Tidy a DDML Object

DML-specific tidy method. Adds ensemble_type column labeling based on the estimator's learner/ensemble configuration. Delegates to tidy.ral for the base table computation.

Usage

# S3 method for class 'ddml'
tidy(
  x,
  ensemble_idx = 1,
  conf.int = FALSE,
  conf.level = 0.95,
  type = "HC1",
  uniform = FALSE,
  bootstraps = 999L,
  ...
)

Arguments

x

A ddml object.

ensemble_idx

Integer index of the ensemble type to report. Defaults to 1. Set to NULL for all.

conf.int

Logical. Include confidence intervals? Default FALSE.

conf.level

Confidence level. Default 0.95.

type

Character. HC type. Default "HC1".

uniform

Logical. Uniform CIs? Default FALSE.

bootstraps

Integer. Bootstrap draws. Default 999.

...

Currently unused.

Value

A data.frame with columns term, estimate, std.error, statistic, p.value, and ensemble_type.

Examples

# \donttest{
y = AE98[, "worked"]
D = AE98[, "morekids"]
X = AE98[, c("age","agefst","black","hisp","othrace")]
plm_fit = ddml_plm(y, D, X,
                learners = list(what = ols),
                sample_folds = 2, silent = TRUE)
tidy(plm_fit)
#>          term      estimate   std.error    statistic      p.value
#> 1          D1 -0.1545738155 0.014698010 -10.51664935 7.240272e-26
#> 2 (Intercept) -0.0001075169 0.006908269  -0.01556351 9.875826e-01
#>         ensemble_type
#> 1 single base learner
#> 2 single base learner
tidy(plm_fit, conf.int = TRUE)
#>          term      estimate   std.error    statistic      p.value
#> 1          D1 -0.1545738155 0.014698010 -10.51664935 7.240272e-26
#> 2 (Intercept) -0.0001075169 0.006908269  -0.01556351 9.875826e-01
#>         ensemble_type    conf.low   conf.high
#> 1 single base learner -0.18338139 -0.12576625
#> 2 single base learner -0.01364748  0.01343244
# }