Tidy a sensitivity analysis — regsen

A broom-style tidier. tidy() returns one row per point of the sensitivity sweep; glance() returns a one-row summary of the analysis as a whole.

Usage

regsen_tidy(x, conf.int = TRUE, ...)

regsen_glance(x, ...)

Arguments

x: A regsensitivity object.
conf.int: Ignored; present for signature compatibility with other tidiers. Sensitivity bounds are not confidence intervals – see regsen_boot() for sampling uncertainty in the breakdown point.
...: Ignored.

Value

regsen_tidy() returns a data.frame with the sensitivity parameters plus conf.low/conf.high holding the identified set, using broom's column names so downstream packages recognise them. regsen_glance() returns a one-row data.frame.

Details

Implementing these makes regsensitivity objects usable by anything that speaks the broom vocabulary – most usefully modelsummary, which is how many economists assemble their tables.

The methods are registered on generics' tidy() and glance() only when that package is installed, so it stays an optional dependency. Without it, call regsen_tidy() and regsen_glance() directly.

Examples

# \donttest{
data(bfg2020)
res <- regsen_bounds(
    avgrep2000to2016 ~ tye_tfe890_500kNI_100_l6 + log_area_2010 + lat + lon,
    data = bfg2020, compare = c("log_area_2010", "lat", "lon"),
    cbar = c(0.1, 0.5)
)
head(regsen_tidy(res))
#>                       term estimate   conf.low conf.high     rxbar rybar cbar
#> 1 tye_tfe890_500kNI_100_l6 1.385416  1.3854162  1.385416 0.0000000   Inf  0.1
#> 2 tye_tfe890_500kNI_100_l6 1.385416  0.5047744  2.266058 0.2531195   Inf  0.1
#> 3 tye_tfe890_500kNI_100_l6 1.385416 -0.4434758  3.214308 0.5062390   Inf  0.1
#> 4 tye_tfe890_500kNI_100_l6 1.385416 -1.4932901  4.264122 0.7593584   Inf  0.1
#> 5 tye_tfe890_500kNI_100_l6 1.385416 -2.6971380  5.467970 1.0124779   Inf  0.1
#> 6 tye_tfe890_500kNI_100_l6 1.385416 -4.1441420  6.914974 1.2655974   Inf  0.1
regsen_glance(res)
#>     analysis subcommand          outcome                treatment nobs
#> 1 DMP (2026)     bounds avgrep2000to2016 tye_tfe890_500kNI_100_l6 2036
#>   beta_medium breakdown hypothesis n_gridpoints
#> 1    1.385416        NA   Beta > 0           22
# }