r_value: Robustness value

In tipr: Tipping Point Analyses

Robustness value

Description

This function wraps the sensemakr::robustness_value() function

Usage

r_value(effect_observed, se, df, ...)

Arguments

effect_observed	Numeric. Observed exposure - outcome effect from a regression model. This is the point estimate (beta coefficient)
se	Numeric. Standard error of the effect_observed in the previous parameter.
df	Numeric positive value. Residual degrees of freedom for the model used to estimate the observed exposure - outcome effect. This is the total number of observations minus the number of parameters estimated in your model. Often for models estimated with an intercept this is N - k - 1 where k is the number of predictors in the model.
...	Optional arguments passed to the sensemakr::robustness_value() function.

Value

Numeric. Robustness value

References

Carlos Cinelli, Jeremy Ferwerda and Chad Hazlett (2021). sensemakr: Sensitivity Analysis Tools for Regression Models. R package version 0.1.4. https://CRAN.R-project.org/package=sensemakr

Examples

r_value(0.5, 0.1, 102)

tipr documentation built on Sept. 5, 2022, 5:09 p.m.