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New developments for net‐effect plots

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Abstract We review the graphics for studying the net‐effects of predictors, including both the global and local net‐effect plots. Then some new definitions of net‐effects and corresponding graphical methods are introduced for studying and visualizing the main and interaction net‐effects of the mean functions and the distribution functions. A sufficient dimension reduction method, called central solution subspace bivariate sliced inverse regression (CSS‐BiSIR), is proposed for reducing the size of the graphical problem. This facilitates the graphical interpretations of the net‐effects, and also allows us to visualize the net‐effects of nonelliptically distributed predictors. WIREs Comput Stat 2013, 5:105–113. doi: 10.1002/wics.1247 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Dimension Reduction Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization

(a) Marginal scatterplot of {Y ,X2}; (b) net‐effect plot of {Y ,X2} on X1, slicing on X1 and linking to the scatterplot of {Y,X2}, where three slices are classified, as indicated by different colors and icons.

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Comparison between net‐effect plots from CSS‐BiSIR (c and d) and the classical method BiSIR (b).

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Marginal plot (a), mean and distributional main effect plot (b), mean interaction net‐effect plot (c), and distributional interaction net‐effect plot (d) for nonelliptically distributed predictors.

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Scatterplot matrix of nonelliptically distributed predictors.

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Combining local net‐effect plot (a) to form a global net‐effect plot (b).

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Statistical and Graphical Methods of Data Analysis > Dimensional Reduction
Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization

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