Builds condition probability matrices for Horvitz-Thompson estimation from permutation matrix
Builds condition probability matrices for Horvitz-Thompson estimation from permutation matrix
permutations_to_condition_pr_mat(permutations)
permutations |
A matrix where the rows are units and the columns are different treatment permutations; treated units must be represented with a 1 and control units with a 0 |
This function takes a matrix of permutations, for example from
the obtain_permutation_matrix function in
randomizr or through simulation and returns a 2n*2n matrix that can
be used to fully specify the design for horvitz_thompson
estimation. You can read more about these matrices in the documentation for
the declaration_to_condition_pr_mat function.
This is done by passing this matrix to the condition_pr_mat argument
of
a numeric 2n*2n matrix of marginal and joint condition treatment
probabilities to be passed to the condition_pr_mat argument of
horvitz_thompson.
# Complete randomization perms <- replicate(1000, sample(rep(0:1, each = 50))) comp_pr_mat <- permutations_to_condition_pr_mat(perms) # Arbitrary randomization possible_treats <- cbind( c(1, 1, 0, 1, 0, 0, 0, 1, 1, 0), c(0, 1, 1, 0, 1, 1, 0, 1, 0, 1), c(1, 0, 1, 1, 1, 1, 1, 0, 0, 0) ) arb_pr_mat <- permutations_to_condition_pr_mat(possible_treats) # Simulating a column to be realized treatment z <- possible_treats[, sample(ncol(possible_treats), size = 1)] y <- rnorm(nrow(possible_treats)) horvitz_thompson(y ~ z, condition_pr_mat = arb_pr_mat)
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