开源软件名称：

tlverse/origami

开源软件地址：

https://github.com/tlverse/origami

开源编程语言：

R 93.5%

开源软件介绍：

R/`origami`

High-powered framework for cross-validation. Fold your data like it’s paper!

Authors: Jeremy Coyle, Nima Hejazi, Ivana Malenica, and Rachael Phillips

What’s `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.

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:

devtools::install_github("tlverse/origami")

Usage

For details on how best to use origami, please consult the package documentation and introductory vignette online, or do so from within R.

Example

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] 15.18558

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}
    }