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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 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 |
Import data, fit a nonlinear regression, test its quality, modify it to improve the quality, and make predictions based on the model.
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.
This example shows two ways of fitting a nonlinear logistic regression model.
Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables.