Hi Jeanne,
I’m not sure I totally follow your question about calipers, but the caliper option in
MatchIt is the number of standard deviations of the distance measure:
https://r.iq.harvard.edu/docs/matchit/2.4-14/Additional_Arguments_f3.html
So, e.g., if you say caliper=0.2 it will be 0.2 standard deviations of whatever distance
measure is being used (which could be logit or or probability, or something else). You
are right that in general the recommendation is to use the SD of the logit, so then you
would want to use the logit distance option and then just give the caliper size you want.
Re the mahalanobis distance matrix, I don’t think there is an easy way to pull it out of
the matchit object. Sorry!
Liz
First, thank you so much for your beautiful work on the MatchIt package. I use MatchIt
regularly in research and am also teaching a course on Quasi-Experimental Designs and
Propensity Score matching this semester. Your work has been of great help.
Second, I have a question about caliper matching. In the current literature (e.g.,
Austin's work) are recommendations for calipers that are 0.2 sd of the logit. In the
past, I computed the standard deviation of the propensity score (m.out$distance in the
probability metric). Recently, based upon the literature recommendations, I have been
begun computing the standard deviation on the logit of the propensity score (logit metric,
rather than probability metric), which makes more sense, given that the logit is linear.
However, I wondered what distance metric the MatchIt package is using to conduct the
matching process (e.g., Nearest Neighbor). Is it being conducted on the propensity score
metric or the logit metric? The standard deviations of the propensity score can be quite
different from the standard deviation of the logit. Moreover, I struggle with whether it
makes sense to even compute a standard deviation of the propensity score in the
probability metric.
Any guidance, would be helpful. I also noticed that the matchit code offers the option to
simply state the caliper distance -- is that on the metric of the logit or the propensity
score (probability metric)?
Third, when conducting Mahalanobis distance, is the matrix of Mahalanobis distances stored
anywhere accessible? Depending on sample size, the matrix would have to be huge, so
I'm not sure what I would even do with it, other than illustrate it to my class. (I
have not peeked into the source code to check.)
Thank you for any help or guidance you can offer! My students also thank you.
Best,
Jeanne
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
S. Jeanne Horst, PhD
Center for Assessment and Research Studies
Associate Professor, Department of Graduate Psychology
James Madison University
1122 Lakeview Hall; 298 Port Republic Road; MSC 6806
Harrisonburg, VA 22807
Office Phone: (540) 568-7103
http://www.psyc.jmu.edu/assessment/people/horst.html
http://www.jmu.edu/assessment/
http://www.jmu.edu/learningimprovement/
"Learning to live with ambiguity is learning to live with how life really is, full of
complexities and strange surprises.” James Hollis
"Fall seven times, stand up eight." Japanese Proverb
"Walk to the beat of your own tuba." Dove Chocolate
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