rbounds (version 0.2)

hlsens: Demostration Autocorrelation Function

Description

Function to calculate Rosenbaum bounds for continuous or ordinal outcomes based on Hodges-Lehmann point estimate.

Usage

#Default Method 
hlsens(x, y=NULL, pr=.1, Gamma=6, GammaInc=1)

Arguments

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.

pr

Search precision parameter.

Gamma

Upper-bound on gamma parameter.

GammaInc

To set user specified increments for gamma parameter.

Details

For large data sets this function can be quite slow if pr is set to low. If the data set is larger, it is best to set pr to .5 before trying values such as .01. Generally, the results from the function are insensitive to the value for pr.

References

Rosenbaum, Paul R. (2002) Observational Studies. Springer-Verlag.

See Also

See also data.prep, binarysens, psens, Match, mcontrol

Examples

#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)

hlsens(trt, ctrl)

#
#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
#
hlsens(mDW, pr=.1, Gamma=2, GammaInc=.1)