Supervised Factor Conversions into Linear Functions using Bayesian Likelihood EncodingsSource:
step_lencode_mixed() 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 mixed model.
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
step_lencode_mixed, this indicates the variables to be encoded into a numeric format. See
recipes::selections()for more details. For the
tidymethod, these are not currently used.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A call to
varsto specify which variable is used as the outcome in the generalized linear model. Only numeric and two-level factors are currently supported.
A list of tibble results that define the encoding. This is
NULLuntil the step is trained by
A logical. Should the step be skipped when the recipe is baked by
recipes::bake()? While all operations are baked when
recipes::prep()is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using
skip = TRUEas it may affect the computations for subsequent operations
A character string that is unique to this step to identify it.
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
terms (the selectors or variables for encoding),
factor levels), and
value (the encodings).
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 log-odds units. The coefficients are created using a no intercept model and, when two factor outcomes are used, the log-odds reflect the event of interest being the first level of the factor.
For novel levels, a slightly timmed average of the coefficients is returned.
lmer(outcome ~ 1 + (1 | predictor), data = data, ...)
... include the
family argument (automatically set by the step)
as well as any arguments given to the
options argument to the step.
Relevant options include
control and others.
tidy() this step, a tibble with columns
(the selectors or variables selected),
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
Micci-Barreca D (2001) "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems," ACM SIGKDD Explorations Newsletter, 3(1), 27-32.
Zumel N and Mount J (2017) "vtreat: a data.frame Processor for Predictive Modeling," arXiv:1611.09477