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 --------------------------------------------- #> #> N = 500 #> #> Added variable: ID #> N_missing N_unique #> 0 500 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -3.42 0.05 0.03 2.85 0.99 0 500 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -3.42 0.05 0.03 2.85 0.99 0 500 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -5.53 1.91 1.98 7.74 2.26 0 500 #> #> Step 3 (sampling): my_sampling ------------------------------------------------- #> #> N = 250 (250 subtracted) #> #> Added variable: S_inclusion_prob #> 0.5 NA #> Frequency 250 0 #> Proportion 1.00 0.00 #> #> Altered variable: ID #> Before: #> N_missing N_unique #> 0 500 #> #> After: #> N_missing N_unique #> 0 250 #> #> Altered variable: noise #> Before: #> min median mean max sd N_missing N_unique #> -3.42 0.05 0.03 2.85 0.99 0 500 #> #> After: #> min median mean max sd N_missing N_unique #> -3.04 0.06 0.01 2.55 1.00 0 250 #> #> Altered variable: Y_Z_0 #> Before: #> min median mean max sd N_missing N_unique #> -3.42 0.05 0.03 2.85 0.99 0 500 #> #> After: #> min median mean max sd N_missing N_unique #> -3.04 0.06 0.01 2.55 1.00 0 250 #> #> Altered variable: Y_Z_1 #> Before: #> min median mean max sd N_missing N_unique #> -5.53 1.91 1.98 7.74 2.26 0 500 #> #> After: #> min median mean max sd N_missing N_unique #> -4.88 1.97 1.99 6.97 2.23 0 250 #> #> Step 4 (estimand): my_estimand ------------------------------------------------- #> #> A single draw of the estimand: #> estimand_label estimand #> ATE 1.982106 #> #> Step 5 (declare step): dplyr::mutate(noise_sq = noise^2) ----------------------- #> #> Added variable: noise_sq #> min median mean max sd N_missing N_unique #> 0.00 0.38 0.99 9.26 1.36 0 250 #> #> Step 6 (assignment): my_assignment --------------------------------------------- #> #> Added variable: Z #> 0 1 NA #> Frequency 225 25 0 #> Proportion 0.90 0.10 0.00 #> #> Added variable: Z_cond_prob #> 0.1 0.9 NA #> Frequency 25 225 0 #> Proportion 0.10 0.90 0.00 #> #> Step 7 (reveal outcomes): reveal_outcomes -------------------------------------- #> #> Added variable: Y #> min median mean max sd N_missing N_unique #> -4.88 0.16 0.19 6.18 1.22 0 250 #> #> Step 8 (estimator): my_estimator ----------------------------------------------- #> #> Formula: Y ~ Z #> #> A single draw of the estimator: #> estimator_label coefficient_name est se p ci_lower #> my_estimator Z 1.147999 0.475402 0.02341815 0.1687111 #> ci_upper estimand_label #> 2.127286 ATE #>