# Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings

Source:`R/lencode_bayes.R`

`step_lencode_bayes.Rd`

`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.

## Usage

```
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")
)
```

## Arguments

- 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

`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.- 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

`vars`

to specify which variable is used as the outcome in the generalized linear model. Only numeric and two-level factors are currently supported.- options
A list of options to pass to

`rstanarm::stan_glmer()`

.- verbose
A logical to control the default printing by

`rstanarm::stan_glmer()`

.- mapping
A list of tibble results that define the encoding. This is

`NULL`

until the step is trained by`recipes::prep()`

.- skip
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 = TRUE`

as it may affect the computations for subsequent operations- id
A character string that is unique to this step to identify it.

## Value

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).

## Details

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

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.

## Tidying

When you `tidy()`

this step, a tibble is retruned with
columns `level`

, `value`

, `terms`

, and `id`

:

- level
character, the factor levels

- value
numeric, the encoding

- terms
character, the selectors or variables selected

- id
character, id of this step

## Case weights

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`

.

## References

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://CRAN.R-project.org/package=rstanarm/vignettes/pooling.html

"Prior Distributions for `rstanarm`

Models"
http://mc-stan.org/rstanarm/reference/priors.html

"Estimating Generalized (Non-)Linear Models with Group-Specific Terms with
`rstanarm`

" http://mc-stan.org/rstanarm/articles/glmer.html