nbpInference (version 1.0.3)

nbp.caliper: non-bipartite matching with treatment assignment caliper

Description

This function creates a I x 2 dataframe containing the indices of observations that form our set of matched pairs. It uses the nbpMatch package lu2011optimalnbpInference along with a p-matrix in order to create I matched pairs using a treatment assignment caliper. A p-matrix can be created using the make.pmatrix function.

Usage

nbp.caliper(Z, X, pmat, xi = 0, M = 0)

Value

I x 2 dataframe

Arguments

Z

a 2I-length vector of treatment values, which must be numeric.

X

a 2I x k matrix of covariate values, which must be numeric.

pmat

a 2I x 2I symmetric matrix where the diagonals equal zero, and the off-diagonal elements (i, j) contain the probability the ith observation has Z = max(Z_i, Z_j) and the jth observation has Z = min(Z_i, Z_j). A p-matrix can be made using the make.pmatrix function.

xi

a number in the range 0 to 0.5, the cutoff related to the treatment assignment probability caliper.

M

an integer determining the penalty of the treatment assignment probability caliper. If a potential matched pair between observations i and j has treatment assignment probability less than xi or greater than 1-xi, add M to the distance matrix in the (i, j) and (j, i) entry.

See Also

Other inference: bias.corrected.neyman(), classic.neyman(), covAdj.variance(), make.pmatrix()

Examples

set.seed(12345)
X <- rnorm(100, 0, 5)
Z <- X + rnorm(100, 0, (1+sqrt(abs(X))))
pmat <- make.pmatrix(Z, X)
nbp.caliper(Z, X, pmat, xi = 0.1, M = 10000)