Convert the persistent aspects of a BCF model to (in-memory) JSON string

Usage

saveBCFModelToJsonString(object)

Arguments

object: Object of type bcfmodel containing draws of a Bayesian causal forest model and associated sampling outputs.

Value

JSON string

Examples

n <- 500
p <- 5
X <- matrix(runif(n*p), ncol = p)
mu_x <- (
    ((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5) + 
    ((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5) + 
    ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5) + 
    ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5)
)
pi_x <- (
    ((0 <= X[,1]) & (0.25 > X[,1])) * (0.2) + 
    ((0.25 <= X[,1]) & (0.5 > X[,1])) * (0.4) + 
    ((0.5 <= X[,1]) & (0.75 > X[,1])) * (0.6) + 
    ((0.75 <= X[,1]) & (1 > X[,1])) * (0.8)
)
tau_x <- (
    ((0 <= X[,2]) & (0.25 > X[,2])) * (0.5) + 
    ((0.25 <= X[,2]) & (0.5 > X[,2])) * (1.0) + 
    ((0.5 <= X[,2]) & (0.75 > X[,2])) * (1.5) + 
    ((0.75 <= X[,2]) & (1 > X[,2])) * (2.0)
)
Z <- rbinom(n, 1, pi_x)
E_XZ <- mu_x + Z*tau_x
snr <- 3
rfx_group_ids <- rep(c(1,2), n %/% 2)
rfx_coefs <- matrix(c(-1, -1, 1, 1), nrow=2, byrow=TRUE)
rfx_basis <- cbind(1, runif(n, -1, 1))
rfx_term <- rowSums(rfx_coefs[rfx_group_ids,] * rfx_basis)
y <- E_XZ + rfx_term + rnorm(n, 0, 1)*(sd(E_XZ)/snr)
test_set_pct <- 0.2
n_test <- round(test_set_pct*n)
n_train <- n - n_test
test_inds <- sort(sample(1:n, n_test, replace = FALSE))
train_inds <- (1:n)[!((1:n) %in% test_inds)]
X_test <- X[test_inds,]
X_train <- X[train_inds,]
pi_test <- pi_x[test_inds]
pi_train <- pi_x[train_inds]
Z_test <- Z[test_inds]
Z_train <- Z[train_inds]
y_test <- y[test_inds]
y_train <- y[train_inds]
mu_test <- mu_x[test_inds]
mu_train <- mu_x[train_inds]
tau_test <- tau_x[test_inds]
tau_train <- tau_x[train_inds]
rfx_group_ids_test <- rfx_group_ids[test_inds]
rfx_group_ids_train <- rfx_group_ids[train_inds]
rfx_basis_test <- rfx_basis[test_inds,]
rfx_basis_train <- rfx_basis[train_inds,]
rfx_term_test <- rfx_term[test_inds]
rfx_term_train <- rfx_term[train_inds]
mu_params <- list(sample_sigma_leaf = TRUE)
tau_params <- list(sample_sigma_leaf = FALSE)
bcf_model <- bcf(X_train = X_train, Z_train = Z_train, y_train = y_train, 
                 propensity_train = pi_train, 
                 rfx_group_ids_train = rfx_group_ids_train, 
                 rfx_basis_train = rfx_basis_train, X_test = X_test, 
                 Z_test = Z_test, propensity_test = pi_test, 
                 rfx_group_ids_test = rfx_group_ids_test,
                 rfx_basis_test = rfx_basis_test, 
                 num_gfr = 10, num_burnin = 0, num_mcmc = 10, 
                 prognostic_forest_params = mu_params, 
                 treatment_effect_forest_params = tau_params)
saveBCFModelToJsonString(bcf_model)
#> [1] 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