DATA= SAS-data-set

A TYPE=COV, TYPE=UCOV, TYPE=CORR, or TYPE=UCORR data set can be created by the CORR procedure or various other procedures. It contains means, standard deviations, the sample size, the covariance or correlation matrix, and possibly other statistics depending on which procedure is used.

If your data set has many observations and you plan to run PROC CALIS several times, you can save computer time by first creating a TYPE=COV, TYPE=UCOV, TYPE=CORR, or TYPE=UCORR data set and using it as input to PROC CALIS. For example, assuming that PROC CALIS is first run with an OUTRAM=MOD option, you can run the following statements:

   * create TYPE=COV data set;
   proc corr cov nocorr data=raw outp=cov(type=cov);
   run;
   * analysis using correlations;
   proc calis data=cov inram=mod;
   run;
   * analysis using covariances;
   proc calis cov data=cov inram=mod;
   run;

Most procedures automatically set the TYPE= option of an output data set appropriately. However, the CORR procedure sets TYPE=CORR unless an explicit TYPE= option is used. Thus, (TYPE=COV) is needed in the preceding PROC CORR request, since the output data set is a covariance matrix. If you use a DATA step with a SET statement to modify this data set, you must declare the TYPE=COV, TYPE=UCOV, TYPE=CORR, or TYPE=UCORR attribute in the new data set.

You can use a VAR statement with PROC CALIS when reading a TYPE=COV, TYPE=UCOV, TYPE=CORR, TYPE=UCORR, or TYPE=SSCP data set to select a subset of the variables or change the order of the variables.

Caution:Problems can arise from using the CORR procedure when there are missing data. By default, PROC CORR computes each covariance or correlation from all observations that have values present for the pair of variables involved ("pairwise deletion"). The resulting covariance or correlation matrix can have negative eigenvalues. A correlation or covariance matrix with negative eigenvalues is recognized as a singular matrix in PROC CALIS, and you cannot compute (default) generalized least squares or maximum likelihood estimates. You can specify the RIDGE option to ridge the diagonal of such a matrix to obtain a positive-definite data matrix. If the NOMISS option is used with the CORR procedure, observations with any missing values are completely omitted from the calculations ("listwise deletion"), and there is no possibility of negative eigenvalues (but still a chance for a singular matrix).

PROC CALIS can also create a TYPE=COV, TYPE=UCOV, TYPE=CORR, or TYPE=UCORR data set that includes all the information needed for repeated analyses. If the data set DATA=RAW does not contain missing values, the following statements should give the same PROC CALIS results as the previous example:

   * using correlations;
   proc calis data=raw outstat=cov inram=mod;
   run;
   * using covariances;
   proc calis cov data=cov inram=mod;
   run;

You can create a TYPE=COV, TYPE=UCOV, TYPE=CORR, TYPE=UCORR, or TYPE=SSCP data set in a DATA step. Be sure to specify the TYPE= option in parentheses after the data set name in the DATA statement, and include the _TYPE_ and _NAME_ variables. If you want to analyze the covariance matrix but your DATA= data set is a TYPE=CORR or TYPE=UCORR data set, you should include an observation with _TYPE_=STD giving the standard deviation of each variable. If you specify the COV option, PROC CALIS analyzes the recomputed covariance matrix:

   data correl(type=corr);
      input _type_ $ _name_ $ X1-X3;
      datalines;
   std   .   4.  2.  8.
   corr  X1  1.0  .   .
   corr  X2   .7 1.0  .
   corr  X3   .5  .4 1.0
   ;
   proc calis cov inram=model;
   run;

If you want to analyze the UCOV or UCORR matrix but your DATA= data set is a TYPE=COV or TYPE=CORR data set, you should include observations with _TYPE_=STD and _TYPE_=MEAN giving the standard deviation and mean of each variable.

INEST= SAS-data-set

You can use the INEST= (or INVAR= or ESTDATA=) input data set to specify the initial values of the parameters used in the optimization and to specify boundary constraints and the more general linear constraints that can be imposed on these parameters.

The variables of the INEST= data set must correspond to the following variables:

• a character variable _TYPE_ that indicates the type of the observation

• numeric variables with the parameter names used in the specified PROC CALIS model

• the BY variables that are used in a DATA= input data set

• a numeric variable _RHS_ (right-hand side) (needed only if linear constraints are used)

• additional variables with names corresponding to constants used in the programming statements

The content of the _TYPE_ variable defines the meaning of the observation of the INEST= data set. PROC CALIS recognizes observations with the following _TYPE_ specifications.

PARMS

specifies initial values for parameters that are defined in the model statements of PROC CALIS. The _RHS_ variable is not used. Additional variables can contain the values of constants that are referred to in programming statements. At the beginning of each run of PROC CALIS, the values of the constants are read from the PARMS observation initializing the constants in the program statements.

UPPERBD | UB

specifies upper bounds with nonmissing values. The use of a missing value indicates that no upper bound is specified for the parameter. The _RHS_ variable is not used.

LOWERBD | LB

specifies lower bounds with nonmissing values. The use of a missing value indicates that no lower bound is specified for the parameter. The _RHS_ variable is not used.

LE |  | <

specifies the linear constraint . The parameter values contain the coefficients , and the _RHS_ variable contains the right-hand-side . The use of a missing value indicates a zero coefficient .

GE |  | >

specifies the linear constraint . The parameter values contain the coefficients , and the _RHS_ variable contains the right-hand-side . The use of a missing value indicates a zero coefficient .

EQ | 

specifies the linear constraint . The parameter values contain the coefficients , and the _RHS_ variable contains the right-hand-side . The use of a missing value indicates a zero coefficient .

The constraints specified in the INEST=, INVAR=, or ESTDATA= data set are added to the constraints specified in BOUNDS and LINCON statements.

You can use an OUTEST= data set from a PROC CALIS run as an INEST= data set in a new run. However, be aware that the OUTEST= data set also contains the boundary and general linear constraints specified in the previous run of PROC CALIS. When you are using this OUTEST= data set without changes as an INEST= data set, PROC CALIS adds the constraints from the data set to the constraints specified by a BOUNDS and LINCON statement. Although PROC CALIS automatically eliminates multiple identical constraints, you should avoid specifying the same constraint a second time.

INRAM= SAS-data-set

This data set is usually created in a previous run of PROC CALIS. It is useful if you want to reanalyze a problem in a different way, such as by using a different estimation method. You can alter an existing OUTRAM= data set, either in the DATA step or using the FSEDIT procedure, to create the INRAM= data set describing a modified model. For more details on the INRAM= data set, see the section OUTRAM= SAS-data-set.

In the case of a RAM or LINEQS analysis of linear structural equations, the OUTRAM= data set always contains the variable names of the model specified. These variable names and the model specified in the INRAM= data set are the basis of the automatic variable selection algorithm performed after reading the INRAM= data set.

INWGT= SAS-data-set

This data set enables you to specify a weight matrix other than the default matrix for the generalized, weighted, and diagonally weighted least squares estimation methods. The specification of any INWGT= data set for unweighted least squares or maximum likelihood estimation is ignored. For generalized and diagonally weighted least squares estimation, the INWGT= data set must contain a _TYPE_ and a _NAME_ variable as well as the manifest variables used in the analysis. The value of the _NAME_ variable indicates the row index of the weight . For weighted least squares, the INWGT= data set must contain _TYPE_, _NAME_, _NAM2_, and _NAM3_ variables as well as the manifest variables used in the analysis. The values of the _NAME_, _NAM2_, and _NAM3_ variables indicate the three indices of the weight . You can store information other than the weight matrix in the INWGT= data set, but only observations with _TYPE_=WEIGHT are used to specify the weight matrix . This property enables you to store more than one weight matrix in the INWGT= data set. You can then run PROC CALIS with each of the weight matrices by changing only the _TYPE_ observation in the INWGT= data set with an intermediate DATA step.

For more details on the INWGT= data set, see the section OUTWGT= SAS-data-set.