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): declare_population(N = 500, noise = rnorm(N)) -------------
#>
#> N = 500
#>
#>  N_missing N_unique     class
#>          0      500 character
#>
#>    min median mean  max   sd N_missing N_unique
#>  -2.72   -0.1 -0.1 2.89 0.99         0      500
#>
#> Step 2 (potential outcomes): declare_potential_outcomes(Y_Z_0 = noise, Y_Z_1 = noise + rnorm(N,     mean = 2, sd = 2))
#>
#>    min median mean  max   sd N_missing N_unique
#>  -2.72   -0.1 -0.1 2.89 0.99         0      500
#>
#>    min median mean max   sd N_missing N_unique
#>  -5.23   1.85 1.85 8.4 2.27         0      500
#>
#> Step 3 (sampling): declare_sampling(n = 250) -----------------------------------
#>
#> N = 250 (250 subtracted)
#>
#>   0.5
#>   250
#>  1.00
#>
#> Altered variable: ID
#>   Before:
#>  N_missing N_unique     class
#>          0      500 character
#>
#>   After:
#>  N_missing N_unique     class
#>          0      250 character
#>
#> Altered variable: noise
#>   Before:
#>    min median mean  max   sd N_missing N_unique
#>  -2.72   -0.1 -0.1 2.89 0.99         0      500
#>
#>   After:
#>    min median  mean  max   sd N_missing N_unique
#>  -2.72  -0.11 -0.16 2.38 0.98         0      250
#>
#> Altered variable: Y_Z_0
#>   Before:
#>    min median mean  max   sd N_missing N_unique
#>  -2.72   -0.1 -0.1 2.89 0.99         0      500
#>
#>   After:
#>    min median  mean  max   sd N_missing N_unique
#>  -2.72  -0.11 -0.16 2.38 0.98         0      250
#>
#> Altered variable: Y_Z_1
#>   Before:
#>    min median mean max   sd N_missing N_unique
#>  -5.23   1.85 1.85 8.4 2.27         0      500
#>
#>   After:
#>    min median mean max   sd N_missing N_unique
#>  -4.39   1.94 1.96 8.4 2.21         0      250
#>
#> Step 4 (estimand): declare_estimand(ATE = mean(Y_Z_1 - Y_Z_0)) -----------------
#>
#> A single draw of the estimand:
#>  estimand_label estimand
#>             ATE 2.118752
#>
#> Step 5 (wrapped): ~dplyr::mutate(noise_sq = noise^2) ---------------------------
#>
#>  min median mean  max   sd N_missing N_unique
#>    0   0.51 0.98 7.42 1.26         0      250
#>
#> Step 6 (assignment): declare_assignment(m = 25) --------------------------------
#>
#>     0    1
#>   225   25
#>  0.90 0.10
#>
#>   0.1  0.9
#>    25  225
#>  0.10 0.90
#>
#> Step 7 (reveal outcomes): reveal_outcomes() ------------------------------------
#>
#>