Thank you so much! [Software]

posted by PharmCat  – Russia, 2019-12-01 17:21 (1602 d 02:45 ago) – Posting: # 20904
Views: 3,847

(edited by PharmCat on 2019-12-02 01:58)

Hi Helmut! Thank you for appreciation of the work! :cool:

❝ Julia is a nasty beast (IMHO, more than SAS and R combined). :-D

Agree, Sometimes)

Hint: Using Juno IDE (Atom based) makes Julia much more appeasable.

In the Random statement, TYPE=FA0(2) could possibly be replaced by TYPE=CSH.

I understand and agree, that "possibly" has an indefinite connotation. But, for example, in SPSS it is a only opportunity to fit model with variance-covariance structure that include free covariance. And in current case FA0 (2) == CSH, results is equal. There is no big broblem to implement FA0 (2), but it little more computationally complex matrix and this get only decrease of performance. If "variance-covariance structure police" will bring charges of violation of covariation law - ok, there will be no other way out but to realize this. ;-):-D

❝ In some of our reference datasets (and terrible ones posted by John in the forum) of the partial replicate TRR|RTR|RRT we ran into convergence issues with FA0(2) but never with FA0(1) and CSH.

Very ironic, given that FA0(x) is often touted as a more stable version of CSH. :-D

❝ Do I understand correctly that you have given up to support partial replicates as all?

With partial replicates design there is good news and bad. Good news: fixed effects, variance components, REML value, SE for all coefficients is full complient in all datasets with SPSS/SAS. And in some cases when convergence fail in SPSS/SAS in Julia you can get better results. Bad news: Satterthwaite degree of freedom is full complient only in designes mantioned above. In other datasets we have miserable deviations but they leads to small changing in CI (± 0.01%), really upset. In other side a similar situation is observed when comparing Phoenix with SAS - the difference in the assessment of degrees of freedom on some datasets, but this does not bother anyone. Anyway, at this moment I can't recommend to use this package with partial replicated designs untill there is any evidence of the absence of significance of these deviations. If you are not limited by regulators in use only Satterthwaite DF - you can use "contain" DF or define it manually and get resulst for any dataset you want.

Edit: Merged with a later (now deleted) post. You can edit your posts within 24 hours. [Helmut]

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