The output from the summary() command includes the following
elements, when applicable:
- The original assignment model call.
- sum.all: a data frame that contains variable names and
interactions down the row names, and summary statistics on all
observations in each of the columns. The columns in
sum.all contain:
- means of all covariates
for treated and control units,
where Means Treated
and Means Control
,
- standard deviation in the control group for all covariates
, where applicable,
- balance statistics of the original data (before matching),
which compare treated and control covariate distributions. If standardize = FALSE, balance measures will be presented on the
original scale. Specifically, mean differences (Mean
Diff.) as well as the median, mean, and maximum value of
differences in empirical quantile functions for each covariate
will be given (eQQ Med, eQQ Mean, and
eQQ Max, respectively). If standardize = TRUE, the
balance measures will be standardized. Standardized mean
differences (Std. Mean Diff.), defined as
, as well as the
median, mean, and maximum value of differences in empirical
cumulative distribution functions for each covariate will be given
(eCDF Med, eCDF Mean, and eCDF Max,
respectively).
- sum.matched: a data frame which contains variable names
down the row names, and summary statistics on only the matched
observations in each of the columns. Specifically, the columns
in sum.matched contain the following elements:
- weighted means for matched treatment units and matched control units of all covariates
and their interactions, where Means Treated
and Means
Control
,
- weighted standard deviations in the matched control group for
all covariates
, where applicable, where SD
, where
is the weighted mean of
in the matched
control group, and
- balance statistics of the matched data (after matching), which
compare treated and control covariate distributions. If standardize = FALSE, balance measures will be presented on the
original scale. Specifically, mean differences (Mean
Diff.) as well as the median, mean, and maximum value of
differences in empirical quantile functions for each covariate
will be given (eQQ Med, eQQ Mean, and
eQQ Max, respectively). If standardize = TRUE, the
balance measures will be standardized. Standardized mean
differences (Std. Mean Diff.), defined as
, as well as the
median, mean, and maximum value of differences in empirical
cumulative distribution functions for each covariate will be given
(eCDF Med, eCDF Mean, and eCDF Max,
respectively).
where
represents the vector of weights.
- reduction: the percent reduction in the difference in
means achieved in each of the balance measures in sum.all
and sum.matched, defined as
, where
was the value of the balance measure before matching and
is the
value of the balance measure after matching.
- nn: the sample sizes in the full and matched samples
and the number of discarded units, by treatment and control.
- q.table: an array that contains the same information
as sum.matched by subclass.
- qn: the sample sizes in the full and matched
samples and the number of discarded units, by subclass and by
treatment and control.
- match.matrix: the same object is contained in the
output of matchit().
Gary King
2011-04-26