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#### Point and variance for binary treatment ##############################################
###########################################################################################
binest<-function(ps.formula=NULL,ps.estimate=NULL,zname=NULL,yname,data,trtgrp=NULL,augmentation=FALSE,bootstrap=FALSE,R=50,out.formula=NULL,out.estimate=NULL,family=NULL,weight="overlap",ps.method='glm',ps.control=list(),out.method='glm',out.control=list()){
#preprocess formula and extract y
out.formula<-as.formula(out.formula)
y<-unlist(data[yname])
#set ordered group
facz<-as.factor(unlist(data[zname]))
#set the treatment label
oldlevel<-levels(facz)
newlevel<-oldlevel
z<-as.numeric(facz)-1
n<-length(z) #total obs
#set group for ATT
if(weight=="treated"){
if(is.null(trtgrp)){
trtgrp<-oldlevel[2]
}else{
newlevel<-rev(unique(c(trtgrp,oldlevel)))
facz<-factor(unlist(data[zname]),levels = newlevel)
z<-as.numeric(facz)-1
}
}
matchlevel<-match(oldlevel,newlevel) #match level for ATT
#reassign value as numeric
data[zname]<-z
data_p<-data[,colnames(data)!=yname] #data without outcome
# obtain ps estimation
# estimate with formula
if(is.null(ps.estimate)){
fit<-do.call(PSmethod,list(ps.formula = ps.formula, method=ps.method, data=data_p,ncate=2,ps.control=ps.control))
W<- model.matrix(ps.formula,data_p) # design matrix
e.h <- as.numeric(fit$e.h[,2])
beta.h<-as.numeric(fit$beta.h)
if(ps.method!='glm'){
ps.estimate<-fit$e.h
}
}else{
#the name for the propensity score
if(length(ps.estimate)==n){
e.h<-c(ps.estimate)
}else if(!setequal(colnames(ps.estimate),newlevel)){
ps.estimate<-as.matrix(ps.estimate)
e.h<-c(ps.estimate[,2])
warning("wrong column name set for ps.estimate, treatment set as: ",newlevel[1], " , ", newlevel[2])
}else{
ps.estimate<-ps.estimate[,match(newlevel,colnames(ps.estimate))]
e.h<-c(ps.estimate[,2])
}
}
#weight entropy needs extra clipping
if(weight=="entropy"){
e.h<-pmax(e.h,1e-6)
e.h<-pmin(e.h,1-1e-6)
}
#compute outcome regression for augmentation
if(augmentation){
#no outcome estimation provided
if(is.null(out.estimate)){
#fit two outcome regression model for different treatment groups
offset.e<-rep(1,n) #for poisson regression
dataaug<-data[,colnames(data)!=zname]
dataaug0<-dataaug[z==0,]
dataaug1<-dataaug[z==1,]
XY<-model.matrix(formula(out.formula),data=dataaug)
#predict outcome
fitout0<-do.call(OUTmethod,list(out.formula=out.formula,y=y[z==0], out.method=out.method, family=family, datain=dataaug0, dataout=dataaug,out.control=out.control))
m0.h<-fitout0$m.est
gamma0.h<-fitout0$gamma.h
fitout1<-do.call(OUTmethod,list(out.formula=out.formula,y=y[z==1], out.method=out.method, family=family, datain=dataaug1, dataout=dataaug,out.control=out.control))
m1.h<-fitout1$m.est
gamma1.h<-fitout1$gamma.h
if(family=='poisson'){
offsetlog<-model.extract(model.frame(out.formula,data = dataaug),'offset')
if(!is.null(offsetlog)){offset.e<-exp(offsetlog)}
}
if(out.method!='glm'){
out.estimate<-cbind(m0.h,m1.h)
colnames(out.estimate)<-newlevel
}
}else{
#the name for the outcome regression
if(!setequal(colnames(out.estimate),newlevel)){
out.estimate<-as.matrix(out.estimate)
m0.h<-out.estimate[,1]
m1.h<-out.estimate[,2]
warning("wrong column name set for out.estimate, treatment set as: ",newlevel[1], " , ", newlevel[2])
}else{
out.estimate<-out.estimate[,match(newlevel,colnames(out.estimate))]
m0.h<-out.estimate[,1]
m1.h<-out.estimate[,2]
}
}
}
#tilting function
ftilt<-tiltbin(weight = weight)
##No Augmentation###############################################################
if(augmentation==FALSE){
muhat<-ptbin(e.h,z,y,ftilt)
##No bootstrap###############################################################
if(bootstrap==FALSE){
conser<-1 #choose conservative or not
#use ps formula
if(is.null(ps.estimate)){
tryCatch( {
theta.h<-c(muhat,beta.h)
covmu<-sand_bin(z,y,n,ftilt,theta.h,W,type='e')
conser<-0 #is pd
},error = function(w) {
warning("The sandwich matrix not pd, therefore not invertable, use conservative variance instead, please double check")
})
}
#use conservative
if(conser==1){
theta.h<-muhat
covmu<-sand_bin(z,y,n,ftilt,theta.h,eest=e.h,type='ec')
}
names(muhat)<-newlevel
colnames(covmu)<-rownames(covmu)<-newlevel
muboot<-NULL
}else{
##With bootstrap###############################################################
muboot<-NULL
for(i in 1:R){
if(i %% 50==0){
message("bootstrap ", i, " samples")
}
# estimate ps
samp.b<-sample(n,n,replace = TRUE)
data.b<-data_p[samp.b,]
y.b<-y[samp.b]
z.b<-z[samp.b]
fit.b<-do.call(PSmethod,list(ps.formula = ps.formula, method=ps.method, data=data.b,ncate=2,ps.control=ps.control))
e.b <- as.numeric(fit.b$e.h[,2])
if(weight=="entropy"){
e.b<-pmax(e.b,1e-6)
e.b<-pmin(e.b,1-1e-6)
}
# point estimate
mu.b <- ptbin(e.b,z.b,y.b,ftilt)
muboot<-rbind(muboot,mu.b)
}
covmu<-cov(muboot)
names(muhat)<-newlevel
colnames(covmu)<-rownames(covmu)<-newlevel
colnames(muboot)<-newlevel
rownames(muboot)<-NULL
}
}
##Augmentation###############################################################
if(augmentation==TRUE){
#calculate point estimate
augest <- ptbin(e.h,z,y,ftilt,m0.h,m1.h)
muhat <- tail(augest,2)
##No bootstrap###############################################################
if(bootstrap==FALSE){
conser<-1 #choose conservative or not
if(is.null(ps.estimate) & is.null(out.estimate)){
## both with formula
tryCatch({
theta.h<-c(augest[1:6],beta.h,gamma0.h,gamma1.h)
covmu<-sand_bin(z,y,n,ftilt,theta.h,W,XY,family=family,offset.e=offset.e,type='ea')
conser<-0 #is pd
},error = function(w) {
warning("The sandwich matrix not pd, therefore not invertable, use conservative variance instead, please double check")
})
}else if(!is.null(ps.estimate) & is.null(out.estimate)){
## formula on outcome regression only
tryCatch( {
theta.h<-c(augest[1:6],gamma0.h,gamma1.h)
covmu<-sand_bin(z,y,n,ftilt,theta.h,XY=XY,eest=e.h,family=family,offset.e=offset.e,type='eca')
conser<-0 #is pd
},error = function(w) {
warning("The sandwich matrix not pd, therefore not invertable, use conservative variance instead, please double check")
})
}else if(is.null(ps.estimate) & !is.null(out.estimate)){
## formula on ps only
#when not pd
tryCatch( {
theta.h<-c(augest[1:6],beta.h)
covmu<-sand_bin(z,y,n,ftilt,theta.h,W,m0est=m0.h,m1est=m1.h,type='eac')
conser<-0 #is pd
},error = function(w) {
warning("The sandwich matrix not pd, therefore not invertable, use conservative variance instead, please double check")
})
}
#if not pd then use conservative
if(conser==1){
theta.h<-augest[1:6]
covmu<-sand_bin(z,y,n,ftilt,theta.h,eest=e.h,m0est=m0.h,m1est=m1.h,type='ecac')
}
muboot<-NULL
names(muhat)<-newlevel
colnames(covmu)<-rownames(covmu)<-newlevel
}else{
#use bootstrap for aumentation
dataaug<-data[,colnames(data)!=zname]
muboot<-NULL
#bootstrap runs
for(i in 1:R){
if(i %% 50==0){
message("bootstrap ", i, " samples")
}
# estimate ps
samp.b<-sample(n,n,replace = TRUE)
data.b<-data_p[samp.b,]
y.b<-y[samp.b]
z.b<-z[samp.b]
fit.b<-do.call(PSmethod,list(ps.formula = ps.formula, method=ps.method, data=data.b,ncate=2,ps.control=ps.control))
e.b <- as.numeric(fit.b$e.h[,2])
if(weight=="entropy"){
e.b<-pmax(e.b,1e-6)
e.b<-pmin(e.b,1-1e-6)
}
#calculate the augmentation term and updata tau
dataaug.b<-dataaug[samp.b,]
dataaug0.b<-dataaug.b[z.b==0,]
dataaug1.b<-dataaug.b[z.b==1,]
#predict outcome
fitout0.b<-do.call(OUTmethod,list(out.formula=out.formula,y=y.b[z.b==0], out.method=out.method, family=family, datain=dataaug0.b, dataout=dataaug.b,out.control=out.control))
m0.b<-fitout0.b$m.est
fitout1.b<-do.call(OUTmethod,list(out.formula=out.formula,y=y.b[z.b==1], out.method=out.method, family=family, datain=dataaug1.b, dataout=dataaug.b,out.control=out.control))
m1.b<-fitout1.b$m.est
mu.b <- ptbin(e.b,z.b,y.b,ftilt,m0.b,m1.b)
muboot <- rbind(muboot,tail(mu.b,2))
}
covmu<-cov(muboot)
names(muhat)<-newlevel
colnames(covmu)<-rownames(covmu)<-newlevel
colnames(muboot)<-newlevel
rownames(muboot)<-NULL
}
}
e.h<-cbind(1-e.h,e.h)
colnames(e.h)<-newlevel
#match back to original levels
e.h<-e.h[,matchlevel]
muhat<-muhat[matchlevel]
covmu<-covmu[matchlevel,matchlevel]
muboot<-muboot[,matchlevel]
out<-list(propensity=e.h, muhat=muhat, covmu=covmu, muboot=muboot, group=c(oldlevel), trtgrp=trtgrp)
class(out)<-'PSweight'
out
}
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