RBestMatch (version 0.1.1)

rbestdist: Creates a penalized distance list for r-best matching.

Description

Computes a penalized Mahalanobis distance (or its robust version) list for use in r-best matching.

Usage

rbestdist(dist,r,penalty=NULL,beta=100)

Arguments

dist

A distance list used for matching. dist contains three elements, d: a distance object for each pair of treated and control; start: the treated subject for each distance; end: the control subject for each distance.

r

A vector of rank with length length(dist$d), denoting the preference for each treated-control pair considered in dist (1: best; 2: second best; etc).

penalty

A vector of penalty with length max(r) for different edge sets to conduct r-best matching. If NULL, use the tuning parameter beta to construct the penalty

beta

If penalty is not specified, then it is calculated based on the tuning parameter beta.

Value

newdist

A new distance object with d being the penalized distance and start and end same as the original distance object dist.

fbpenalty

The penalty for fine balance in r-best matching.

Details

The r-best matching problem is solved as a network flow problem with penalized distances, with preferance to edges that rank high. This function is used to construct the new distance object.

Examples

# NOT RUN {
data(rhc)
z=as.numeric(rhc$swang1=='RHC')
attach(rhc)
female=as.numeric(sex=="Female")
race_black=as.numeric(race=="black")
race_other=as.numeric(race=="other")
income1=as.numeric(income=='$11-$25k')
income2=as.numeric(income=='$25-$50k')
income3=as.numeric(income=='> $50k')
ins_care=as.numeric(ninsclas=='Medicare')
ins_pcare=as.numeric(ninsclas=='Private & Medicare')
ins_caid=as.numeric(ninsclas=='Medicaid')
ins_no=as.numeric(ninsclas=='No insurance')
ins_carecaid=as.numeric(ninsclas=='Medicare & Medicaid')
cat1_copd=as.numeric(cat1=='COPD')
cat1_mosfsep=as.numeric(cat1=='MOSF w/Sepsis')
cat1_mosfmal=as.numeric(cat1=='MOSF w/Malignancy')
cat1_chf=as.numeric(cat1=='CHF')
cat1_coma=as.numeric(cat1=='Coma')
cat1_cirr=as.numeric(cat1=='Cirrhosis')
cat1_lung=as.numeric(cat1=='Lung Cancer')
cat1_colon=as.numeric(cat1=='Colon Cancer')
cat2_mosfsep=as.numeric(match(cat2,'MOSF w/Sepsis',nomatch = 0)>0)
cat2_coma=as.numeric(match(cat2,'Coma',nomatch = 0)>0)
cat2_mosfmal=as.numeric(match(cat2,'MOSF w/Malignancy',nomatch = 0)>0)
cat2_lung=as.numeric(match(cat2,'Lung Cancer',nomatch = 0)>0)
cat2_cirr=as.numeric(match(cat2,'Cirrhosis',nomatch = 0)>0)
cat2_colon=as.numeric(match(cat2,'Colon Cancer',nomatch = 0)>0)
adld3p_na=as.numeric(is.na(adld3p))
adld3p_impute=adld3p
adld3p_impute[is.na(adld3p)]=mean(adld3p,na.rm=TRUE)
ca_yes=as.numeric(ca=='Yes')
ca_meta=as.numeric(ca=='Metastatic')
wt0=as.numeric(wtkilo1==0)
urin1_na=as.numeric(is.na(urin1))
urin1_impute=urin1
urin1_impute[is.na(urin1)]=mean(urin1,na.rm=TRUE)
NumComorbid=cardiohx+chfhx+dementhx+psychhx+chrpulhx+renalhx+liverhx+
  gibledhx+malighx+immunhx+transhx+amihx
Resp=as.numeric(resp=='Yes')
Card=as.numeric(card=='Yes')
Neuro=as.numeric(neuro=='Yes')
Gastr=as.numeric(gastr=='Yes')
Renal=as.numeric(renal=='Yes')
Meta=as.numeric(meta=='Yes')
Hema=as.numeric(hema=='Yes')
Seps=as.numeric(seps=='Yes')
Trauma=as.numeric(trauma=='Yes')
Ortho=as.numeric(ortho=='Yes')
Dnr1=as.numeric(dnr1=='Yes')

pr<-glm(z~age+female+race_black+race_other+edu+income1+income2+income3+
          ins_care+ins_pcare+ins_caid+ins_no+ins_carecaid+
          cat1_copd+cat1_mosfsep+cat1_mosfmal+cat1_chf+cat1_coma+cat1_cirr+cat1_lung+cat1_colon+
          cat2_mosfsep+cat2_coma+cat2_mosfmal+cat2_lung+cat2_cirr+cat2_colon+
          Resp+Card+Neuro+Gastr+Renal+Meta+Hema+Seps+Trauma+Ortho+
          adld3p_impute+adld3p_na+das2d3pc+Dnr1+ca_yes+ca_meta+surv2md1+aps1+scoma1+
          wtkilo1+wt0+temp1+meanbp1+resp1+hrt1+pafi1+paco21+ph1+wblc1+hema1+
          sod1+pot1+crea1+bili1+alb1+urin1_impute+urin1_na+
          cardiohx+chfhx+dementhx+psychhx+chrpulhx+renalhx+liverhx+
          gibledhx+malighx+immunhx+transhx+amihx,family=binomial)$fitted.values

X<-cbind(aps1,surv2md1,age,NumComorbid,adld3p_impute,adld3p_na,das2d3pc,temp1,hrt1,meanbp1,
         resp1,wblc1,pafi1,paco21,ph1,crea1,alb1,scoma1,
         cat1_copd,cat1_mosfsep,cat1_mosfmal,cat1_chf,cat1_coma,cat1_cirr,cat1_lung,cat1_colon)

dm0=mahad(z,X,pr,0.1)
pr1=pr[z==1]
pr0=pr[z==0]
prd=abs(pr1[dm0$start]-pr0[dm0$end-sum(z)])
calip1<-prd<=(.01)&(dm0$d<=quantile(dm0$d,0.01))
calip2<-prd<=(.02)&(dm0$d<=quantile(dm0$d,0.1))
calip3<-prd<=(.03)&(dm0$d<=quantile(dm0$d,0.25))
calip4<-prd<=(.03) &(dm0$d<=quantile(dm0$d,0.5))
r=rep(5,length(dm0$d))
r[calip4]=4
r[calip3]=3
r[calip2]=2
r[calip1]=1
which4=which(r<=4)
dm0$d=dm0$d[which4]
dm0$start=dm0$start[which4]
dm0$end=dm0$end[which4]
r4=r[which4]
dm=rbestdist(dm0,r4,beta=50)
head(dm$newdist$d)
dm$fbpenalty
detach(rhc)
# }