Mixed interest [General Sta­tis­tics]

posted by Helmut Homepage – Vienna, Austria, 2010-07-13 21:19 (5421 d 11:08 ago) – Posting: # 5620
Views: 14,329

Dear D. Labes!

...

The Mixed Procedure


                     Iteration History


Iteration    Evaluations    -2 Res Log Like       Criterion

        5              1       272.91032497      0.00000001


OK, I end up after 7 iterations at:
-2* REML log(likelihood)      251.724
Same result if use the default convergence criterion (10-10), yours ((10-8), or approach numeric resolution.

        Covariance Parameter Estimates


Cov Parm     Subject    Group          Estimate


FA(1,1)      Subject                     0.5756

FA(2,1)      Subject                     0.4485

FA(2,2)      Subject                     0.3981

Residual     Subject    Treatment R      0.1862 (CV=0.4524)

Residual     Subject    Treatment T      0.3269


Closer match than we had in our previous example...
Final variance parameter estimates:
                 lambda(1,1)_11     0.575597
                 lambda(1,2)_11     0.448489
                 lambda(2,2)_11     0.355511
Var(PERIOD*TREATMEN*SUBJECT)_21     0.186160
Var(PERIOD*TREATMEN*SUBJECT)_22     0.359059


❝ And just more interesting: Analysis according to D. Brown (EMA)...

...

The GLM Procedure


Dependent Variable: ln_Cmax   ln_Cmax


                            Sum of

Source            DF       Squares   Mean Square   F Value Pr > F


Model             45   38.86413212    0.86364738      4.73 <.0001

Error             42    7.66318204    0.18245672

Corrected Total   87   46.52731416


Confirmed.
       Total Observations :    88
        Observations Used :    88
              Residual SS :     7.66318204
              Residual df :    42
Final variance parameter estimates:
            Var(Residual)       0.18245672


❝ Very near to the MIXED results. But maybe this coincidence is by chance.


Who knows?

❝ But never trust estimates with questionable convergence in REML (WINNONLIN

❝ definitely warns at least "Output is suspect", R's lme() will throw an error if

❝ not converged and will not give you any parameter estimate or will not give

❝ any CI for the covariance parameters if VarCov is not positive definite, but

❝ the incredible [image] tells all estimates

❝ as if nothing happens :angry:).


Well, if I ask PHX/WNL for intermediate results (besides many pages of Matrazen aka matrices) I got an additional
Warning 11090: Asymptotic covariance matrix not computed. Information matrix is deemed singular.

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