Runs the design once and reports what each step did: the rows a data step produced or kept and the columns it added, dropped, or changed; the value each inquiry took; the estimate each estimator returned. The run itself, estimates joined to estimands, comes after the steps, then the parameters and objects the design refers to. Everything the printout shows is on the returned object, so a document or an app can use the pieces.
Usage
# S3 method for class 'design'
summary(object, run = TRUE, ...)Value
A summary.design: a list with steps (a tibble with one row per
step: step, label, type, call, and one_run, the account of that
step on this run), parameters and notes (as design_parameters()
and design_notes() return them), ran (logical), and when the design
was run,
data (the final data), inquiries and estimates (as run_design()
returns them).
Details
The run is one random draw, so the numbers differ from call to call. A
design whose estimator is slow can be summarized without running it:
run = FALSE gives the steps and the parameters alone, which is what
print() shows.
Examples
n_units <- 30
design <- declare_model(N = n_units, U = rnorm(N), Y = U) +
declare_inquiry(mu = mean(Y)) +
declare_estimator(Y ~ 1, .method = lm, term = "(Intercept)",
inquiry = "mu", label = "ols")
summary(design)
#> Research design with 3 steps
#>
#> Step 1 (model): declare_model(N = n_units, U = rnorm(N), Y = U)
#> N = 30 rows; adds ID, U, Y
#> Step 2 (inquiry): declare_inquiry(mu = mean(Y))
#> mu = 0.0466
#> Step 3 (estimator): declare_estimator(Y ~ 1, .method = lm, term = "(Intercept)", inquiry = "mu", label = "ols")
#> (Intercept) = 0.0466 (std.error 0.185)
#>
#> One run of the design:
#> # A tibble: 1 × 10
#> term estimate std.error statistic p.value conf.low conf.high estimator
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 (Intercept) 0.0466 0.185 0.252 0.803 -0.332 0.425 ols
#> # ℹ 2 more variables: inquiry <chr>, estimand <dbl>
#>
#> Parameters and objects the design refers to:
#> name value kind declared steps
#> n_units 30 scalar FALSE 1
summary(design, run = FALSE)
#> Research design with 3 steps
#>
#> Step 1 (model): declare_model(N = n_units, U = rnorm(N), Y = U)
#> Step 2 (inquiry): declare_inquiry(mu = mean(Y))
#> Step 3 (estimator): declare_estimator(Y ~ 1, .method = lm, term = "(Intercept)", inquiry = "mu", label = "ols")
#>
#> Parameters and objects the design refers to:
#> name value kind declared steps
#> n_units 30 scalar FALSE 1
summary(design)$steps
#> # A tibble: 3 × 5
#> step label type call one_run
#> <int> <chr> <chr> <chr> <chr>
#> 1 1 model model "declare_model(N = n_units, U = rnorm(N), Y = U… N = 30…
#> 2 2 mu inquiry "declare_inquiry(mu = mean(Y))" mu = 0…
#> 3 3 ols estimator "declare_estimator(Y ~ 1, .method = lm, term = … (Inter…