Mean means [🇷 for BE/BA]
Hi ElMaestro,
Didn’t say that.
If we have a four-period, two-sequence replicate design, balanced, and complete data: marginal treatment means = adjusted means (SAS-lingo: LSMs).
Why the heck? IMHO, for this dataset all means should agree, regardless the model – which they do in Vienna (to 15 significant digits; not shown):
So why do we get…
… which are ~3.8% higher for both treatments? No, I’m not satisfied if only the PE agrees – I expect to get a nasty surprise once we start to deal with unbalanced data.
❝ "If it doesn't match SAS then it must be wrong", right?
Didn’t say that.

If we have a four-period, two-sequence replicate design, balanced, and complete data: marginal treatment means = adjusted means (SAS-lingo: LSMs).
❝ I must read up on the c.c. matter when I have got the time. By the way I wonder if "LS Means" have any meaningful interpretation for mixed models when we are not working with least squares minimisation??
Why the heck? IMHO, for this dataset all means should agree, regardless the model – which they do in Vienna (to 15 significant digits; not shown):
R T
Method A (EMA, subjects fixed) 7.328141 7.391880
Method B (EMA, subjects random) 7.328141 7.391880
Method C (FDA = mixed efects) 7.328141 7.391880
Marginal means (Gnumeric) 7.328141 7.391880
So why do we get…
bear 7.356274 7.420013
… which are ~3.8% higher for both treatments? No, I’m not satisfied if only the PE agrees – I expect to get a nasty surprise once we start to deal with unbalanced data.
—
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Dif-tor heh smusma 🖖🏼 Довге життя Україна!
![[image]](https://static.bebac.at/pics/Blue_and_yellow_ribbon_UA.png)
Helmut Schütz
![[image]](https://static.bebac.at/img/CC by.png)
The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

Complete thread:
- Reproducing Bear results in Phoenix mittyri 2015-04-14 08:35 [🇷 for BE/BA]
- Bug in bear? Helmut 2015-04-14 14:07
- Bug in bear? mittyri 2015-04-14 20:43
- Bug in bear? Helmut 2015-04-15 02:17
- use the same dataset? yjlee168 2015-04-20 10:13
- Bug in bear? mittyri 2015-04-14 20:43
- Bugs in bear with replicated demo data set? yjlee168 2015-04-15 09:38
- Bugs fixed in bear? yjlee168 2015-04-17 01:21
- Bugs fixed in bear? Helmut 2015-04-17 14:25
- Bugs fixed in bear? yjlee168 2015-04-17 18:50
- Bugs fixed in bear? ElMaestro 2015-04-17 23:14
- lme() in bear yjlee168 2015-04-18 11:12
- lme() in bear ElMaestro 2015-04-18 11:28
- lme() in bear yjlee168 2015-04-18 12:02
- lme() in bear ElMaestro 2015-04-18 13:42
- Mean means Helmut 2015-04-18 14:08
- Mean means ElMaestro 2015-04-18 16:01
- Mean meansHelmut 2015-04-19 00:59
- Mean means ElMaestro 2015-04-19 09:14
- LL and AIC Helmut 2015-04-19 11:08
- Confused ElMaestro 2015-04-19 11:42
- LL and AIC Helmut 2015-04-19 11:08
- lsmeans for mixed model in R yjlee168 2015-04-19 23:36
- lsmeans() & lme() Helmut 2015-04-20 01:28
- lsmeans() & lme() yjlee168 2015-04-20 10:23
- lsmeans() & lme() ElMaestro 2015-04-20 10:35
- keep it simple! Helmut 2015-04-20 14:34
- keep it simple! ElMaestro 2015-04-20 15:39
- ML vs. REML Helmut 2015-04-20 16:35
- keep it simple! ElMaestro 2015-04-20 15:39
- keep it simple! Helmut 2015-04-20 14:34
- lsmeans() & lme() Helmut 2015-04-20 01:28
- Mean means ElMaestro 2015-04-19 09:14
- Mean meansHelmut 2015-04-19 00:59
- Mean means ElMaestro 2015-04-18 16:01
- Mean means Helmut 2015-04-18 14:08
- lme() in bear ElMaestro 2015-04-18 13:42
- lme() in bear yjlee168 2015-04-18 12:02
- lme() with NA in R yjlee168 2015-04-19 23:44
- lme() in bear ElMaestro 2015-04-18 11:28
- lme() in bear yjlee168 2015-04-18 11:12
- Dataset Helmut 2015-04-18 13:30
- Dataset and lme() in bear yjlee168 2015-04-18 23:14
- Bugs fixed in bear? ElMaestro 2015-04-17 23:14
- Bugs fixed in bear? yjlee168 2015-04-17 18:50
- Bugs fixed in bear? Helmut 2015-04-17 14:25
- Bug in bear? Helmut 2015-04-14 14:07