README.Rmd

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output: github_document
---

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knitr::opts_chunk$set(
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# nestedmodels <img src="man/figures/logo.png" align="right" height="139" />

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The goal of nestedmodels is to allow the modelling of nested data.
Some models only accept certain predictors. For panel data, it is often desirable to create a model for each panel. nestedmodels enhances the '[tidymodels](https://www.tidymodels.org/)' set of packages by allowing the user to classify a model as 'nested'.

## Installation

``` r
# Install the released version on CRAN
install.packages("nestedmodels")

# Or install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("ashbythorpe/nestedmodels")
```

## Example

```{r setup}
library(nestedmodels)
```

Nested models are often best used on panel data.

```{r data}
data <- example_nested_data

nested_data <- tidyr::nest(example_nested_data, data = -id)

nested_data
```

The `nested_resamples()` function makes sure that the testing and training data
contain every unique value of 'id'.

```{r}
split <- nested_resamples(nested_data, rsample::initial_split())

data_tr <- rsample::training(split)
data_tst <- rsample::testing(split)
```

Fitting a nested model to this data is very simple.

```{r}
model <- parsnip::linear_reg() %>%
  nested()

fit <- fit(model, z ~ x + y + a + b, 
           tidyr::nest(data_tr, data = -id))

predict(fit, data_tst)
```

If you don't want to nest your data manually, use `step_nest()` inside a workflow:

```{r}
recipe <- recipes::recipe(data_tr, z ~ x + y + a + b + id) %>%
  step_nest(id)

wf <- workflows::workflow() %>%
  workflows::add_model(model) %>%
  workflows::add_recipe(recipe)

wf_fit <- fit(wf, data_tr)

predict(wf_fit, data_tst)
```

Please note that the nestedmodels project is released with a [Contributor Code of Conduct](https://ashbythorpe.github.io/nestedmodels/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.