### Download and install MatchIt
install.packages("MatchIt")
library(MatchIt)


### Read your data into R 
trialdata=read.csv("nameofyourfile.csv")

### First let's include ONLY the variables we will be working with 
### So let's get rid of "OutpatientCost", "InpatientCost" and 
### "Residential" ...keep olny total cost 
trialdata=trialdata[,-(1:3)]

### Perform the following regression 
m.out=matchit(trialdata[,2]~ trialdata[,3] + trialdata[,4] + trialdata[,5] + trialdata[,6] + trialdata[,7] + trialdata[,8] 
+trialdata[,9] + trialdata[,10] + trialdata[,11] + trialdata[,12] + trialdata[,13] + trialdata[,14]
+  trialdata[,15] + trialdata[,16] + trialdata[,17] + trialdata[,18] + trialdata[,19] + trialdata[,20] 
+ trialdata[,21] + trialdata[,22] + trialdata[,23] + trialdata[,24] + trialdata[,25] + trialdata[,26]
+ trialdata[,27] + trialdata[,28] + trialdata[,29] + trialdata[,30] + trialdata[,31] + trialdata[,32] 
+trialdata[,33] + trialdata[,34] + trialdata[,35] + trialdata[,36] + trialdata[,37] + trialdata[,38]
+  trialdata[,39] + trialdata[,40] + trialdata[,41]
, data=trialdata,method="exact")


### Now let's find the weights generated by propensity score matching for 
### the entire data set 
m.data=match.data(m.out)
fix(m.data)


### Now perform a weighted regression 
gweights=lm(m.data[,1]~.,data=m.data[,(2:41)],weights=m.data[,42])
summary(gweights)


### and once without weights 
gnoweights=lm(m.data[,1]~.,data=m.data[,(2:41)])
summary(gnoweights)