Matching (version 4.6-2)

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. If standardize==TRUE, the empirical CDF is used instead of the empirical-QQ plot. The later retains the scale of the variable.

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. 2007. ``Multivariate and Propensity Score Matching Software with Automated Balance Optimization.'' Journal of Statistical Software. http://sekhon.berkeley.edu/papers/MatchingJSS.pdf 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 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)