Description Usage Arguments Details Value See Also Examples
View source: R/helper_condition_pr_matrix.R
Builds condition probability matrices for HorvitzThompson estimation from randomizr declaration
1 2  declaration_to_condition_pr_mat(ra_declaration, condition1 = NULL,
condition2 = NULL, prob_matrix = NULL)

ra_declaration 
An object of class 
condition1 
The name of the first condition, often the control group. If 
condition2 
The name of the second condition, often the treatment group. If 
prob_matrix 
An optional probability matrix to override the one in

This function takes a "ra_declaration"
, generated
by the declare_ra
function in randomizr and
returns a 2n*2n matrix that can be used to fully specify the design for
horvitz_thompson
estimation. This is done by passing this
matrix to the condition_pr_mat
argument of
horvitz_thompson
.
Currently, this function can learn the condition probability matrix for a wide variety of randomizations: simple, complete, simple clustered, complete clustered, blocked, blockclustered.
A condition probability matrix is made up of four submatrices, each of which corresponds to the joint and marginal probability that each observation is in one of the two treatment conditions.
The upperleft quadrant is an n*n matrix. On the diagonal is the marginal probability of being in condition 1, often control, for every unit (Pr(Z_i = Condition1) where Z represents the vector of treatment conditions). The offdiagonal elements are the joint probabilities of each unit being in condition 1 with each other unit, Pr(Z_i = Condition1, Z_j = Condition1) where i indexes the rows and j indexes the columns.
The upperright quadrant is also an n*n matrix. On the diagonal is the joint probability of a unit being in condition 1 and condition 2, often the treatment, and thus is always 0. The offdiagonal elements are the joint probability of unit i being in condition 1 and unit j being in condition 2, Pr(Z_i = Condition1, Z_j = Condition2).
The lowerleft quadrant is also an n*n matrix. On the diagonal is the joint probability of a unit being in condition 1 and condition 2, and thus is always 0. The offdiagonal elements are the joint probability of unit i being in condition 2 and unit j being in condition 1, Pr(Z_i = Condition2, Z_j = Condition1).
The lowerright quadrant is an n*n matrix. On the diagonal is the marginal probability of being in condition 2, often treatment, for every unit (Pr(Z_i = Condition2)). The offdiagonal elements are the joint probability of each unit being in condition 2 together, Pr(Z_i = Condition2, Z_j = Condition2).
a numeric 2n*2n matrix of marginal and joint condition treatment
probabilities to be passed to the condition_pr_mat
argument of
horvitz_thompson
. See details.
permutations_to_condition_pr_mat
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  # Learn condition probability matrix from complete blocked design
library(randomizr)
n < 100
dat < data.frame(
blocks = sample(letters[1:10], size = n, replace = TRUE),
y = rnorm(n)
)
# Declare complete blocked randomization
bl_declaration < declare_ra(blocks = dat$blocks, prob = 0.4, simple = FALSE)
# Get probabilities
block_pr_mat < declaration_to_condition_pr_mat(bl_declaration, 0, 1)
# Do randomiztion
dat$z < conduct_ra(bl_declaration)
horvitz_thompson(y ~ z, data = dat, condition_pr_mat = block_pr_mat)
# When you pass a declaration to horvitz_thompson, this function is called
# Equivalent to above call
horvitz_thompson(y ~ z, data = dat, ra_declaration = bl_declaration)

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