woe_table.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/woe.R
\name{woe_table}
\alias{woe_table}
\title{Crosstable with woe between a binary outcome and a predictor variable.}
\usage{
woe_table(predictor, outcome, Laplace = 1e-06, call = rlang::caller_env(0))
}
\arguments{
\item{predictor}{A atomic vector, usualy with few distinct values.}

\item{outcome}{The dependent variable. A atomic vector with exactly 2
distinct values.}

\item{Laplace}{The \code{pseudocount} parameter of the Laplace Smoothing
estimator. Default to 1e-6. Value to avoid -Inf/Inf from predictor category
with only one outcome class. Set to 0 to allow Inf/-Inf.}

\item{call}{The execution environment of a currently running function, e.g.
\code{caller_env()}. The function will be mentioned in error messages as the
source of the error. See the call argument of \code{\link[rlang:abort]{rlang::abort()}} for more
information.}
}
\value{
a tibble with counts, proportions and woe. Warning: woe can possibly
be -Inf. Use 'Laplace' arg to avoid that.
}
\description{
Calculates some summaries and the WoE (Weight of Evidence) between a binary
outcome and a given predictor variable. Used to biuld the dictionary.
}
\references{
Kullback, S. (1959). \emph{Information Theory and Statistics.} Wiley, New York.

Hastie, T., Tibshirani, R. and Friedman, J. (1986). \emph{Elements of Statistical
Learning}, Second Edition, Springer, 2009.

Good, I. J. (1985), "Weight of evidence: A brief survey", \emph{Bayesian
Statistics}, 2, pp.249-270.
}