# 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 `nlintool`.

## Functions

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 `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 (Since R2020b) `plotPartialDependence` Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots `plotResiduals` Plot residuals of nonlinear regression model
 `nlinfit` Nonlinear regression `nlintool` Interactive nonlinear regression `nlparci` Nonlinear regression parameter confidence intervals `nlpredci` Nonlinear regression prediction confidence intervals
 `nlmefit` Nonlinear mixed-effects estimation `nlmefitsa` Fit nonlinear mixed-effects model with stochastic EM algorithm
 `dummyvar` Create dummy variables `hougen` Hougen-Watson model `statset` Create statistics options structure `statget` Access values in statistics options structure

## Objects

 `NonLinearModel` Nonlinear regression model

## Topics

### 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 the `stats` structure, which is returned by both `nlmefit` and `nlmefitsa`, to determine the quality of your model.