The CATMOD Procedure
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Overview
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Getting Started
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Syntax
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DetailsMissing ValuesInput Data SetsOrdering of Populations and ResponsesSpecification of EffectsOutput Data SetsLogistic AnalysisLog-Linear Model AnalysisRepeated Measures AnalysisGeneration of the Design MatrixCautionsComputational MethodComputational FormulasMemory and Time RequirementsDisplayed OutputODS Table Names
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ExamplesLinear Response Function, r=2 ResponsesMean Score Response Function, r=3 ResponsesLogistic Regression, Standard Response FunctionLog-Linear Model, Three Dependent VariablesLog-Linear Model, Structural and Sampling ZerosRepeated Measures, 2 Response Levels, 3 PopulationsRepeated Measures, 4 Response Levels, 1 PopulationRepeated Measures, Logistic Analysis of Growth CurveRepeated Measures, Two Repeated Measurement FactorsDirect Input of Response Functions and Covariance MatrixPredicted Probabilities
- References
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.