OptSig: Optimal Level of Significance for Regression and Other
Statistical Tests
Calculates the optimal level of significance based on a decision-theoretic approach.
The optimal level is chosen so that the expected loss from hypothesis testing is minimized.
A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model.
The details are covered in Kim, Jae H. and Choi, In, 2020, Choosing the Level of Significance: A Decision-Theoretic Approach, Abacus.
See also Kim, Jae H., 2020, Decision-theoretic hypothesis testing: A primer with R package OptSig, The American Statistician.
| Version: |
2.1 |
| Imports: |
pwr |
| Published: |
2020-04-18 |
| Author: |
Jae H. Kim |
| Maintainer: |
Jae H. Kim <J.Kim at latrobe.edu.au> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
OptSig results |
Documentation:
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