knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
origami
High-powered framework for cross-validation. Fold your data like it's paper!
Authors: Jeremy Coyle, Nima Hejazi, Ivana Malenica, and Rachael Phillips
origami
?The origami
R package provides a general framework for the application of
cross-validation schemes to particular functions. By allowing arbitrary lists of
results, origami
accommodates a range of cross-validation applications.
For standard use, we recommend installing the package from CRAN via
install.packages("origami")
You can install a stable release of origami
from GitHub via
devtools
with:
devtools::install_github("tlverse/origami")
For details on how best to use origami
, please consult the package
documentation and introductory
vignette
online, or do so from within R.
This minimal example shows how to use origami
to apply cross-validation to the
computation of a simple descriptive statistic using a sample data set. In
particular, we obtain a cross-validated estimate of the mean:
library(stringr) library(origami) set.seed(4795) data(mtcars) head(mtcars) # build a cv_fun that wraps around lm cv_lm <- function(fold, data, reg_form) { # get name and index of outcome variable from regression formula out_var <- as.character(unlist(str_split(reg_form, " "))[1]) out_var_ind <- as.numeric(which(colnames(data) == out_var)) # split up data into training and validation sets train_data <- training(data) valid_data <- validation(data) # fit linear model on training set and predict on validation set mod <- lm(as.formula(reg_form), data = train_data) preds <- predict(mod, newdata = valid_data) # capture results to be returned as output out <- list(coef = data.frame(t(coef(mod))), SE = ((preds - valid_data[, out_var_ind])^2)) return(out) } folds <- make_folds(mtcars) results <- cross_validate(cv_fun = cv_lm, folds = folds, data = mtcars, reg_form = "mpg ~ .") mean(results$SE)
For details on how to write wrappers (cv_fun
s) for use with
origami::cross_validate
, please consult the documentation and vignettes that
accompany the package.
If you encounter any bugs or have any specific feature requests, please file an issue.
Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.
After using the origami
R package, please cite it:
@article{coyle2018origami, author = {Coyle, Jeremy R and Hejazi, Nima S}, title = {origami: A Generalized Framework for Cross-Validation in R}, journal = {The Journal of Open Source Software}, volume = {3}, number = {21}, month = {January}, year = {2018}, publisher = {The Open Journal}, doi = {10.21105/joss.00512}, url = {https://doi.org/10.21105/joss.00512} }
© 2017-2021 Jeremy R. Coyle
The contents of this repository are distributed under the GPL-3 license. See
file LICENSE
for details.
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