doi:10.1002/sta4.454> explains why differing how we take folds based on survey design is useful.">

surveyCV: Cross Validation Based on Survey Design (original) (raw)

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.

Version: 0.2.0
Depends: R (≥ 4.0)
Imports: survey (≥ 4.1), magrittr (≥ 2.0)
Suggests: dplyr (≥ 1.0), ggplot2 (≥ 3.3), grid (≥ 4.0), gridExtra (≥ 2.3), ISLR (≥ 1.2), knitr (≥ 1.29), rmarkdown (≥ 2.2), rpms (≥ 0.5), splines (≥ 4.0), testthat (≥ 3.1)
Published: 2022-03-15
DOI: 10.32614/CRAN.package.surveyCV
Author: Cole Guerin [aut], Thomas McMahon [aut], Jerzy Wieczorek ORCID iD [cre, aut], Hunter Ratliff [ctb]
Maintainer: Jerzy Wieczorek
BugReports: https://github.com/ColbyStatSvyRsch/surveyCV/issues
License: GPL-2 | GPL-3
URL: https://github.com/ColbyStatSvyRsch/surveyCV/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: surveyCV results

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