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randomizr: Easy-to-Use Tools for Common Forms of Random Assignment and Sampling

Source: R/randomizr-package.r
randomizr-package.Rd

randomizr generates random assignments for common experimental designs and random samples for common sampling designs. The functions are named for the procedure they implement, and each has a `_probabilities` companion that returns the probability of each unit falling into each condition, which is what inverse-probability weights are built from.

Random assignment

  • [simple_ra()] assigns each unit independently, so the number treated varies from draw to draw.

  • [complete_ra()] fixes the number treated on every draw.

  • [block_ra()] conducts complete assignment separately within blocks of similar units, which increases precision.

  • [cluster_ra()] assigns whole groups together, for interventions that cannot be delivered to individuals.

  • [block_and_cluster_ra()] does both at once.

  • [balanced_ra()] (experimental) holds condition counts (and, with formula, covariate totals) at their targets while keeping each unit's probability exact.

  • [declare_ra()] describes a design once so it can be reused by [conduct_ra()] to draw assignments and by [obtain_condition_probabilities()] to recover the probabilities. Balanced assignment is opt-in: ra_type = "balanced", prob_unit_each, or formula.

Random sampling

The sampling functions mirror the assignment ones: [simple_rs()], [complete_rs()], [strata_rs()], [cluster_rs()] and [strata_and_cluster_rs()], with [declare_rs()], [draw_rs()] and [obtain_inclusion_probabilities()] playing the roles that [declare_ra()], [conduct_ra()] and [obtain_condition_probabilities()] play for assignment.

Randomization inference

[obtain_permutation_matrix()] enumerates or samples the assignments a design could have produced, and [obtain_num_permutations()] counts them.

References

Blair, G., Cooper, J., Coppock, A. and Humphreys, M. (2019). Declaring and Diagnosing Research Designs. American Political Science Review 113(3), 838-859. doi:10.1017/S0003055419000194

Gerber, A. S. and Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. New York: W. W. Norton.

See also

Useful links:

Author

Maintainer: Alexander Coppock acoppock@gmail.com (ORCID)

Authors:

Other contributors:

Examples

# Complete random assignment: exactly 50 of 100 units treated, every draw.
Z <- complete_ra(N = 100, m = 50)
table(Z)
#> Z
#>  0  1 
#> 50 50 

# Blocking on a covariate usually buys precision.
blocks <- rep(c("small", "large"), times = c(60, 40))
Z <- block_ra(blocks = blocks)
table(blocks, Z)
#>        Z
#> blocks   0  1
#>   large 20 20
#>   small 30 30

# Declare once, then draw and recover probabilities from the same object.
declaration <- declare_ra(N = 100, m = 50)
Z <- conduct_ra(declaration)
probs <- obtain_condition_probabilities(declaration, Z)
table(probs)
#> probs
#> 0.5 
#> 100