High-powered framework for cross-validation. Fold your data like it’s paper!
Authors: Jeremy Coyle, Nima Hejazi, Ivana Malenica, and Rachael Phillips
origami is an R package that 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)
#> origami v1.0.4: Generalized Framework for Cross-Validation
set.seed(4795)
data(mtcars)
head(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# 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)
#> [1] 15.18558For details on how to write wrappers (cv_funs) 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.
It is our hope that origami will grow to be adopted as a backend for
most any procedure requiring cross-validation, including its integration
into larger machine learning frameworks. To that end, contributions are
very welcome, though we ask that interested contributors 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}
}
© 2017-2020 Jeremy R. Coyle
The contents of this repository are distributed under the GPL-3 license.
See file LICENSE for details.
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