-9,-8,0,6,7,8))] <- 4
PayType2 <- factor(PayType2)
levels(PayType2) = c("Private","GovEmp","Self","Other")
barplot(table(PayType2))
Temp <- ERdat$temp/10
hist(Temp,breaks=20)
Pulse <- ERdat$pulse
hist(Pulse,breaks=20)
SBP <- ERdat$sbp
hist(SBP,breaks=20)
DBP <- ERdat$dbp
hist(DBP,breaks=20)
Pain <- as.factor(ERdat$pain)
barplot(table(Pain))
Residence <- as.factor(ERdat$resid)
barplot(table(Residence))
ArrivalMode <- as.factor(ERdat$arrmode)
barplot(table(ArrivalMode))
ArrivalTime <- as.factor(floor(ERdat$arrtime/100))
ArrivalTime2 <- floor(ERdat$arrtime/100)
ArrivalTime2[which(ArrivalTime2 >8 & ArrivalTime2 < 20)] <- "AM"
ArrivalTime2[which(ArrivalTime2 != "AM")] <- "PM"
barplot(table(ArrivalTime))
ArrivalTime2 = as.factor(ArrivalTime2)
barplot(table(ArrivalTime2))
Region <- as.factor(ERdat$region)
levels(Region) <- c("NE","MW","S","W")
barplot(table(Region))
Metro <- as.factor(ERdat$metro)
levels(Metro) <- c("Yes", "No")
barplot(table(Metro))
Owner <- as.factor(ERdat$owner)
barplot(table(Owner))
HospCode <- ERdat$hosp
hist(HospCode, breaks=30)
RecentVisit <- rep("N",length(HospCode))
if(length(ERdat$disch7 ) == 0) { RecentVisit[which(ERdat$seen72 == 1)] <- "Y"
} else { RecentVisit[which(ERdat$seen72 == 1 | ERdat$disch7 ==1)] <- "Y" }
RecentVisit <- as.factor(RecentVisit)
table(RecentVisit)
dframe <- data.frame(y,Sex,Month,DOW,Race2,Age,PayType2,Temp,Pulse,
SBP,DBP,Pain,Residence,ArrivalMode,ArrivalTime2,
Region, Metro, #Owner,
HospCode, RecentVisit)
qs <- c(seq(0.05, 0.95, by=0.05))
# no batch effect
linmod <- y~ Sex+Race2+Age+Region+Metro+
PayType2+ArrivalTime2+DOW+RecentVisit
ncols <- nlevels(Sex)-1+nlevels(Race2)-1+1+
nlevels(PayType2)-1+
nlevels(Region)-1+nlevels(Metro)-1+nlevels(ArrivalTime2)-1+
nlevels(DOW)-1+nlevels(RecentVisit)-1+1
res1 <- matrix(0,nrow=length(qs), ncol=2*ncols)
qqp <- matrix(0, nrow=length(qs), ncol=3)
for (i in 1:length(qs)) {
cat(i,qs[i],"\n")
qremFit <- QREM(lm,linmod, dframe, qs[i])
varKED <- bcov(qremFit, linmod=linmod, dframe, qs[i])
res1[i,] <- c(as.numeric(qremFit$fitted.mod$coefficients), sqrt(diag(varKED)))
}
# hospital is a random effect:
linmodrnd <- y~ Sex+Race2+Age+Region+Metro+
PayType2+ArrivalTime2+DOW+
RecentVisit+ (1|HospCode)
ncols2 <- nlevels(Sex)-1+nlevels(Race2)-1+1+
nlevels(PayType2)-1+
nlevels(Region)-1+nlevels(Metro)-1+nlevels(ArrivalTime2)-1+
nlevels(DOW)-1+nlevels(RecentVisit)-1+1
# set onlyEstimate = FALSE if you want to get regression coefficient estimates
# without running the bootstrap (which takes a long time):
res2 <- matrix(0,nrow=length(qs), ncol=2*ncols2)
onlyEstimate <- TRUE
if (onlyEstimate) {
for (i in 1:length(qs)) {
cat(i,qs[i],"\n")
qremFit <- QREM(lmer,linmodrnd, dframe, qs[i], maxit = 2000)
res2[i,] <- c(as.numeric(qremFit$coef$beta), rep(0,ncols2))
}
} else {
B <- 99
for (i in 1:length(qs)) {
cat(i,qs[i],"\n")
bsv <- boot.QREM(lmer, linmodrnd, dframe, qs[i], 100, #length(unique(HospCode)),
"HospCode", maxit = 2000, B=B, seedno=336621, showEst = TRUE)
res2[i,] <- c(colMeans(bsv), apply(bsv,2,sd))
}
}
#save(res1,res2,file="ERresults1120.RData")
# parameter estimates with 95% confidence intervals for each predictor, by quantile
# for the two models (with/without random effect) using smooth splines
# names(fixef(qremFit$fitted.mod))
varnames <- c("(Intercept)", "SexM", "Race2B", "Race2O", "Age", "RegionMW",
"RegionS", "RegionW", "MetroNo", "PayType2GovEmp", "PayType2Self", "PayType2Other" ,
"ArrivalTime2PM", "DOW2", "DOW3", "DOW4", "DOW5", "DOW6",
"DOW7", "RecentVisitY")
ciCols <- c("navyblue","darkred","orange")
sspldf=10
for (j in 1:(ncol(res1)/2)) {
mm <- min(res1[,j]-abs(zalpha)*res1[,j+ncol(res1)/2], res2[,j]-abs(zalpha)*res2[,j+ncol(res2)/2])
mm <- mm - abs(mm)*0.1
MM <- max(res1[,j]+abs(zalpha)*res1[,j+ncol(res1)/2], res2[,j]+abs(zalpha)*res2[,j+ncol(res2)/2])
MM <- MM + abs(MM)*0.1
#pdf(sprintf("fig/ER%02d.pdf",j),width = 5, height = 5)
plot(smooth.spline(qs,res1[,j],df=sspldf),type='l', axes=F, ylim=c(mm,MM),
main=varnames[j], ylab="Coef.", xlab="quantile",col=ciCols[1],lwd=2)
axis(1,labels=seq(0,1,by=0.1), at=seq(0,1,by=.1)); axis(2)
lines(smooth.spline(qs,res2[,j],df=sspldf),col=ciCols[2],lwd=2,lty=2)
yyl <- c(res1[,j]-abs(zalpha)*res1[,j+ncol(res1)/2])
yyu <- c(res1[,j]+abs(zalpha)*res1[,j+ncol(res1)/2])
sspl <- smooth.spline(qs, yyl, df=sspldf)
sspu <- smooth.spline(qs, yyu, df=sspldf)
xx <- c(sspl$x, rev(sspu$x))
yy <- c(sspl$y, rev(sspu$y))
polygon(xx, yy, col = adjustcolor(ciCols[1], alpha.f=0.1),
border = ciCols[1], lty=1)
yyl <- c(res2[,j]-abs(zalpha)*res2[,j+ncol(res2)/2])
yyu <- c(res2[,j]+abs(zalpha)*res2[,j+ncol(res2)/2])
sspl <- smooth.spline(qs, yyl, df=sspldf)
sspu <- smooth.spline(qs, yyu, df=sspldf)
xx <- c(sspl$x, rev(sspu$x))
yy <- c(sspl$y, rev(sspu$y))
polygon(xx, yy, col = adjustcolor(ciCols[2], alpha.f=0.1),
border = ciCols[2], lty=1)
abline(h=0,lwd=2,col="grey66")
#dev.off()
}