Declare Data Strategy: Assignment
Usage
declare_assignment(..., handler = assignment_handler, label = NULL)
assignment_handler(data, ..., legacy = FALSE)Value
A function that takes a data.frame as an argument and returns a data.frame with assignment columns appended.
Examples
# declare_assignment 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 0.006027208 0.07908535 0.07621143 0.9393278
#> conf.low conf.high df outcome
#> 1 -0.1499305 0.1619849 198 Y
# Set up population to assign
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)
)
)
# Assignment procedures
## Complete random assignment
design <-
model +
declare_assignment(Z = complete_ra(N = N, m = 1000))
head(draw_data(design))
#> villages N_households households N_members individuals age gender Z
#> 1 01 88 0001 3 0001 33 0 0
#> 2 01 88 0001 3 0002 34 0 0
#> 3 01 88 0001 3 0003 77 1 0
#> 4 01 88 0002 1 0004 34 0 0
#> 5 01 88 0003 2 0005 58 0 0
#> 6 01 88 0003 2 0006 90 0 0
## Cluster random assignment
design <-
model +
declare_assignment(Z = cluster_ra(clusters = villages,
n = 15))
head(draw_data(design))
#> villages N_households households N_members individuals age gender Z
#> 1 01 72 0001 2 0001 36 1 T15
#> 2 01 72 0001 2 0002 52 0 T15
#> 3 01 72 0002 1 0003 22 1 T15
#> 4 01 72 0003 2 0004 81 0 T15
#> 5 01 72 0003 2 0005 57 1 T15
#> 6 01 72 0004 1 0006 69 1 T15
## Block and cluster random assignment
design <-
model +
declare_assignment(Z = block_and_cluster_ra(
blocks = villages,
clusters = households,
block_m = rep(20, 30)
))
head(draw_data(design))
#> villages N_households households N_members individuals age gender Z
#> 1 01 94 0001 4 0001 84 1 0
#> 2 01 94 0001 4 0002 59 0 0
#> 3 01 94 0001 4 0003 78 1 0
#> 4 01 94 0001 4 0004 25 0 0
#> 5 01 94 0002 1 0005 67 1 0
#> 6 01 94 0003 4 0006 70 0 0
## Block random assignment
design <-
model +
declare_assignment(Z = block_ra(blocks = gender, m = 100))
head(draw_data(design))
#> villages N_households households N_members individuals age gender Z
#> 1 01 82 0001 4 0001 86 0 1
#> 2 01 82 0001 4 0002 74 1 0
#> 3 01 82 0001 4 0003 56 0 0
#> 4 01 82 0001 4 0004 26 1 0
#> 5 01 82 0002 2 0005 72 0 0
#> 6 01 82 0002 2 0006 88 1 0
## Block random assignment using probabilities
design <-
model +
declare_assignment(Z = block_ra(blocks = gender,
block_prob = c(1 / 3, 2 / 3)))
head(draw_data(design))
#> villages N_households households N_members individuals age gender Z
#> 1 01 89 0001 2 0001 78 0 1
#> 2 01 89 0001 2 0002 19 1 1
#> 3 01 89 0002 3 0003 84 0 1
#> 4 01 89 0002 3 0004 42 0 1
#> 5 01 89 0002 3 0005 39 1 1
#> 6 01 89 0003 4 0006 62 1 0
## Factorial assignment
design <-
model +
declare_assignment(Z1 = complete_ra(N = N, m = 100),
Z2 = block_ra(blocks = Z1))
head(draw_data(design))
#> villages N_households households N_members individuals age gender Z1 Z2
#> 1 01 81 0001 4 0001 86 1 0 1
#> 2 01 81 0001 4 0002 85 0 0 0
#> 3 01 81 0001 4 0003 63 0 0 1
#> 4 01 81 0001 4 0004 31 0 0 1
#> 5 01 81 0002 3 0005 34 0 0 1
#> 6 01 81 0002 3 0006 24 1 0 1
## Assignment using functions outside of randomizr
design <-
model +
declare_assignment(Z = rbinom(n = N, size = 1, prob = 0.35))
head(draw_data(design))
#> villages N_households households N_members individuals age gender Z
#> 1 01 55 0001 3 0001 50 0 1
#> 2 01 55 0001 3 0002 31 1 1
#> 3 01 55 0001 3 0003 43 1 0
#> 4 01 55 0002 4 0004 19 1 1
#> 5 01 55 0002 4 0005 38 0 1
#> 6 01 55 0002 4 0006 19 1 1