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Stratified CMR assignment

Source: R/cmr_stratified.R
cmr_stratified.Rd

Estimate cell-specific variance confidence intervals from pilot data and return the stratified CMR assignment across treatment/control by stratum cells.

Usage

cmr_stratified(
  y,
  d,
  strata,
  strata_share,
  alpha = 0.05,
  method = c("auto", "bounded", "bernoulli", "maurer_pontil", "mp", "bernoulli_exact",
    "martinez_taboada_ramdas", "mtr"),
  beta = NULL,
  normalize = FALSE,
  lower = NULL,
  upper = NULL,
  na.rm = TRUE,
  tol = 1e-11,
  solver_control = list(),
  max_vertices = 65536L
)

Arguments

y

Pilot outcomes.

d

Pilot treatment indicator; treatment is 1 and control is 0.

strata

Pilot stratum labels.

strata_share

Named stratum population shares that sum to one.

alpha

Target joint error level.

method

Confidence-set method. "auto" chooses exact Bernoulli bounds for 0/1 outcomes and bounded Maurer–Pontil bounds otherwise.

beta

Optional endpoint error allocation. If NULL, Bonferroni error is split across all lower and upper treatment/control by stratum endpoints.

normalize

If TRUE, normalize bounded outcomes to [0, 1] before computing variances. For guarantee-bearing bounded CMR on a non-unit scale, provide known lower and upper bounds.

lower, upper

Optional lower and upper outcome bounds used when normalize = TRUE. If either is omitted, the pilot minimum and/or maximum is used with a warning; that data-dependent normalization is exploratory and does not carry the finite-sample bounded-outcome CMR guarantee.

na.rm

If TRUE, drop rows with missing y, d, or strata.

tol

Numerical tolerance for exact Bernoulli bound inversion.

solver_control

Optional list of solver controls for the general vertex epigraph solver.

max_vertices

Maximum number of hyperrectangle vertices to enumerate.

Value

A list of class cmr_stratified with total cell assignment shares pi, matrix form pi_matrix, sampling and treatment margins, CMR certificate U_CMR, confidence set, pilot summaries, endpoint error allocation, and diagnostics.

See also

Other CMR rules: binary_rectangle_corners(), cmr_multiarm(), cmr_multiarm_from_rectangle(), cmr_multiple_outcomes(), cmr_proxy(), cmr_stratified_from_rectangle(), cmr_two_arm(), cmr_unbounded(), cmr_unbounded_from_rectangle(), print.cmr_two_arm()

Examples

set.seed(7)
strata <- rep(c("A", "B"), each = 40)
d <- rep(rep(c(1, 0), each = 20), 2)
y <- c(rbeta(20, 2, 6), rbeta(20, 4, 4),
       rbeta(20, 5, 3), rbeta(20, 3, 5))
cmr_stratified(y, d, strata, strata_share = c(A = 0.45, B = 0.55))
#> <cmr_stratified>
#>   pi: 1:A=0.225, 0:A=0.225, 1:B=0.275, 0:B=0.275
#>   U_CMR: 0.25
#>   method: bounded
#>   n: 80