Tidy a diagnosis

Returns the diagnosands in long form, one row per group per diagnosand, with the bootstrap standard error and percentile confidence interval alongside. This is the shape to plot from: ggplot(aes(estimate, diagnosand)) + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)).

Usage

# S3 method for class 'diagnosis'
tidy(x, conf.int = TRUE, conf.level = 0.95, ...)

Arguments

x

(required) A diagnosis.

conf.int

(optional) Whether to include the confidence interval. Defaults to TRUE.

conf.level

(optional) Confidence level for the interval. Defaults to 0.95.

...

(optional) Ignored.

Value

A tibble with one row per group per diagnosand: the grouping columns (design, inquiry, estimator, outcome, term, and any redesign parameters), diagnosand, estimate, and, when bootstrapped, std.error, conf.low, and conf.high.

Details

std.error, conf.low, and conf.high come from the bootstrap replicates, so they appear only when the diagnosis was run with bootstrap_sims > 0.

Examples

design <- declare_model(N = 30, Y = rnorm(N), Z = rep(0:1, 15)) +
  declare_inquiry(ATE = 0) +
  declare_estimator(Y ~ Z, .method = lm, term = "Z", inquiry = "ATE",
                    label = "ols")
d <- diagnose_design(design, sims = 5, bootstrap_sims = 0)
tidy(d)
#> # A tibble: 7 × 6
#>   design inquiry estimator term  diagnosand    estimate
#>   <chr>  <chr>   <chr>     <chr> <chr>            <dbl>
#> 1 design ATE     ols       Z     mean_estimand   0     
#> 2 design ATE     ols       Z     mean_estimate   0.0788
#> 3 design ATE     ols       Z     bias            0.0788
#> 4 design ATE     ols       Z     sd_estimate     0.581 
#> 5 design ATE     ols       Z     rmse            0.526 
#> 6 design ATE     ols       Z     power           0.2   
#> 7 design ATE     ols       Z     coverage        0.8