Generates diagnosands from a design or simulations of a design.

diagnose_design( ..., diagnosands = NULL, sims = 500, bootstrap_sims = 100, add_grouping_variables = NULL ) diagnose_designs( ..., diagnosands = NULL, sims = 500, bootstrap_sims = 100, add_grouping_variables = NULL )

... | A design or set of designs typically created using the + operator, or a |
---|---|

diagnosands | A set of diagnosands created by |

sims | The number of simulations, defaulting to 500. sims may also be a vector indicating the number of simulations for each step in a design, as described for |

bootstrap_sims | Number of bootstrap replicates for the diagnosands to obtain the standard errors of the diagnosands, defaulting to |

add_grouping_variables | Variables used to generate groups of simulations for diagnosis. Added to list default list: c("design_label", "estimand_label", "estimator_label", "term") |

a list with a data frame of simulations, a data frame of diagnosands, a vector of diagnosand names, and if calculated, a data frame of bootstrap replicates.

If the diagnosand function contains a `group_by`

attribute, it will be used to split-apply-combine diagnosands rather than the intersecting column names.

If `sims`

is named, or longer than one element, a fan-out strategy is created and used instead.

If the packages `future`

and `future.apply`

are installed, you can set `plan`

to run multiple simulations in parallel.

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_assignment <- declare_assignment() my_estimand <- declare_estimand(ATE = mean(Y_Z_1 - Y_Z_0)) my_reveal <- reveal_outcomes() my_estimator <- declare_estimator(Y ~ Z, estimand = my_estimand) design <- my_population + my_potential_outcomes + my_estimand + my_assignment + my_reveal + my_estimator if (FALSE) { # using built-in defaults: diagnosis <- diagnose_design(design) diagnosis } # using a user-defined diagnosand my_diagnosand <- declare_diagnosands(absolute_error = mean(abs(estimate - estimand))) if (FALSE) { diagnosis <- diagnose_design(design, diagnosands = my_diagnosand) diagnosis get_diagnosands(diagnosis) get_simulations(diagnosis) } # Using an existing data frame of simulations if (FALSE) { simulations <- simulate_design(designs, sims = 2) diagnosis <- diagnose_design(simulations_df = simulations_df) }