step_lencode_bayes | R Documentation |
step_lencode_bayes
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 estimated using
Bayesian analysis.
step_lencode_bayes( recipe, ..., role = NA, trained = FALSE, outcome = NULL, options = list(seed = sample.int(10^5, 1)), verbose = FALSE, mapping = NULL, skip = FALSE, id = rand_id("lencode_bayes") )
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 |
options |
A list of options to pass to |
verbose |
A logical to control the default printing by
|
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 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.
A hierarchical generalized linear model is fit using
rstanarm::stan_glmer()
and no intercept via
stan_glmer(outcome ~ (1 | predictor), data = data, ...)
where the ...
include the family
argument (automatically
set by the step, unless passed in by options
) as well as any arguments
given to the options
argument to the step. Relevant options include
chains
, iter
, cores
, and arguments for the priors (see the links
in the References below). prior_intercept
is the argument that has the
most effect on the amount of shrinkage.
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
.
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
"Hierarchical Partial Pooling for Repeated Binary Trials" https://tinyurl.com/stan-pooling
"Prior Distributions for 'rstanarm“ Models" https://tinyurl.com/stan-priors
"Estimating Generalized (Non-)Linear Models with Group-Specific
Terms with rstanarm
" https://tinyurl.com/stan-glm-grouped
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_bayes(sponsor_code, outcome = vars(class))
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