maxsubset_weights_cluster: Find maximum effective sample size balanced set

In ebenmichael/balancer: Matrix constraints for covariate balance

Find maximum effective sample size balanced set

Description

Find maximum effective sample size balanced set

Usage

maxsubset_weights_cluster(
  ind_covs,
  clus_covs,
  trt,
  clusters,
  lambda = 0,
  icc = 0,
  lowlim = 0,
  uplim = 1,
  verbose = TRUE,
  eps_abs = 1e-05,
  eps_rel = 1e-05,
  ...
)

Arguments

ind_covs	n x d1 matrix of covariates for individual units
clus_covs	n x d2 matrix of covariates for clusters
trt	Vector of treatment assignments
clusters	n vector of cluster assignments
lambda	Regularization hyper parameter, default 0
icc	Intraclass correlation coefficient for regularization
lowlim	Lower limit on weights, default 0
uplim	Upper limit on weights, default 1 * number of treated/control units
verbose	Whether to show messages, default T
eps_abs	Absolute error tolerance for solver
eps_rel	Relative error tolerance for solver
...	Extra arguments for osqp solver

Value

• weights Estimated weights as a length n vector

• imbalance Imbalance in covariates as a d X J matrix

• global_imbalanceOverall imbalance in covariates, as a length d vector

ebenmichael/balancer documentation built on Jan. 17, 2024, 2:56 p.m.