Text Summary of a Design

# S3 method for design
summary(object, ...)

Arguments

object

a design object created by declare_design

...

optional arguments to be sent to summary function

Examples

my_population <- declare_population(N = 500, noise = rnorm(N)) my_potential_outcomes <- declare_potential_outcomes( Y_Z_0 = noise, Y_Z_1 = noise + rnorm(N, mean = 2, sd = 2)) my_sampling <- declare_sampling(n = 250) my_assignment <- declare_assignment(m = 25) my_estimand <- declare_estimand(ATE = mean(Y_Z_1 - Y_Z_0)) my_estimator <- declare_estimator(Y ~ Z, estimand = my_estimand) design <- declare_design(my_population, my_potential_outcomes, my_sampling, my_estimand, dplyr::mutate(noise_sq = noise^2), my_assignment, reveal_outcomes, my_estimator) summary(design)
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> Added variable: ID #> N_missing N_unique #> 0 500 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -3.23 0.02 -0.02 2.72 0.98 0 500 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -3.23 0.02 -0.02 2.72 0.98 0 500 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -4.48 1.75 1.76 7.26 2.26 0 500 #> #> Step 3 (sampling): my_sampling ------------------------------------------------- #> #> #> Random sampling procedure: Complete random sampling #> Number of units: 500 #> The inclusion probabilities are constant across units. #> #> Added variable: S_inclusion_prob #> 0.5 #> Frequency 250 #> Proportion 1.00 #> #> Step 4 (estimand): my_estimand ------------------------------------------------- #> #> A single draw of the estimand: #> estimand_label estimand #> ATE 1.669259 #> #> Step 5 (custom data modification): dplyr::mutate(noise_sq = noise^2) ----------- #> #> Added variable: noise_sq #> min median mean max sd N_missing N_unique #> 0.00 0.56 1.07 10.41 1.49 0 250 #> #> Step 6 (assignment): my_assignment --------------------------------------------- #> #> #> Random assignment procedure: Complete random assignment #> Number of units: 250 #> Number of treatment arms: 2 #> The possible treatment categories are 0 and 1. #> The probabilities of assignment are constant across units. #> #> Added variable: Z #> 0 1 #> Frequency 225 25 #> Proportion 0.90 0.10 #> #> Added variable: Z_cond_prob #> 0.1 0.9 #> Frequency 25 225 #> Proportion 0.10 0.90 #> #> Step 7 (reveal outcomes): reveal_outcomes -------------------------------------- #> #> Added variable: Y #> min median mean max sd N_missing N_unique #> -3.73 0.06 0.12 5.63 1.29 0 250 #> #> Step 8 (estimator): my_estimator ----------------------------------------------- #> #> A single draw of the estimator: #> estimator_label est se p ci_lower ci_upper df #> my_estimator 1.343188 0.496138 0.007255215 0.3660068 2.32037 248 #> estimand_label #> ATE #>