• Previous Page
  • |
  • Next Page
Missing Values
Previous Page | Next Page

The GAM Procedure

  • Overview
  • Getting Started
  • Syntax Procedure Syntax
    PROC GAM Statement BY Statement CLASS Statement FREQ Statement MODEL Statement OUTPUT Statement SCORE Statement
  • Details Procedure Details
    Missing Values Nonparametric Regression Additive Models and Generalized Additive Models Forms of Additive Models Estimates from PROC GAM Backfitting and Local Scoring Algorithms Smoothers Selection of Smoothing Parameters Confidence Intervals for Smoothers Distribution Family and Canonical Link Dispersion Parameter Computational Resources ODS Table Names ODS Graphics
  • Examples Procedure Examples
    Generalized Additive Model with Binary Data Poisson Regression Analysis of Component Reliability Comparing PROC GAM with PROC LOESS
  • References
 
Missing Values

When fitting a model, PROC GAM excludes any observation with missing values for an explanatory variable, offset variable, or dependent variable. However, if only the response is missing, predicted values can be computed and output to a data set by using the OUTPUT or SCORE statement.

Previous Page | Next Page | Top of Page
Copyright © SAS Institute Inc. All rights reserved.
  • Previous Page
  • |
  • Next Page
  • |
  • Top of Page