비선형 모델

다중 예측 변수를 사용하는 비선형 회귀

클래스

NonLinearModel Nonlinear regression model class

`fitnlm`	비선형 회귀 모델 피팅
`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 모델
`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

`nlinfit`	비선형 회귀
`nlintool`	Interactive nonlinear regression
`nlparci`	Nonlinear regression parameter confidence intervals
`nlpredci`	Nonlinear regression prediction confidence intervals

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.
가중 비선형 회귀
이 예제에서는 상수가 아닌 오차 분산을 갖는 데이터에 비선형 회귀 모델을 피팅하는 방법을 보여줍니다.
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.
비선형 로지스틱 회귀
이 예제에서는 비선형 로지스틱 회귀 모델을 피팅하는 두 가지 방법을 보여줍니다.

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