diagnose_design: Diagnose the design
In DeclareDesign/ddoldversion: Declare and Diagnose Research Designs

Description Usage Arguments Details Value Examples

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 `data.frame` of simulations, typically created by `simulate_design`.
`diagnosands`	A set of diagnosands created by `declare_diagnosands`. By default, these include bias, root mean-squared error, power, frequentist coverage, the mean and standard deviation of the estimate(s), the "type S" error rate (Gelman and Carlin 2014), and the mean of the estimand(s).
`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 `simulate_design`
`bootstrap_sims`	Number of bootstrap replicates for the diagnosands to obtain the standard errors of the diagnosands, defaulting to `100`. Set to FALSE to turn off bootstrapping.
`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")

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.

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.

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 <- declare_reveal()

my_estimator <- declare_estimator(Y ~ Z, estimand = my_estimand)

design <- my_population +
   my_potential_outcomes +
   my_estimand +
   my_assignment +
   my_reveal +
   my_estimator

## Not run: 
# using built-in defaults:
diagnosis <- diagnose_design(design)
diagnosis

## End(Not run)

# using a user-defined diagnosand
my_diagnosand <- declare_diagnosands(absolute_error = mean(abs(estimate - estimand)))

## Not run: 
diagnosis <- diagnose_design(design, diagnosands = my_diagnosand)
diagnosis

get_diagnosands(diagnosis)

get_simulations(diagnosis)


## End(Not run)
# Using an existing data frame of simulations
## Not run: 
simulations <- simulate_design(designs, sims = 2)
diagnosis   <- diagnose_design(simulations_df = simulations_df)


## End(Not run)