Create a Generalized Linear Model

A generalized linear model (GLM) is an extension of a traditional linear model that allows the population mean to depend on a linear predictor through a nonlinear link function. A GLM requires that you specify a distribution and a link function. The distribution should match the distribution of the response variable. The link function is used to relate the response variable to the effect variables.
To create the GLM for this example, complete the following steps:
  1. In the Data pane, select the variable Emission of Total Hydrocarbons (g/mi). Click Show actions, and select Measure Details.
  2. In the Measure Details window, notice that the distribution of emissions of total hydrocarbons (g/mi) is not normal. The shape of the distribution suggests that an exponential distribution might be appropriate for the GLM.
    Close the Measure Details window.
  3. Click New Visualization to create a new visualization.
  4. Click new generalized linear model to specify that this visualization is a GLM. Maximize the visualization.
  5. Drag and drop the variable Emission of Total Hydrocarbons (g/mi) into the Response field on the Roles tab.
  6. Select the Properties tab in the right pane. Select Informative missingness.
  7. For the Distribution property, select Exponential.
  8. Drag and drop the variables Vehicle Clusters, Vehicle Manufacturer, Vehicle Axle Ratio, Vehicle Cylinders, Vehicle Gears, Vehicle MPG, and Vehicle Weight (lbs) onto the visualization. SAS Visual Analytics automatically creates a GLM using these variables as the effects.
  9. Drag and drop the variable Vehicle Type into the Group By field on the Roles tab. This specifies that Vehicle Type is used as a segmentation variable.
    The results windows are updated. As with the linear regression, separate models are created based on a vehicle’s classification as a car, a truck, or both.
  10. Save the exploration.
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Last updated: August 16, 2017