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Régression non linéaire
R2026aModèles de régression non linéaire à effets fixes et mixtes
Dans un modèle de régression non linéaire, il n’est pas nécessaire d’exprimer la variable de réponse comme une combinaison linéaire des coefficients du modèle et des variables prédictives. Vous pouvez effectuer une telle régression avec ou sans l’objet NonLinearModel ou avec l’outil interactif Nonlinear Regression Fitter Tool.
Fonctions
Objets
NonLinearModel | Nonlinear regression model |
Rubriques
Modèles non linéaires
- 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.
Effets mixtes
- 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.
