Description: 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' and 'stepwise' steps. Procedure uses Akaike information criterion, the small-sample-size corrected version of Akaike information criterion, Bayesian information criterion, Hannan and Quinn information criterion, the corrected form of Hannan and Quinn information criterion, Schwarz criterion and significance levels as selection criteria, where the significance levels for entry and for stay are set to 0.15 as default. Multicollinearity detection in regression model are performed by checking tolerance value, which is set to 1e-7 as default. Continuous variables nested within class effect and weighted stepwise regression are also considered in this package.

Description: 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' and 'stepwise' steps. 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 in this package.

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

Description: 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.