resultsBinaryMatchThenGreedySearch

Returns unique allocation vectors that are binary matched

Computes experimental designs for a
two-arm experiment with covariates via a number of methods:
(0) complete randomization and randomization with forced-balance,
(1) Greedily optimizing a
balance objective function via pairwise switching. This optimization
provides lower variance for the treatment effect estimator (and higher
power) while preserving a design that is close to complete randomization.
We return all iterations of the designs for use in a permutation test,
(2) The second is via numerical optimization
(via 'gurobi' which must be installed, see <https://www.gurobi.com/documentation/9.1/quickstart_windows/r_ins_the_r_package.html>)
a la Bertsimas and Kallus,
(3) rerandomization,
(4) Karp's method for one covariate,
(5) exhaustive enumeration to find the
optimal solution (only for small sample sizes),
(6) Binary pair matching using the 'nbpMatching' library,
(7) Binary pair matching plus design number (1) to further optimize balance,
(8) Binary pair matching plus design number (3) to further optimize balance,
(9) Hadamard designs,
(10) Simultaneous Multiple Kernels.
In (1-9) we allow for three objective functions:
Mahalanobis distance,
Sum of absolute differences standardized and
Kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in
Krieger, A, Azriel, D and Kapelner, A "Nearly Random Designs with Greatly Improved Balance" (2016) <arXiv:1612.02315>,
Krieger, A, Azriel, D and Kapelner, A "Better Experimental Design by Hybridizing Binary Matching with Imbalance
Optimization" (2021) <arXiv:2012.03330>.

Adam Kapelner

GreedyExperimentalDesign

Greedy Experimental Design Construction

David Azriel

Abba Krieger

resultsBinaryMatchThenGreedySearch function

<dl><dt>obj</dt>
<dd>The <code>binary_then_greedy_experimental_design</code> object where the pairs are computed.</dd>
<dt>num_vectors</dt>
<dd>How many random allocation vectors you wish to return. The default is <code>NULL</code> indicating you want all of them.</dd>
<dt>compute_obj_vals</dt>
<dd>Should we compute all the objective values for each allocation? Default is <code>FALSE</code>.</dd>
<dt>form</dt>
<dd>Which form should it be in? The default is <code>one_zero</code> for 1/0's or <code>pos_one_min_one</code> for +1/-1's.</dd></dl>

Arguments

Author

Returns unique allocation vectors that are binary matched — resultsBinaryMatchThenGreedySearch

<dl>

<dt>obj</dt>
<dd>The <code>binary_then_greedy_experimental_design</code> object where the pairs are computed.</dd>


<dt>num_vectors</dt>
<dd>How many random allocation vectors you wish to return. The default is <code>NULL</code> indicating you want all of them.</dd>


<dt>compute_obj_vals</dt>
<dd>Should we compute all the objective values for each allocation? Default is <code>FALSE</code>.</dd>


<dt>form</dt>
<dd>Which form should it be in? The default is <code>one_zero</code> for 1/0's or <code>pos_one_min_one</code> for +1/-1's.</dd>

</dl>

resultsBinaryMatchThenGreedySearch: Returns unique allocation vectors that are binary matched

Description

Usage

Arguments

Author