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.
Installation
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:
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 lmcv_lm<-function(fold, data, reg_form) {
# get name and index of outcome variable from regression formulaout_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 setstrain_data<- training(data)
valid_data<- validation(data)
# fit linear model on training set and predict on validation setmod<- lm(as.formula(reg_form), data=train_data)
preds<- predict(mod, newdata=valid_data)
# capture results to be returned as outputout<-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] 15.18558
For details on how to write wrappers (cv_funs) for use with
origami::cross_validate, please consult the documentation and
vignettes that accompany the package.
Issues
If you encounter any bugs or have any specific feature requests, please
file an issue.
Contributions
Contributions are very welcome. Interested contributors should consult
our contribution
guidelines
prior to submitting a pull request.
Citation
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}
}
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