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Compute quick descriptive statistics on the effective sample

idid_contrib_stats.Rd

Reports the total, nominal and effective sample size, as well as the proportion of observations from the nominal sample that are part of the effective one.

Usage

idid_contrib_stats(
  reg,
  var_interest,
  contrib_threshold,
  threshold_change = 0.05,
  ...
)

Arguments

reg

A regression object.

var_interest

A string. The name of the main variable of interest.

contrib_threshold

A numeric (optional). Weight below which observations are deemed to be non-contributing. If not provided, will be determined by running ididvar::idid_contrib_threshold

threshold_change

A double (between 0 and 1). The change threshold in estimate and s.e. when dropping observations.

...

Additional elements to pass to the regression function when partialling out controls.

Value

A dataframe with 4 columns:

  • n_total: the total sample size, before dropping observations with missing values

  • n_nominal: the nominal sample size, ie the number of observations in the regression

  • n_effective: the effective sample size, after removing non contributing observations

  • prop_effective: the ratio of the effective to the nominal sample sizes

Examples

reg_ex <- ggplot2::txhousing |>
  lm(formula = log(sales) ~ median + listings + city + as.factor(date))

idid_contrib_stats(reg_ex, "median")
#> Searching for the contribution threshold
#>   n_initial n_nominal n_effective prop_effective
#> 1      8602      7168        3943      0.5500837