Matching (version 3.3-3)

qqstats: QQ Summary Statistics

Description

This function calculates a set of summary statistics for the QQ plot of two samples of data. The summaries are useful for determining if the two samples are from the same distribution.

Usage

qqstats(x, y, standardize=TRUE, summary.func)

Arguments

x

The first sample.

y

The second sample.

standardize

A logical flag for whether the statistics should be standardized by the empirical cumulative distribution functions of the two samples.

summary.func

A user provided function to summarize the difference between the two distributions. The function should expect a vector of the differences as an argument and return summary statistic. For example, the q

Value

• meandiff
  The mean difference between the QQ plots of the two samples.
• mediandiff
  The median difference between the QQ plots of the two samples.
• maxdiff
  The maximum difference between the QQ plots of the two samples.
• summarydiff
  If the user provides a summary.func, the user requested summary difference is returned.
• summary.func
  If the user provides a summary.func, the function is returned.

References

Sekhon, Jasjeet S. 2006. ``Alternative Balance Metrics for Bias Reduction in Matching Methods for Causal Inference.'' Working Paper. http://sekhon.berkeley.edu/papers/SekhonBalanceMetrics.pdf Sekhon, Jasjeet S. 2006. ``Matching: Algorithms and Software for Multivariate and Propensity Score Matching with Balance Optimization via Genetic Search.'' http://sekhon.berkeley.edu/matching/

Diamond, Alexis and Jasjeet S. Sekhon. 2005. ``Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies.'' Working Paper. http://sekhon.berkeley.edu/papers/GenMatch.pdf

See Also

Also see ks.boot, balanceUV, Match, GenMatch, MatchBalance, balanceMV, GerberGreenImai, lalonde

Examples

#
# Replication of Dehejia and Wahba psid3 model
#
# Dehejia, Rajeev and Sadek Wahba. 1999.``Causal Effects in Non-Experimental Studies: Re-Evaluating the
# Evaluation of Training Programs.''Journal of the American Statistical Association 94 (448): 1053-1062.
#
data(lalonde)

#
# Estimate the propensity model
#
glm1  <- 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
#
X  <- glm1$fitted
Y  <- lalonde$re78
Tr  <- lalonde$treat

#
# one-to-one matching with replacement (the "M=1" option).
# Estimating the treatment effect on the treated (the "estimand" option which defaults to 0).
#
rr  <- Match(Y=Y,Tr=Tr,X=X,M=1);
summary(rr)

#
# Do we have balance on 1975 income after matching?
#
qqout  <- qqstats(lalonde$re75[rr$index.treated], lalonde$re75[rr$index.control])
print(qqout)