cace: Compute the Posterior Distribution of the CACE

In noncomplyR: Bayesian Analysis of Randomized Experiments with Non-Compliance

Description

cace takes a sample from the posterior distribution of the model parameters and computes the corresponding posterior distribution of the Complier Average Causal Effect.

Usage

cace(chain, outcome_model, strong_access)

Arguments

chain	a matrix containing the draws from the posterior distribution of the model parameters. The matrix should either be the result of a call to compliance_chain or have the same structure as one.
outcome_model	a character string indicating which outcome model was used in fitting the model, either "binary" for a dichotomous outcome or "normal" for the Normal model.
strong_access	a logical indicating whether the strong access monotonicity assumption was made when fitting the model

Value

a vector containing the draws from the posterior distribution of the CACE

Examples

# CACE based on a subset of the vitaminA dataset
set.seed(4923)
chain <- compliance_chain(vitaminA[sample(1:nrow(vitaminA), 1000),], outcome_model = "binary",
 exclusion_restriction = TRUE, strong_access = TRUE, n_iter = 10, n_burn = 1)

cace(chain, outcome_model = "binary", strong_access = TRUE)

# matrix representing the samples from the posterior distribution of the model parameters
posterior_mat <- matrix(rnorm(10*8, mean = 10), nrow = 10, ncol = 8)
cace(posterior_mat, "normal", strong_access = TRUE)

noncomplyR documentation built on May 2, 2019, 6:01 a.m.