sensitivity_stats {sensemakr}

Sensitivity statistics for regression coefficients

Description

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

Usage

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.

Value

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

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).

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)