The OUTEST= (or OUTVAR=) data set is of TYPE=EST and contains the final parameter estimates, the gradient, the Hessian, and boundary and linear constraints. For METHOD=ML, METHOD=GLS, and METHOD=WLS, the OUTEST= data set also contains the approximate standard errors, the information matrix (crossproduct Jacobian), and the approximate covariance matrix of the parameter estimates ((generalized) inverse of the information matrix). If there are linear or nonlinear equality or active inequality constraints at the solution, the OUTEST= data set also contains Lagrange multipliers, the projected Hessian matrix, and the Hessian matrix of the Lagrange function.
The OUTEST= data set can be used to save the results of an optimization by PROC CALIS for another analysis with either PROC CALIS or another SAS procedure. Saving results to an OUTEST= data set is advised for expensive applications that cannot be repeated without considerable effort.
The OUTEST= data set contains the BY variables, two character variables _TYPE_
and _NAME_
, t numeric variables corresponding to the parameters used in the model, a numeric variable _RHS_
(righthand side) that is used for the righthandside value of a linear constraint or for the value of the objective function at the final point of the parameter space, and a numeric variable _ITER_
that is set to zero for initial values, set to the iteration number for the OUTITER output, and set to missing for the result
output.
The _TYPE_
observations in Table 29.5 are available in the OUTEST= data set, depending on the request.
Table 29.5: _TYPE_
Observations in the OUTEST= Data Set

Description 


ACTBC 
If there are active boundary constraints at the solution , three observations indicate which of the parameters are actively constrained, as follows:


COV 
Contains the approximate covariance matrix of the parameter estimates; used in computing the approximate standard errors. 

COVRANK 
contains the rank of the covariance matrix of the parameter estimates. 

CRPJ_LF 
Contains the Hessian matrix of the Lagrange function (based on CRPJAC). 

CRPJAC 
Contains the approximate Hessian matrix used in the optimization process. This is the inverse of the information matrix. 

EQ 
If linear constraints are used, this observation contains the ith linear constraint . The parameter variables contain the coefficients , , the 

GE 
If linear constraints are used, this observation contains the ith linear constraint . The parameter variables contain the coefficients , , and the 

GRAD 
Contains the gradient of the estimates. 

GRAD_LF 
Contains the gradient of the Lagrange function. The 

HESSIAN 
Contains the Hessian matrix. 

HESS_LF 
Contains the Hessian matrix of the Lagrange function (based on HESSIAN). 

INFORMAT 
Contains the information matrix of the parameter estimates (only for METHOD=ML, METHOD=GLS, or METHOD=WLS). 

INITGRAD 
Contains the gradient of the starting estimates. 

INITIAL 
Contains the starting values of the parameter estimates. 

JACNLC 
Contains the Jacobian of the nonlinear constraints evaluated at the final estimates. 

LAGM BC 
Contains Lagrange multipliers for masks and active boundary constraints.


LAGM LC 
Contains Lagrange multipliers for linear equality and active inequality constraints in pairs of observations containing the constraint number and the value of the Lagrange multiplier.


LAGM NLC 
contains Lagrange multipliers for nonlinear equality and active inequality constraints in pairs of observations that contain the constraint number and the value of the Lagrange multiplier.


LE 
If linear constraints are used, this observation contains the ith linear constraint . The parameter variables contain the coefficients , , and the 

LOWERBD 
If boundary constraints are used, this observation contains the lower bounds. Those parameters not subjected to lower bounds
contain missing values. The 

NACTBC 
All parameter variables contain the number of active boundary constraints at the solution . The 

NACTLC 
All parameter variables contain the number of active linear constraints at the solution that are recognized as linearly independent. The 

NLC_EQ 
Contains values and residuals of nonlinear constraints. The


NLDACTBC 
Contains the number of active boundary constraints at the solution that are recognized as linearly dependent. The 

NLDACTLC 
Contains the number of active linear constraints at the solution that are recognized as linearly dependent. The 

_NOBS_ 
Contains the number of observations. 

PARMS 
Contains the final parameter estimates. The 

PCRPJ_LF 
Contains the projected Hessian matrix of the Lagrange function (based on CRPJAC). 

PHESS_LF 
Contains the projected Hessian matrix of the Lagrange function (based on HESSIAN). 

PROJCRPJ 
Contains the projected Hessian matrix (based on CRPJAC). 

PROJGRAD 
If linear constraints are used in the estimation, this observation contains the values of the projected gradient in the variables corresponding to the first parameters. The 

PROJHESS 
Contains the projected Hessian matrix (based on HESSIAN). 

STDERR 
Contains approximate standard errors (only for METHOD=ML, METHOD=GLS, or METHOD=WLS). 

TERMINAT 
The 

UPPERBD 
If boundary constraints are used, this observation contains the upper bounds. Those parameters not subjected to upper bounds
contain missing values. The 
If the technique specified by the OMETHOD= option cannot be performed (for example, no feasible initial values can be computed or the function value or derivatives cannot be evaluated at the starting point), the OUTEST= data set can contain only some of the observations (usually only the PARMS and GRAD observations).
The OUTMODEL= (or OUTRAM=) data set is of TYPE=CALISMDL and contains the model specification, the computed parameter estimates, and the standard error estimates. This data set is intended to be reused as an INMODEL= data set to specify good initial values in a subsequent analysis by PROC CALIS.
The OUTMODEL= data set contains the following variables:
the BY variables, if any
an _MDLNUM_
variable for model numbers, if used
a character variable _TYPE_
, which takes various values that indicate the type of model specification
a character variable _NAME_
, which indicates the model type, parameter name, or variable name
a character variable _MATNR_
, which indicates the matrix number (COSAN models only)
a character variable _VAR1_
, which is the name or number of the first variable in the specification
a character variable _VAR2_
, which is the name or number of the second variable in the specification
a numerical variable _ESTIM_
for the final estimate of the parameter location
a numerical variable _STDERR_
for the standard error estimate of the parameter location
a numerical variable _SDEST_
for the final standardized estimate of the parameter location
a numerical variable _SDSE_
for the standard error of the standardized estimate of the parameter location
Although the _SDEST_
and _SDSE_
variables are created for COSAN models, the values for these two variables are always missing because there are no rules
to carry out the standardization of COSAN models.
Each observation (record) of the OUTMODEL= data set contains a piece of information regarding the model specification. Depending
on the type of the specification indicated by the value of the _TYPE_
variable, the meanings of _NAME_
, _VAR1_
, and _VAR2_
differ. The following tables summarize the meanings of the _NAME_
, _MATNR_
(COSAN models only), _VAR1_
, and _VAR2_
variables for each value of the _TYPE_
variable, given the type of the model.

Description 





MDLTYPE 
Model type 
COSAN 

VAR 
Variable 
Variable name 
Matrix number 
Column location 

MATRIX 
Matrix 
Matrix name 
Matrix number 
Number of rows 
Number of columns 
MODEL 
Model formula 
COV or MEAN 
Matrix number 
Term number 
Location in term 
ESTIM 
Parameters 
Parameter name 
Matrix number 
Row number 
Column number 
The value of the _NAME_
variable is COSAN
for the _TYPE_
=MDLTYPE observation.
The _TYPE_
=VAR observations store the information about the column variables in matrices. The _NAME_
variable stores the variable names. The value of _VAR1_
indicates the column location of the variable in the matrix with the matrix number stored in _MATNR_
.
The _TYPE_
=MATRIX observations store the information about the model matrices. The _NAME_
variable stores the matrix names. The value of _MATNR_
indicates the corresponding matrix number. The values of_VAR1_
and _VAR2_
indicates the numbers of rows and columns, respectively, of the matrix.
The _TYPE_
=MODEL observations store the covariance and mean structure formulas. The _NAME_
variable indicates whether the mean (MEAN) or covariance (COV) structure information is stored. The value of _MATNR_
indicates the matrix number in the mean or covariance structure formula. The _VAR1_
variable indicates the term number, and the _VAR2_
variable indicates the location of the matrix in the term.
The _TYPE_
=ESTIM observations store the information about the parameters and their estimates. The _NAME_
variable stores the parameter names. The value of _MATNR_
indicates the matrix number. The values of _VAR1_
and _VAR2_
indicate the associated row and column numbers, respectively, of the parameter.

Description 




MDLTYPE 
Model type 
Model type 

FACTVAR 
Variable 
Variable name 
Variable number 
Variable type 
LOADING 
Factor loading 
Parameter name 
Manifest variable 
Factor variable 
COV 
Covariance 
Parameter name 
First variable 
Second variable 
PVAR 
(Partial) variance 
Parameter name 
Variable 

MEAN 
Mean or intercept 
Parameter name 
Variable 

ADDCOV 
Added covariance 
Parameter name 
First variable 
Second variable 
ADDPVAR 
Added (partial) variance 
Parameter name 
Variable 

ADDMEAN 
Added mean or intercept 
Parameter name 
Variable 
For factor models, the value of the _NAME_
variable is either EFACTOR
(exploratory factor model) or CFACTOR
(confirmatory factor model) for the _TYPE_
=MDLTYPE observation.
The _TYPE_
=FACTVAR observations store the information about the variables in the model. The _NAME_
variable stores the variable names. The value of _VAR1_
indicates the variable number. The value of _VAR2_
indicates the type of the variable: either DEPV
for dependent observed variables or INDF
for latent factors.
Other observations specify the parameters and their estimates in the model. The _NAME_
values for these observations are the parameter names. Observation with _TYPE_
=LOADING
, _TYPE_
=COV
, or _TYPE_
=ADDCOV
are for parameters that are associated with two variables. The _VAR1_
and _VAR2_
values of these two types of observations indicate the variables involved.
Observations with _TYPE_
=PVAR
, _TYPE_
=MEAN
, _TYPE_
=ADDPVAR
, or _TYPE_
=ADDMEAN
are for parameters that are associated with a single variable. The value of _VAR1_
indicates the variable involved.

Description 




MDLTYPE 
Model type 
LINEQS 

EQSVAR 
Variable 
Variable name 
Variable number 
Variable type 
EQUATION 
Path coefficient 
Parameter 
Outcome variable 
Predictor variable 
COV 
Covariance 
Parameter 
First variable 
Second variable 
VARIANCE 
Variance 
Parameter 
Variable 

MEAN 
Mean 
Parameter 
Variable 

ADDCOV 
Added covariance 
Parameter 
First variable 
Second variable 
ADDVARIA 
Added variance 
Parameter 
Variable 

ADDINTE 
Added intercept 
Parameter 
Variable 

ADDMEAN 
Added mean 
Parameter 
Variable 
The value of the _NAME_
variable is LINEQS
for the _TYPE_
=MDLTYPE observation.
The _TYPE_
=EQSVAR observations store the information about the variables in the model. The _NAME_
variable stores the variable names. The value of _VAR1_
indicates the variable number. The value of _VAR2_
indicates the type of the variable. There are six types of variables in the LINEQS model:
DEPV
for dependent observed variables
INDV
for independent observed variables
DEPF
for dependent latent factors
INDF
for independent latent factors
INDD
for independent error terms
INDE
for independent disturbance terms
Other observations specify the parameters and their estimates in the model. The _NAME_
values for these observations are the parameter names. Observation with _TYPE_
=EQUATION
, _TYPE_
=COV
, or _TYPE_
=ADDCOV
are for parameters that are associated with two variables. The _VAR1_
and _VAR2_
values of these two types of observations indicate the variables involved.
Observations with _TYPE_
=VARIANCE
, _TYPE_
=MEAN
, _TYPE_
=ADDVARIA
, _TYPE_
=ADDINTE
, or _TYPE_
=ADDMEAN
are for parameters associated with a single variable. The value of _VAR1_
indicates the variable involved.

Description 




MDLTYPE 
model type 
LISMOD 

XVAR 
variable 
Variable 
Variable number 

YVAR 
variable 
Variable 
Variable number 

ETAVAR 
variable 
Variable 
Variable number 

XIVAR 
variable 
Variable 
Variable number 

ALPHA 
_ALPHA_ entry 
Parameter 
Row number 

BETA 
_BETA_ entry 
Parameter 
Row number 
Column number 
GAMMA 
_BETA_ entry 
Parameter 
Row number 
Column number 
KAPPA 
_KAPPA_ entry 
Parameter 
Row number 

LAMBDAX 
_LAMBDAX_ entry 
Parameter 
Row number 
Column number 
LAMBDAY 
_LAMBDAY_ entry 
Parameter 
Row number 
Column number 
NUX 
_NUX_ entry 
Parameter 
Row number 

NUY 
_NUY_ entry 
Parameter 
Row number 

PHI 
_PHI_ entry 
Parameter 
Row number 
Column number 
PSI 
_PSI_ entry 
Parameter 
Row number 
Column number 
THETAX 
_THETAX_ entry 
Parameter 
Row number 
Column number 
THETAY 
_THETAY_ entry 
Parameter 
Row number 
Column number 
ADDALPHA 
Added _ALPHA_ entry 
Parameter 
Row number 

ADDKAPPA 
Added _KAPPA_ entry 
Parameter 
Row number 

ADDNUX 
Added _NUX_ entry 
Parameter 
Row number 

ADDNUY 
Added _NUY_ entry 
Parameter 
Row number 

ADDPHI 
Added _PHI_ entry 
Parameter 
Row number 
Column number 
ADDPSI 
Added _PSI_ entry 
Parameter 
Row number 
Column number 
ADTHETAX 
Added _THETAX_ entry 
Parameter 
Row number 
Column number 
ADTHETAY 
Added _THETAY_ entry 
Parameter 
Row number 
Column number 
The value of the _NAME_
variable is LISMOD
for the _TYPE_
=MDLTYPE observation. Other observations specify either the variables or the parameters in the model.
Observations with _TYPE_
values equal to XVAR
, YVAR
, ETAVAR
, and XIVAR
indicate the variables in the respective lists in the model. The _NAME_
variable of these observations stores the names of the variables, and the _VAR1_
variable stores the variable numbers in the respective list. The variable numbers in this data set are not arbitrary—that
is, they define the variable orders in the rows and columns of the LISMOD model matrices. The _VAR2_
variable of these observations is not used.
All other observations in this data set specify the parameters in the model. The _NAME_
values of these observations are the parameter names. The corresponding _VAR1_
and _VAR2_
values of these observations indicate the row and column locations of the parameters in the LISMOD model matrices that are
specified in the _TYPE_
variable. For example, when the value of _TYPE_
is ADDPHI
or PHI
, the parameter specified is located in the _PHI_ matrix, with its row and column numbers indicated by the _VAR1_
and _VAR2_
values, respectively. Some observations for specifying parameters do not have values in the _VAR2_
variable. This means that the associated LISMOD matrices are vectors so that the column numbers are always 1 for these observations.

Description 




MDLTYPE 
Model type 
MSTRUCT 

VAR 
Variable 
Variable 
Variable number 

COVMAT 
Covariance 
Parameter 
Row number 
Column number 
MEANVEC 
Mean 
Parameter 
Row number 

ADCOVMAT 
Added covariance 
Parameter 
Row number 
Column number 
AMEANVEC 
Added mean 
Parameter 
Row number 
The value of the _NAME_
variable is MSTRUCT
for the _TYPE_
=MDLTYPE observation. Other observations specify either the variables or the parameters in the model.
Observations with _TYPE_
values equal to VAR
indicate the variables in the model. The _NAME_
variable of these observations stores the names of the variables, and the _VAR1_
variable stores the variable numbers in the variable list. The variable numbers in this data set are not arbitrary—that is,
they define the variable orders in the rows and columns of the mean and covariance matrices. The _VAR2_
variable of these observations is not used.
All other observations in this data set specify the parameters in the model. The _NAME_
values of these observations are the parameter names. The corresponding _VAR1_
and _VAR2_
values of these observations indicate the row and column locations of the parameters in the mean or covariance matrix, as
specified in the _TYPE_
model. For example, when _TYPE_
=COVMAT
, the parameter specified is located in the covariance matrix, with its row and column numbers indicated by the _VAR1_
and _VAR2_
values, respectively. For observations with _TYPE_
=MEANVEC, the _VAR2_
variable is not used because the column numbers are always 1 for parameters in the mean vector.

Description 




MDLTYPE 
Model type 
PATH 

PATHVAR 
Variable 
Variable name 
Variable number 
Variable type 
LEFT 
Path coefficient 
Parameter 
Outcome variable 
Predictor variable 
RIGHT 
Path coefficient 
Parameter 
Predictor variable 
Outcome variable 
PCOV 
(Partial) covariance 
Parameter 
First variable 
Second variable 
PCOVPATH 
(Partial) covariance path 
Parameter 
First variable 
Second variable 
PVAR 
(Partial) variance 
Parameter 
Variable 

PVARPATH 
(Partial) variance path 
Parameter 
Variable 
Variable 
MEAN 
Mean or intercept 
Parameter 
Variable 

ONEPATH 
Mean or intercept path 
Parameter 
_ONE_ 
Variable 
ADDPCOV 
Added (partial) covariance 
Parameter 
First variable 
Second variable 
ADDPVAR 
Added (partial) variance 
Parameter 
Variable 

ADDMEAN 
Added mean 
Parameter 
Variable 
The value of the _NAME_
variable is PATH
for the _TYPE_
=MDLTYPE observation.
The _TYPE_
=PATHVAR observations store the information about the variables in the model. The _NAME_
variable stores the variable names. The value of _VAR1_
indicates the variable number. The value of _VAR2_
indicates the type of the variable. There are four types of variables in the PATH model:
DEPV
for dependent observed variables
INDV
for independent observed variables
DEPF
for dependent latent factors
INDF
for independent latent factors
Other observations specify the parameters in the model. The _NAME_
values for these observations are the parameter names. Observation with _TYPE_
=LEFT
, _TYPE_
=RIGHT
, _TYPE_
=PCOV
, or _TYPE_
=ADDPCOV
are for parameters that are associated with two variables. The _VAR1_
and _VAR2_
values of these two types of observations indicate the variables involved.
Observations with _TYPE_
=PVAR
, _TYPE_
=MEAN
, _TYPE_
=ADDPVAR
, or _TYPE_
=ADDMEAN
are for parameters that are associated with a single variable. The value of _VAR1_
indicates the variable involved.

Description 




MDLTYPE 
Model type 
RAM 

RAMVAR 
Variable name 
Variable 
Variable number 
Variable type 
_A_ 

Parameter 
Row number 
Column number 
_P_ 

Parameter 
Row number 
Column number 
_W_ 

Parameter 
Row number 
Column number 
ADD_P_ 
Added 
Parameter 
Row number 
Column number 
ADD_W_ 
Added 
Parameter 
Row number 
Column number 
The value of the _NAME_
variable is RAM
for the _TYPE_
=MDLTYPE observation.
For the _TYPE_
=RAMVAR observations, the _NAME_
variable stores the variable names, the _VAR1_
variable stores the variable number, and the _VAR2_
variable stores the variable type. There are four types of variables in the PATH model:
DEPV
for dependent observed variables
INDV
for independent observed variables
DEPF
for dependent latent factors
INDF
for independent latent factors
Other observations specify the parameters in the model. The _NAME_
variable stores the parameter name. The _TYPE_
variable indicates the associated matrix with the row number indicated in _VAR1_
and column number indicated in _VAR2_
.
When the OUTMODEL= data set is treated as an INMODEL= data set in subsequent analyses, you need to pay attention to observations with _TYPE_
values prefixed by “ADD
”, “AD
”, or “A
” (for example, ADDCOV
, ADTHETAY
, or AMEANVEC
). These observations represent default parameter locations that are generated by PROC CALIS in a previous run. Because the
context of the new analyses might be different, these observations for added parameter locations might no longer be suitable
in the new runs. Hence, these observations are not read as input model information. Fortunately, after reading the INMODEL= specification in the new analyses, CALIS analyzes the new model specification again. It then adds an appropriate set of parameters
in the new context when necessary. If you are certain that the added parameter locations in the INMODEL= data set are applicable,
you can force the input of these observations by using the READADDPARM option in the PROC CALIS statement. However, you must be very careful about using the READADDPARM option. The added parameters from the INMODEL= data set might have the same parameter names as those for the generated parameters
in the new run. This might lead to unnecessary constraints in the model.
The OUTSTAT= data set is similar to the TYPE=COV, TYPE=UCOV, TYPE=CORR, or TYPE=UCORR data set produced by the CORR procedure. The OUTSTAT= data set contains the following variables:
the BY variables, if any
the _GPNUM_ variable for groups numbers, if used in the analysis
two character variables, _TYPE_
and _NAME_
the manifest and the latent variables analyzed
The OUTSTAT= data set contains the following information (when available) in the observations:
the mean and standard deviation
the skewness and kurtosis (if the DATA= data set is a raw data set and the KURTOSIS option is specified)
the number of observations
if the WEIGHT statement is used, sum of the weights
the correlation or covariance matrix to be analyzed
the robust covariances, standard deviations, and means for robust estimation
the predicted correlation or covariance matrix
the standardized or normalized residual correlation or covariance matrix
if the model contains latent variables, the predicted covariances between latent and manifest variables and the latent variable (or factor) score regression coefficients (see the PLATCOV option )
In addition, for FACTOR models the OUTSTAT= data set contains:
If effects are analyzed, the OUTSTAT= data set also contains:
direct, indirect, and total effects and their standard error estimates
standardized direct, indirect, and total effects and their standard error estimates
Each observation in the OUTSTAT= data set contains some type of statistic as indicated by the _TYPE_
variable. The values of the _TYPE_
variable are shown in the following tables:
Value of 
Contents 

ADJCOV 
Adjusted covariances 
ADJSTD 
Adjusted standard deviations 
CORR 
Correlations 
COV 
Covariances 
KURTOSIS 
Univariate kurtosis 
MEAN 
Means 
N 
Sample size 
NPARTIAL 
Number of partial variables 
PARTCOV 
Covariances after partialling 
PARTCORR 
Correlations after partialling 
PARTMEAN 
Means after partialling 
PARTSTD 
Standard deviations after partialling 
ROBCOV 
Robust covariances 
ROBMEAN 
Robust means 
ROBSTD 
Robust standard deviations 
SKEWNESS 
Univariate skewness 
STD 
Standard deviations 
SUMWGT 
Sum of weights (if the WEIGHT statement is used) 
VARDIV 
Variance divisor 
VARDIVAJ 
Variance divisor adjustment 
For the _TYPE_
=CORR
or COV
observations, the _NAME_
variable contains the name of the manifest variable that corresponds to each row for the covariance or correlation. For other
observations, _NAME_
is blank.
value of 
Contents 

METHOD=DWLS 

DWLSPRED 
DWLS predicted moments 
DWLSRES 
DWLS raw residuals 
DWLSSRES 
DWLS variance standardized residuals 
METHOD=GLS 

GLSASRES 
GLS asymptotically standardized residuals 
GLSNRES 
GLS normalized residuals 
GLSPRED 
GLS predicted moments 
GLSRES 
GLS raw residuals 
GLSSRES 
GLS variance standardized residuals 
METHOD=ML or FIML 

MAXASRES 
ML asymptotically standardized residuals 
MAXNRES 
ML normalized residuals 
MAXPRED 
ML predicted moments 
MAXRES 
ML raw residuals 
MAXSRES 
ML variance standardized residuals 
METHOD=ULS 

ULSPRED 
ULS predicted moments 
ULSRES 
ULS raw residuals 
ULSSRES 
ULS variance standardized residuals 
METHOD=WLS 

WLSASRES 
WLS asymptotically standardized residuals 
WLSNRES 
WLS normalized residuals 
WLSPRED 
WLS predicted moments 
WLSRES 
WLS raw residuals 
WLSSRES 
WLS variance standardized residuals 
For residuals or predicted moments of means, the _NAME_
variable is a fixed value denoted by _Mean_
. For residuals or predicted moments for covariances or correlations, the _NAME_
variable is used for names of variables.
Value of 
Contents 

Unstandardized Effects 

DEFFECT 
Direct effects 
DEFF_SE 
Standard error estimates for direct effects 
IEFFECT 
Indirect effects 
IEFF_SE 
Standard error estimates for indirect effects 
TEFFECT 
Total effects 
TEFF_SE 
Standard error estimates for total effects 
Standardized Effects 

SDEFF 
Standardized direct effects 
SDEFF_SE 
Standard error estimates for standardized direct effects 
SIEFF 
Standardized indirect effects 
SIEFF_SE 
Standard error estimates for standardized indirect effects 
STEFF 
Standardized total effects 
STEFF_SE 
Standard error estimates for standardized total effects 
Latent Variable Scores Coefficients 

LSSCORE 
Latent variable (or factor) scores regression coefficients for ULS method 
SCORE 
Latent variable (or factor) scores regression coefficients other than ULS method 
For latent variable or factor scores coefficients, the _NAME_
variable contains factor or latent variables in the observations. For other observations, the _NAME_
variable contains manifest or latent variable names.
You can use the latent variable score regression coefficients with PROC SCORE to compute factor scores.
If the analyzed matrix is a covariance rather than a correlation matrix, the _TYPE_
=STD observation is not included in the OUTSTAT= data set. In this case, the standard deviations can be obtained from the diagonal elements of the covariance matrix. Dropping
the _TYPE_
=STD observation prevents PROC SCORE from standardizing the observations before computing the factor scores.
Value of 
Contents 

ERRVAR 
Error variances 
FCOV 
Factor correlations or covariances 
LOADINGS 
Unrotated factor loadings 
RFCOV 
Rotated factor correlations or covariances 
RLOADING 
Rotated factor loadings 
ROTMAT 
Rotation matrix 
STDERVAR 
Error variances in standardized solutions 
STDFCOV 
Standardized factor correlations 
STDLOAD 
Standardized factor loadings 
STDRFCOV 
Standardized rotated factor correlations or covariances 
STDRLOAD 
Standardized rotated factor loadings 
For the _TYPE_
=ERRVAR
observation, the _NAME_
variable is blank. For all other observations, the _NAME_
variable contains factor names.
You can create an OUTWGT= data set that is of TYPE=WEIGHT and contains the weight matrix used in generalized, weighted, or diagonally weighted least
squares estimation. The OUTWGT= data set contains the weight matrix on which the WRIDGE= and the WPENALTY= options are applied. However, if you input the inverse of the weight matrix with the INWGT= and INWGTINV options (or the INWGT(INV)= option alone) in the same analysis, the OUTWGT= data set contains the same elements of the inverse of the weight matrix. For unweighted least squares or maximum likelihood
estimation, no OUTWGT= data set can be written. The weight matrix used in maximum likelihood estimation is dynamically updated during optimization.
When the ML solution converges, the final weight matrix is the same as the predicted covariance or correlation matrix,
which is included in the OUTSTAT= data set (observations with _TYPE_
=MAXPRED).
For generalized and diagonally weighted least squares estimation, the weight matrices of the OUTWGT= data set contain all elements , where the indices i and j correspond to all manifest variables used in the analysis. Let be the name of the ith variable in the analysis. In this case, the OUTWGT= data set contains n observations with the variables shown in the following table:
Variable 
Contents 


WEIGHT (character) 

Name of variable (character) 

Weight for variable (numeric) 



Weight for variable (numeric) 
For weighted least squares estimation, the weight matrix of the OUTWGT= data set contains only the nonredundant elements . In this case, the OUTWGT= data set contains observations with the variables shown in the following table:
Variable 
Contents 


WEIGHT (character) 

Name of variable (character) 

Name of variable (character) 

Name of variable (character) 

Weight for variable (numeric) 



Weight for variable (numeric) 
Symmetric redundant elements are set to missing values.
You can create an OUTFIT= data set that is of TYPE=CALISFIT and that contains the values of the fit indices of your analysis. If you use two estimation methods such as LSML or LSWLS, the fit indices are for the second analysis. An OUTFIT=data set contains the following variables:
a character variable _TYPE_
for the types of fit indices
a numerical variable IndexCode
for the codes of the fit indices
a character variable FitIndex
for the names of the fit indices
a numerical variable FitValue
for the numerical values of the fit indices
a character variable PrintChar
for the characterformatted fit index values.
The possible values of _TYPE_
are:
ModelInfo
:basic modeling statistics and information
Absolute
:standalone fit indices
Parsimony
:fit indices that take model parsimony into account
Incremental
:fit indices that are based on comparison with a baseline model
Value of 
Description 

Number of Observations 
Number of observations used in the analysis 
Number of Complete Observations 
Number of complete observations (METHOD=FIML) 
Number of Incomplete Observations 
Number of incomplete observations (METHOD=FIML) 
Number of Variables 
Number of variables 
Number of Moments 
Number of mean or covariance elements 
Number of Parameters 
Number of parameters 
Number of Active Constraints 
Number of active constraints in the solution 
Saturated Model Estimation 
Estimation status of the saturated model (METHOD=FIML) 
Saturated Model Function Value 
Saturated model function value (METHOD=FIML) 
Saturated Model 2 LogLikelihood 
Saturated model –2 loglikelihood function value (METHOD=FIML) 
Baseline Model Estimation 
Estimation status of the baseline model (METHOD=FIML) 
Baseline Model Function Value 
Baseline model function value 
Baseline Model 2 LogLikelihood 
Baseline model –2 loglikelihood function value (METHOD=FIML) 
Baseline Model ChiSquare 
Baseline model chisquare value 
Baseline Model ChiSquare DF 
Baseline model chisquare degrees of freedom 
Baseline Model DF 
Baseline model degrees of freedom (METHOD=ULS or METHOD=DWLS) 
Pr > Baseline Model ChiSquare 
p value of the baseline model chisquare 
Value of 
Description 

Fit Function 
Fit function value 
2 LogLikelihood 
–2 loglikelihood function value for the model (METHOD=FIML) 
ChiSquare 
Model chisquare value 
ChiSquare DF 
Degrees of freedom for the model chisquare test 
Model DF 
Degrees of freedom for model (METHOD=ULS or METHOD=DWLS) 
Pr > ChiSquare 
Probability of obtaining a larger chisquare than the observed value 
Percent Contribution to ChiSquare 
Percentage contribution to the chisquare value 
Percent Contribution to Likelihood 
Percentage contribution to the –2 loglikelihood function value (METHOD=FIML) 
Elliptic Corrected ChiSquare 
Ellipticcorrected chisquare value 
Pr > Elliptic Corr. ChiSquare 
Probability of obtaining a larger ellipticcorrected chisquare value 
Ztest of Wilson and Hilferty 
Ztest of Wilson and Hilferty 
Hoelter Critical N 
N value that makes a significant chisquare when multiplied to the fit function value 
Root Mean Square Residual (RMR) 
Root mean square residual 
Standardized RMR (SRMR) 
Standardized root mean square residual 
Goodness of Fit Index (GFI) 
Jöreskog and Sörbom goodnessoffit index 
Value of 
Description 

Adjusted GFI (AGFI) 
Goodnessoffit index adjusted for the degrees of freedom of the model 
Parsimonious GFI 
Mulaik et al. (1989) modification of the GFI 
RMSEA Estimate 
Steiger and Lind (1980) root mean square error of approximation 
RMSEA Lower r% Confidence Limit 
Lower r% confidence limit for RMSEA 
RMSEA Upper r% Confidence Limit 
Upper r% confidence limit for RMSEA 
Probability of Close Fit 
Browne and Cudeck (1993) test of close fit 
ECVI Estimate 
Expected crossvalidation index 
ECVI Lower r% Confidence Limit 
Lower r% confidence limit for ECVI 
ECVI Upper r% Confidence Limit 
Upper r% confidence limit for ECVI 
Akaike Information Criterion 
Akaike information criterion 
Bozdogan CAIC 
Bozdogan (1987) consistent AIC 
Schwarz Bayesian Criterion 
Schwarz (1978) Bayesian criterion 
McDonald Centrality 
McDonald and Marsh (1988) measure of centrality 
1. The value of r is one minus the ALPHARMS= value. By default, r=90. 2. The value of r is one minus the ALPHAECV= value. By default, r=90.
Value of 
Description 

Bentler Comparative Fit Index 
Bentler (1985) comparative fit index 
BentlerBonett NFI 
Bentler and Bonett (1980) normed fit index 
BentlerBonett Nonnormed Index 
Bentler and Bonett (1980) nonnormed fit index 
Bollen Normed Index Rho1 
Bollen normed 
Bollen Nonnormed Index Delta2 
Bollen nonnormed 
James et al. Parsimonious NFI 
James, Mulaik, and Brett (1982) parsimonious normed fit index 