Results: bmi2.sas

BMI and Health: Use the Double Measurement Design

Full Model

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.HEALTH
N Records Read 500
N Records Used 500
N Obs 500
Model Type LINEQS
Analysis Covariances

BMI and Health: Use the Double Measurement Design

Full Model

The CALIS Procedure

Covariance Structure Analysis: Descriptive Statistics

Descriptive Statistics

Covariances

Covariance Matrix (DF = 500)
  age1 bmi1 fat1 cholest1 diastol1 age2 bmi2 fat2 cholest2 diastol2
age1 167.52510 8.92158 26.00226 19.04716 48.22078 147.92434 3.61434 25.24749 8.12150 34.57954
bmi1 8.92158 21.84976 29.71269 56.60201 35.58611 5.02565 13.16763 21.37916 54.76676 24.33292
fat1 26.00226 29.71269 60.04656 124.24172 54.28907 23.49520 20.57226 45.04269 110.67378 55.51732
cholest1 19.04716 56.60201 124.24172 3097.1185 123.72798 20.99279 61.46665 130.04808 2883.7848 104.58672
diastol1 48.22078 35.58611 54.28907 123.72798 325.01316 37.58229 25.53910 57.44539 120.41482 128.96085
age2 147.92434 5.02565 23.49520 20.99279 37.58229 153.77528 3.10259 22.21992 11.58506 36.85896
bmi2 3.61434 13.16763 20.57226 61.46665 25.53910 3.10259 14.31233 19.26437 56.91286 27.03997
fat2 25.24749 21.37916 45.04269 130.04808 57.44539 22.21992 19.26437 55.05661 113.95079 51.57859
cholest2 8.12150 54.76676 110.67378 2883.7848 120.41482 11.58506 56.91286 113.95079 3216.5713 90.88486
diastol2 34.57954 24.33292 55.51732 104.58672 128.96085 36.85896 27.03997 51.57859 90.88486 176.09704
Determinant 4.4858554E18 Ln 42.947461

Optimization

BMI and Health: Use the Double Measurement Design

Full Model

The CALIS Procedure

Covariance Structure Analysis: Optimization

Levenberg-Marquardt Optimization

Scaling Update of More (1978)

Optimization Problem

Parameter Estimates 45
Functions (Observations) 55
Lower Bounds 15
Upper Bounds 0

Iteration Start

Optimization Start
Active Constraints 0 Objective Function 0.2768223813
Max Abs Gradient Element 0.1015687357 Radius 1

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   0.10492 0.1719 0.0424 0 0.674
2   0 8 0   0.01737 0.0875 0.00443 0 1.073
3   0 10 0   0.00932 0.00805 0.000201 0 1.093
4   0 12 0   0.00931 0.000013 0.000164 0 1.019
5   0 14 0   0.00931 5.719E-8 1.441E-6 0 1.025

Iteration Stop

Optimization Results
Iterations 5 Function Calls 17
Jacobian Calls 7 Active Constraints 0
Objective Function 0.0093074908 Max Abs Gradient Element 1.4407197E-6
Lambda 0 Actual Over Pred Change 1.0254679474
Radius 0.0018060845    
Convergence criterion (ABSGCONV=0.00001) satisfied.

BMI and Health: Use the Double Measurement Design

Full Model

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Fit

Fit Summary

Fit Summary
Modeling Info Number of Observations 500
  Number of Variables 10
  Number of Moments 55
  Number of Parameters 45
  Number of Active Constraints 0
  Baseline Model Function Value 8.1272
  Baseline Model Chi-Square 4063.5787
  Baseline Model Chi-Square DF 45
  Pr > Baseline Model Chi-Square <.0001
Absolute Index Fit Function 0.0093
  Chi-Square 4.6537
  Chi-Square DF 10
  Pr > Chi-Square 0.9131
  Z-Test of Wilson & Hilferty -1.3607
  Hoelter Critical N 1966
  Root Mean Square Residual (RMR) 5.7883
  Standardized RMR (SRMR) 0.0114
  Goodness of Fit Index (GFI) 0.9982
Parsimony Index Adjusted GFI (AGFI) 0.9899
  Parsimonious GFI 0.2218
  RMSEA Estimate 0.0000
  RMSEA Lower 90% Confidence Limit 0.0000
  RMSEA Upper 90% Confidence Limit 0.0185
  Probability of Close Fit 0.9987
  ECVI Estimate 0.1934
  ECVI Lower 90% Confidence Limit 0.2045
  ECVI Upper 90% Confidence Limit 0.2075
  Akaike Information Criterion 94.6537
  Bozdogan CAIC 329.3111
  Schwarz Bayesian Criterion 284.3111
  McDonald Centrality 1.0054
Incremental Index Bentler Comparative Fit Index 1.0000
  Bentler-Bonett NFI 0.9989
  Bentler-Bonett Non-normed Index 1.0060
  Bollen Normed Index Rho1 0.9948
  Bollen Non-normed Index Delta2 1.0013
  James et al. Parsimonious NFI 0.2220

BMI and Health: Use the Double Measurement Design

Full Model

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Predicted Covariances

Predicted Covariances
  age1 bmi1 fat1 cholest1 diastol1 age2 bmi2 fat2 cholest2 diastol2
age1 165.91444 8.15828 25.70966 21.92140 46.09152 147.33013 4.16046 23.32030 19.21823 35.52966
bmi1 8.15828 22.00545 29.94458 61.22107 34.79903 4.16046 13.34085 20.97589 62.10470 24.73891
fat1 25.70966 29.94458 60.60980 132.11865 53.34193 23.32030 20.97589 44.48516 124.19534 56.26978
cholest1 21.92140 61.22107 132.11865 3097.4142 108.50328 19.21823 62.10470 124.19534 2897.3074 108.59996
diastol1 46.09152 34.79903 53.34193 108.50328 323.36959 35.52966 24.73891 56.26978 108.59996 128.32927
age2 147.33013 4.16046 23.32030 19.21823 35.52966 154.19102 3.49973 20.61679 17.25526 37.80286
bmi2 4.16046 13.34085 20.97589 62.10470 24.73891 3.49973 14.43023 19.12637 59.45181 27.39103
fat2 23.32030 20.97589 44.48516 124.19534 56.26978 20.61679 19.12637 53.81671 112.81926 51.43138
cholest2 19.21823 62.10470 124.19534 2897.3074 108.59996 17.25526 59.45181 112.81926 3241.7583 99.62629
diastol2 35.52966 24.73891 56.26978 108.59996 128.32927 37.80286 27.39103 51.43138 99.62629 176.67800
Determinant 4.5278025E18 Ln 42.956768

BMI and Health: Use the Double Measurement Design

Full Model

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

ML Estimation

Equations

Linear Equations
Fcholest =   -0.3197 (ns) Fage + 0.3935 (ns) Fbmi + 2.7739 (**) Ffat + 1.0000   epsilon1
Fdiastol =   0.0204 (ns) Fage + -0.4795 (ns) Fbmi + 1.4803 (**) Ffat + 1.0000   epsilon2
age1 =   1.0000   Fage + 1.0000   e11                
bmi1 =   1.0000   Fbmi + 1.0000   e12                
fat1 =   1.0000   Ffat + 1.0000   e13                
cholest1 =   1.0000   Fcholest + 1.0000   e14                
diastol1 =   1.0000   Fdiastol + 1.0000   e15                
age2 =   1.0000   Fage + 1.0000   e21                
bmi2 =   1.0000   Fbmi + 1.0000   e22                
fat2 =   1.0000   Ffat + 1.0000   e23                
cholest2 =   1.0000   Fcholest + 1.0000   e24                
diastol2 =   1.0000   Fdiastol + 1.0000   e25                

Linear Effects

Effects in Linear Equations
Variable Predictor Parameter Estimate Standard
Error		 t Value Pr > |t|
Fcholest Fage beta11 -0.31974 0.22776 -1.4038 0.1604
Fcholest Fbmi beta12 0.39355 1.70786 0.2304 0.8178
Fcholest Ffat beta13 2.77389 0.98043 2.8293 0.0047
Fdiastol Fage beta21 0.02038 0.05008 0.4070 0.6840
Fdiastol Fbmi beta22 -0.47951 0.41875 -1.1451 0.2522
Fdiastol Ffat beta23 1.48033 0.23452 6.3123 <.0001
age1 Fage   1.00000      
bmi1 Fbmi   1.00000      
fat1 Ffat   1.00000      
cholest1 Fcholest   1.00000      
diastol1 Fdiastol   1.00000      
age2 Fage   1.00000      
bmi2 Fbmi   1.00000      
fat2 Ffat   1.00000      
cholest2 Fcholest   1.00000      
diastol2 Fdiastol   1.00000      

Variance Parms

Estimates for Variances of Exogenous Variables
Variable
Type		 Variable Parameter Estimate Standard
Error		 t Value Pr > |t|
Latent Fage phi11 147.33013 9.69906 15.1901 <.0001
  Fbmi phi22 13.34085 0.98617 13.5280 <.0001
  Ffat phi33 44.48516 3.10108 14.3451 <.0001
Disturbance epsilon1 psi11 2535 171.25775 14.7994 <.0001
  epsilon2 psi22 56.16992 9.22072 6.0917 <.0001
Error e11 omega111 18.58431 2.91383 6.3780 <.0001
  e12 omega122 8.66460 0.70794 12.2392 <.0001
  e13 omega133 16.12464 1.65945 9.7168 <.0001
  e14 omega144 200.10685 57.42189 3.4849 0.0005
  e15 omega155 195.04032 14.32319 13.6171 <.0001
  e21 omega211 6.86089 2.70107 2.5401 0.0111
  e22 omega222 1.08938 0.49076 2.2198 0.0264
  e23 omega233 9.33155 1.53876 6.0643 <.0001
  e24 omega244 344.45088 60.28977 5.7133 <.0001
  e25 omega255 48.34873 8.24562 5.8636 <.0001

Covariance Parms

Covariances Among Exogenous Variables
Var1 Var2 Parameter Estimate Standard
Error		 t Value Pr > |t|
Fage Fbmi phi12 4.16046 2.14059 1.9436 0.0519
Fage Ffat phi13 23.32030 3.98559 5.8511 <.0001
Fbmi Ffat phi23 20.97589 1.58378 13.2442 <.0001
epsilon1 epsilon2 psi12 -45.86173 24.96841 -1.8368 0.0662
e11 e12 omega112 3.99782 0.94487 4.2311 <.0001
e11 e13 omega113 2.38936 1.50537 1.5872 0.1125
e11 e14 omega114 2.70317 9.09144 0.2973 0.7662
e11 e15 omega115 10.56186 3.82395 2.7620 0.0057
e12 e13 omega123 8.96869 0.95597 9.3818 <.0001
e12 e14 omega124 -0.88363 4.17825 -0.2115 0.8325
e12 e15 omega125 10.06012 2.27406 4.4239 <.0001
e13 e14 omega134 7.92331 6.74144 1.1753 0.2399
e13 e15 omega135 -2.92785 3.40916 -0.8588 0.3904
e14 e15 omega145 -0.09668 16.90694 -0.00572 0.9954
e21 e22 omega212 -0.66074 0.73496 -0.8990 0.3686
e21 e23 omega213 -2.70351 1.36924 -1.9745 0.0483
e21 e24 omega214 -1.96296 8.96205 -0.2190 0.8266
e21 e25 omega215 2.27320 2.70992 0.8388 0.4016
e22 e23 omega223 -1.84952 0.70488 -2.6239 0.0087
e22 e24 omega224 -2.65289 3.47643 -0.7631 0.4454
e22 e25 omega225 2.65213 1.48666 1.7839 0.0744
e23 e24 omega234 -11.37608 6.54647 -1.7377 0.0823
e23 e25 omega235 -4.83840 2.53639 -1.9076 0.0564
e24 e25 omega245 -8.97366 12.60469 -0.7119 0.4765

Sq. Mult. Correlations

Squared Multiple Correlations
Variable Error Variance Total Variance R-Square
Fcholest 2535 2897 0.1252
Fdiastol 56.16992 128.32927 0.5623
age1 18.58431 165.91444 0.8880
bmi1 8.66460 22.00545 0.6063
fat1 16.12464 60.60980 0.7340
cholest1 200.10685 3097 0.9354
diastol1 195.04032 323.36959 0.3969
age2 6.86089 154.19102 0.9555
bmi2 1.08938 14.43023 0.9245
fat2 9.33155 53.81671 0.8266
cholest2 344.45088 3242 0.8937
diastol2 48.34873 176.67800 0.7263

BMI and Health: Use the Double Measurement Design

Reduced Model with beta12=beta22=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.HEALTH
N Records Read 500
N Records Used 500
N Obs 500
Model Type LINEQS
Analysis Covariances

BMI and Health: Use the Double Measurement Design

Reduced Model with beta12=beta22=0

The CALIS Procedure

Covariance Structure Analysis: Descriptive Statistics

Descriptive Statistics

Covariances

Covariance Matrix (DF = 500)
  age1 bmi1 fat1 cholest1 diastol1 age2 bmi2 fat2 cholest2 diastol2
age1 167.52510 8.92158 26.00226 19.04716 48.22078 147.92434 3.61434 25.24749 8.12150 34.57954
bmi1 8.92158 21.84976 29.71269 56.60201 35.58611 5.02565 13.16763 21.37916 54.76676 24.33292
fat1 26.00226 29.71269 60.04656 124.24172 54.28907 23.49520 20.57226 45.04269 110.67378 55.51732
cholest1 19.04716 56.60201 124.24172 3097.1185 123.72798 20.99279 61.46665 130.04808 2883.7848 104.58672
diastol1 48.22078 35.58611 54.28907 123.72798 325.01316 37.58229 25.53910 57.44539 120.41482 128.96085
age2 147.92434 5.02565 23.49520 20.99279 37.58229 153.77528 3.10259 22.21992 11.58506 36.85896
bmi2 3.61434 13.16763 20.57226 61.46665 25.53910 3.10259 14.31233 19.26437 56.91286 27.03997
fat2 25.24749 21.37916 45.04269 130.04808 57.44539 22.21992 19.26437 55.05661 113.95079 51.57859
cholest2 8.12150 54.76676 110.67378 2883.7848 120.41482 11.58506 56.91286 113.95079 3216.5713 90.88486
diastol2 34.57954 24.33292 55.51732 104.58672 128.96085 36.85896 27.03997 51.57859 90.88486 176.09704
Determinant 4.4858554E18 Ln 42.947461

Optimization

BMI and Health: Use the Double Measurement Design

Reduced Model with beta12=beta22=0

The CALIS Procedure

Covariance Structure Analysis: Optimization

Levenberg-Marquardt Optimization

Scaling Update of More (1978)

Optimization Problem

Parameter Estimates 45
Functions (Observations) 55
Lower Bounds 15
Upper Bounds 0
Linear Constraints 2

Iteration Start

Optimization Start
Active Constraints 2 Objective Function 0.4178440369
Max Abs Gradient Element 0.3660107658 Radius 1

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 2   0.02411 0.3937 0.0180 0 0.983
2   0 6 2   0.01233 0.0118 0.00354 0 1.088
3   0 8 2   0.01229 0.000037 0.000266 0 1.032
4   0 10 2   0.01229 2.904E-7 0.000035 0 1.043
5   0 12 2   0.01229 2.601E-9 2.183E-6 0 1.047

Iteration Stop

Optimization Results
Iterations 5 Function Calls 15
Jacobian Calls 7 Active Constraints 2
Objective Function 0.0122914464 Max Abs Gradient Element 2.183169E-6
Lambda 0 Actual Over Pred Change 1.0474386893
Radius 0.0002628856    
Convergence criterion (ABSGCONV=0.00001) satisfied.

BMI and Health: Use the Double Measurement Design

Reduced Model with beta12=beta22=0

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Fit

Fit Summary

Fit Summary
Modeling Info Number of Observations 500
  Number of Variables 10
  Number of Moments 55
  Number of Parameters 45
  Number of Active Constraints 2
  Baseline Model Function Value 8.1272
  Baseline Model Chi-Square 4063.5787
  Baseline Model Chi-Square DF 45
  Pr > Baseline Model Chi-Square <.0001
Absolute Index Fit Function 0.0123
  Chi-Square 6.1457
  Chi-Square DF 12
  Pr > Chi-Square 0.9086
  Z-Test of Wilson & Hilferty -1.3331
  Hoelter Critical N 1710
  Root Mean Square Residual (RMR) 6.0043
  Standardized RMR (SRMR) 0.0117
  Goodness of Fit Index (GFI) 0.9975
Parsimony Index Adjusted GFI (AGFI) 0.9887
  Parsimonious GFI 0.2660
  RMSEA Estimate 0.0000
  RMSEA Lower 90% Confidence Limit 0.0000
  RMSEA Upper 90% Confidence Limit 0.0183
  Probability of Close Fit 0.9993
  ECVI Estimate 0.1882
  ECVI Lower 90% Confidence Limit 0.2004
  ECVI Upper 90% Confidence Limit 0.2039
  Akaike Information Criterion 92.1457
  Bozdogan CAIC 316.3739
  Schwarz Bayesian Criterion 273.3739
  McDonald Centrality 1.0059
Incremental Index Bentler Comparative Fit Index 1.0000
  Bentler-Bonett NFI 0.9985
  Bentler-Bonett Non-normed Index 1.0055
  Bollen Normed Index Rho1 0.9943
  Bollen Non-normed Index Delta2 1.0014
  James et al. Parsimonious NFI 0.2663

BMI and Health: Use the Double Measurement Design

Reduced Model with beta12=beta22=0

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

Predicted Covariances

Predicted Covariances
  age1 bmi1 fat1 cholest1 diastol1 age2 bmi2 fat2 cholest2 diastol2
age1 165.94962 8.20571 25.70354 21.30821 46.85625 147.35916 4.20875 23.25988 18.95005 36.00814
bmi1 8.20571 22.03596 29.92016 59.72729 36.18872 4.20875 13.38439 20.88452 60.74377 25.97210
fat1 25.70354 29.92016 60.51905 132.18141 53.98491 23.25988 20.88452 44.53047 124.64266 56.08746
cholest1 21.30821 59.72729 132.18141 3098.5013 106.42453 18.95005 60.74377 124.64266 2898.3698 107.18909
diastol1 46.85625 36.18872 53.98491 106.42453 326.89912 36.00814 25.97210 56.08746 107.18909 131.28285
age2 147.35916 4.20875 23.25988 18.95005 36.00814 154.17131 3.50218 20.60135 17.37979 37.60943
bmi2 4.20875 13.38439 20.88452 60.74377 25.97210 3.50218 14.44514 19.06599 58.27550 27.96575
fat2 23.25988 20.88452 44.53047 124.64266 56.08746 20.60135 19.06599 53.91216 113.45616 51.16047
cholest2 18.95005 60.74377 124.64266 2898.3698 107.18909 17.37979 58.27550 113.45616 3242.6924 99.42254
diastol2 36.00814 25.97210 56.08746 107.18909 131.28285 37.60943 27.96575 51.16047 99.42254 176.52160
Determinant 4.5413333E18 Ln 42.959752

BMI and Health: Use the Double Measurement Design

Reduced Model with beta12=beta22=0

The CALIS Procedure

Covariance Structure Analysis: Maximum Likelihood Estimation

ML Estimation

Equations

Linear Equations
Fcholest =   -0.3414 (ns) Fage + 0   Fbmi + 2.9773 (**) Ffat + 1.0000   epsilon1
Fdiastol =   0.0496 (ns) Fage + 0   Fbmi + 1.2336 (**) Ffat + 1.0000   epsilon2
age1 =   1.0000   Fage + 1.0000   e11                
bmi1 =   1.0000   Fbmi + 1.0000   e12                
fat1 =   1.0000   Ffat + 1.0000   e13                
cholest1 =   1.0000   Fcholest + 1.0000   e14                
diastol1 =   1.0000   Fdiastol + 1.0000   e15                
age2 =   1.0000   Fage + 1.0000   e21                
bmi2 =   1.0000   Fbmi + 1.0000   e22                
fat2 =   1.0000   Ffat + 1.0000   e23                
cholest2 =   1.0000   Fcholest + 1.0000   e24                
diastol2 =   1.0000   Fdiastol + 1.0000   e25                

Linear Effects

Effects in Linear Equations
Variable Predictor Parameter Estimate Standard
Error		 t Value Pr > |t|
Fcholest Fage beta11 -0.34136 0.20455 -1.6688 0.0951
Fcholest Fbmi beta12 0 0 . .
Fcholest Ffat beta13 2.97735 0.37942 7.8471 <.0001
Fdiastol Fage beta21 0.04964 0.04196 1.1831 0.2368
Fdiastol Fbmi beta22 0 0 . .
Fdiastol Ffat beta23 1.23360 0.08227 14.9949 <.0001
age1 Fage   1.00000      
bmi1 Fbmi   1.00000      
fat1 Ffat   1.00000      
cholest1 Fcholest   1.00000      
diastol1 Fdiastol   1.00000      
age2 Fage   1.00000      
bmi2 Fbmi   1.00000      
fat2 Ffat   1.00000      
cholest2 Fcholest   1.00000      
diastol2 Fdiastol   1.00000      

Variance Parms

Estimates for Variances of Exogenous Variables
Variable
Type		 Variable Parameter Estimate Standard
Error		 t Value Pr > |t|
Latent Fage phi11 147.35916 9.69954 15.1924 <.0001
  Fbmi phi22 13.38439 0.98699 13.5608 <.0001
  Ffat phi33 44.53047 3.10022 14.3636 <.0001
Disturbance epsilon1 psi11 2534 171.18262 14.8014 <.0001
  epsilon2 psi22 60.30586 8.34990 7.2223 <.0001
Error e11 omega111 18.59046 2.91561 6.3762 <.0001
  e12 omega122 8.65157 0.70705 12.2362 <.0001
  e13 omega133 15.98859 1.64362 9.7277 <.0001
  e14 omega144 200.13158 57.43752 3.4843 0.0005
  e15 omega155 195.61627 14.35032 13.6315 <.0001
  e21 omega211 6.81215 2.70141 2.5217 0.0117
  e22 omega222 1.06075 0.48836 2.1721 0.0298
  e23 omega233 9.38169 1.53936 6.0946 <.0001
  e24 omega244 344.32264 60.29115 5.7110 <.0001
  e25 omega255 45.23875 7.75633 5.8325 <.0001

Covariance Parms

Covariances Among Exogenous Variables
Var1 Var2 Parameter Estimate Standard
Error		 t Value Pr > |t|
Fage Fbmi phi12 4.20875 2.14275 1.9642 0.0495
Fage Ffat phi13 23.25988 3.98543 5.8362 <.0001
Fbmi Ffat phi23 20.88452 1.58006 13.2175 <.0001
epsilon1 epsilon2 psi12 -47.51098 23.93790 -1.9848 0.0472
e11 e12 omega112 3.99696 0.94583 4.2259 <.0001
e11 e13 omega113 2.44366 1.50389 1.6249 0.1042
e11 e14 omega114 2.35816 9.05843 0.2603 0.7946
e11 e15 omega115 10.84811 3.83970 2.8252 0.0047
e12 e13 omega123 9.03564 0.95299 9.4813 <.0001
e12 e14 omega124 -1.01648 4.17350 -0.2436 0.8076
e12 e15 omega125 10.21662 2.28539 4.4704 <.0001
e13 e14 omega134 7.53875 6.62506 1.1379 0.2552
e13 e15 omega135 -2.10255 3.35670 -0.6264 0.5311
e14 e15 omega145 -0.76456 16.94646 -0.0451 0.9640
e21 e22 omega212 -0.70657 0.73483 -0.9615 0.3363
e21 e23 omega213 -2.65853 1.36904 -1.9419 0.0522
e21 e24 omega214 -1.57026 8.90599 -0.1763 0.8600
e21 e25 omega215 1.60129 2.65633 0.6028 0.5466
e22 e23 omega223 -1.81853 0.70191 -2.5908 0.0096
e22 e24 omega224 -2.46827 3.45117 -0.7152 0.4745
e22 e25 omega225 1.99366 1.38840 1.4359 0.1510
e23 e24 omega234 -11.18649 6.48216 -1.7257 0.0844
e23 e25 omega235 -4.92698 2.52151 -1.9540 0.0507
e24 e25 omega245 -7.76655 12.45466 -0.6236 0.5329

Sq. Mult. Correlations

Squared Multiple Correlations
Variable Error Variance Total Variance R-Square
Fcholest 2534 2898 0.1258
Fdiastol 60.30586 131.28285 0.5406
age1 18.59046 165.94962 0.8880
bmi1 8.65157 22.03596 0.6074
fat1 15.98859 60.51905 0.7358
cholest1 200.13158 3099 0.9354
diastol1 195.61627 326.89912 0.4016
age2 6.81215 154.17131 0.9558
bmi2 1.06075 14.44514 0.9266
fat2 9.38169 53.91216 0.8260
cholest2 344.32264 3243 0.8938
diastol2 45.23875 176.52160 0.7437

BMI and Health: Use the Double Measurement Design

Calculate Likelihood ratio test of H0: beta12=beta22=0

The IML Procedure

G2_pval

G2 pval
1.4919778 0.4742651

_LIT1006

Or better, difference between 'Chi-Square' values

diff

  diff
6.1457-4.6537 = 1.492