The Fit Curve Report | JMP 13.2

JMP Support 800.450.0135 (US)

Documentation Feedback
Your feedback is important to us. us any comments about our documentation.

Predictive and Specialized Modeling • Fit Curve • The Fit Curve Report

•

The Fit Curve Report

The Fit Curve report initially contains only a plot of Y versus X (Fit Curve Reports: No Grouping Variable (left) and with Group Variable (right)). If you specify a Group variable, the report includes overlaid and individual plots for each group of the fitted model (shown in Fit Curve Reports: No Grouping Variable (left) and with Group Variable (right) on the right).

Fit Curve Reports: No Grouping Variable (left) and with Group Variable (right)

Select any of the following built-in models from the Fit Curve red triangle menu:

Fits first degree to fifth degree polynomials.

Fits Logistic, Probit, and Gompertz models. These models are S-shaped and have both upper and lower asymptotes. The Logistic 2P, 3P, and 4P and Probit 2P and 4P models are symmetric. The Logistic 5P and both Gompertz models are not symmetric. The Logistic 2P is available only when the response is between 0 and 1. Examples of Sigmoid curves include learning curves and modeling tumor growth, both of which increase initially and then taper off.

Exponential Growth and Decay

Fits Exponential, Biexponential, and Mechanistic Growth models. The Exponential 2P and 3P are similar, but the 3P model has an asymptote. The Biexponential models assume there are two separate growth or decay processes. The Mechanistic Growth and Exponential 3P models always increase, but the rate of growth slows so that the model has an asymptote. Examples of exponential growth and decay functions are virus spread and drug half-life, respectively.

Fits Gaussian Peak and Lorentzian Peak models. These models increase up to a peak and then decrease. The Gaussian Peak model is a scaled version of the Gaussian probability density function (PDF). The Lorentzian Peak model is a scaled version of the Cauchy distribution, a continuous probability distribution. These models can be used for some chemical concentration assays and artificial neural networks.

Pharmacokinetic Models

Fits the One Compartment Oral Dose model, the Two Compartment IV Bolus Dose model, and the Biexponential 4P model. This option is used to model the concentration of drugs in the body.

Fit Michaelis-Menten

Fits the Michaelis-Menten biochemical kinetics model, which relates the rate of enzymatic reactions to substrate concentration.

See Local Data Filter, Redo Menus, and Save Script Menusin the Using JMP book for more information about the following options:

Local Data Filter

Shows or hides the local data filter that enables you to filter the data used in a specific report.

Contains options that enable you to repeat or relaunch the analysis. In platforms that support the feature, the Automatic Recalc option immediately reflects the changes that you make to the data table in the corresponding report window.

Contains options that enable you to save a script that reproduces the report to several destinations.

Save By-Group Script

Contains options that enable you to save a script that reproduces the platform report for all levels of a By variable to several destinations. Available only when a By variable is specified in the launch window.

•

Help created on 9/19/2017