非线性回归

非线性固定效应回归模型和非线性混合效应回归模型

在非线性回归模型中，响应变量不需要表示为模型系数和预测变量的线性组合。您可以使用或不使用 NonLinearModel 对象来执行非线性回归，也可以使用交互式工具 Nonlinear Regression Fitter 来执行非线性回归。

函数

`fitnlm`	Fit nonlinear regression model
`feval`	Evaluate nonlinear regression model prediction
`predict`	Predict response of nonlinear regression model
`random`	Simulate responses for nonlinear regression model
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`profileLikelihood`	Profile loglikelihood for nonlinear regression model (自 R2025a 起)
`plotProfileLikelihood`	Plot profile loglikelihood for nonlinear regression model (自 R2025a 起)
`plotResiduals`	Plot residuals of nonlinear regression model

`nlinfit`	非线性回归
`Nonlinear Regression Fitter`	Interactive nonlinear regression fitting
`nlparci`	Nonlinear regression parameter confidence intervals
`nlpredci`	Nonlinear regression prediction confidence intervals

`nlmefit`	Fit nonlinear mixed-effects estimation
`nlmefitsa`	Fit nonlinear mixed-effects model with stochastic EM algorithm

`dummyvar`	Create dummy variables
`hougen`	豪根-瓦特森模型
`statset`	Create statistics options structure
`statget`	Access values in statistics options structure

NonLinearModel 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.
加权非线性回归
此示例说明如何将非线性回归模型与具有非常量误差方差的数据进行拟合。
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
Examine the stats structure, which is returned by both nlmefit and nlmefitsa, to determine the quality of your model.