cstari: Compute cstar from published least squares results

In cstar: Substantive significance testing for regression estimates and marginal effects.

Description

cstari computes the minimum actionable effect size under a kinked linear loss function with user-specified degree of risk aversion. It computes this value from a beta, its associated standard error, and the degrees of freedom of a regression, and gamma, e.g. enables researchers to compute this quantity from a table of published results.

Usage

cstari(b, v, degfr, r)

Arguments

b	The beta of interest from a linear regression model.
v	The standard error associated with the beta of interest.
degfr	The degrees of freedom of the regression.
r	The degree of loss aversion; must be a non-negative number. This parameter maps to gamma in Esarey and Danneman (2014).

Value

The expected value for the utility of acting on the evidence encapsulated by the beta and standard error of the estimated regression, given the researcher's stated level of loss aversion.

References

Esarey and Danneman (2014). A Quantitative Method for Substantive Robustness Assessment. Political Science Research and Methods.

Examples

cstari(2.5, 1, 50, 2)

cstar documentation built on May 1, 2019, 8:44 p.m.