cvms

cvms 0.1.0 released on CRAN

After a fairly long life on GitHub, my R package, cvms, for cross-validating linear and logistic regression, is finally on CRAN! With a few additions in the past months, this is a good time to catch you all up on the included functionality. For examples, check out the readme on GitHub! The main purpose of cvms is to allow researchers to quickly compare their models with cross-validation, with a tidy output containing the relevant metrics.

Running cross_validate from cvms in parallel

The cvms package is useful for cross-validating a list of linear and logistic regression model formulas in R. To speed up the process, I’ve added the option to cross-validate the models in parallel. In this post, I will walk you through a simple example and introduce the combine_predictors() function, which generates model formulas by combining a list of fixed effects. We will be using the simple participant.scores dataset from cvms.

Repeated cross-validation in cvms and groupdata2

I have spent the last couple of days adding functionality for performing repeated cross-validation to cvms and groupdata2. In this quick post I will show an example. (Please note: At the moment, you need to use the github version of groupdata2. I hope to update it on CRAN this month.) In cross-validation, we split our training set into a number (often denoted “k”) of groups called folds. We repeatedly train our machine learning model on k-1 folds and test it on the last fold, such that each fold becomes test set once.