startcost

Creates an distance matrix of zeros of dimensions compatible with the treatment indicator vector z.

Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are 'aHDL' from Rosenbaum (2023a) <doi:10.1111/biom.13558> and 'bingeM' from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data 'bingeM' from 'binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'.

Paul Rosenbaum

iTOS

Methods and Examples from Introduction to the Theory of
Observational Studies

startcost function

<dl> <dt>z</dt>
<dd>A vector with z[i]=1 if individual i is treated or z[i]=0 if individual i is control. The rows of costmatrix refer to treated individuals and the columns refer to controls. Although not strictly required, it is best that z has names that are the same as the names of the data frame dat that will be used in matching.</dd></dl>

Arguments

Author

Initialize a Distance Matrix. — startcost

<dl>

 <dt>z</dt>
<dd>A vector with z[i]=1 if individual i is treated or z[i]=0 if individual i is control. The rows of costmatrix refer to treated individuals and the columns refer to controls. Although not strictly required, it is best that z has names that are the same as the names of the data frame dat that will be used in matching.</dd>

</dl>

startcost: Initialize a Distance Matrix.

Description

Usage

Value

Arguments

Author

Examples