The CALIS Procedure
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Overview
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Getting Started
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Syntax
Classes of Statements in PROC CALIS Single-Group Analysis Syntax Multiple-Group Multiple-Model Analysis Syntax PROC CALIS Statement BOUNDS Statement BY Statement COSAN Statement COV Statement DETERM Statement EFFPART Statement FACTOR Statement FITINDEX Statement FREQ Statement GROUP Statement LINCON Statement LINEQS Statement LISMOD Statement LMTESTS Statement MATRIX Statement MEAN Statement MODEL Statement MSTRUCT Statement NLINCON Statement NLOPTIONS Statement OUTFILES Statement PARAMETERS Statement PARTIAL Statement PATH Statement PCOV Statement PVAR Statement RAM Statement REFMODEL Statement RENAMEPARM Statement SAS Programming Statements SIMTESTS Statement STD Statement STRUCTEQ Statement TESTFUNC Statement VAR Statement VARIANCE Statement VARNAMES Statement WEIGHT Statement
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Details
Input Data Sets Output Data Sets The COSAN Model The FACTOR Model The LINEQS Model The LISMOD Model and Submodels The MSTRUCT Model The PATH Model The RAM Model Naming Variables and Parameters Setting Constraints on Parameters Automatic Variable Selection Estimation Criteria Relationships among Estimation Criteria Gradient, Hessian, Information Matrix, and Approximate Standard Errors Counting the Degrees of Freedom Assessment of Fit Total, Direct, and Indirect Effects Standardized Solutions Modification Indices Missing Values and the Analysis of Missing Patterns Measures of Multivariate Kurtosis Initial Estimates Use of Optimization Techniques Computational Problems Displayed Output ODS Table Names ODS Graphics
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Examples
Estimating Covariances and Correlations Estimating Covariances and Means Simultaneously Testing Uncorrelatedness of Variables Testing Covariance Patterns Testing Some Standard Covariance Pattern Hypotheses Linear Regression Model Multivariate Regression Models Measurement Error Models Testing Specific Measurement Error Models Measurement Error Models with Multiple Predictors Measurement Error Models Specified As Linear Equations Confirmatory Factor Models Confirmatory Factor Models: Some Variations The Full Information Maximum Likelihood Method Comparing the ML and FIML Estimation Path Analysis: Stability of Alienation Simultaneous Equations with Mean Structures and Reciprocal Paths Fitting Direct Covariance Structures Confirmatory Factor Analysis: Cognitive Abilities Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis Testing Equality of Covariance and Mean Matrices between Independent Groups Illustrating Various General Modeling Languages Testing Competing Path Models for the Career Aspiration Data Fitting a Latent Growth Curve Model Higher-Order and Hierarchical Factor Models Linear Relations among Factor Loadings Multiple-Group Model for Purchasing Behavior Fitting the RAM and EQS Models by the COSAN Modeling Language Second-Order Confirmatory Factor Analysis Linear Relations among Factor Loadings: COSAN Model Specification Ordinal Relations among Factor Loadings Longitudinal Factor Analysis
- References
| VARNAMES Statement |
- VARNAMES name_assignment <, name_assignment ...> ;
- VARNAME name_assignment <, name_assignment ...> ;
- VNAMES name_assignment <, name_assignment ...> ;
where name_assignment represents one of the following forms:
matrix_name variable_names
matrix_name = [variable_names]
matrix_name = matrix_name
You can use the VARNAMES statement in connection with the COSAN modeling language to assign variable names for matrices. The matrix_name refers to any matrix you define in the COSAN statement. The variable_names that follow the matrix_name are assigned to the column variables of the matrix of interest. This applies to the first two types of VARNAMES specifications. For example,
varnames F f1-f3;
is exactly the same as
varnames F = [ f1-f3 ];
Both of these assign f1, f2, and f3 as the names for the first three column variables of matrix F.
You can also use another kind of name_assignment in connection with a COSAN statement. Two matrix names equated by an equal sign assign the column names of the matrix on the right-hand side to the column names of the matrix on the left-hand side. This assignment assumes that the column names of at least one of the two matrices are already defined. For example, assuming that 
and 
are model matrices defined in a COSAN statement, the following VARNAMES statement specification specifies that both and have the same set of column variable names V1–V6 and F1–F3:
varnames J = [ V1-V6 F1-F3 ] ,
A = J ;
This is the same as the following specification:
varnames J = [ V1-V6 F1-F3 ] ,
A = [ V1-V6 F1-F3 ] ;
The VARNAMES statement appears to enable you to specify only the column variable names for matrices. However, PROC CALIS also uses these column variable names to assign row variable names of the related matrices in the covariance and mean structure formulas for the COSAN model. PROC CALIS uses the following rules to determine the row variable names of a matrix in the model:
If a matrix is the first matrix of any term in the covariance or mean structure formula, the row variable names are the names of the manifest variables.
If a matrix is the central covariance matrix of any term in the covariance structure formula, the row variable names are the same as the column variable names.
For any other matrices, the row variable names are the same as the column variable names of the preceding matrix in the multiplicative formula for the covariance or mean structures.