Nonlinear Models

Nonlinear regression with multiple predictor variables

Classes

NonLinearModel Nonlinear regression model class

`fitnlm`	Fit nonlinear regression model
`disp`	Display nonlinear regression model
`feval`	Evaluate nonlinear regression model prediction
`predict`	Predict response of nonlinear regression model
`random`	Simulate responses for nonlinear regression model

`dummyvar`	Create dummy variables
`hougen`	Hougen-Watson model
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`statset`	Create statistics options structure
`statget`	Access values in statistics options structure

`nlinfit`	Nonlinear regression
`nlintool`	Interactive nonlinear regression
`nlparci`	Nonlinear regression parameter confidence intervals
`nlpredci`	Nonlinear regression prediction confidence intervals

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.