Predictive and Specialized Modeling > Fit Curve > Overview of the Fit Curve Platform
Publication date: 04/12/2021

Overview of the Fit Curve Platform

Some models are linear in the parameters (for example, a quadratic or other polynomial); others can be transformed to be such (for example, when you use a log transformation of x). The Fit Model or Fit Y by X platforms are more appropriate in these situations. An example in Example of a Regression Analysis Using Fit Model in Fitting Linear Models shows a significant linear relationship between oxygen uptake and time spent running. For more information about Fit Model, see Model Specification in Fitting Linear Models. For more information about Fit Y by X, see Introduction to Fit Y by X in Basic Analysis.

The Fit Curve platform enables you to fit models that are nonlinear in the parameters. The initial example in this chapter shows the analysis of a nonlinear relationship: drug toxicity as a function of concentration. The effect of concentration on toxicity changes from low to high doses, so this relationship is nonlinear.

The following are examples of equations for linear and nonlinear functions.

Linear function: Equation shown here

Nonlinear function: Equation shown here

The Fit Curve platform provides predefined models, such as polynomial, logistic, probit, Gompertz, exponential, peak, and pharmacokinetic models. Specifying a grouping variable lets you estimate separate model parameters for each level of the grouping variable. The fitted models and estimated parameters can be compared across the levels of the grouping variable.

Fit Curve also enables you to build a model to create the prediction formula. Then you set upper and lower parameter limits in Nonlinear. See Example of Setting Parameter Limits. In JMP Pro, you can also specify a set of supplementary variables and fit a generalized regression model within the Fit Curve platform to determine how these variables affect the response.

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