The CATMOD Procedure
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Overview
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Getting Started
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Syntax
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Details
Missing Values Input Data Sets Ordering of Populations and Responses Specification of Effects Output Data Sets Logistic Analysis Log-Linear Model Analysis Repeated Measures Analysis Generation of the Design Matrix Cautions Computational Method Computational Formulas Memory and Time Requirements Displayed Output ODS Table Names
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Examples
Linear Response Function, r=2 Responses Mean Score Response Function, r=3 Responses Logistic Regression, Standard Response Function Log-Linear Model, Three Dependent Variables Log-Linear Model, Structural and Sampling Zeros Repeated Measures, 2 Response Levels, 3 Populations Repeated Measures, 4 Response Levels, 1 Population Repeated Measures, Logistic Analysis of Growth Curve Repeated Measures, Two Repeated Measurement Factors Direct Input of Response Functions and Covariance Matrix Predicted Probabilities
- References
| Types of Input Data |
The data that PROC CATMOD analyzes are usually supplied in one of two ways. First, you can supply raw data, where each observation is a subject. Second, you can supply cell count data, where each observation is a cell in a contingency table. (A third way, which uses direct input of the covariance matrix, is also available; details are given in the section Inputting Response Functions and Covariances Directly.)
Suppose detergent brand preference is related to three other categorical variables: water softness, water temperature, and previous use of a brand of detergent. In the raw data case, each observation in the input data set identifies a given respondent in the study and contains information about all four variables. The data set contains the same number of observations as the survey had respondents. In the cell count case, each observation identifies a given cell in the four-way table of water softness, water temperature, previous use of brand, and brand preference. A fifth variable contains the number of respondents in the cell. In the analysis, this fifth variable is identified in a WEIGHT statement. The data set contains the same number of observations as the number of cross-classifications formed by the four categorical variables. For more about this particular example, see Example 29.1. For additional details, see the section Input Data Sets.
Most of the examples in this chapter use cell counts as input and use a WEIGHT statement.