Breakdown frontier for a regression coefficient hypothesis

Find the smallest sensitivity-parameter value at which a given hypothesis about the long-regression coefficient first fails. For DMP, this is rxbar as a function of (cbar, rybar, beta). For Oster, this is |delta| as a function of R-squared(long), beta and (optionally) maxovb.

Usage

regsen_breakdown(
  formula,
  data,
  analysis = c("dmp", "oster"),
  compare = NULL,
  nocompare = NULL,
  cbar = 1,
  rybar = Inf,
  rybar_expr = NULL,
  r2long = 1,
  maxovb = NA,
  r2long_type = c("eq", "relative"),
  maxovb_type = c("bound", "relative"),
  beta = "sign",
  ngrid = 200L,
  subset = NULL
)

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.

analysis

Which sensitivity analysis to run: "dmp" (default) or "oster".

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.

cbar, rybar, rybar_expr

(DMP) Same as in regsen_bounds().

r2long, maxovb

(Oster) Same as in regsen_bounds().

r2long_type

One of "eq" (the default) or "relative". When "relative", values are multiplied by R-squared(medium).

maxovb_type

One of "bound" (default) or "relative". When "relative", values are multiplied by |Beta(medium)|.

beta

Hypothesis spec. One of:

• "sign" – the hypothesis that sign(beta_long) = sign(beta_med).

• a numeric scalar or vector. Use the helpers bnd_lb(), bnd_ub(), bnd_eq() to set the direction, e.g. beta = bnd_lb(0) for the hypothesis beta > 0.

ngrid

Resolution of the finer grid stored in the result. Default 200.

subset

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

Value

A regsensitivity object.

Examples

# \donttest{
data(bfg2020)
bk <- regsen_breakdown(
  avgrep2000to2016 ~ tye_tfe890_500kNI_100_l6 +
    log_area_2010 + lat + lon + temp_mean + rain_mean + elev_mean +
    d_coa + d_riv + d_lak + ave_gyi,
  data = bfg2020,
  cbar = seq(0, 1, 0.1)
)
print(bk)
#> 
#> Regression Sensitivity Analysis ----- Breakdown Frontier
#> ------------------------------------------------------------------------
#> Analysis:          DMP (2026)
#> Treatment:         tye_tfe890_500kNI_100_l6
#> Outcome:           avgrep2000to2016
#> N (obs):           2036
#> Hypothesis:        Beta > 0
#> 
#> --- Summary statistics ----------------------------------
#>   Beta (short)                  1.7078
#>   Beta (medium)                 1.5864
#>   R2 (short)                    0.0269
#>   R2 (medium)                   0.1345
#>   Var(Y)                      136.3204
#>   Var(X)                        1.2574
#>   Var(X_Residual)               1.0903
#> 
#> --- Results ---------------------------------------------
#>   index breakdown
#>       0   0.38508
#>     0.1    0.3726
#>     0.2   0.36438
#>     0.3   0.36007
#>     0.4   0.35936
#>     0.5   0.35936
#>     0.6   0.35936
#>     0.7   0.35936
#>     0.8   0.35936
#>     0.9   0.35936
#>       1   0.35936
# }