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
#> 0.08495106 0.08735526 0.09188577 0.09188018 0.10634567
#> seq.3459.49 seq.3865.56 seq.3363.21 seq.4487.88 seq.5994.84
#> 0.09348696 0.09575663 0.09103747 0.09282479 0.09005451
#> seq.9011.72 seq.2902.23 seq.2260.48 seq.4936.96 seq.2277.95
#> 0.08922376 0.10315964 0.09214161 0.09374182 0.09969196
#> seq.2953.31 seq.3032.11 seq.4330.4 seq.4914.10 seq.3896.5
#> 0.07875422 0.09379076 0.08887630 0.09248241 0.08995053
#> seq.5002.7 seq.3476.4 seq.1130.49 seq.6356.60 seq.4579.40
#> 0.08732796 0.09252996 0.08751564 0.09146692 0.09139596
#> seq.8344.24 seq.8441.53 seq.9360.55 seq.7841.8 seq.8142.63
#> 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
#> 0.08873767 0.08925680 0.08993740 0.09374001 0.08576509
#> seq.6615.18 seq.8797.98 seq.9879.88 seq.8993.16 seq.9373.82
#> 0.08677730 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
#> 3.438979 3.439728 3.423638 3.428196 3.427512
#> seq.3459.49 seq.3865.56 seq.3363.21 seq.4487.88 seq.5994.84
#> 3.388480 3.388280 3.388948 3.388497 3.388886
#> seq.9011.72 seq.2902.23 seq.2260.48 seq.4936.96 seq.2277.95
#> 3.388982 3.387285 3.388674 3.388382 3.387792
#> seq.2953.31 seq.3032.11 seq.4330.4 seq.4914.10 seq.3896.5
#> 3.399252 3.381620 3.385452 3.382065 3.385052
#> seq.5002.7 seq.3476.4 seq.1130.49 seq.6356.60 seq.4579.40
#> 3.388270 3.384922 3.388647 3.382381 3.383859
#> seq.8344.24 seq.8441.53 seq.9360.55 seq.7841.8 seq.8142.63
#> 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
#> 3.387160 3.410809 3.388310 3.387717 3.397427
#> seq.6615.18 seq.8797.98 seq.9879.88 seq.8993.16 seq.9373.82
#> 3.380865 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
#> 3.438979 3.439728 3.423638 3.428196 3.427512
#> seq.3459.49 seq.3865.56 seq.3363.21 seq.4487.88 seq.5994.84
#> 3.388480 3.388280 3.388948 3.388497 3.388886
#> seq.9011.72 seq.2902.23 seq.2260.48 seq.4936.96 seq.2277.95
#> 3.388982 3.387285 3.388674 3.388382 3.387792
#> seq.2953.31 seq.3032.11 seq.4330.4 seq.4914.10 seq.3896.5
#> 3.399252 3.381620 3.385452 3.382065 3.385052
#> seq.5002.7 seq.3476.4 seq.1130.49 seq.6356.60 seq.4579.40
#> 3.388270 3.384922 3.388647 3.382381 3.383859
#> seq.8344.24 seq.8441.53 seq.9360.55 seq.7841.8 seq.8142.63
#> 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
#> 3.387160 3.410809 3.388310 3.387717 3.397427
#> seq.6615.18 seq.8797.98 seq.9879.88 seq.8993.16 seq.9373.82
#> 3.380865 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
#> 0.08495106 0.08735526 0.09188577 0.09188018 0.10634567
#> seq.3459.49 seq.3865.56 seq.3363.21 seq.4487.88 seq.5994.84
#> 0.09348696 0.09575663 0.09103747 0.09282479 0.09005451
#> seq.9011.72 seq.2902.23 seq.2260.48 seq.4936.96 seq.2277.95
#> 0.08922376 0.10315964 0.09214161 0.09374182 0.09969196
#> seq.2953.31 seq.3032.11 seq.4330.4 seq.4914.10 seq.3896.5
#> 0.07875422 0.09379076 0.08887630 0.09248241 0.08995053
#> seq.5002.7 seq.3476.4 seq.1130.49 seq.6356.60 seq.4579.40
#> 0.08732796 0.09252996 0.08751564 0.09146692 0.09139596
#> seq.8344.24 seq.8441.53 seq.9360.55 seq.7841.8 seq.8142.63
#> 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
#> 0.08873767 0.08925680 0.08993740 0.09374001 0.08576509
#> seq.6615.18 seq.8797.98 seq.9879.88 seq.8993.16 seq.9373.82
#> 0.08677730 0.10461108 0.08302976 0.09531872 0.08283751