Learn R Programming

glmdisc (version 0.6)

Discretization and Grouping for Logistic Regression

Description

A Stochastic-Expectation-Maximization (SEM) algorithm (Celeux et al. (1995) ) associated with a Gibbs sampler which purpose is to learn a constrained representation for logistic regression that is called quantization (Ehrhardt et al. (2019) ). Continuous features are discretized and categorical features' values are grouped to produce a better logistic regression model. Pairwise interactions between quantized features are dynamically added to the model through a Metropolis-Hastings algorithm (Hastings, W. K. (1970) ).

Copy Link

Version

Install

install.packages('glmdisc')

Monthly Downloads

39

Version

0.6

License

GPL (>= 2)

Maintainer

Adrien Ehrhardt

Last Published

September 30th, 2020

Functions in glmdisc (0.6)

glmdisc-class

Class glmdisc
predict

Prediction on a raw test set of the best logistic regression model on discretized data.
glmdisc-package

glmdisc: A package for discretizing continuous features, grouping categorical features' values and optimizing it for logistic regression.
predictlogisticRegression

Predicting using a logistic regression fitted with RCpp::fast_LR.
plot

Plots for the discretized / grouped data.
contr.ltfr

Wrapper for contr.ltfr
cutpoints

Obtaining the cutpoints and / or regroupments of a discretization.
glmdisc

Model-based multivariate discretization for logistic regression.
discretize

Prediction on a raw test set of the best logistic regression model on discretized / grouped data.
normalizedGini

Calculating the normalized Gini index