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 Using
|Fit nonlinear regression model
|Evaluate nonlinear regression model prediction
|Predict response of nonlinear regression model
|Simulate responses for nonlinear regression model
|Compute partial dependence (Since R2020b)
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
|Plot residuals of nonlinear regression model
Nonlinear Regression Without Using Object
|Nonlinear regression model
- 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 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
statsstructure, which is returned by both
nlmefitsa, to determine the quality of your model.