|Nonlinear regression model class|
|Fit nonlinear regression model|
|Display nonlinear regression model|
|Evaluate nonlinear regression model prediction|
|Predict response of nonlinear regression model|
|Simulate responses for nonlinear regression model|
|Create dummy variables|
|Compute partial dependence|
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
|Create statistics options structure|
|Access values in statistics options structure|
Import data, fit a nonlinear regression, test its quality, modify it to improve the quality, and make predictions based on the model.
This example shows how to fit a nonlinear regression model for data with nonconstant error variance.
This example shows pitfalls that can occur when fitting a nonlinear model by transforming to linearity.
This example shows two ways of fitting a nonlinear logistic regression model.
Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables.