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"))# S3 method for step_lencode_bayes
tidy(x, ...)
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_bayes, this indicates the variables to be encoded
into a numeric format. See recipes::selections() for more details. For
the tidy method, 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 vars to 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 options to pass to rstanarm::stan_glmer().
A logical to control the default printing by
rstanarm::stan_glmer().
A list of tibble results that define the
encoding. This is NULL until the step is trained by
recipes::prep.recipe().
A logical. Should the step be skipped when the
recipe is baked by recipes::bake.recipe()? While all operations are baked
when recipes::prep.recipe() 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 = TRUE as it may affect
the computations for subsequent operations
A character string that is unique to this step to identify it.
A step_lencode_bayes object.
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).
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) 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.
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
# NOT RUN {
library(recipes)
library(dplyr)
data(okc)
reencoded <- recipe(Class ~ age + location, data = okc) %>%
step_lencode_bayes(location, outcome = vars(Class))
# See https://tidymodels.github.io/embed/ for examples
# }
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