surveyCV: Cross Validation Based on Survey Design

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.

Getting started

Package details

AuthorCole Guerin [aut], Thomas McMahon [aut], Jerzy Wieczorek [cre, aut] (<https://orcid.org/0000-0002-2859-6534>), Hunter Ratliff [ctb]
MaintainerJerzy Wieczorek <jawieczo@colby.edu>
LicenseGPL-2 | GPL-3
Version0.2.0
URL https://github.com/ColbyStatSvyRsch/surveyCV/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("surveyCV")

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surveyCV documentation built on March 18, 2022, 5:15 p.m.