Modelos no lineales

Regresión no lineal con múltiples variables predictoras

Clases

NonLinearModel Nonlinear regression model class

`fitnlm`	Ajustar modelo de regresión no lineal
`disp`	Display nonlinear regression model
`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
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`statset`	Create statistics options structure
`statget`	Access values in statistics options structure

`nlinfit`	Regresión no lineal
`nlintool`	Interactive nonlinear regression
`nlparci`	Nonlinear regression parameter confidence intervals
`nlpredci`	Nonlinear regression prediction confidence intervals

Import data, fit a nonlinear regression, test its quality, modify it to improve the quality, and make predictions based on the model.

En este ejemplo se muestra cómo ajustar un modelo de regresión no lineal para datos con varianza de error no constante.

This example shows pitfalls that can occur when fitting a nonlinear model by transforming to linearity.

En este ejemplo se muestran dos formas de ajustar un modelo de regresión logística no lineal.

Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables.