Choose the best model with minimal information criteria statistics in forward selection or maximal ones in backward elimination
stepOne(findIn, p, n, sigma, tolerance, Ftrace, criteria, Y,X1, X0, k, SST)Logical value, if FALSE then add independent variable to regression model, otherwise remove independent variable from regression model
The number of independent variable entered in regression
The sample size
Pure error variance from full regressoin model for Bayesian information criterion(BIC)
Tolerance value for multicollinearity
Statistic of multivariate regression including Wilks` lambda, Pillai trace and Hotelling-lawley trace
Information criterion including AIC, AICc, BIC, SBC, HQ, HQc and SL
Data set for dependent variable
Data set for independent variables not in regression model
Data set for independent variables entered in regression model
Forces the first k effects entered in regression model, and the selection methods are performed on the other effects in the data set
Total sum of squares corrected for the mean for the dependent variable
P value or Information Criteria statistic value
Pointer for independent variable enter or eliminate
Maximum or minimum of SSE
Rank changed or not
This function can compute probability value or information criteria statistics with multivariate and univariate regression using least square method
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