psens {rbounds} | R Documentation |
Function to calculate Rosenbaum bounds for continuous or ordinal outcomes based on Wilcoxon sign rank test.
#Default Method psens(x, y=NULL, Gamma=6, GammaInc=1)
x |
Treatment group outcomes in same order as treatment group outcomes or an objects from Match(). |
y |
Control group outcomes in same order as treatment group outcomes unnecessary when using Match() object. |
Gamma |
Upper-bound on gamma parameter. |
GammaInc |
To set user specified increments for gamma parameter. |
Luke Keele, Ohio State University, keele.4@osu.edu
Rosenbaum, Paul R. (2002) Observational Studies. Springer-Verlag.
See also data.prep
, binarysens
, hlsens
, Match
, mcontrol
#Replication of Rosenbaum Sensitivity Tests From Chapter 4 of Observational Studies #Data: Matched Data of Lead Blood Levels in Children trt <- c(38,23,41,18,37,36,23,62,31,34,24,14,21,17,16,20,15,10,45,39,22,35,49,48,44,35,43,39,34,13,73,25,27) ctrl <- c(16,18,18,24,19,11,10,15,16,18,18,13,19,10,16,16,24,13,9,14,21,19,7,18,19,12,11,22,25,16,13,11,13) psens(trt, ctrl) #Example With Match() # #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) # # One should check balance, but let's skip that step for now. # # # Sensitivity Test # psens(mDW, Gamma=2, GammaInc=.1)