Functions to generate K-fold cross validation (CV) folds and CV test error estimates that take into account how a survey dataset's sampling design was constructed (SRS, clustering, stratification, and/or unequal sampling weights). You can input linear and logistic regression models, along with data and a type of survey design in order to get an output that can help you determine which model best fits the data using K-fold cross validation. Our paper on "K-Fold Cross-Validation for Complex Sample Surveys" by Wieczorek, Guerin, and McMahon (2022) <doi:10.1002/sta4.454> explains why differing how we take folds based on survey design is useful.
Package details |
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Author | Cole Guerin [aut], Thomas McMahon [aut], Jerzy Wieczorek [cre, aut] (<https://orcid.org/0000-0002-2859-6534>), Hunter Ratliff [ctb] |
Maintainer | Jerzy Wieczorek <jawieczo@colby.edu> |
License | GPL-2 | GPL-3 |
Version | 0.2.0 |
URL | https://github.com/ColbyStatSvyRsch/surveyCV/ |
Package repository | View on CRAN |
Installation |
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