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https://github.com/aaronpeikert/iv

Independend Validation (IV) with 'rsample'

https://github.com/aaronpeikert/iv

Science Score: 23.0%

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Keywords from Contributors

structural-equation-modeling
Last synced: 11 months ago · JSON representation

Repository

Independend Validation (IV) with 'rsample'

Basic Info
  • Host: GitHub
  • Owner: aaronpeikert
  • License: other
  • Language: R
  • Default Branch: master
  • Size: 27.3 KB
Statistics
  • Stars: 1
  • Watchers: 1
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  • Open Issues: 1
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Created almost 7 years ago · Last pushed almost 3 years ago
Metadata Files
Readme Contributing License Code of conduct

README.Rmd 

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# Independend Validation (IV)


[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ask Me Anything
\!](https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg)](https://github.com/aaronpeikert/iv/issues/new)


Independend Validation is a procedure proposed by von Oertzen (in prep), which produces independend assessment sets. This property is assumed when performing most statistical tests on the performance measures associated with the assesment sets. Importantly classical resampling procedures (like cross validation or bootstrapping) do violate this assumption, because even when the original sample are independend, the resulting assesment and holdout sets are not.

## Installation

You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("aaronpeikert/iv")
```

## Examples

```{r data}
# install.packages("modeldata")
library(modeldata)
data("attrition")
# downsample
attrition <- attrition[sample(seq_len(nrow(attrition)), 100), ]
```

```{r iv}
library(iv)
library(rsample)
iv_obj <- iv(attrition, m = 20)
iv_obj
```

```{r lm_func}
mod_form <- as.formula(Attrition ~ JobSatisfaction + Gender + MonthlyIncome)
## splits will be the `rsplit` object
holdout_results <- function(splits, ...) {
  # Fit the model to the 90%
  mod <- glm(..., data = analysis(splits), family = binomial)
  # Save the 10%
  holdout <- assessment(splits)
  # `augment` will save the predictions with the holdout data set
  res <- broom::augment(mod, newdata = holdout)
  # Class predictions on the assessment set from class probs
  lvls <- levels(holdout$Attrition)
  predictions <- factor(ifelse(res$.fitted > 0, lvls[2], lvls[1]),
                        levels = lvls)
  # Calculate whether the prediction was correct
  res$correct <- predictions == holdout$Attrition
  # Return the assessment data set with the additional columns
  res
}
```

```{r model_purrr, warning=FALSE}
library(purrr)
iv_obj$results <- map(iv_obj$splits,
                      holdout_results,
                      mod_form)
iv_obj$accuracy <- map_dbl(iv_obj$results, function(x) mean(x$correct))
summary(iv_obj$accuracy)
```


by [Aaron Peikert![ORCID
iD](https://orcid.org/sites/default/files/images/orcid_16x16.png)](https://orcid.org/0000-0001-7813-818X)
and [Andreas Brandmaier![ORCID
iD](https://orcid.org/sites/default/files/images/orcid_16x16.png)](http://orcid.org/0000-0001-8765-6982).

Owner

  • Name: Aaron Peikert
  • Login: aaronpeikert
  • Kind: user

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