data.prep {rbounds} | R Documentation |
This function reshapes the output from Match() to create the necessary objects for mcontrol()
#Default Method data.prep(obj, Y=NULL, group.size=3)
obj |
An object from the Match() function. |
Y |
Internal argument to the Match object. |
group.size |
The size of the matched groups. Three for one treated unit and two control units. |
This functions takes a Match() object and formats it for use with the mcontrol() function. The output is a list with the three objects needed for the arguments of the mcontrol() function.
Y |
The matched outcomes |
id |
A vector which identifies the matched groups: 1, 1, 1 for matched group one; 2, 2, 2 for match group 2, etc. |
treat |
A vector with 1's for treated units and 0's for control units |
Luke Keele, Ohio State University, keele.4@osu.edu
Rosenbaum, Paul R. (2002) Observational Studies. Springer-Verlag.
See also binarysens
, psens
, hlsens
, Match
, mcontrol
# #Load Matching Software and Data # library(Matching) data(lalonde) # # Estimate Propensity Score # DWglm <- glm(treat~age + I(age^2) + educ + I(educ^2) + black + hisp + married + nodegr + re74 + I(re74^2) + re75 + I(re75^2) + u74 + u75, family=binomial, data=lalonde) # #save data objects # Y <- lalonde$re78 #the outcome of interest Tr <- lalonde$treat #the treatment of interest # # Match # mDW <- Match(Y=Y, Tr=Tr, X=DWglm$fitted, M=2) # # One should check balance, but let's skip that step for now. # #Create Data Object tmp <- data.prep(mDW, group.size=3) # # Sensitivity Test # mcontrol(tmp$Y, tmp$id, tmp$treat, group.size=3)