Main Content

Nonlinear Models

Nonlinear regression with multiple predictor variables


NonLinearModelNonlinear regression model class


fitnlmFit nonlinear regression model
dispDisplay nonlinear regression model
fevalEvaluate nonlinear regression model prediction
predictPredict response of nonlinear regression model
randomSimulate responses for nonlinear regression model
dummyvarCreate dummy variables
hougenHougen-Watson model
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
statsetCreate statistics options structure
statgetAccess values in statistics options structure
nlinfitNonlinear regression
nlintoolInteractive nonlinear regression
nlparciNonlinear regression parameter confidence intervals
nlpredciNonlinear regression prediction confidence intervals

Examples and How To

Nonlinear Regression Workflow

Import data, fit a nonlinear regression, test its quality, modify it to improve the quality, and make predictions based on the model.

Weighted Nonlinear Regression

This example shows how to fit a nonlinear regression model for data with nonconstant error variance.

Pitfalls in Fitting Nonlinear Models by Transforming to Linearity

This example shows pitfalls that can occur when fitting a nonlinear model by transforming to linearity.

Nonlinear Logistic Regression

This example shows two ways of fitting a nonlinear logistic regression model.


Nonlinear Regression

Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables.