# permutations_to_condition_pr_mat: Builds condition probability matrices for Horvitz-Thompson... In estimatr: Fast Estimators for Design-Based Inference

 permutations_to_condition_pr_mat R Documentation

## Builds condition probability matrices for Horvitz-Thompson estimation from permutation matrix

### Description

Builds condition probability matrices for Horvitz-Thompson estimation from permutation matrix

### Usage

``````permutations_to_condition_pr_mat(permutations)
``````

### Arguments

 `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

### Details

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

### Value

a numeric 2n*2n matrix of marginal and joint condition treatment probabilities to be passed to the `condition_pr_mat` argument of `horvitz_thompson`.

`declare_ra`, `declaration_to_condition_pr_mat`

### Examples

``````
# 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)

``````

estimatr documentation built on May 29, 2024, 7:23 a.m.