Regression Analysis with R

Beyond Linearity – When Curving Is Much Better

Some problems cannot be solved with linear models. Often, we must go beyond the simple linearity of models by introducing features that take into account the complexity of the phenomenon. Nonlinear models are more complex (and more prone to overfitting), but sometimes they are the only solution.

In this chapter, we will see an introduction to the most used ones, how to train them, and how to apply them. First, a nonlinear least squares method will be treated, where the parameters of the regression function to be estimated are nonlinear. In this technique, given the nonlinearity of the coefficients, the solution of the problem occurs by means of iterative numerical calculation methods. Then