Nonlinear Models
Nonlinear regression with multiple predictor variables
Classes
NonLinearModel |
Nonlinear regression model class |
Functions
fitnlm |
Fit nonlinear regression model |
disp |
显示非线性回归模型 |
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 |
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.
Concepts
- Nonlinear Regression
Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables.