sensitivity_stats function

Sensitivity statistics for regression coefficients

Convenience function that computes the robustness_value, partial_r2 and partial_f2 of the coefficient of interest.

sensitivity_stats(...)

## S3 method for class 'lm'
sensitivity_stats(
  model,
  treatment,
  q = 1,
  alpha = 0.05,
  reduce = TRUE,
  invert = FALSE,
  ...
)

## S3 method for class 'fixest'
sensitivity_stats(
  model,
  treatment,
  q = 1,
  alpha = 0.05,
  reduce = TRUE,
  invert = FALSE,
  message = T,
  ...
)

## S3 method for class 'numeric'
sensitivity_stats(
  estimate,
  se,
  dof,
  treatment = "treatment",
  q = 1,
  alpha = 0.05,
  reduce = TRUE,
  invert = FALSE,
  ...
)

Arguments

• ...: arguments passed to other methods.
• model: An lm or fixest object with the outcome regression.
• treatment: A character vector with the name of the treatment variable of the model.
• q: percent change of the effect estimate that would be deemed problematic. Default is 1, which means a reduction of 100% of the current effect estimate (bring estimate to zero). It has to be greater than zero.
• alpha: significance level.
• reduce: should the bias adjustment reduce or increase the absolute value of the estimated coefficient? Default is TRUE.
• invert: should IRV be computed instead of RV? (i.e. is the estimate insignificant?). Default is FALSE.
• message: should messages be printed? Default = TRUE.
• estimate: Coefficient estimate.
• se: standard error of the coefficient estimate.
• dof: residual degrees of freedom of the regression.

Returns

A data.frame containing the following quantities:

• treatment: a character with the name of the treatment variable
• estimate: a numeric vector with the estimated effect of the treatment
• se: a numeric vector with the estimated standard error of the treatment effect
• t_statistics: a numeric vector with the t-value of the treatment
• r2yd.x: a numeric vector with the partial R2 of the treatment and the outcome, see details in partial_r2
• rv_q: a numeric vector with the robustness value of the treatment, see details in robustness_value
• rv_qa: a numeric vector with the robustness value of the treatment considering statistical significance, see details in robustness_value
• f2yd.x: a numeric vector with the partial (Cohen's) f2 of the treatment with the outcome, see details in partial_f2
• dof: a numeric vector with the degrees of freedom of the model

Examples

## loads data
data("darfur")

## fits model
model <- lm(peacefactor ~ directlyharmed + age + farmer_dar + herder_dar +
             pastvoted + hhsize_darfur + female + village, data = darfur)

## sensitivity stats for directly harmed
sensitivity_stats(model, treatment = "directlyharmed")

## you can  also pass the numeric values directly
sensitivity_stats(estimate = 0.09731582, se = 0.02325654, dof = 783)

References

Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology).