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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) | ||
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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)) | ||
} | ||
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zalpha <- qnorm(0.025) | ||
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# data from | ||
# ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS | ||
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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$paytype) | ||
# 0 = Blank, 1 = Private insurance, 2 = Medicare, 3 = Medicaid/SCHIP | ||
# 4 = Worker's Compensation, 5 = Self-pay, 6 = No charge/charity | ||
# 7 = Other, 8 = Unknown | ||
barplot(table(PayType)) | ||
# combine 0,7,8, | ||
PayType2 <- PayType | ||
PayType2[which(as.numeric(as.character(PayType)) %in% c(2,3,4))] <- 2 | ||
PayType2[which(as.numeric(as.character(PayType)) %in% c(5))] <- 3 | ||
PayType2[which(as.numeric(as.character(PayType)) %in% c(-9,-8,0,6,7,8))] <- 4 | ||
PayType2 <- factor(PayType2) | ||
levels(PayType2) = c("Private","GovEmp","Self","Other") | ||
barplot(table(PayType2)) | ||
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Temp <- ERdat$temp/10 | ||
hist(Temp,breaks=20) | ||
Pulse <- ERdat$pulse | ||
hist(Pulse,breaks=20) | ||
SBP <- ERdat$sbp | ||
hist(SBP,breaks=20) | ||
DBP <- ERdat$dbp | ||
hist(DBP,breaks=20) | ||
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Pain <- as.factor(ERdat$pain) | ||
barplot(table(Pain)) | ||
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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)) | ||
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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) | ||
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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) | ||
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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 | ||
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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))) | ||
} | ||
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# 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 | ||
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# 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)) | ||
} | ||
} | ||
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#save(res1,res2,file="ERresults1120.RData") | ||
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# 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$y)) | ||
polygon(xx, yy, col = adjustcolor(ciCols[1], alpha.f=0.1), | ||
border = ciCols[1], lty=1) | ||
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yyl <- c(res2[,j]-abs(zalpha)*res2[,j+ncol(res2)/2]) | ||
yyu <- c(res2[,j]+abs(zalpha)*res2[,j+ncol(res2)/2]) | ||
sspl <- smooth.spline(qs, yyl, df=sspldf) | ||
sspu <- smooth.spline(qs, yyu, df=sspldf) | ||
xx <- c(sspl$x, rev(sspu$x)) | ||
yy <- c(sspl$y, rev(sspu$y)) | ||
polygon(xx, yy, col = adjustcolor(ciCols[2], alpha.f=0.1), | ||
border = ciCols[2], lty=1) | ||
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abline(h=0,lwd=2,col="grey66") | ||
#dev.off() | ||
} |
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library(QREM) | ||
library(SEMMS) | ||
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res <- list() | ||
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qn <- 0.25 # change to 0.5 and 0.75 | ||
maxReps <- 20 | ||
fn <- "riboflavin1.csv" | ||
responseCol <- 1 | ||
predictors <- 2:4089 | ||
dataYXZ <- readInputFile(fn, ycol=responseCol, Zcols=predictors) | ||
nn <- 10 | ||
n <- dataYXZ$N | ||
y0 <- dataYXZ$Y | ||
K <- dataYXZ$K | ||
# initialize: | ||
zval <- rep(0, K) | ||
for (i in seq(1,K,by=5)) { | ||
if (i %% 101 == 0) { cat(i,"...\n") } | ||
preds <- paste0(colnames(dataYXZ$Z)[i:min(i+4,K)], collapse = " + ") | ||
linmod <- as.formula(paste("Y ~", preds)) | ||
dframetmp <- data.frame(cbind(dataYXZ$Y, dataYXZ$Z[,i:min(i+4,K)])) | ||
colnames(dframetmp) <- c("Y",colnames(dataYXZ$Z)[i:min(i+4,K)]) | ||
qremFit <- QREM(lm,linmod, dframetmp, qn, maxInvLambda = 1000) | ||
zval[i:min(i+4,K)] <- qremFit$coef$beta[-1]/sqrt(diag(bcov(qremFit,linmod,dframetmp,qn)))[-1] | ||
} | ||
nnset <- order(abs(zval),decreasing = TRUE)[1:nn] | ||
#cat("initial set", nnset,"\n") | ||
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for (rep in 1:maxReps) { | ||
# create a subset of the selected columns and run QREM | ||
preds <- paste(colnames(dataYXZ$Z)[nnset], collapse = "+") | ||
linmod <- as.formula(paste("Y ~", preds)) | ||
dframetmp <- data.frame(cbind(dataYXZ$Y, dataYXZ$Z[,nnset])) | ||
colnames(dframetmp) <- c("Y",colnames(dataYXZ$Z)[nnset]) | ||
qremFit <- QREM(lm,linmod, dframetmp, qn, maxInvLambda = 1000) | ||
# apply the weights found by QREM and rerun SEMMS | ||
dataYXZtmp <- dataYXZ | ||
#dataYXZtmp$Z <- diag(sqrt(qremFit$weights))%*%dataYXZ$Z | ||
dataYXZtmp$Y <- (dataYXZ$Y-(1-2*qn)/qremFit$weights)#*sqrt(qremFit$weights) | ||
#plot(dataYXZ$Y, dataYXZtmp$Y); abline(0,1,col=2) | ||
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fittedVSnew <- fitSEMMS(dataYXZtmp,distribution = 'N', mincor=0.8,rnd=F, | ||
nnset=nnset, minchange = 1, maxst = 20) | ||
foundSEMMSnew <- sort(union(which(fittedVSnew$gam.out$lockedOut != 0), | ||
fittedVSnew$gam.out$nn)) | ||
cat(rep,"\n",fittedVSnew$gam.out$nn,"\n",nnset,"\n", qremFit$empq,"\n\n") | ||
if (length(fittedVSnew$gam.out$nn) == length(nnset)) { | ||
if (all(fittedVSnew$gam.out$nn == nnset)) { | ||
break | ||
} | ||
} | ||
nnset <- fittedVSnew$gam.out$nn | ||
} | ||
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# after the loop | ||
fittedGLM <- runLinearModel(dataYXZtmp,nnset, "N") | ||
print(summary(fittedGLM$mod)) | ||
plotMDS(dataYXZ, fittedVSnew, fittedGLM, ttl="... Data") | ||
plotFit(fittedGLM) | ||
plot(y0, col=(2+(qremFit$ui>0)), cex=0.7, pch=19) | ||
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if(length(nnset) > 0) { | ||
for (i in 1:length(nnset)) { | ||
plot(dataYXZ$Z[,nnset[i]],dataYXZ$Y, col=1+(qremFit$ui>0)) | ||
} | ||
} | ||
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res[[1]] <- list(y0, nnset, qremFit, fittedGLM) | ||
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# loop over all the columns, and get the estimates for q=0.25, 0.5, 0.75 | ||
# | ||
# This part can take a while... 4088 quantile regression models with | ||
# variable selection | ||
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for (k in 1:K) { | ||
cat(k,"...\n") | ||
dataYXZ <- readInputFile(fn, ycol=k+1, Zcols=setdiff(predictors,k+1)) | ||
n <- dataYXZ$N | ||
y0 <- dataYXZ$Y | ||
K <- dataYXZ$K | ||
zval <- rep(0, K) | ||
for (i in seq(1,K,by=5)) { | ||
if (i %% 101 == 0) { cat(i,"...\n") } | ||
preds <- paste0(colnames(dataYXZ$Z)[i:min(i+4,K)], collapse = " + ") | ||
linmod <- as.formula(paste("Y ~", preds)) | ||
dframetmp <- data.frame(cbind(dataYXZ$Y, dataYXZ$Z[,i:min(i+4,K)])) | ||
colnames(dframetmp) <- c("Y",colnames(dataYXZ$Z)[i:min(i+4,K)]) | ||
qremFit <- QREM(lm,linmod, dframetmp, qn, maxInvLambda = 1000) | ||
zval[i:min(i+4,K)] <- qremFit$coef$beta[-1]/sqrt(diag(bcov(qremFit,linmod,dframetmp,qn)))[-1] | ||
} | ||
nnset = order(abs(zval),decreasing = TRUE)[1:nn] | ||
#cat("initial set", nnset,"\n") | ||
for (rep in 1:maxReps) { | ||
# create a subset of the selected columns and run QREM | ||
preds <- paste(colnames(dataYXZ$Z)[nnset], collapse = "+") | ||
linmod <- as.formula(paste("Y ~", preds)) | ||
dframetmp <- data.frame(cbind(dataYXZ$Y, dataYXZ$Z[,nnset])) | ||
colnames(dframetmp) <- c("Y",colnames(dataYXZ$Z)[nnset]) | ||
qremFit <- QREM(lm,linmod, dframetmp, qn, maxInvLambda = 1000) | ||
# apply the weights found by QREM and rerun SEMMS | ||
dataYXZtmp <- dataYXZ | ||
#dataYXZtmp$Z <- diag(sqrt(qremFit$weights))%*%dataYXZ$Z | ||
dataYXZtmp$Y <- (dataYXZ$Y-(1-2*qn)/qremFit$weights)#*sqrt(qremFit$weights) | ||
fittedVSnew <- fitSEMMS(dataYXZtmp,distribution = 'N', mincor=0.8,rnd=F, | ||
nnset=nnset, minchange = 1, maxst = 20) | ||
foundSEMMSnew <- sort(union(which(fittedVSnew$gam.out$lockedOut != 0), | ||
fittedVSnew$gam.out$nn)) | ||
cat(rep,"\n",fittedVSnew$gam.out$nn,"\n",nnset,"\n", qremFit$empq,"\n\n") | ||
if (length(fittedVSnew$gam.out$nn) == length(nnset)) { | ||
if (all(fittedVSnew$gam.out$nn == nnset)) { | ||
break | ||
} | ||
} | ||
nnset <- fittedVSnew$gam.out$nn | ||
if (length(nnset) == 0) | ||
break | ||
} | ||
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if (length(nnset) == 0) { | ||
res[[k+1]] <- list() | ||
next | ||
} | ||
fittedGLM <- runLinearModel(dataYXZtmp,nnset, "N") | ||
res[[k+1]] <- list(y0, nnset, qremFit, fittedGLM) | ||
} | ||
save(res, file="B2q25.RData") # change to B2q50 and B2q75 when using different | ||
# quartiles |
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