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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’ set of packages by allowing the user to classify a model as ‘nested’.
# Install the released version on CRAN
install.packages("nestedmodels")
# Or install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("ashbythorpe/nestedmodels")library(nestedmodels)Nested models are often best used on panel data.
data <- example_nested_data
nested_data <- tidyr::nest(example_nested_data, data = -id)
nested_data
#> # A tibble: 20 × 2
#> id data
#> <int> <list>
#> 1 1 <tibble [50 × 6]>
#> 2 2 <tibble [50 × 6]>
#> 3 3 <tibble [50 × 6]>
#> 4 4 <tibble [50 × 6]>
#> 5 5 <tibble [50 × 6]>
#> 6 6 <tibble [50 × 6]>
#> 7 7 <tibble [50 × 6]>
#> 8 8 <tibble [50 × 6]>
#> 9 9 <tibble [50 × 6]>
#> 10 10 <tibble [50 × 6]>
#> 11 11 <tibble [50 × 6]>
#> 12 12 <tibble [50 × 6]>
#> 13 13 <tibble [50 × 6]>
#> 14 14 <tibble [50 × 6]>
#> 15 15 <tibble [50 × 6]>
#> 16 16 <tibble [50 × 6]>
#> 17 17 <tibble [50 × 6]>
#> 18 18 <tibble [50 × 6]>
#> 19 19 <tibble [50 × 6]>
#> 20 20 <tibble [50 × 6]>The nested_resamples() function makes sure that the
testing and training data contain every unique value of ‘id’.
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.
model <- parsnip::linear_reg() %>%
nested()
fit <- fit(model, z ~ x + y + a + b,
tidyr::nest(data_tr, data = -id))
predict(fit, data_tst)
#> # A tibble: 260 × 1
#> .pred
#> <dbl>
#> 1 35.0
#> 2 27.7
#> 3 35.0
#> 4 39.4
#> 5 30.4
#> 6 29.5
#> 7 33.8
#> 8 33.1
#> 9 26.3
#> 10 18.9
#> # ℹ 250 more rowsIf you don’t want to nest your data manually, use
step_nest() inside a workflow:
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)
#> # A tibble: 260 × 1
#> .pred
#> <dbl>
#> 1 35.0
#> 2 27.7
#> 3 35.0
#> 4 39.4
#> 5 30.4
#> 6 29.5
#> 7 33.8
#> 8 33.1
#> 9 26.3
#> 10 18.9
#> # ℹ 250 more rowsPlease note that the nestedmodels project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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