Abstract
We now focus on models for the joint effect of two continuous predictor variables. Additive models are convenient, but there is no reason to assume that they are always adequate. In the general bivariate models studied in this chapter, the joint effect of the two variables is a smooth, but otherwise unrestricted, function of these variables. Therefore, these models allow interactions so that the effect of one predictor depends upon the value of the other predictor.
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Harezlak, J., Ruppert, D., Wand, M.P. (2018). Bivariate Function Extensions. In: Semiparametric Regression with R. Use R!. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8853-2_5
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