Workarounds for v6.3+ [Software]

posted by Helmut Homepage – Vienna, Austria, 2018-01-08 00:40  – Posting: # 18148
Views: 5,241

Hi Mittyri and Antigoni,

» As I see the problem is in quantile function, looks like approximation is not correct for low DF

Confirmed. I checked the 64bit-versions of PHX/WNL 6.3, 6.4, 7.0, and 8.0 (Linear Mixed Effects and WNL Linear Model 502, 1–7 degrees of freedom). The critical t-value is not reported in Model 502. But we can ­calculate it from the confidence limit(s), the estimate, and its standard error as    (UnivarCI_Upper – Estimate) / StdError or (Estimate – UnivarCI_Lower) / StdError.
 Interesting  Scary that the reported critical t-value in Linear Mixed Effects is also wrong! I didn’t expect that… Results are identical (full numeric precision) in all versions of PHX/WNL. The custom function tinv(0.025, df) introduced in v6.4 is practically correct.

df   R/OO/Gnumeric    LME (rep’d)      %RE     502 (calc)       %RE  tinv(0.025, df)    %RE 
 1 12.706204736175 11.300117888951 –11.0661 16.500679825590 +29.8632 12.706204736175 ±0.0000
 2  4.302652729749  4.270551416408  –0.7461  4.327589042282  +0.5796  4.302652729750 +0.0000
 3  3.182446305284  3.178617037716  –0.1203  3.183479996442  +0.0325  3.182434925786 –0.0004
 4  2.776445105198  2.775582256369  –0.0311  2.776407538431  –0.0014  2.776445033314 –0.0000
 5  2.570581835636  2.570308254272  –0.0106  2.570544577883  –0.0014  2.570581685935 –0.0000
 6  2.446911851145  2.446804416220  –0.0044  2.446913617243  +0.0001  2.446911763977 –0.0000
 7  2.364624251593  2.364575396335  –0.0021  2.364643234749  +0.0008  2.364624210734 –0.0000


Given that, use only LME (map time as Regressor, concentration as Dependent, drag time from Regressors/Covariates to Model Specification). After execution I suggest these workarounds:In BE we don’t need the CI of fixed effects and the degrees of freedom are generally high. However, I’m dumbfounded by such defects in commercial software. Even for high degrees of freedom tinv(x, y) is three orders of magnitude “better” than what is given by LME/BE. Why is it not used?
df   R/OO/Gnumeric       LME = BE        %RE        tinv(0.025, 28)   %RE
28 2.04840714179524  2.04840711367364 –1.37·10–6  2.04840714175209 –2.11·10–9


If you don’t trust in the open source software I used, below an excerpt of R.A. Fisher’s Statistical Methods for Research Workers (1934), the Geigy tables (1980), WolframAlpha, and UsableStats:
df             t(p 0.025)
 1  12.706 12.7062 12.706 12.70620474
 2   4.303  4.3027  4.303  4.30265273
 3   3.182  3.1824  3.182  3.18244912
 4   2.776  2.7764  2.776  2.77644504
 5   2.571  2.5706  2.571  2.57058169
 6   2.447  2.4469  2.447  2.44691177
 7   2.365  2.3646  2.365  2.36462421
28   2.048  2.0484  2.048  2.04840714

Cheers,
Helmut Schütz
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