ACER {ivdesign}

Two-sided test for the average cluster effect ratio estimand

Description

ACER tests (two-sided) if the average cluster effect ratio (ACER) is equal to lambda.

Usage

ACER(
  num_t,
  num_c,
  R_t,
  R_c,
  d_t,
  d_c,
  lambda,
  alpha = 0.05,
  kappa = 0.1,
  gap = 0.05,
  verbose = TRUE
)

Arguments

num_t

A length-K vector where K is equal to the number of clusters and the kth entry equal to the number of units in the encouraged cluster of the kth matched pair of two clusters.

num_c

A length-K vector with the kth entry equal to the number of units in the control cluster of the kth matched pair of two clusters.

R_t

A length-K vector with kth entry equal to the sum of unit-level outcomes in the encouraged cluster of the kth matched pair of two clusters.

R_c

A length-K vector with the kth entry equal to the sum of unit-level outcomes in the control cluster of the kth matched pair of two clusters.

d_t

A length-K vector with the kth entry equal to the sum of unit-level treatment received in the encouraged cluster of the kth matched pair of two clusters.

d_c

A length-K vector with the kth entry equal to the sum of unit-level treatment received in the control cluster of the kth matched pair of two clusters.

lambda

The magnitude of the average cluster effect ratio (ACER) to be tested.

alpha

The level of the test.

kappa

Minimum compliance rate.

gap

Relative MIP optimality gap.

verbose

If true, the solver output is enabled; otherwise, the solver output is disabled.

Value

A list of three elements: the optimal solution, the optimal objective value, and an indicator of whether or not the test is rejected.

Examples

## Not run: 
# To run the following example, Gurobi must be installed.

R_t = encouraged_clusters$aggregated_outcome
R_c = control_clusters$aggregated_outcome
d_t = encouraged_clusters$aggregated_treatment
d_c = control_clusters$aggregated_treatment
num_t = encouraged_clusters$number_units
num_c = control_clusters$number_units

# Test at level 0.05 if the ACER is equal
# to 0.2. Assume the minimum compliance rate across
# K clusters is at least 0.2. Set verbose = FALSE
# to suppress the output.
res = ACER(num_t, num_c, R_t, R_c, d_t, d_c,
          lambda = 0.2, alpha = 0.05, kappa = 0.2,
          verbose = FALSE)

# The test is rejected
res$Reject

## End(Not run)