LogicForest: Logic Forest
Version 2.1.0

Two classification ensemble methods based on logic regression models. LogForest uses a bagging approach to construct an ensemble of logic regression models. LBoost uses a combination of boosting and cross-validation to construct an ensemble of logic regression models. Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome.

AuthorBethany Wolf
Date of publication2014-09-19 00:46:31
MaintainerBethany Wolf <wolfb@musc.edu>
LicenseGPL-2
Version2.1.0
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("LogicForest")

Getting started

Package overview

Popular man pages

CV.data: Internal Logic Forest Functions
LBoost.fit: Example of a LBoost Model
LBoost.PIs: Internal Logic Forest Functions
Perm.PIimp: Internal Logic Forest Functions
Perms: Internal Logic Forest Functions
pimp.import: Internal Logic Forest Functions
Pred.imp: Internal Logic Forest Functions
See all...

All man pages Function index File listing

Man pages

ada.pred: Internal Logic Forest Functions
ada.weights: Internal Logic Forest Functions
BoostVimp.plot: Variable and Interaction Importance Plots for a LBoost Model
CV.data: Internal Logic Forest Functions
CV.err: Internal Logic Forest Functions
CV.ids: Internal Logic Forest Functions
LBoost: LBoost
LBoost.fit: Example of a LBoost Model
LBoost.PIs: Internal Logic Forest Functions
LF.data: Example Data for Logic Forest and LBoost
LF.testdata: Example of New Test Data for Prediction in Logic Forest and...
list.PIs: Internal Logic Forest Functions
logforest: Logic Forest
logforest.fit: Example of a Logic Forest Model
LogicForest-package: Logic Forest
p.combos: Internal Logic Forest Functions
Perm.PIimp: Internal Logic Forest Functions
Perms: Internal Logic Forest Functions
persistence.plot: Plot Persistence of a Variable of Interaction
persistence.prep: Internal Logic forest Functions
persist.match: Internal Logic Forest Functions
pimp.import: Internal Logic Forest Functions
pimp.mat: Internal Logic Forest Functions
PlusMinus.PIimp: Internal Logic Forest Functions
predict.LBoost: Prediction of Response Using LBoost
predict.logforest: Prediction of Response Using Logic Forest
Pred.imp: Internal Logic Forest Functions
prime.imp: Internal Logic Forest Functions
print.LBoost: Prints Output for and LBoost Model
print.LFprediction: Prints Logic Forest Prediction Output
print.logforest: Prints Output for a Logic Forest Model
proportion.positive: Internal Logic Forest Functions
submatch.plot: Plot of Variable/Interaction Frequency
subs: Internal Logic Forest Functions
TTab: Internal Logic Forest Functions
vimp.plot: Variable and Interaction Importance Plots for a Logic Forest...

Functions

BoostVimp.plot Man page Source code
CV.data Man page Source code
CV.err Man page Source code
CV.ids Man page Source code
LBoost Man page Source code
LBoost.PIs Man page Source code
LBoost.fit Man page
LF.data Man page
LF.testdata Man page
LogicForest Man page
LogicForest-package Man page
Perm.PIimp Man page Source code
Perms Man page Source code
PlusMinus.PIimp Man page Source code
Pred.imp Man page Source code
TTab Man page Source code
ada.pred Man page Source code
ada.weights Man page Source code
list.PIs Man page Source code
logforest Man page Source code
logforest.fit Man page
p.combos Man page Source code
persist.match Man page Source code
persistence.plot Man page Source code
persistence.prep Man page Source code
pimp.import Man page Source code
pimp.mat Man page Source code
predict.LBoost Man page Source code
predict.logforest Man page Source code
prime.imp Man page Source code
print.LBoost Man page Source code
print.LFprediction Man page Source code
print.logforest Man page Source code
proportion.positive Man page Source code
submatch.plot Man page Source code
subs Man page Source code
vimp.plot Man page Source code

Files

NAMESPACE
data
data/LF.testdata.rda
data/LF.data.rda
data/datalist
data/LBoost.fit.rda
data/logforest.fit.rda
R
R/Pred.imp.R
R/pimp.mat.R
R/print.LFprediction.R
R/Perms.R
R/logforest.R
R/print.LBoost.R
R/predict.LBoost.R
R/prime.imp.R
R/persistence.plot.R
R/pimp.import.R
R/persist.match.R
R/print.logforest.R
R/LBoost.PIs.R
R/Perm.PIimp.R
R/CV.data.R
R/proportion.positive.R
R/submatch.plot.R
R/list.PIs.R
R/TTab.R
R/ada.weights.R
R/CV.err.R
R/LBoost.R
R/persistence.prep.R
R/CV.ids.R
R/vimp.plot.R
R/ada.pred.R
R/PlusMinus.PIimp.R
R/BoostVimp.plot.R
R/predict.logforest.R
R/subs.R
R/p.combos.R
MD5
DESCRIPTION
man
man/CV.data.Rd
man/predict.logforest.Rd
man/vimp.plot.Rd
man/ada.weights.Rd
man/CV.err.Rd
man/persistence.prep.Rd
man/LBoost.fit.Rd
man/prime.imp.Rd
man/submatch.plot.Rd
man/LF.testdata.Rd
man/predict.LBoost.Rd
man/TTab.Rd
man/pimp.import.Rd
man/PlusMinus.PIimp.Rd
man/persist.match.Rd
man/pimp.mat.Rd
man/LF.data.Rd
man/print.logforest.Rd
man/print.LFprediction.Rd
man/proportion.positive.Rd
man/ada.pred.Rd
man/BoostVimp.plot.Rd
man/logforest.Rd
man/print.LBoost.Rd
man/p.combos.Rd
man/persistence.plot.Rd
man/CV.ids.Rd
man/list.PIs.Rd
man/Pred.imp.Rd
man/LogicForest-package.Rd
man/Perms.Rd
man/Perm.PIimp.Rd
man/LBoost.PIs.Rd
man/subs.Rd
man/LBoost.Rd
man/logforest.fit.Rd
LogicForest documentation built on May 20, 2017, 12:55 a.m.

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