LF.data

A data frame containing 200 observations and 52 variables with value 0 or 1.

data

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. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.

Melica Nikahd

LogicForest

Logic Forest

Bethany Wolf

Madison Hyer

LF.data function

Bethany Wolf <a href="/link/wolf%40musc.edu?package=LogicForest&version=2.1.1" data-mini-rdoc="LogicForest::wolf@musc.edu">wolf@musc.edu</a>

Author

LF.data — LF.data

Bethany Wolf <a href='mailto:wolf@musc.edu'>wolf@musc.edu</a>

LF.data: LF.data

Description

Arguments

Author

Details

References