Nonlinear Regression
Nonlinear fixed- and mixed-effects regression models
In a nonlinear regression model, the response variable does not need to be
expressed as a linear combination of model coefficients and predictor variables.
You can perform a nonlinear regression with or without the
NonLinearModel object or by using the interactive tool
Nonlinear Regression
Fitter.
Functions
Objects
NonLinearModel | Nonlinear regression model |
Topics
Nonlinear Models
- Nonlinear Regression
Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables. - 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.
Mixed Effects
- Mixed-Effects Models
Mixed-effects models account for both fixed effects (which represent population parameters, assumed to be the same each time data is collected) and random effects (which act like additional error terms). - Mixed-Effects Models Using nlmefit and nlmefitsa
Fit a mixed-effects model, plot predictions and residuals, and interpret the results. - Examining Residuals for Model Verification
Examine thestatsstructure, which is returned by bothnlmefitandnlmefitsa, to determine the quality of your model.
