Results: Circle.sas

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

With psi1 neq psi2, fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Model and Initial Values

The CALIS Procedure

Modeling Specification

Modeling Info

Modeling Information
Maximum Likelihood Estimation
Data Set WORK.BIZARRE
N Records Read 250
N Records Used 250
N Obs 250
Model Type LINEQS
Analysis Covariances

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

With psi1 neq psi2, fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Descriptive Statistics

Descriptive Statistics

Covariances

Covariance Matrix (DF = 250)
  Y1 Y2
Y1 1.72246 0.96515
Y2 0.96515 2.16043
Determinant 2.789736 Ln 1.025947

Optimization

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

With psi1 neq psi2, fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Optimization

Levenberg-Marquardt Optimization

Scaling Update of More (1978)

Optimization Problem

Parameter Estimates 4
Functions (Observations) 3

Iteration Start

Optimization Start
Active Constraints 0 Objective Function 0.3332353081
Max Abs Gradient Element 1.2287976625 Radius 1.877894481

Iteration History

Iteration   Restarts Function
Calls		 Active
Constraints		   Objective
Function		 Objective
Function
Change		 Max Abs
Gradient
Element		 Lambda Ratio
Between
Actual
and
Predicted
Change
1 * 0 5 0   0.04251 0.2907 0.2231 1.110 0.669
2 * 0 7 0   0.0000238 0.0425 0.00886 111E-16 0.858
3 * 0 9 0   0 0.000024 0.000023 111E-16 1.005

Iteration Stop

Optimization Results
Iterations 3 Function Calls 12
Jacobian Calls 5 Active Constraints 0
Objective Function 0 Max Abs Gradient Element 0.0000226838
Lambda 1.110223E-14 Actual Over Pred Change 1.0046121167
Radius 1.8425156463    
Convergence criterion (GCONV2=0) satisfied.

Note:The Moore-Penrose inverse is used in computing the covariance matrix for parameter estimates.

Note:Covariance matrix for the estimates is not full rank.

Note:The variance of some parameter estimates is zero or some parameter estimates are linearly related to other parameter estimates as shown in the following equations:

Linear Dependence

beta2 = 0.367032 + 1.621439 * beta1

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

With psi1 neq psi2, fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Fit

Fit Summary

Fit Summary
WARNING: Indices for models with negative
degrees of freedom may not be interpretable.
Modeling Info Number of Observations 250
  Number of Variables 2
  Number of Moments 3
  Number of Parameters 4
  Number of Active Constraints 0
  Baseline Model Function Value 0.2881
  Baseline Model Chi-Square 72.0287
  Baseline Model Chi-Square DF 1
  Pr > Baseline Model Chi-Square <.0001
Absolute Index Fit Function 0.0000
  Chi-Square 0.0000
  Chi-Square DF -1
  Pr > Chi-Square .
  Z-Test of Wilson & Hilferty .
  Hoelter Critical N .
  Root Mean Square Residual (RMR) 0.0000
  Standardized RMR (SRMR) 0.0000
  Goodness of Fit Index (GFI) 1.0000
Parsimony Index Adjusted GFI (AGFI) .
  Parsimonious GFI -1.0000
  RMSEA Estimate .
  Probability of Close Fit .
  ECVI Estimate 0.0243
  ECVI Lower 90% Confidence Limit .
  ECVI Upper 90% Confidence Limit .
  Akaike Information Criterion 8.0000
  Bozdogan CAIC 26.0858
  Schwarz Bayesian Criterion 22.0858
  McDonald Centrality 0.9980
Incremental Index Bentler Comparative Fit Index 0.9859
  Bentler-Bonett NFI 1.0000
  Bentler-Bonett Non-normed Index .
  Bollen Normed Index Rho1 .
  Bollen Non-normed Index Delta2 0.9863
  James et al. Parsimonious NFI -1.0000

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

With psi1 neq psi2, fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Predicted Covariances

Predicted Covariances
  Y1 Y2
Y1 1.72249 0.96518
Y2 0.96518 2.16046
Determinant 2.789785 Ln 1.025964

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

With psi1 neq psi2, fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

ML Estimation

Equations

Linear Equations
Y1 =   0.3417 (**) F1 + 0.9211 (**) F2 + 1.0000   epsilon1
Y2 =   0.3417 (**) F1 + 0.9211 (**) F2 + 1.0000   epsilon2

Linear Effects

Effects in Linear Equations
Variable Predictor Parameter Estimate Standard
Error		 t Value Pr > |t|
Y1 F1 beta1 0.34171 0.02415 14.1496 <.0001
Y1 F2 beta2 0.92109 0.06510 14.1496 <.0001
Y2 F1 beta1 0.34171 0.02415 14.1496 <.0001
Y2 F2 beta2 0.92109 0.06510 14.1496 <.0001

Variance Parms

Estimates for Variances of Exogenous Variables
Variable
Type		 Variable Parameter Estimate Standard
Error		 t Value Pr > |t|
Error epsilon1 psi1 0.75731 0.12549 6.0349 <.0001
  epsilon2 psi2 1.19528 0.15030 7.9529 <.0001
Latent F1   1.00000      
  F2   1.00000      

Covariance Parms

Covariances Among Exogenous Variables
Var1 Var2 Estimate Standard
Error		 t Value Pr > |t|
F1 F2 0      

Sq. Mult. Correlations

Squared Multiple Correlations
Variable Error Variance Total Variance R-Square
Y1 0.75731 1.72249 0.5603
Y2 1.19528 2.16046 0.4467

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Passes parameter count rule with psi1=psi1=psi

The CALIS Procedure

Covariance Structure Analysis: Model and Initial Values

The CALIS Procedure

Modeling Specification

Modeling Info

Modeling Information
Maximum Likelihood Estimation
Data Set WORK.BIZARRE
N Records Read 250
N Records Used 250
N Obs 250
Model Type LINEQS
Analysis Covariances

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Passes parameter count rule with psi1=psi1=psi

The CALIS Procedure

Covariance Structure Analysis: Descriptive Statistics

Descriptive Statistics

Covariances

Covariance Matrix (DF = 250)
  Y1 Y2
Y1 1.72246 0.96515
Y2 0.96515 2.16043
Determinant 2.789736 Ln 1.025947

Optimization

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Passes parameter count rule with psi1=psi1=psi

The CALIS Procedure

Covariance Structure Analysis: Optimization

Levenberg-Marquardt Optimization

Scaling Update of More (1978)

Optimization Problem

Parameter Estimates 3
Functions (Observations) 3

Iteration Start

Optimization Start
Active Constraints 0 Objective Function 0.3332353081
Max Abs Gradient Element 2.1047853132 Radius 4.4055158095

Iteration History

Iteration   Restarts Function
Calls		 Active
Constraints		   Objective
Function		 Objective
Function
Change		 Max Abs
Gradient
Element		 Lambda Ratio
Between
Actual
and
Predicted
Change
1 * 0 6 0   0.08867 0.2446 0.4874 1.181 0.680
2 * 0 8 0   0.01726 0.0714 0.0245 111E-16 0.848
3 * 0 10 0   0.01704 0.000217 0.000199 111E-16 1.014
4 * 0 12 0   0.01704 1.398E-8 4.379E-6 111E-16 1.000

Iteration Stop

Optimization Results
Iterations 4 Function Calls 15
Jacobian Calls 6 Active Constraints 0
Objective Function 0.017043464 Max Abs Gradient Element 4.3789451E-6
Lambda 1.110223E-14 Actual Over Pred Change 0.9996298357
Radius 3.6478775864    
Convergence criterion (ABSGCONV=0.00001) satisfied.

Note:The Moore-Penrose inverse is used in computing the covariance matrix for parameter estimates.

Note:Covariance matrix for the estimates is not full rank.

Note:The variance of some parameter estimates is zero or some parameter estimates are linearly related to other parameter estimates as shown in the following equations:

Linear Dependence

beta2 = 0.257970 + 1.318141 * beta1

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Passes parameter count rule with psi1=psi1=psi

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Fit

Fit Summary

Fit Summary
Modeling Info Number of Observations 250
  Number of Variables 2
  Number of Moments 3
  Number of Parameters 3
  Number of Active Constraints 0
  Baseline Model Function Value 0.2881
  Baseline Model Chi-Square 72.0287
  Baseline Model Chi-Square DF 1
  Pr > Baseline Model Chi-Square <.0001
Absolute Index Fit Function 0.0170
  Chi-Square 4.2609
  Chi-Square DF 0
  Pr > Chi-Square .
  Z-Test of Wilson & Hilferty .
  Hoelter Critical N .
  Root Mean Square Residual (RMR) 0.1788
  Standardized RMR (SRMR) 0.0939
  Goodness of Fit Index (GFI) 0.9834
Parsimony Index Adjusted GFI (AGFI) .
  Parsimonious GFI 0.0000
  RMSEA Estimate .
  Probability of Close Fit .
  ECVI Estimate 0.0243
  ECVI Lower 90% Confidence Limit .
  ECVI Upper 90% Confidence Limit .
  Akaike Information Criterion 10.2609
  Bozdogan CAIC 23.8252
  Schwarz Bayesian Criterion 20.8252
  McDonald Centrality 0.9915
Incremental Index Bentler Comparative Fit Index 0.9400
  Bentler-Bonett NFI 0.9408
  Bentler-Bonett Non-normed Index .
  Bollen Normed Index Rho1 .
  Bollen Non-normed Index Delta2 0.9408
  James et al. Parsimonious NFI 0.0000

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Passes parameter count rule with psi1=psi1=psi

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Predicted Covariances

Predicted Covariances
  Y1 Y2
Y1 1.94145 0.96516
Y2 0.96516 1.94145
Determinant 2.837700 Ln 1.042994

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Passes parameter count rule with psi1=psi1=psi

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

ML Estimation

Equations

Linear Equations
Y1 =   0.4620 (**) F1 + 0.8670 (**) F2 + 1.0000   epsilon1
Y2 =   0.4620 (**) F1 + 0.8670 (**) F2 + 1.0000   epsilon2

Linear Effects

Effects in Linear Equations
Variable Predictor Parameter Estimate Standard
Error		 t Value Pr > |t|
Y1 F1 beta1 0.46203 0.03282 14.0772 <.0001
Y1 F2 beta2 0.86700 0.06159 14.0772 <.0001
Y2 F1 beta1 0.46203 0.03282 14.0772 <.0001
Y2 F2 beta2 0.86700 0.06159 14.0772 <.0001

Variance Parms

Estimates for Variances of Exogenous Variables
Variable
Type		 Variable Parameter Estimate Standard
Error		 t Value Pr > |t|
Error epsilon1 psi 0.97629 0.08732 11.1803 <.0001
  epsilon2 psi 0.97629 0.08732 11.1803 <.0001
Latent F1   1.00000      
  F2   1.00000      

Covariance Parms

Covariances Among Exogenous Variables
Var1 Var2 Estimate Standard
Error		 t Value Pr > |t|
F1 F2 0      

Sq. Mult. Correlations

Squared Multiple Correlations
Variable Error Variance Total Variance R-Square
Y1 0.97629 1.94145 0.4971
Y2 0.97629 1.94145 0.4971

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Psi1 neq psi2 again, start at beta1 = -0.9824205, beta2=0

The CALIS Procedure

Covariance Structure Analysis: Model and Initial Values

The CALIS Procedure

Modeling Specification

Modeling Info

Modeling Information
Maximum Likelihood Estimation
Data Set WORK.BIZARRE
N Records Read 250
N Records Used 250
N Obs 250
Model Type LINEQS
Analysis Covariances

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Psi1 neq psi2 again, start at beta1 = -0.9824205, beta2=0

The CALIS Procedure

Covariance Structure Analysis: Descriptive Statistics

Descriptive Statistics

Covariances

Covariance Matrix (DF = 250)
  Y1 Y2
Y1 1.72246 0.96515
Y2 0.96515 2.16043
Determinant 2.789736 Ln 1.025947

Optimization

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Psi1 neq psi2 again, start at beta1 = -0.9824205, beta2=0

The CALIS Procedure

Covariance Structure Analysis: Optimization

Levenberg-Marquardt Optimization

Scaling Update of More (1978)

Optimization Problem

Parameter Estimates 4
Functions (Observations) 3

Iteration Start

Optimization Start
Active Constraints 0 Objective Function 0
Max Abs Gradient Element 2.436141E-6 Radius 1

Iteration Stop

Optimization Results
Iterations 0 Function Calls 4
Jacobian Calls 1 Active Constraints 0
Objective Function 0 Max Abs Gradient Element 2.436141E-6
Lambda 0 Actual Over Pred Change 0
Radius 1    
Convergence criterion (ABSGCONV=0.00001) satisfied.

Note:The Moore-Penrose inverse is used in computing the covariance matrix for parameter estimates.

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Psi1 neq psi2 again, start at beta1 = -0.9824205, beta2=0

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Fit

Fit Summary

Fit Summary
WARNING: Indices for models with negative
degrees of freedom may not be interpretable.
Modeling Info Number of Observations 250
  Number of Variables 2
  Number of Moments 3
  Number of Parameters 4
  Number of Active Constraints 0
  Baseline Model Function Value 0.2881
  Baseline Model Chi-Square 72.0287
  Baseline Model Chi-Square DF 1
  Pr > Baseline Model Chi-Square <.0001
Absolute Index Fit Function 0.0000
  Chi-Square 0.0000
  Chi-Square DF -1
  Pr > Chi-Square .
  Z-Test of Wilson & Hilferty .
  Hoelter Critical N .
  Root Mean Square Residual (RMR) 0.0000
  Standardized RMR (SRMR) 0.0000
  Goodness of Fit Index (GFI) 1.0000
Parsimony Index Adjusted GFI (AGFI) .
  Parsimonious GFI -1.0000
  RMSEA Estimate .
  Probability of Close Fit .
  ECVI Estimate 0.0243
  ECVI Lower 90% Confidence Limit .
  ECVI Upper 90% Confidence Limit .
  Akaike Information Criterion 8.0000
  Bozdogan CAIC 26.0858
  Schwarz Bayesian Criterion 22.0858
  McDonald Centrality 0.9980
Incremental Index Bentler Comparative Fit Index 0.9859
  Bentler-Bonett NFI 1.0000
  Bentler-Bonett Non-normed Index .
  Bollen Normed Index Rho1 .
  Bollen Non-normed Index Delta2 0.9863
  James et al. Parsimonious NFI -1.0000

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Psi1 neq psi2 again, start at beta1 = -0.9824205, beta2=0

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Predicted Covariances

Predicted Covariances
  Y1 Y2
Y1 1.72246 0.96515
Y2 0.96515 2.16043
Determinant 2.789740 Ln 1.025948

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = 0.7071068, beta2 = -0.7071068, psi1 = psi2 = 1

Psi1 neq psi2 again, start at beta1 = -0.9824205, beta2=0

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

ML Estimation

Equations

Linear Equations
Y1 =   -0.9824 (**) F1 + 0   F2 + 1.0000   epsilon1
Y2 =   -0.9824 (**) F1 + 0   F2 + 1.0000   epsilon2

Linear Effects

Effects in Linear Equations
Variable Predictor Parameter Estimate Standard
Error		 t Value Pr > |t|
Y1 F1 beta1 -0.98242 0.06943 -14.1494 <.0001
Y1 F2 beta2 0 0 . .
Y2 F1 beta1 -0.98242 0.06943 -14.1494 <.0001
Y2 F2 beta2 0 0 . .

Variance Parms

Estimates for Variances of Exogenous Variables
Variable
Type		 Variable Parameter Estimate Standard
Error		 t Value Pr > |t|
Error epsilon1 psi1 0.75731 0.12549 6.0349 <.0001
  epsilon2 psi2 1.19528 0.15029 7.9529 <.0001
Latent F1   1.00000      
  F2   1.00000      

Covariance Parms

Covariances Among Exogenous Variables
Var1 Var2 Estimate Standard
Error		 t Value Pr > |t|
F1 F2 0      

Sq. Mult. Correlations

Squared Multiple Correlations
Variable Error Variance Total Variance R-Square
Y1 0.75731 1.72246 0.5603
Y2 1.19528 2.16043 0.4467

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = beta2 = 0, psi1 = psi2 = 1

Fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Model and Initial Values

The CALIS Procedure

Modeling Specification

Modeling Info

Modeling Information
Maximum Likelihood Estimation
Data Set WORK.BIZARRE
N Records Read 250
N Records Used 250
N Obs 250
Model Type LINEQS
Analysis Covariances

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = beta2 = 0, psi1 = psi2 = 1

Fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Descriptive Statistics

Descriptive Statistics

Covariances

Covariance Matrix (DF = 250)
  Y3 Y4
Y3 0.73234 -0.06743
Y4 -0.06743 1.08540
Determinant 0.790329 Ln -0.235307

Optimization

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = beta2 = 0, psi1 = psi2 = 1

Fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Optimization

Levenberg-Marquardt Optimization

Scaling Update of More (1978)

Optimization Problem

Parameter Estimates 4
Functions (Observations) 3

Iteration Start

Optimization Start
Active Constraints 0 Objective Function 0.3773072194
Max Abs Gradient Element 1.7788941288 Radius 3.4494465031

Iteration History

Iteration   Restarts Function
Calls		 Active
Constraints		   Objective
Function		 Objective
Function
Change		 Max Abs
Gradient
Element		 Lambda Ratio
Between
Actual
and
Predicted
Change
1 * 0 4 0   0.01160 0.3657 0.1066 111E-16 0.577
2 * 0 8 0   0.00697 0.00463 0.0450 0.954 1.032
3 * 0 11 0   0.00581 0.00116 0.00670 0.185 0.890
4 * 0 14 0   0.00574 0.000068 0.00200 0.322 0.649
5 * 0 17 0   0.00574 2.616E-6 0.000681 0.434 0.978
6 * 0 20 0   0.00574 2.668E-7 0.000214 0.387 0.987
7 * 0 23 0   0.00574 2.381E-8 0.000078 0.492 0.999
8 * 0 26 0   0.00574 3.503E-9 0.000016 0.219 0.995
9 * 0 29 0   0.00574 1.46E-10 4.028E-6 0.283 0.979

Iteration Stop

Optimization Results
Iterations 9 Function Calls 32
Jacobian Calls 11 Active Constraints 0
Objective Function 0.0057358354 Max Abs Gradient Element 4.0276129E-6
Lambda 0.2829550366 Actual Over Pred Change 0.9794165443
Radius 0.000027147    
Convergence criterion (ABSGCONV=0.00001) satisfied.

Note:The Moore-Penrose inverse is used in computing the covariance matrix for parameter estimates.

Note:Covariance matrix for the estimates is not full rank.

Note:The variance of some parameter estimates is zero or some parameter estimates are linearly related to other parameter estimates as shown in the following equations:

Linear Dependence

beta1 = -388462 + 213706 * psi1 + 213706 * psi2
beta2 = -318623 + 175285 * psi1 + 175285 * psi2

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = beta2 = 0, psi1 = psi2 = 1

Fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Fit

Fit Summary

Fit Summary
WARNING: Indices for models with negative
degrees of freedom may not be interpretable.
Modeling Info Number of Observations 250
  Number of Variables 2
  Number of Moments 3
  Number of Parameters 4
  Number of Active Constraints 0
  Baseline Model Function Value 0.0057
  Baseline Model Chi-Square 1.4340
  Baseline Model Chi-Square DF 1
  Pr > Baseline Model Chi-Square 0.2311
Absolute Index Fit Function 0.0057
  Chi-Square 1.4340
  Chi-Square DF -1
  Pr > Chi-Square .
  Z-Test of Wilson & Hilferty .
  Hoelter Critical N .
  Root Mean Square Residual (RMR) 0.0389
  Standardized RMR (SRMR) 0.0437
  Goodness of Fit Index (GFI) 0.9943
Parsimony Index Adjusted GFI (AGFI) .
  Parsimonious GFI -0.9943
  RMSEA Estimate .
  Probability of Close Fit .
  ECVI Estimate 0.0243
  ECVI Lower 90% Confidence Limit .
  ECVI Upper 90% Confidence Limit .
  Akaike Information Criterion 9.4340
  Bozdogan CAIC 27.5198
  Schwarz Bayesian Criterion 23.5198
  McDonald Centrality 0.9951
Incremental Index Bentler Comparative Fit Index 0.0000
  Bentler-Bonett NFI 0.0000
  Bentler-Bonett Non-normed Index .
  Bollen Normed Index Rho1 .
  Bollen Non-normed Index Delta2 0.0000
  James et al. Parsimonious NFI 0.0000

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = beta2 = 0, psi1 = psi2 = 1

Fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Predicted Covariances

Predicted Covariances
  Y3 Y4
Y3 0.73234 0.00000
Y4 0.00000 1.08540
Determinant 0.794878 Ln -0.229566

Try to fit a non-identified model

Jerry Brunner: Student Number 999999999

True beta1 = beta2 = 0, psi1 = psi2 = 1

Fails parameter count rule

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

ML Estimation

Equations

Linear Equations
Y3 =   4.474E-7 (**) F1 + 5.455E-7 (**) F2 + 1.0000   epsilon1
Y4 =   4.474E-7 (**) F1 + 5.455E-7 (**) F2 + 1.0000   epsilon2

Linear Effects

Effects in Linear Equations
Variable Predictor Parameter Estimate Standard
Error		 t Value Pr > |t|
Y3 F1 beta1 4.47396E-7 1.04791E-7 4.2694 <.0001
Y3 F2 beta2 5.45461E-7 1.2776E-7 4.2694 <.0001
Y4 F1 beta1 4.47396E-7 1.04791E-7 4.2694 <.0001
Y4 F2 beta2 5.45461E-7 1.2776E-7 4.2694 <.0001

Variance Parms

Estimates for Variances of Exogenous Variables
Variable
Type		 Variable Parameter Estimate Standard
Error		 t Value Pr > |t|
Error epsilon1 psi1 0.73234 0.06550 11.1803 <.0001
  epsilon2 psi2 1.08540 0.09708 11.1803 <.0001
Latent F1   1.00000      
  F2   1.00000      

Covariance Parms

Covariances Among Exogenous Variables
Var1 Var2 Estimate Standard
Error		 t Value Pr > |t|
F1 F2 0      

Sq. Mult. Correlations

Squared Multiple Correlations
Variable Error Variance Total Variance R-Square
Y3 0.73234 0.73234 0
Y4 1.08540 1.08540 0