Remove a step from an existing design

remove_step(...)

Arguments

...

bare (unquoted) names of step(s) to remove from a design

Details

see modify_design for details.

Examples

my_population <- declare_population(N = 100, noise = rnorm(N)) my_potential_outcomes <- declare_potential_outcomes(Y_Z_0 = noise, Y_Z_1 = noise + rnorm(N, mean = 2, sd = 2)) my_assignment <- declare_assignment(m = 50) my_assignment_2 <- declare_assignment(m = 25) design <- declare_design(my_population, my_potential_outcomes, my_assignment) design
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -1.95 -0.01 0.02 3.15 1.03 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -1.95 -0.01 0.02 3.15 1.03 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -4.19 1.91 1.97 7.62 2.26 0 100 #> #> Step 3 (assignment): my_assignment --------------------------------------------- #> #> Random assignment procedure: Complete random assignment #> Number of units: 100 #> 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 50 50 #> Proportion 0.50 0.50 #> #> Added variable: Z_cond_prob #> 0.5 #> Frequency 100 #> Proportion 1.00 #>
modify_design(design, replace_step(my_assignment_2, replace = my_assignment))
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -1.96 -0.02 -0.04 2.50 1.01 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -1.96 -0.02 -0.04 2.50 1.01 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -3.11 1.81 2.01 7.91 2.28 0 100 #> #> Step 3 (assignment): my_assignment_2 ------------------------------------------- #> #> Random assignment procedure: Complete random assignment #> Number of units: 100 #> 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 75 25 #> Proportion 0.75 0.25 #> #> Added variable: Z_cond_prob #> 0.25 0.75 #> Frequency 25 75 #> Proportion 0.25 0.75 #>
modify_design(design, add_step(dplyr::mutate(income = noise^2), after = my_assignment))
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -2.28 -0.17 -0.11 3.37 1.03 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -2.28 -0.17 -0.11 3.37 1.03 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -4.08 1.93 2.25 8.10 2.18 0 100 #> #> Step 3 (assignment): my_assignment --------------------------------------------- #> #> Random assignment procedure: Complete random assignment #> Number of units: 100 #> 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 50 50 #> Proportion 0.50 0.50 #> #> Added variable: Z_cond_prob #> 0.5 #> Frequency 100 #> Proportion 1.00 #> #> Step 4 (custom data modification): dplyr::mutate(income = noise^2) ------------- #> #> Added variable: income #> min median mean max sd N_missing N_unique #> 0.00 0.49 1.06 11.39 1.79 0 100 #>
modify_design(design, add_step(dplyr::mutate(income = noise^2), before = my_assignment))
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -2.37 0.16 0.07 2.44 1.05 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -2.37 0.16 0.07 2.44 1.05 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -3.98 2.22 2.26 7.92 2.40 0 100 #> #> Step 3 (custom data modification): dplyr::mutate(income = noise^2) ------------- #> #> Added variable: income #> min median mean max sd N_missing N_unique #> 0.00 0.46 1.10 5.96 1.41 0 100 #> #> Step 4 (assignment): my_assignment --------------------------------------------- #> #> Random assignment procedure: Complete random assignment #> Number of units: 100 #> 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 50 50 #> Proportion 0.50 0.50 #> #> Added variable: Z_cond_prob #> 0.5 #> Frequency 100 #> Proportion 1.00 #>
modify_design(design, remove_step(my_assignment))
#> #> Design Summary #> #> Step 1 (population): my_population --------------------------------------------- #> #> N = 100 #> #> Added variable: ID #> N_missing N_unique #> 0 100 #> #> Added variable: noise #> min median mean max sd N_missing N_unique #> -2.88 0.05 0.01 2.23 0.99 0 100 #> #> Step 2 (potential outcomes): my_potential_outcomes ----------------------------- #> #> Added variable: Y_Z_0 #> min median mean max sd N_missing N_unique #> -2.88 0.05 0.01 2.23 0.99 0 100 #> #> Added variable: Y_Z_1 #> min median mean max sd N_missing N_unique #> -2.53 2.14 2.33 8.77 2.20 0 100 #>