Description Usage Arguments Value References Examples
View source: R/sensitivity_stats.R
Convenience function that computes the robustness_value
,
partial_r2
and partial_f2
of the coefficient of interest.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | sensitivity_stats(...)
## S3 method for class 'lm'
sensitivity_stats(model, treatment, q = 1, alpha = 0.05, reduce = TRUE, ...)
## S3 method for class 'numeric'
sensitivity_stats(
estimate,
se,
dof,
treatment = "treatment",
q = 1,
alpha = 0.05,
reduce = TRUE,
...
)
|
... |
Arguments passed to other methods. First argument should either be an
|
model |
An |
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 |
alpha |
significance level. |
reduce |
Should the bias adjustment reduce or increase the
absolute value of the estimated coefficient? Default is |
estimate |
Coefficient estimate. |
se |
Standard error of the coefficient estimate. |
dof |
Residual degrees of freedom of the regression. |
A data.frame
containing the following quantities:
a character with the name of the treatment variable
a numeric vector with the estimated effect of the treatment
a numeric vector with the estimated standard error of the treatment effect
a numeric vector with the t-value of the treatment
a numeric vector with the partial R2 of the treatment and the outcome, see details in partial_r2
a numeric vector with the robustness value of the treatment, see details in robustness_value
a numeric vector with the robustness value of the treatment considering statistical significance, see details in robustness_value
a numeric vector with the partial (Cohen's) f2 of the treatment with the outcome, see details in partial_f2
a numeric vector with the degrees of freedom of the model
Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology).
1 2 3 4 5 6 7 8 9 10 11 12 | ## 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)
|
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