rematchSchools: Repeat School Match Only

In matchMulti: Optimal Multilevel Matching using a Network Algorithm

Repeat School Match Only

Description

After matchMulti has been called, repeats the school match (with possibly different parameters) without repeating the more computationally intensive student match.

Usage

rematchSchools(
  match.out,
  students,
  school.fb = NULL,
  verbose = FALSE,
  keep.target = NULL,
  school.penalty = NULL,
  tol = 0.001
)

Arguments

match.out	an object returned by a call to matchMulti.
students	a dataframe containing student and school covariates, with a different row for each student.
school.fb	an optional list of character vectors, each containing a subset of the column names of students. Each element of the list should contain all the names in previous elements (producing a nested structure).
verbose	a logical value indicating whether detailed output should be printed.
keep.target	an optional numeric value specifying the number of treated schools desired in the final match.
school.penalty	an optional numeric value, treated as the cost (to the objective function in the underlying optimization problem) of excluding a treated school. If it is set lower, more schools will be excluded.
tol	a numeric tolerance value for comparing distances. It may need to be raised above the default when matching with many levels of refined balance.

Details

The school.fb argument encodes a refined covariate balance constraint: the matching algorithm optimally balances the interaction of the variables in the first list element, then attempts to further balance the interaction in the second element, and so on. As such variables should be added in order of priority for balance.

The keep.target and school.penalty parameters allow optimal subset matching within the school match. When the keep.target argument is specified, the school match is repeated for different values of the school.penalty parameter in a form of binary search until an optimal match is obtained with the desired number of treated schools or a stopping rule is reached. The tol parameter controls the stopping rule; smaller values provide a stronger guarantee of obtaining the exact number of treated schools desired but may lead to greater computational costs.

It is not recommended that users specify the school.penalty parameter directly in most cases. Instead the keep.target parameter provides an easier way to consider excluding schools.

Author(s)

Luke Keele, Penn State University, [e-mail]

Sam Pimentel, University of Pennsylvania, [e-mail]

References

Rosenbaum, Paul R. (2002). Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2012) "Optimal Matching of an Optimally Chosen Subset in Observational Studies." Journal of Computational and Graphical Statistics, 21.1, 57-71.

See Also

matchMulti.

Examples

## Not run: 
# Load Catholic school data
data(catholic_schools)

student.cov <- c('minority','female','ses')
school.cov <- c('minority_mean','female_mean', 'ses_mean', 'size', 'acad')

#Match schools but not students within schools
match.simple <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = FALSE)

#Check balance after matching - this checks both student and school balance
balanceMulti(match.simple, student.cov = student.cov, school.cov = school.cov)

#now rematch excluding 2 schools
match.trimmed <- rematchSchools(match.simple, catholic_schools, keep.target = 13)
match.trimmed$dropped$schools.t

## End(Not run)

matchMulti documentation built on May 31, 2023, 9:13 p.m.