Deprecated. Please use the reveal_outcomes function within a declare_measurement declaration.

declare_reveal(..., handler = declare_reveal_handler, label = NULL)

declare_reveal_handler(
data = NULL,
outcome_variables = Y,
assignment_variables = Z,
attrition_variables = NULL,
...
)

Arguments

...

arguments to be captured, and later passed to the handler

handler

a tidy-in, tidy-out function

label

a string describing the step

data

A data.frame containing columns for assignment and potential outcomes.

outcome_variables

The outcome prefix(es) of the potential outcomes.

assignment_variables

Unquoted name(s) of the assignment variable(s).

attrition_variables

Unquoted name of the attrition variable.

Details

Potential outcomes declarations indicate what outcomes would obtain for different possible values of assignment variables. But realized outcomes need to be "revealed." declare_reveal generates these realized outcomes using information on potential outcomes (for instance generated via declare_potential_outcomes) and the relevant assignment variables (for example created by declare_assignment). Revelation steps are usefully included after declaration of all assignments of conditions required to determine the realized outcome. If a revelation is not declared, DeclareDesign will try to guess appropriate revelations. Explicit revelation is recommended however.

declare_reveal declares how outcomes should be realized. A "revelation" uses the random assignment to pluck out the correct potential outcomes (Gerber and Green 2012, Chapter 2). Revelation requires that every named outcome variable is a function of every named assignment variable within a step. Thus if multiple outcome variables depend on different assignment variables, multiple revelations are needed.

Examples


design <-
declare_model(
N = 100,
U = rnorm(N),
Y_Z_0 = U,
Y_Z_1 = U + rnorm(N, mean = 2, sd = 2)
) +
declare_assignment(Z = complete_ra(N, m = 50)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z))

# Declaring multiple assignment variables or multiple outcome variables

design   <-
declare_model(
N = 10,
potential_outcomes(Y1 ~ Z),
potential_outcomes(Y2 ~ 1 + 2 * Z),
potential_outcomes(Y3 ~ 1 - X * Z, conditions = list(X = 0:1, Z = 0:1))
) +
declare_assignment(Z = complete_ra(N)) +
declare_assignment(X = complete_ra(N)) +
declare_measurement(Y1 = reveal_outcomes(Y1 ~ Z),
Y2 = reveal_outcomes(Y2 ~ Z),
Y3 = reveal_outcomes(Y3 ~ X + Z))

design <-
declare_model(
N = 100,
age = sample(18:95, N, replace = TRUE),
potential_outcomes(Y ~ .25 * Z + .01 * age * Z),
potential_outcomes(R ~ rbinom(n = N, size = 1, prob = pnorm(Y_Z_0)))
) +
declare_assignment(Z = complete_ra(N, m = 25))
declare_measurement(R = reveal_outcomes(R ~ Z),
Y = reveal_outcomes(Y ~ Z),
Y = ifelse(R == 1, Y, NA))
#> declare_measurement(R = reveal_outcomes(R ~ Z), Y = reveal_outcomes(Y ~
#>     Z), Y = ifelse(R == 1, Y, NA))