Nonlinear Regression Fitter Tool

Interactive nonlinear regression fitting

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Description

The Nonlinear Regression Fitter tool provides a graphical user interface for simple nonlinear fitting with the nlinfit function. For more complex workflows, you can use plotSlice with the fitnlm function (see Nonlinear Regression Workflow). The interface displays plots of the fitted response against each predictor, with the other predictors held fixed. Dashed red curves show 95% simultaneous confidence bounds for the function. The fixed values are displayed in the text boxes below each predictor axis. Change the fixed values by entering new values or by dragging the vertical lines in the plots to new positions. When you change the value of a predictor, the tool updates all plots to display the model at the new point in the predictor space. Use the Export button to export specified variables to the workspace.

Required Products

  • MATLAB®

  • Statistics and Machine Learning Toolbox™

Nonlinear Regression Fitter tool

Open the Nonlinear Regression Fitter Tool

  • At the MATLAB command prompt, enter nlintool.

Examples

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Open Live Script

Perform nonlinear regression and display fitted model responses for different predictor values using the Nonlinear Regression Fitter tool.

Load the reaction kinetics data set. The observations in reactants are partial pressures of the three chemical reactants listed in xn. The corresponding reaction rates (responses) are stored in rate.

load reaction

Fit the Hougen-Watson model to the data using the initial model coefficient values in beta. Specify to show 99% confidence bounds and to label the plot axes.

nlintool(reactants,rate,@hougen,beta,Alpha=0.01,XName=xn,YName=yn)

Figure Nonlinear Fit contains 3 axes objects and other objects of type uimenu, uicontrol. Axes object 1 contains 5 objects of type line. Axes object 2 contains 5 objects of type line. Axes object 3 contains 5 objects of type line.

The tool displays plots of the fitted response against each predictor. The solid green curve shows the predicted response for that predictor when the other predictor values are fixed. You can change the fixed values by entering new values in the text boxes, or by dragging the vertical lines in the plots to new positions. When you change the value of a predictor the tool updates all plots to display the model at the new point in the predictor space. The dashed red lines indicate the 99% confidence bounds.

Related Examples

Programmatic Use

nlintool opens the Nonlinear Regression Fitter tool with a nonlinear Hougen-Watson model (hougen) fit to sample reaction rate data (see Nonlinear Regression Fitting with Interactive Tool).

nlintool(X,Y,modelfun,beta0) opens the Nonlinear Regression Fitter tool and displays the fitted response Y against each predictor in X, with the other predictors held fixed. The software generates the fitted responses using the model specified by modelfun and the initial coefficient values beta0. You must specify modelfun as a function handle (see modelfun). Dashed red curves show 95% simultaneous confidence bounds for the function.

nlintool(___,Name=Value) specifies additional options using one or more of the following name-value arguments.

  • Alpha — Significance level for the confidence bounds, specified as a nonnegative scalar. Dashed red curves show 100(1 – Alpha)% simultaneous confidence bounds for the function. The default value of Alpha is 0.05.

  • XName — Predictor names, specified as a string array, cell array of strings, or character array.

  • YName — Response name, specified as a string scalar, cell array containing one character vector, or character vector.

  • PlotData — Flag to display the data on the plot, specified as "on" or "off". If X contains more than one predictor, then you cannot specify PlotData="on".

Tips

  • Use the Bounds menu in the tool window to select the type of confidence bounds: simultaneous or non-simultaneous, and curve or observation.

    • Simultaneous or Non-Simultaneous

      • Simultaneous (default) — nlintool computes confidence bounds for the curve of the response values using Scheffé's method. The range between the upper and lower confidence bounds contains the curve consisting of true response values with 95% confidence.

      • Non-Simultaneous — nlintool computes confidence bounds for the response value at each observation. The confidence interval for a response value at a specific predictor value contains the true response value with 95% confidence.

      With simultaneous bounds, the entire curve of true response values is within the bounds at high confidence. By contrast, non-simultaneous bounds require only the response value at a single predictor value to be within the bounds at high confidence. Therefore, simultaneous bounds are wider than non-simultaneous bounds.

    • Curve or Observation

      A regression model for the predictor variables X and the response variable Y has the form

      Y = f(X) + ε,

      where f is a function of X and ε is a random noise term.

      • Curve (default) — nlintool plots confidence bounds for the fitted responses f(X).

      • Observation — nlintool plots confidence bounds for the response observations Y.

      The bounds for Y are wider than the bounds for f(X) because of the noise term.

    • If you do not want the plot to display confidence bounds, you can select No Bounds.

Version History

Introduced before R2006a

See Also

Functions

Classes

Tools

Topics