Declare sampling procedure
Usage
declare_sampling(..., handler = sampling_handler, label = NULL)
sampling_handler(data, ..., legacy = FALSE)Value
A sampling declaration, which is a function that takes a data.frame as an argument and returns a data.frame subsetted to sampled observations and (optionally) augmented with inclusion probabilities and other quantities.
Examples
# 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)
#> inquiry estimand estimator term estimate std.error statistic p.value
#> 1 ATE 0.2 estimator Z 0.1371534 0.07941205 1.72711 0.08570732
#> conf.low conf.high df outcome
#> 1 -0.01944857 0.2937553 198 Y
# 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))
#> villages N_households households N_members individuals age gender S
#> 2 01 64 0001 3 0002 86 1 1
#> 10 01 64 0005 2 0010 65 0 1
#> 11 01 64 0005 2 0011 85 1 1
#> 24 01 64 0011 1 0024 73 0 1
#> 39 01 64 0019 3 0039 43 1 1
#> 45 01 64 0021 1 0045 62 0 1
## Cluster random sampling
design <- model +
declare_sampling(S = cluster_rs(clusters = villages,
n = 15))
head(draw_data(design))
#> villages N_households households N_members individuals age gender S
#> 1 01 65 0001 3 0001 18 1 1
#> 2 01 65 0001 3 0002 74 1 1
#> 3 01 65 0001 3 0003 38 1 1
#> 4 01 65 0002 1 0004 29 0 1
#> 5 01 65 0003 3 0005 24 0 1
#> 6 01 65 0003 3 0006 53 0 1
## 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))
#> villages N_households households N_members individuals age gender S
#> 1 01 54 0001 1 0001 55 1 1
#> 6 01 54 0004 4 0006 21 0 1
#> 7 01 54 0004 4 0007 58 0 1
#> 8 01 54 0004 4 0008 30 0 1
#> 9 01 54 0004 4 0009 23 1 1
#> 12 01 54 0006 4 0012 86 1 1
## Stratified random sampling
design <- model +
declare_sampling(S = strata_rs(strata = gender, n = 100))
head(draw_data(design))
#> villages N_households households N_members individuals age gender S
#> 44 01 53 0020 4 0044 30 1 1
#> 56 01 53 0023 4 0056 20 0 1
#> 70 01 53 0030 3 0070 70 0 1
#> 103 01 53 0044 2 0103 29 1 1
#> 129 01 53 0052 3 0129 38 1 1
#> 150 02 96 0059 4 0150 63 1 1