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Add a model to an epi_workflow

Source: R/model-methods.R
Add_model.Rd

Add a model to an epi_workflow

Usage

Add_model(x, spec, ..., formula = NULL)

Remove_model(x)

Update_model(x, spec, ..., formula = NULL)

# S3 method for class 'epi_workflow'
Add_model(x, spec, ..., formula = NULL)

# S3 method for class 'epi_workflow'
Remove_model(x)

# S3 method for class 'epi_workflow'
Update_model(x, spec, ..., formula = NULL)

# S3 method for class 'workflow'
Add_model(x, spec, ..., formula = NULL)

# S3 method for class 'workflow'
Remove_model(x)

# S3 method for class 'workflow'
Update_model(x, spec, ..., formula = NULL)

add_model(x, spec, ..., formula = NULL)

remove_model(x)

update_model(x, spec, ..., formula = NULL)

Arguments

x

An epi_workflow.

spec

A parsnip model specification.

...

Not used.

formula

An optional formula override to specify the terms of the model. Typically, the terms are extracted from the formula or recipe preprocessing methods. However, some models (like survival and bayesian models) use the formula not to preprocess, but to specify the structure of the model. In those cases, a formula specifying the model structure must be passed unchanged into the model call itself. This argument is used for those purposes.

Value

x, updated with a new, updated, or removed model.

Details

Has the same behaviour as workflows::add_model() but also ensures that the returned object is an epi_workflow.

This family is called Add_* / Update_* / Remove_* to avoid masking the related functions in {workflows}. We also provide aliases with the lower-case names. However, in the event that {workflows} is loaded after {epipredict}, these may fail to function properly.

See also

workflows::add_model()

  • Add_model() adds a parsnip model to the epi_workflow.

  • Remove_model() removes the model specification as well as any fitted model object. Any extra formulas are also removed.

  • Update_model() first removes the model then adds the new specification to the workflow.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
jhu <- case_death_rate_subset %>%
  filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))

r <- epi_recipe(jhu) %>%
  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
  step_epi_ahead(death_rate, ahead = 7)

rf_model <- rand_forest(mode = "regression")

wf <- epi_workflow(r)

wf <- wf %>% Add_model(rf_model)
wf
#> 
#> ══ Epi Workflow ════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: rand_forest()
#> Postprocessor: None
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 
#> 2 Recipe steps.
#> 1. step_epi_lag()
#> 2. step_epi_ahead()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Random Forest Model Specification (regression)
#> 
#> Computational engine: ranger 
#> 
#> 

lm_model <- linear_reg()

wf <- Update_model(wf, lm_model)
wf
#> 
#> ══ Epi Workflow ════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> Postprocessor: None
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 
#> 2 Recipe steps.
#> 1. step_epi_lag()
#> 2. step_epi_ahead()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: lm 
#> 
#> 

wf <- Remove_model(wf)
wf
#> 
#> ══ Epi Workflow ════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: None
#> Postprocessor: None
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 
#> 2 Recipe steps.
#> 1. step_epi_lag()
#> 2. step_epi_ahead()
#> 
#>