| ,Strategy of improving the goodness of fit of the regression model(Ⅳ) ——the optimal scoring transformation and the other variable transformations[J].SICHUAN MENTAL HEALTH,2019,32(1):21-28 |
| Strategy of improving the goodness of fit of the regression model(Ⅳ) ——the optimal scoring transformation and the other variable transformations |
DOI：10.11886/j.issn.1007-3256.2019.01.004 |
| English keywords:Optimal scoring transformation Spline transformation Monotonic transformation Box - cox transformation |
| Fund projects:国家高技术研究发展计划课题资助(2015AA020102) |
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| The purpose of this paper was to introduce the fourth strategy of improving the goodness of fit of the regression models, the optimal scoring transformation and the other variable transformations. The concrete approaches were as follows: ①“ The optimal scoring transformation” was adopted to the multi - value nominal independent variable. ②“ The monotonic transformation” and “ the optimal scoring transformation” were adopted to the multi - value ordered independent variable, respectively. ③ “ The spline transformation” and “ the monotonic spline transformation” were adopted to the quantitative independent variables, respectively. ④“ The spline transformation ” “ the monotonic spline transformation ” and “ the BOX - COX transformation ” were adopted to the quantitative dependent variable, respectively. There were twelve variable transformation ways, so the twelve multiple nonlinear regression models were built. One best regression model, which was “ the model one” in this article, was selected from the twelve models mentioned above in terms of the results of the goodness of fit evaluation. The results were as follows: Root MSE = 0. 30935, R - Square = 0. 9586, and the adjusted R - Square = 0. 9527. Combined the results of this article with the other results of the previous three articles in the similar titles in this journal, the final conclusions were acquired as follows: ① “ The quantitative dependent variable” “ the quantitative independent variables” and “ the multi - value ordered independent variables” should be transformed in an appropriate form. ②The derived variables should be introduced as many as possible in fitting the regression model. ③No intercept term should be applied in fitting the regression models. ④The strategies of screening independent variables should be adopted as many as possible during fitting the regression models, such as “ forward selection” “ backward selection” and “ stepwise selection” . |
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