mlr {MatchLinReg}

Creating a series of matched data sets with different calibration parameters

Description

Creating a series of matched data sets with different calibration parameters. The output of this function can be supplied to summary.mlr and then plot.summary.mlr methods to generate diagnostic and calibration plots.

Usage

mlr(tr, Z.i = NULL, Z.o = mlr.generate.Z.o(Z.i), psm = TRUE
  , caliper.vec = c(0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 5, Inf)
  , ...)

Arguments

tr

Binary treatment indicator vector (1=treatment, 0=control), whose coefficient in the linear regression model is TE.

Z.i

Matrix of adjustment covariates included in linear regression. We must have nrow(Z.i) == length(tr).

Z.o

Matrix of adjustment covariates (present in generative model but) omitted from regression estimation. We must have nrow(Z.o) == length(tr).

psm

Boolean flag, indicating whether propensity score matching should be used (TRUE) or Mahalanobis matching (FALSE).

caliper.vec

Vector of matching calipers used.

...

Other parameters passed to mlr.match.

Value

A list with the following fields:

tr

Same as input.

Z.i

Same as input.

Z.o

Same as input.

idx.list

List of observation indexes for each matched data set.

caliper.vec

Same as input.

Author(s)

Alireza S. Mahani, Mansour T.A. Sharabiani

References

Link to a draft paper, documenting the supporting mathematical framework, will be provided in the next release.