Stepwise Regression Analysis

Stepwise regression analysis for variable selection can be used to get the best candidate final regression model in univariate or multivariate regression analysis with the 'forward', 'backward' and 'bidirection' steps. Besides, best subset selection is included in this package. Procedure can use Akaike information criterion, corrected Akaike information criterion, Bayesian information criterion, Hannan and Quinn information criterion, corrected Hannan and Quinn information criterion, Schwarz criterion and significance levels as selection criteria. Multicollinearity detection in regression model are performed by checking tolerance value. Continuous variables nested within class effect and weighted stepwise regression are also considered.


Reference manual

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1.3.2 by Junhui Li, 11 days ago

Browse source code at

Authors: Junhui Li , Xiaohuan Lu , Kun Cheng , Wenxin Liu

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp

Linking to Rcpp, RcppEigen

See at CRAN