standardize: Re-weight groups to target population means

In ebenmichael/balancer: Matrix constraints for covariate balance

Re-weight groups to target population means

Description

Re-weight groups to target population means

Usage

standardize(
  X,
  target,
  Z,
  lambda = 0,
  lowlim = 0,
  uplim = 1,
  scale_sample_size = T,
  data_in = NULL,
  verbose = TRUE,
  return_data = TRUE,
  exact_global = T,
  init_uniform = F,
  eps_abs = 1e-05,
  eps_rel = 1e-05,
  ...
)

Arguments

X	n x d matrix of covariates
target	Vector of population means to re-weight to
Z	Vector of group indicators with J levels
lambda	Regularization hyper parameter, default 0
lowlim	Lower limit on weights, default 0
uplim	Upper limit on weights, default 1
scale_sample_size	Whether to scale the dispersion penalty by the sample size of each group, default T
data_in	Optional list containing pre-computed objective matrix/vector and constraints (without regularization term)
verbose	Whether to show messages, default T
return_data	Whether to return the objective matrix and vector and constraints, default T
exact_global	Whether to enforce exact balance for overall population
init_uniform	Wheter to initialize solver with uniform weights
eps_abs	Absolute error tolerance for solver
eps_rel	Relative error tolerance for solver
...	Extra arguments for osqp solver

Value

• weights Estimated primal weights as an n x J matrix

• imbalance Imbalance in covariates as a d X J matrix

• data_out List containing elements of QP min 0.5 x'Px + q'x st l <= Ax <= u

  • P, q

  • constraints A, l , u

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