plotSlice

Load the carsmall sample data set.

load carsmall.mat

The variables Cylinders, MPG, and Weight contain data for the number of engine cylinders, miles per gallon, and weight for each car.

Create a table from the data in Cylinders, MPG, and Weight by using the categorical and table functions. Fit a generalized linear regression model using Cylinders and MPG as predictors and Weight as the response.

Cylinders = categorical(Cylinders);
tbl = table(Cylinders,MPG,Weight,VariableNames=["Cylinders" "MPG" "Weight"]);
mdl = fitglm(tbl,"Weight ~ MPG + Cylinders");

mdl is a GeneralizedLinearModel object that contains the result of fitting a linear regression model to the data.

Create a slice plot for mdl.

plotSlice(mdl)

Each plot shows Weight as a function of a single predictor variable, Cylinders in the plot on the left and MPG in the plot on the right. The predictor is fixed at the value shown in the x-axis of each plot. The 95% confidence intervals for the categorical predictor Cylinders are represented by error bars. The 95% confidence intervals for the continuous predictor MPG 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 Cylinders to 4.

The right plot shows that cars with four cylinders have smaller values for Weight, relative to cars with six cylinders, across all values of MPG.

Load the patients sample data set.

load patients.mat

The variables Age, Weight, and Smoker contain data for the age, weight, and smoking status of each patient.

Create a table from the data in Age, Weight, and Smoker by using the table function. Fit a generalized linear regression model to the data using Age and Weight as predictors and Smoker as the response.

tbl = table(Age,Weight,Smoker,VariableNames=["Age" "Weight" "Smoker"]);
mdl = fitglm(tbl,"Smoker ~ Age*Weight");

mdl is a GeneralizedLinearModel object that contains the results of fitting a linear regression model to the data.

Generate new predictor data for patient age and weight.

Age_New = 25:35;
Weight_New = 100:10:200;
Xnew = [Age_New; Weight_New]';

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

plotSlice(mdl,Xnew)

The left plot shows Smoker as a function of Age with Weight fixed at 150. The right plot shows Smoker as a function of Weight with Age fixed at 30.

Load the census sample data set.

load census

The variables pop and cdate contain data for the population size and the year the census was taken, respectively.

Fit a generalized linear regression model using cdate as the predictor and pop as the response.

mdl = fitglm(cdate,pop);

mdl is a GeneralizedLinearModel object that contains the fitted linear regression model.

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.