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Bootstrap confidence interval for the breakdown point

Source: R/bootstrap.R
regsen_boot.Rd

Computes a non-parametric (or cluster) bootstrap percentile confidence interval for the breakdown point returned by regsen_breakdown() or the scalar $breakdown field of regsen_bounds().

Usage

regsen_boot(
  formula,
  data,
  ...,
  R = 999L,
  cluster = NULL,
  level = 0.95,
  seed = NULL,
  ncores = 1L,
  show_progress = interactive()
)

Arguments

formula

Two-sided formula: y ~ x + w1 + w2 + .... The first right-hand-side variable is the primary independent variable; the rest are controls.

data

A data.frame.

...

Additional arguments forwarded to regsen_breakdown() (the analysis to bootstrap).

R

Integer. Number of bootstrap replications. Defaults to 999.

cluster

Optional character scalar naming a column of data to resample at the cluster level (e.g. "km_grid_cel_code" for the BFG 2020 application). When NULL the standard non-parametric bootstrap is used.

level

Two-sided confidence level for the percentile CI. Default 0.95.

seed

Optional integer seed for reproducibility. Results are identical for a given seed regardless of ncores: each replicate draws its own seed from a vector generated once up front, so nothing depends on how the work was divided.

ncores

Number of cores for the replications. 1 (default) runs serially. Above 1 the package forks on macOS and Linux and falls back to a PSOCK cluster on Windows, which has no fork. A progress bar is not shown when running in parallel. Capped at R, and at 2 while R CMD check --as-cran is running, which forbids more; results do not depend on the cap.

show_progress

Logical; print progress bar.

Value

An object of class regsensitivity_boot containing: point, replicates, ci, level, R, cluster, na.

Details

For DMP analyses the breakdown point is computed exactly as in regsen_breakdown(); when rxbar, rybar and cbar are all scalar the returned breakdown is the rxbar breakdown for the (scalar) hypothesis on beta. For Oster analyses the breakdown is the |delta| value at which the hypothesis first fails.

Examples

# \donttest{
data(bfg2020)
bfg2020$statea <- factor(bfg2020$statea)
w1 <- c("log_area_2010", "lat", "lon", "temp_mean", "rain_mean",
        "elev_mean", "d_coa", "d_riv", "d_lak", "ave_gyi")
form <- reformulate(c("tye_tfe890_500kNI_100_l6", w1, "statea"),
                    response = "avgrep2000to2016")
set.seed(1)
bb <- regsen_boot(form, bfg2020, compare = w1, cbar = 1,
                   R = 199, cluster = "km_grid_cel_code")
print(bb)
#> Bootstrap confidence interval for the breakdown point
#> ------------------------------------------------------------
#>   R                  : 199
#>   Cluster bootstrap  : km_grid_cel_code
#>   Confidence level   : 95%
#>   Point estimate     : 0.8036
#>   95% CI            : [0.4997, 0.8612]
# }