step_lencode_glm  R Documentation 
step_lencode_glm()
creates a specification of a recipe step that will
convert a nominal (i.e. factor) predictor into a single set of scores derived
from a generalized linear model.
step_lencode_glm(
recipe,
...,
role = NA,
trained = FALSE,
outcome = NULL,
mapping = NULL,
skip = FALSE,
id = rand_id("lencode_glm")
)
recipe 
A recipe object. The step will be added to the sequence of operations for this recipe. 
... 
One or more selector functions to choose variables. For

role 
Not used by this step since no new variables are created. 
trained 
A logical to indicate if the quantities for preprocessing have been estimated. 
outcome 
A call to 
mapping 
A list of tibble results that define the encoding. This is

skip 
A logical. Should the step be skipped when the recipe is baked by

id 
A character string that is unique to this step to identify it. 
For each factor predictor, a generalized linear model is fit to the outcome and the coefficients are returned as the encoding. These coefficients are on the linear predictor scale so, for factor outcomes, they are in logodds units. The coefficients are created using a no intercept model and, when two factor outcomes are used, the logodds reflect the event of interest being the first level of the factor.
For novel levels, a slightly timmed average of the coefficients is returned.
An updated version of recipe
with the new step added to the
sequence of existing steps (if any). For the tidy
method, a tibble with
columns terms
(the selectors or variables for encoding), level
(the
factor levels), and value
(the encodings).
When you tidy()
this step, a tibble with columns terms
(the selectors or variables selected), value
and component
is returned.
This step performs an supervised operation that can utilize case weights.
To use them, see the documentation in recipes::case_weights and the examples on
tidymodels.org
.
MicciBarreca D (2001) "A preprocessing scheme for highcardinality categorical attributes in classification and prediction problems," ACM SIGKDD Explorations Newsletter, 3(1), 2732.
Zumel N and Mount J (2017) "vtreat: a data.frame Processor for Predictive Modeling," arXiv:1611.09477
library(recipes)
library(dplyr)
library(modeldata)
data(grants)
set.seed(1)
grants_other < sample_n(grants_other, 500)
reencoded < recipe(class ~ sponsor_code, data = grants_other) %>%
step_lencode_glm(sponsor_code, outcome = vars(class))
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