Mixed interest [General Sta­tis­tics]

posted by d_labes  – Berlin, Germany, 2010-07-13 17:11 (5421 d 10:09 ago) – Posting: # 5618
Views: 14,600

Dear Helmut!

❝ I found it interesting that ABE's CVWR 0.452397 matched Paolo's

❝ teacher's 0.452 (though I'm not sure what he means by 'reference

❝ variability').


Then you may find this also interesting :cool:.

Results of Proc MIXED FDA code:
...
The Mixed Procedure

                     Iteration History

Iteration    Evaluations    -2 Res Log Like       Criterion
        0              1       306.93584974
        1              3       274.26626278      8.89107758
...
        4              1       272.91032505      0.00000213
        5              1       272.91032497      0.00000001

   Convergence criteria met but final hessian is not positive
                           definite.


                        Estimated G Matrix

 Row    Effect       Treatment    Subject        Col1        Col2

   1    Treatment    R             1           0.3313      0.2581
   2    Treatment    T             1           0.2581      0.3596

        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
...


And just more interesting: Analysis according to D. Brown (EMA) of data under treatment with R only, employing an ANOVA with period, sequence and subject within sequence effects (all fixed):
...
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
...

(All colors by me).
Very near to the MIXED results. But maybe this coincidence is by chance.
Note also that this is not the same as the analysis via intra-subject contrasts R1-R2 with
s2WR= 0.143318, sWR= 0.378573 (Paolos result), CV=0.392551.

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:).

Regards,

Detlew

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