add_variables() specifies the terms of the model through the usage of
tidyselect::select_helpers for the outcomes and predictors.
remove_variables() removes the variables. Additionally, if the model
has already been fit, then the fit is removed.
update_variables() first removes the variables, then replaces the
previous variables with the new ones. Any model that has already been
fit based on the original variables will need to be refit.
add_variables(x, outcomes, predictors, ..., blueprint = NULL) remove_variables(x) update_variables(x, outcomes, predictors, ..., blueprint = NULL)
| x | A workflow |
|---|---|
| outcomes, predictors | Tidyselect expressions specifying the terms
of the model. |
| ... | Not used. |
| blueprint | A hardhat blueprint used for fine tuning the preprocessing. If Note that preprocessing done here is separate from preprocessing that might be done by the underlying model. |
x, updated with either a new or removed variables preprocessor.
To fit a workflow, exactly one of add_formula(), add_recipe(), or
add_variables() must be specified.
library(parsnip) spec_lm <- linear_reg() spec_lm <- set_engine(spec_lm, "lm") workflow <- workflow() workflow <- add_model(workflow, spec_lm) # Add terms with tidyselect expressions. # Outcomes are specified before predictors. workflow1 <- add_variables( workflow, outcomes = mpg, predictors = c(cyl, disp) ) workflow1 <- fit(workflow1, mtcars) workflow1#> ══ Workflow [trained] ══════════════════════════════════════════════════════════ #> Preprocessor: Variables #> Model: linear_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> Outcomes: mpg #> Predictors: c(cyl, disp) #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> #> Call: #> stats::lm(formula = ..y ~ ., data = data) #> #> Coefficients: #> (Intercept) cyl disp #> 34.66099 -1.58728 -0.02058 #># Removing the variables of a fit workflow will also remove the model remove_variables(workflow1)#> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: None #> Model: linear_reg() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Linear Regression Model Specification (regression) #> #> Computational engine: lm #># Variables can also be updated update_variables(workflow1, mpg, starts_with("d"))#> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Variables #> Model: linear_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> Outcomes: mpg #> Predictors: starts_with("d") #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Linear Regression Model Specification (regression) #> #> Computational engine: lm #># The `outcomes` are removed before the `predictors` expression # is evaluated. This allows you to easily specify the predictors # as "everything except the outcomes". workflow2 <- add_variables(workflow, mpg, everything()) workflow2 <- fit(workflow2, mtcars) pull_workflow_mold(workflow2)$predictors#> # A tibble: 32 x 10 #> cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows