Extract A Model Fitted Workflow

Extract a model fitted workflow from a tidyAML model tibble.

Usage

extract_wflw_fit(.data, .model_id = NULL)

Arguments

.data: The model table that must have the class tidyaml_mod_spec_tbl.
.model_id: The model number that you want to select, Must be an integer or sequence of integers, ie. 1 or c(1,3,5) or 1:2

Value

A tibble with the chosen model workflow(s).

Details

This function allows you to get a model fitted workflow or more from a tibble with a class of "tidyaml_mod_spec_tbl". It allows you to select the model by the .model_id column. You can call the model id's by an integer or a sequence of integers.

Author

Steven P. Sanderson II, MPH

Examples

library(recipes)

rec_obj <- recipe(mpg ~ ., data = mtcars)
frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"),
                                           .parsnip_fns = "linear_reg")

extract_wflw_fit(frt_tbl, 1)
#> [[1]]
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)          cyl         disp           hp         drat           wt  
#>  -11.707745    -0.297490     0.016804    -0.001976     0.358231    -4.368291  
#>        qsec           vs           am         gear         carb  
#>    2.194814    -1.765715     4.282072     0.930844    -0.241416  
#> 
#> 
extract_wflw_fit(frt_tbl, 1:2)
#> [[1]]
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#> (Intercept)          cyl         disp           hp         drat           wt  
#>  -11.707745    -0.297490     0.016804    -0.001976     0.358231    -4.368291  
#>        qsec           vs           am         gear         carb  
#>    2.194814    -1.765715     4.282072     0.930844    -0.241416  
#> 
#> 
#> [[2]]
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> 
#> Call:  stats::glm(formula = ..y ~ ., family = stats::gaussian, data = data)
#> 
#> Coefficients:
#> (Intercept)          cyl         disp           hp         drat           wt  
#>  -11.707745    -0.297490     0.016804    -0.001976     0.358231    -4.368291  
#>        qsec           vs           am         gear         carb  
#>    2.194814    -1.765715     4.282072     0.930844    -0.241416  
#> 
#> Degrees of Freedom: 23 Total (i.e. Null);  13 Residual
#> Null Deviance:	    947.7 
#> Residual Deviance: 107.5 	AIC: 128.1
#>