Compound Symmetry - SASian (1) [RSABE / ABEL]

posted by d_labes  – Berlin, Germany, 2013-05-02 16:26 (2975 d 07:15 ago) – Posting: # 10529
Views: 18,541

Dear Helmut,

» What do you SASians get?

Aye, aye Sir, here I am :-D.

log AUCt
     90% CI
     PE=94.74%  [ 87.49% ... 102.59%] [image]

    Covariance Parameter Estimates

  Cov Parm     Subject    Group    Estimate

  Variance     subject              0.008436
  CS           subject              0.1875
  Residual     subject    tmt A     0.07150
  Residual     subject    tmt B     0.05300

NOTE: Convergence criteria met.


log AUCinf
     90% CI
     PE=95.30%  [ 88.32% ... 102.82%]  [image]

    Covariance Parameter Estimates

  Cov Parm     Subject    Group    Estimate

  Variance     subject                    0 (!)
  CS           subject              0.1441
  Residual     subject    tmt A     0.07465
  Residual     subject    tmt B     0.06088

NOTE: Convergence criteria met.
NOTE: Estimated G matrix is not positive definite.
NOTE: Asymptotic variance matrix of covariance parameter estimates has been found to be singular and a generalized inverse was used. Covariance parameters with zero variance do not contribute to degrees of freedom computed by DDFM=SATTERTH.


Please note the 'Variance' parameter in case of AUCinf.
CS Covariance matrix parameterized in Proc MIXED as:
( CS+var  CS
  CS      CS+var)


See this post to notice that we really need 'Variance'=0 in our model (Type CS is only an approximation to that end, hoping the 'Variance' parameter is fitted with a value near zero).
So we eventually have the optimizer to tell that for AUCt. Couldn't figure out in a hurry at moment how to do that.

Regards,

Detlew

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