distmat(t_ind, X_mat, calip_cov = NULL, calip_size = NULL, calip_penalty = NULL,
near_exact_covs = NULL, near_exact_penalties = NULL, digits = 1)0.2*sd(calip_cov).near_exact_penalties has to be equal to the number of columns of near_exact_covs.bmatch functions in the designmatch package.
distmat is a function for building a normalized rank-based Mahalanobis distance matrix with a penalty for caliper violations on a covariate (say, the propensity score) and penalties for near-exact matching.
As explained in Rosenbaum (2010), the use of a rank-based Mahalanobis distance prevents an outlier from inflating the variance for a variable, and it thus decreases its importance in the matching.
In the calculation of the matrix the variances are constrained to not decrease as ties become more common, so that it is not more important to match on a rare binary variable than on a common binary one.
The penalty for caliper violations ensures good balance on the propensity score or the covariate used.
In this way the rank-based Mahalanobis distance with a penalty for caliper violations in the propensity score constitutes a robust distance for matching.
As explained in Zubizarreta et al. (2011), the distance matrix can also be modified for near-exact matching.
Penalties are added to the distance matrix every time that a treated and a control unit have a different value for the corresponding near-exact matching covariate.
Zubizarreta, J. R., Reinke, C. E., Kelz, R. R., Silber, J. H., and Rosenbaum, P. R. (2011), "Matching for Several Sparse Nominal Variables in a Case-Control Study of Readmission Following Surgery," The American Statistician, 65, 229-238.
# Load data
data(germancities)
attach(germancities)
# Treatment indicator
t_ind = treat
# Matrix of covariates
X_mat = cbind(log2pop, popgrowth1939, popgrowth3339, emprate, indrate, rubble,
rubblemiss, flats, flatsmiss, refugees)
# Distance matrix
dist_mat = distmat(t_ind, X_mat)
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