plotSlice

Load the reaction sample data set.

load reaction

The reactants variable contains data for the partial pressures of three chemicals, and the rate variable contains data for the corresponding reaction rates.

Use the hougen function to fit a Hougen-Watson model to the data. Specify reactants as the predictors and rate as the response.

mdl = fitnlm(reactants,rate,@hougen,[1 .05 .02 .1 2]);

mdl is a NonLinearModel object that contains the results of fitting a nonlinear regression model to the data.

Create a slice plot for mdl.

plotSlice(mdl)

Each plot shows rate as a function of a single predictor variable, X1, X2, and X3. Each predictor is fixed at the value shown in the x-axis of each plot. In each plot, the 95% confidence intervals are represented by a blue shaded region. The dashed lines indicate a selected point, and the table to the left of the plots shows the response value and confidence bounds for the selected point. You can select a different point by changing the predictor values in the x-axis labels or by dragging the dashed lines to a different location.

Change the value of X1 to 450.

The plots show that increasing X1 to 450 shifts the regression curves for X2 and X3 toward smaller values for rate.

Load the carbig sample data set.

load carbig

The variables Horsepower and Weight contain data for the horsepower and weight of each car. The variable MPG contains data for miles per gallon.

Create a matrix of predictor data from Horsepower and Weight and define a nonlinear model by using an anonymous function.

X = [Horsepower,Weight];
modelfun = @(b,x)b(1)+b(2)*x(:,1).^b(3)+...
    b(4)*x(:,2).^b(5);

modelfun is a handle to the nonlinear model.

Fit modelfun to the predictor and response data.

mdl = fitnlm(X,MPG,modelfun,[-50 500 -1 500 -1],VarNames=["Horsepower" "Weight" "MPG"]);

mdl is a NonLinearModel object that contains the results of fitting modelfun to the data.

Generate new predictor data for horsepower and weight.

Horsepower_new = 100:200;
Weight_new = 3000:10:4000;
Xnew = [Horsepower_new' Weight_new'];

Create a slice plot for mdl using the new predictor data.

plotSlice(mdl,Xnew)

The left plot shows MPG as a function of Horsepower with Weight fixed at 3500. The right plot shows MPG as a function of Weight with Horsepower fixed at 150. The Confidence Interval Type options indicate that the confidence intervals are simultaneous and for the fitted responses. For more information, see Tips.

Load the carbig sample data set.

load carbig

The variables Horsepower and MPG contain data for the horsepower and miles per gallon of each car.

Define a nonlinear model by using an anonymous function.

modelfun = @(b,x)b(1)+b(2)*x.^b(3);

modelfun is a handle to the nonlinear model.

Fit modelfun to the predictor and response data.

mdl = fitnlm(Horsepower,MPG,modelfun,[-50 500 -1],VarNames=["Horsepower" "MPG"]);

mdl is a NonLinearModel object that contains the results of fitting modelfun to the data.

Create a slice plot for mdl.

plotSlice(mdl)

Because mdl has only one predictor, the slice plot displays a single set of axes containing a scatter plot of the training data. The regression line for mdl is shown in red together with a shaded region representing the 95% confidence intervals. The Confidence Interval Type options indicate that the confidence intervals are simultaneous and for the fitted responses. For more information, see Tips.