Print and summarize a sensitivity analysis

print() shows the headline: the analysis, the medium-regression coefficient, the breakdown point and a thinned results table. summary() returns an object whose print method adds the call, the control and comparison sets, every sensitivity parameter, the summary statistics and the full results table. For an identified-set sweep that table carries a holds column, TRUE where the identified set lies entirely on the hypothesis' side.

Usage

# S3 method for class 'regsensitivity'
print(x, max_rows = 12, ...)

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

# S3 method for class 'summary.regsensitivity'
print(x, max_rows = Inf, ...)

Arguments

x, object

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

max_rows

Rows of the results table to show before thinning it to evenly spaced rows. print() defaults to 12, a summary to all of them.

...

Ignored.

Value

print() returns x invisibly. summary() returns an object of class summary.regsensitivity, a list holding call, analysis, subcommand, outcome, treatment, n, controls, compare, hypothesis, sparams (a data frame describing each sensitivity parameter), statistics, breakdown and results.

See also

breakdown_point(), identified_set() and as.data.frame.regsensitivity() to extract the numbers.

Examples

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 = 0.1
)
res
#> 
#> 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:   0.3906
#> 
#> Results:
#>    rxbar rybar   cbar    bmin   bmax
#>        0  +Inf    0.1  1.3854 1.3854
#>  0.23774  +Inf    0.1 0.55995 2.2109
#>  0.47547  +Inf    0.1 -0.3236 3.0944
#>  0.71321  +Inf    0.1 -1.2923 4.0631
#>  0.95094  +Inf    0.1 -2.3864 5.1572
#>   1.1887  +Inf    0.1 -3.6709 6.4417
#>   1.4264  +Inf    0.1 -5.2616 8.0325
#>   1.6642  +Inf    0.1 -7.3957 10.167
#>   1.9019  +Inf    0.1 -10.676 13.447
#>   2.1396  +Inf    0.1  -17.46 20.231
#>   2.3774  +Inf    0.1    -Inf   +Inf
#> 
#> Use summary() for the specification and full results.
summary(res)
#> 
#> Regression Sensitivity Analysis -- Bounds [DMP (2026)]
#> ------------------------------------------------------------------------
#> Call:
#>   regsen_bounds(formula = avgrep2000to2016 ~ tye_tfe890_500kNI_100_l6 + 
#>       log_area_2010 + lat + lon, data = bfg2020, compare = c("log_area_2010", 
#>       "lat", "lon"), cbar = 0.1)
#> 
#> --- Specification ---------------------------------------
#>   Outcome:             avgrep2000to2016
#>   Treatment:           tye_tfe890_500kNI_100_l6
#>   N (obs):             2036
#>   Controls:            log_area_2010, lat, lon
#>   Comparison set:      log_area_2010, lat, lon
#>   Hypothesis:          Beta > 0
#> 
#> --- Sensitivity parameters ------------------------------
#>   rxbar                11 values in [0.0000, 2.3774]
#>   rybar                +Inf
#>   cbar                 0.1000
#>   clow                 0.0000
#> 
#> --- Summary statistics ----------------------------------
#>   Beta (short)               1.7078
#>   Beta (medium)              1.3854
#>   R2 (short)                 0.0269
#>   R2 (medium)                0.0718
#>   Var(Y)                   136.3204
#>   Var(X)                     1.2574
#>   Var(X_Residual)            1.1391
#> 
#> --- Results ---------------------------------------------
#>   Breakdown point:     0.3906
#>   Hypothesis holds at: 2 of 11 grid points
#> 
#> Results:
#>    rxbar rybar   cbar    bmin   bmax holds
#>        0  +Inf    0.1  1.3854 1.3854  TRUE
#>  0.23774  +Inf    0.1 0.55995 2.2109  TRUE
#>  0.47547  +Inf    0.1 -0.3236 3.0944 FALSE
#>  0.71321  +Inf    0.1 -1.2923 4.0631 FALSE
#>  0.95094  +Inf    0.1 -2.3864 5.1572 FALSE
#>   1.1887  +Inf    0.1 -3.6709 6.4417 FALSE
#>   1.4264  +Inf    0.1 -5.2616 8.0325 FALSE
#>   1.6642  +Inf    0.1 -7.3957 10.167 FALSE
#>   1.9019  +Inf    0.1 -10.676 13.447 FALSE
#>   2.1396  +Inf    0.1  -17.46 20.231 FALSE
#>   2.3774  +Inf    0.1    -Inf   +Inf FALSE