L1.meas {cem} | R Documentation |
Evaluates L1 distance between multidimensional histograms
L1.meas(group, data, drop=NULL, breaks = NULL, weights)
group |
the group variable |
data |
the data |
drop |
a vector of variable names in the data frame to ignore |
breaks |
a list of vectors of cutpoints; if not specified, automatic choice will be made |
weights |
weights |
This function calculates the L1 distance on the k-dimensional histogram in order to measure the level of imbalance in a matching solution.
If breaks
is not specified, the Scott automated bin calculation
is used (which coarsens less than Sturges, which used in
cem
). Please refer to cem
help page. In
this case, breaks are used to calculate the L1 measure.
When choosing breaks
for L1, a very fine coarsening (many cut
points) produces values of L1 close to 1. A very mild coarsening (very
fex cutpoints), is not able to discriminate, i.e. L1 close to 0
(particularly true when the number of observations is small with respect
to the number of continuous variables). The L1.profile
function
shows how to compare matching solutions for any level of (i.e., without
regard to) coarsening.
This code also calculate the Local Common Support (LCS) measure, which is the proportion of non empty k-dimensional cells of the histogram which contain at least one observation per group.
An object of class L1.meas
which is a list with the following fields
L1 |
The numerical value of the L1 measure |
breaks |
A list of cutpoints used to calculate the L1 measure |
LCS |
The numerical value of the Local Common Support proportion |
Stefano Iacus, Gary King, and Giuseppe Porro
Stefano Iacus, Gary King, Giuseppe Porro, ``Matching for Casual Inference Without Balance Checking: Coarsened Exact Matching,'' http://gking.harvard.edu/files/abs/cem-abs.shtml
data(LL) L1.meas(LL$treated,LL, drop=c("treated","re78"))