Download Appendix S1 Example script to run replications of the quantile count

Appendix S1
Example script to run replications of the quantile count model in R
#Script to perform m replications of dithering of count data on multiple taus
#based on Machado and Santos Silva (2005 JASA)
#Results stored in matrix count.model3.rep50t98
#Confidence intervals can be turned off with rq(ci=F) and then reducing the
#dimensions of the count.model3.rep50t98 matrix to ncol=6.
#Data frame used was csss with column variables BC (raw counts),
#WDAVGCM (water depth in cm),and ILAGBC (categorical factors for lag 1 counts
#= 0, 1, 2, >=3.
#Note that undefined weights based on ci=T,iid=F may cause estimation failure
#for some random samples at some taus and require restarting estimation for
#a subset of taus.
taus<-c(0.50,0.60,0.70,0.75,0.80,0.85,0.90,0.91,0.92,0.93,0.94,0.95,
0.96,0.97,0.98)
m <- 100
numtaus<- length(taus)
count.model3.rep50t98<-matrix(0,nrow=m*numtaus,ncol=16)
colnames(count.model3.rep50t98)<-c("tau","b0I0","lwr95b0I0","upr95b0I0",
"b1I0","lwr95b1I0","upr95b1I0","difb0I1","lwr95difb0I1",
"upr95difb0I1","difb0I2","lwr95difb0I2","upr95difb0I2",
"difb0I3","lwr95difb0I3","upr95difb0I3")
for(i in 1:m){
csss$DBC<-(csss$BC + runif(length(csss$BC[!is.na(csss$BC)]),min=0,max=1))
for (j in 1:numtaus){
csss$LOGDBC<-csss$DBC - taus[j]
csss$LOGDBC[csss$LOGDBC<=0.0]<-0.00001
csss$LOGDBC<-log(csss$LOGDBC)
rqfit<- rq(LOGDBC ~ WDAVGCM + ILAGBC,data=csss,tau=taus[j],
ci=T,iid=F,alpha=0.05,contrasts=list(ILAGBC="contr.treatment"))
count.model3.rep50t98[j + (i-1)*numtaus,]<-c(rqfit$tau,rqfit$coef[1,],
rqfit$coef[2,],rqfit$coef[3,],rqfit$coef[4,],rqfit$coef[5,],use.names=F)
}
}
###
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###
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Return quantile regression estimates to y counts for graphing,
where simplified data set with one value for each 1 cm of water depth.
Example for prior year counts = 0, where parameters
come from averaging across m simulated values
x <- matrix(seq(0,90,by=1),nrow=91,ncol=1)
cssslag0<-data.frame(wdavgcm=x)
cssslag0$qrq50<-ceiling(0.50+exp(-2.93014743-0.08969231*cssslag0$wdavgcm)-1)
cssslag0$qrq60<-ceiling(0.60+exp(-2.72949536-0.09616953*cssslag0$wdavgcm)-1)
cssslag0$qrq75<-ceiling(0.75+exp(-2.5123068-0.1003238*cssslag0$wdavgcm)-1)
cssslag0$qrq90<-ceiling(0.90+exp(-2.33264227-0.09125396*cssslag0$wdavgcm)-1)
cssslag0$qrq91<-ceiling(0.91+exp(-1.70786210-0.10857259*cssslag0$wdavgcm)-1)
cssslag0$qrq95<-ceiling(0.95+exp(-0.07856737-0.13185230*cssslag0$wdavgcm)-1)
cssslag0$qrq98<-ceiling(0.98+exp(0.7037903-0.1189377*cssslag0$wdavgcm)-1)
### Graph results for prior year counts = 0 in step function.
plot(cssslag0$wdavgcm,cssslag0$qrq98,type="s",col="red",ylim=c(0,7),bty="l",xl
im=c(0,90),xaxs="i",yaxs="i",yaxt="n",xaxt="n")
axis(1,at=c(0,10,20,30,40,50,60,70,80,90),tcl=0.5)
axis(2,at=c(0,1,2,3,4,5,6,7),tcl=0.5)
lines(cssslag0$wdavgcm,cssslag0$qrq95,type="s",col="blue",bty="l",xaxs="i",yax
s="i",xaxt="n",yaxt="n")
lines(cssslag0$wdavgcm,cssslag0$qrq91,type="s",col="brown",bty="l",xaxs="i",ya
xs="i",xaxt="n",yaxt="n")