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
| Missing Values |
Observations with missing values for any variable listed in the MODEL or POPULATION statement are omitted from the analysis.
If the WEIGHT variable for an observation has a missing value, the observation is by default omitted from the analysis. You can modify this behavior by specifying the MISSING= option in the MODEL statement. The option MISSING=value sets all missing weights to value and all missing cells to value. The option MISSING=SAMPLING causes all missing cells in a contingency table to be treated as sampling zeros.
Any observation with nonpositive weight is also, by default, omitted from the analysis. If it has zero weight, then you can specify the ZERO= option in the MODEL statement.