Get Parameters From A Recipe
get_recipe_params.RdGet mean and standard deviations used to center/scale data from a prepped recipe.
Value
A named numeric vector with means (step = 'center')
or standard deviations (step = scale'). Names correspond
to recipe predictors.
Examples
test <- simdata
feats <- wranglr:::get_analytes(test)
rec <- recipes::recipe(~ ., data = dplyr::select(test, dplyr::all_of(feats))) |>
recipes::step_log(recipes::all_predictors(), base = 10) |>
recipes::step_center(recipes::all_predictors()) |>
recipes::step_scale(recipes::all_predictors()) |>
recipes::prep(training = test)
get_recipe_params(rec, "scale")
#> seq.2802.68 seq.9251.29 seq.1942.70 seq.5751.80 seq.9608.12 seq.3459.49
#> 0.08495106 0.08735526 0.09188577 0.09188018 0.10634567 0.09348696
#> seq.3865.56 seq.3363.21 seq.4487.88 seq.5994.84 seq.9011.72 seq.2902.23
#> 0.09575663 0.09103747 0.09282479 0.09005451 0.08922376 0.10315964
#> seq.2260.48 seq.4936.96 seq.2277.95 seq.2953.31 seq.3032.11 seq.4330.4
#> 0.09214161 0.09374182 0.09969196 0.07875422 0.09379076 0.08887630
#> seq.4914.10 seq.3896.5 seq.5002.7 seq.3476.4 seq.1130.49 seq.6356.60
#> 0.09248241 0.08995053 0.08732796 0.09252996 0.08751564 0.09146692
#> seq.4579.40 seq.8344.24 seq.8441.53 seq.9360.55 seq.7841.8 seq.8142.63
#> 0.09139596 0.09874588 0.09255668 0.09234006 0.08733651 0.09262342
#> seq.4461.56 seq.9297.97 seq.9396.38 seq.3300.26 seq.2772.14 seq.6615.18
#> 0.08873767 0.08925680 0.08993740 0.09374001 0.08576509 0.08677730
#> seq.8797.98 seq.9879.88 seq.8993.16 seq.9373.82
#> 0.10461108 0.08302976 0.09531872 0.08283751
get_recipe_params(rec, "center")
#> seq.2802.68 seq.9251.29 seq.1942.70 seq.5751.80 seq.9608.12 seq.3459.49
#> 3.438979 3.439728 3.423638 3.428196 3.427512 3.388480
#> seq.3865.56 seq.3363.21 seq.4487.88 seq.5994.84 seq.9011.72 seq.2902.23
#> 3.388280 3.388948 3.388497 3.388886 3.388982 3.387285
#> seq.2260.48 seq.4936.96 seq.2277.95 seq.2953.31 seq.3032.11 seq.4330.4
#> 3.388674 3.388382 3.387792 3.399252 3.381620 3.385452
#> seq.4914.10 seq.3896.5 seq.5002.7 seq.3476.4 seq.1130.49 seq.6356.60
#> 3.382065 3.385052 3.388270 3.384922 3.388647 3.382381
#> seq.4579.40 seq.8344.24 seq.8441.53 seq.9360.55 seq.7841.8 seq.8142.63
#> 3.383859 3.395858 3.381571 3.381561 3.394183 3.384688
#> seq.4461.56 seq.9297.97 seq.9396.38 seq.3300.26 seq.2772.14 seq.6615.18
#> 3.387160 3.410809 3.388310 3.387717 3.397427 3.380865
#> seq.8797.98 seq.9879.88 seq.8993.16 seq.9373.82
#> 3.390829 3.394751 3.392975 3.394166
rcp <- create_recipe(test)
#> Warning: NaNs produced
get_recipe_params(rec, "center")
#> seq.2802.68 seq.9251.29 seq.1942.70 seq.5751.80 seq.9608.12 seq.3459.49
#> 3.438979 3.439728 3.423638 3.428196 3.427512 3.388480
#> seq.3865.56 seq.3363.21 seq.4487.88 seq.5994.84 seq.9011.72 seq.2902.23
#> 3.388280 3.388948 3.388497 3.388886 3.388982 3.387285
#> seq.2260.48 seq.4936.96 seq.2277.95 seq.2953.31 seq.3032.11 seq.4330.4
#> 3.388674 3.388382 3.387792 3.399252 3.381620 3.385452
#> seq.4914.10 seq.3896.5 seq.5002.7 seq.3476.4 seq.1130.49 seq.6356.60
#> 3.382065 3.385052 3.388270 3.384922 3.388647 3.382381
#> seq.4579.40 seq.8344.24 seq.8441.53 seq.9360.55 seq.7841.8 seq.8142.63
#> 3.383859 3.395858 3.381571 3.381561 3.394183 3.384688
#> seq.4461.56 seq.9297.97 seq.9396.38 seq.3300.26 seq.2772.14 seq.6615.18
#> 3.387160 3.410809 3.388310 3.387717 3.397427 3.380865
#> seq.8797.98 seq.9879.88 seq.8993.16 seq.9373.82
#> 3.390829 3.394751 3.392975 3.394166
get_recipe_params(rcp, "scale")
#> seq.2802.68 seq.9251.29 seq.1942.70 seq.5751.80 seq.9608.12 seq.3459.49
#> 0.08495106 0.08735526 0.09188577 0.09188018 0.10634567 0.09348696
#> seq.3865.56 seq.3363.21 seq.4487.88 seq.5994.84 seq.9011.72 seq.2902.23
#> 0.09575663 0.09103747 0.09282479 0.09005451 0.08922376 0.10315964
#> seq.2260.48 seq.4936.96 seq.2277.95 seq.2953.31 seq.3032.11 seq.4330.4
#> 0.09214161 0.09374182 0.09969196 0.07875422 0.09379076 0.08887630
#> seq.4914.10 seq.3896.5 seq.5002.7 seq.3476.4 seq.1130.49 seq.6356.60
#> 0.09248241 0.08995053 0.08732796 0.09252996 0.08751564 0.09146692
#> seq.4579.40 seq.8344.24 seq.8441.53 seq.9360.55 seq.7841.8 seq.8142.63
#> 0.09139596 0.09874588 0.09255668 0.09234006 0.08733651 0.09262342
#> seq.4461.56 seq.9297.97 seq.9396.38 seq.3300.26 seq.2772.14 seq.6615.18
#> 0.08873767 0.08925680 0.08993740 0.09374001 0.08576509 0.08677730
#> seq.8797.98 seq.9879.88 seq.8993.16 seq.9373.82
#> 0.10461108 0.08302976 0.09531872 0.08283751