Sensitivity summary (DMP bounds + Oster breakdown)

Runs the default sweep used by Stata's regsensitivity when no subcommand is given: a DMP bounds analysis and an Oster breakdown analysis at a few standard r2long values.

Usage

regsen_summary(formula, data, compare = NULL, nocompare = NULL, subset = NULL)

# S3 method for class 'regsensitivity_summary'
print(x, ...)

# S3 method for class 'regsensitivity_summary'
summary(object, ...)

# S3 method for class 'summary.regsensitivity_summary'
print(x, ...)

# S3 method for class 'regsensitivity_summary'
plot(x, which = c("both", "dmp", "oster"), ...)

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.

compare

Optional character vector of variables to use as the comparison set. Defaults to all controls if neither compare nor nocompare is given.

nocompare

Optional character vector of controls to exclude from the comparison set.

subset

Optional logical or integer vector indicating which rows of data to include in the estimation.

x, object

A regsensitivity_summary object, or for the print() method of its summary, a summary.regsensitivity_summary object.

...

Passed to the methods for the two component analyses.

which

Which panel to draw: "both" (default) side by side, or "dmp" or "oster" alone.

Value

A list with elements dmp_bounds and oster_breakdown, each a regsensitivity object.

plot() returns a ggplot for a single panel. For both it returns a list of the two, of class regsensitivity_plots, whose print method draws them side by side; each element is an ordinary ggplot that can be modified and drawn on its own.

Examples

data(bfg2020)
s <- regsen_summary(
  avgrep2000to2016 ~ tye_tfe890_500kNI_100_l6 +
    log_area_2010 + lat + lon,
  data = bfg2020
)
print(s)
#> 
#> === DMP (2026) bounds ===
#> 
#> Regression Sensitivity Analysis -- Bounds [DMP (2026)]
#> ------------------------------------------------------------------------
#> Model:             avgrep2000to2016 ~ tye_tfe890_500kNI_100_l6
#> N (obs):           2036
#> Beta (medium):     1.3854
#> Hypothesis:        Beta > 0
#> Breakdown point:   0.3749
#> 
#> Results:
#>    rxbar rybar   cbar      bmin   bmax
#>        0  +Inf      1    1.3854 1.3854
#>  0.09518  +Inf      1    1.0605 1.7103
#>  0.19036  +Inf      1   0.72556 2.0453
#>  0.28554  +Inf      1    0.3688  2.402
#>  0.38072  +Inf      1 -0.025422 2.7963
#>   0.4759  +Inf      1  -0.48095 3.2518
#>  0.57108  +Inf      1   -1.0391 3.8099
#>  0.66626  +Inf      1   -1.7832  4.554
#>  0.76144  +Inf      1   -2.9248 5.6956
#>  0.85662  +Inf      1   -5.2891   8.06
#>   0.9518  +Inf      1      -Inf   +Inf
#> 
#> Use summary() for the specification and full results.
#> 
#> === Oster (2019) breakdown ===
#> 
#> Regression Sensitivity Analysis -- Breakdown Frontier [Oster (2019)]
#> ------------------------------------------------------------------------
#> Model:             avgrep2000to2016 ~ tye_tfe890_500kNI_100_l6
#> N (obs):           2036
#> Beta (medium):     1.3854
#> Hypothesis:        Beta > 0
#> 
#> Results:
#>     index breakdown
#>  0.093344         1
#>   0.19334         1
#>   0.29334   0.65067
#>   0.39334   0.45792
#>   0.49334   0.35327
#>   0.59334   0.28756
#>   0.69334   0.24246
#>   0.79334   0.20958
#>   0.89334   0.18456
#>   0.99334   0.16488
#>         1   0.16371
#> 
#> Use summary() for the specification and full results.