lmer: Method B is ready for scaling [🇷 for BE/BA]
❝ mittyri!!!
❝ May I hug you?!


❝ Why didn't you provide the result of the code (that's the way of secret initiation?)
I was so surprized by the results that was in hurry to share my code

❝ Please explain how to get Std.Error and Value with more digits?
FormulationT<-summary(muddle.lmer)$coefficients["FormulationT",]
cat(round(exp(FormulationT[1]+FormulationT[2]*qt(0.05, FormulationT[3]))*100, 2), "-",
round(exp(FormulationT[1]-FormulationT[2]*qt(0.05, FormulationT[3]))*100, 2))
confint is not the right thing here. Here we go:
lmer treats Sequence, Period and Formulation as factors, Random is /~1|Subject/ (close to Method B), then
[1] 0.1460882
PE is 115.73
SAS gives 115.73
what about LSMeans?
PHX gives for Method B 7.67001367911898 and 7.81610190985527
and lmer gives
Formulation Estimate
Formulation R R 7.6700
Formulation T T 7.8161
what about CI? SAS gives 107.17-124.97
107.17 - 124.97
❝ Why does the model have (1|Subject) as random? I also tried (1|Subject/Sequence) and the result (PE and CI) remains the same. Can we use it instead?
Yes, we can. Subject in Sequence is SAS legacy. By the way as ElMaestro wrote, that's meaningless, because the reference grid (G-matrix) doesn't change. It will be useful only if we are enumerating the subjects in each sequence from 1 (Seq ABAB: 1, 2, 3...; Seq BABA: 1, 2, 3...)
❝ The other question is what from the output we can use to estimate R-variance in order to apply scABE?
No, we cannot. Sorry, I must confess I don't have time now to implement this, but I would do it as follows:
- exclude all T data first and then
- build a linear model with lm() using only reference data.
If nobody does it during the next week I'll try to code it by myself.
❝ We are in front of the final step - from now we can make R tutorial to hold scABEL as did Helmut in WinNonLin, am I right? If it is so, Es ist fantastisch!
Ja, new feature to implement in BEAR, Yung-jin will be happy

Kind regards,
Mittyri
Complete thread:
- Bear vs. Phoenix & SAS Helmut 2015-04-20 17:34 [🇷 for BE/BA]
- R vs. Phoenix & SAS? yjlee168 2015-04-20 19:36
- R vs. Phoenix & SAS? Helmut 2015-04-21 01:02
- lme() does not work with all fixed effects yjlee168 2015-04-21 23:41
- lme() does not work with all fixed effects Astea 2016-11-04 00:13
- lmer: Method B (PE catched for imbalanced dataset!!!) and Method C mittyri 2016-11-05 17:38
- lmer: Method B (PE catched for imbalanced dataset!!!) and Method C Astea 2016-11-05 19:27
- lmer: Method B is ready for scalingmittyri 2016-11-05 20:01
- lmer: Method B is ready for scaling Astea 2016-11-06 11:50
- lmer: Method B is ready for scaling mittyri 2016-11-07 06:07
- lmer: Method B is ready for scaling Astea 2016-11-06 11:50
- lmer: Method B is ready for scalingmittyri 2016-11-05 20:01
- lmer: Method B (PE catched for imbalanced dataset!!!) and Method C Astea 2016-11-05 19:27
- lmer: Method B (PE catched for imbalanced dataset!!!) and Method C mittyri 2016-11-05 17:38
- lme() does not work with all fixed effects Astea 2016-11-04 00:13
- lme() does not work with all fixed effects yjlee168 2015-04-21 23:41
- R vs. Phoenix & SAS? Helmut 2015-04-21 01:02
- info for lsmeans yjlee168 2015-04-20 21:34
- info for lsmeans Helmut 2015-04-21 01:15
- once more about R and replicate designes Astea 2016-11-02 23:43
- once more about R and replicate designes VStus 2016-11-06 11:34
- Getting variance components from the lmer output StatR 2017-02-03 13:53
- Getting variance components from the lmer output VStus 2017-02-03 15:47
- Getting variance components from the lmer output StatR 2017-02-03 17:12
- Getting variance components d_labes 2017-02-07 11:16
- Getting variance components StatR 2017-02-07 11:36
- Getting variance components StatR 2017-02-08 08:41
- Getting variance components d_labes 2017-02-08 10:13
- Getting variance components StatR 2017-02-08 10:19
- Data structure Helmut 2017-02-08 10:33
- Data structure StatR 2017-02-08 10:49
- Getting variance components d_labes 2017-02-08 10:13
- Getting variance components d_labes 2017-02-07 11:16
- Getting variance components from the lmer output StatR 2017-02-03 17:12
- Getting variance components from the lmer output VStus 2017-02-03 15:47
- Getting variance components from the lmer output StatR 2017-02-03 13:53
- once more about R and replicate designes VStus 2016-11-06 11:34
- once more about R and replicate designes Astea 2016-11-02 23:43
- info for lsmeans Helmut 2015-04-21 01:15
- R vs. Phoenix & SAS? yjlee168 2015-04-20 19:36