Use to build non-linear models describing the relationship between an explanatory variable and an outcome variable.
Fit Curve
- Select Analyze > Specialized Modeling > Fit Curve.
- Select a continuous variable from Select Columns, and add to Y, Response.
- Select a continuous explanatory variable for X, Regressor
Add a categorical variable to Group to have a separate model fit for each value of a grouping variable (optional). Here we have eight specimens sampled (two from each of four treatments).
- Click OK.
JMP displays a plot of the response vs. predictor values for all the data, and separate graphs for each group if a grouping variable was used.
Note: The red triangle next to Fit Curve displays a collection of non-linear models that can be fit.
See “Statistical Details for the Fit Curve Platform” in JMP Help to see visual representations for different families of nonlinear models to help choose an appropriate one for your data.
The 4-parameter logistic sigmoid model was chosen here. Additional models can be fit and compared.
- Select Custom Inverse Prediction under the red triangle.
Insert values for the response variable (Algae Density) to produce predictions and corresponding confidence intervals for the value of the predictor variable (Days) estimated to result in a given amount of the response variable. Here we chose an Algae Density of 2.
JMP displays predicted values and confidence intervals for the explanatory variable. An Analysis of Means graph comparing these predictions across groups is produced if a grouping variable was used.
Additonal options, such as Actual by Predicted and Residual by Predicted plots, Save Prediction and Residual Formula, are under the red triangle for each fitted model.
Visit Predictive and Specialized Modeling > Fit Curve and Nonlinear Regression in JMP Help to learn more.