| qqstats {Matching} | R Documentation |
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.
qqstats(x, y, standardize=TRUE, summary.func)
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 quantile function is a
legal function to pass in. |
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. |
Jasjeet S. Sekhon, UC Berkeley, sekhon@berkeley.edu, http://sekhon.berkeley.edu/.
Sekhon, Jasjeet S. 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization.” Journal of Statistical Software 42(7): 1-52. http://www.jstatsoft.org/v42/i07/
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
Sekhon, Jasjeet Singh and Richard D. Grieve. 2011. "A Matching Method For Improving Covariate Balance in Cost-Effectiveness Analyses." Health Economics. forthcoming.
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
Also see ks.boot,
balanceUV, Match,
GenMatch,
MatchBalance,
GerberGreenImai, lalonde
#
# 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)