# declare_sampling in use
## Two-arm randomized experiment
design <-
declare_model(
N = 500,
X = rep(c(0, 1), each = N / 2),
U = rnorm(N, sd = 0.25),
potential_outcomes(Y ~ 0.2 * Z + X + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N = N, n = 200)) +
declare_assignment(Z = complete_ra(N = N, m = 100)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
run_design(design)
# Set up population to sample from
model <- declare_model(
villages = add_level(
N = 30,
N_households = sample(c(50:100), N, replace = TRUE)
),
households = add_level(
N = N_households,
N_members = sample(c(1, 2, 3, 4), N,
prob = c(0.2, 0.3, 0.25, 0.25), replace = TRUE)
),
individuals = add_level(
N = N_members,
age = sample(18:90, N, replace = TRUE),
gender = rbinom(n = N, size = 1, prob = .5)
)
)
# Sampling procedures
## Complete random sampling
design <- model +
declare_sampling(S = complete_rs(N = N, n = 1000))
head(draw_data(design))
## Cluster random sampling
design <- model +
declare_sampling(S = cluster_rs(clusters = villages,
n = 15))
head(draw_data(design))
## Strata and cluster random sampling
design <- model +
declare_sampling(S = strata_and_cluster_rs(
strata = villages,
clusters = households,
strata_n = rep(20, 30)))
head(draw_data(design))
## Stratified random sampling
design <- model +
declare_sampling(S = strata_rs(strata = gender, n = 100))
head(draw_data(design))
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