Just thinking loud [Software]
Thanks Yung-jin,
Here I am thinking loud, and usually nothing good results when I do it. But here goes:
By way of a typical anova (forget for a moment the case of imbalance and type III SS which is irrelevant anyway for the actual dataset example) we have:
SStot=SStrt+SSseq+SSper+SSsubj+SSres
where
MSsubj=SSsubj/(n-2) [n=total number of subjects counting both sequences]
and
MSres= SSres/(n-2).
I want to know what the color and taste of "MSsubj-MSres" as prescribed above is:
MSsubj=SSsubj/(n-2)
or
MSsubj=(SStot-SStrt-SSseq-SSper-SSres)/(n-2)
Then
"MSsubj-MSres" = (SStot-SStrt-SSseq-SSper-SSres)/(n-2) - SSres/(n-2)
= (SStot-SStrt-SSseq-SSper-2*SSres)/(n-2)
This is truly a mindf%cker. My brain refuses to cooperate. I am experiencing the cerebral equivalent of a
Proton M rocket launch.
Actually, I am inclined to think there is something wrong with that subtraction in the first place.
❝ ❝ "MSSubject(seq)-MSResidual" enter the game?
❝
❝ Should be this (as presented values in my previous post), I guess.
Here I am thinking loud, and usually nothing good results when I do it. But here goes:
By way of a typical anova (forget for a moment the case of imbalance and type III SS which is irrelevant anyway for the actual dataset example) we have:
SStot=SStrt+SSseq+SSper+SSsubj+SSres
where
MSsubj=SSsubj/(n-2) [n=total number of subjects counting both sequences]
and
MSres= SSres/(n-2).
I want to know what the color and taste of "MSsubj-MSres" as prescribed above is:
MSsubj=SSsubj/(n-2)
or
MSsubj=(SStot-SStrt-SSseq-SSper-SSres)/(n-2)
Then
"MSsubj-MSres" = (SStot-SStrt-SSseq-SSper-SSres)/(n-2) - SSres/(n-2)
= (SStot-SStrt-SSseq-SSper-2*SSres)/(n-2)
This is truly a mindf%cker. My brain refuses to cooperate. I am experiencing the cerebral equivalent of a
Proton M rocket launch.
Actually, I am inclined to think there is something wrong with that subtraction in the first place.
—
Pass or fail!
ElMaestro
Pass or fail!
ElMaestro
Complete thread:
- How to calculate intersubject variability in PHX WinNonlin zan 2014-01-29 23:58 [Software]
- Negative variance component Helmut 2014-01-30 01:16
- Negative variance component zan 2014-01-30 18:11
- Negative variance component zan 2014-01-31 00:16
- Negative variance component ElMaestro 2014-01-31 08:20
- Negative variance component yjlee168 2014-01-31 10:26
- Example data set Helmut 2014-02-01 16:03
- Example data set yjlee168 2014-02-01 17:40
- PHX build 6.3.0.395 / 6.4.0.511 Helmut 2014-02-02 02:04
- PHX build 6.3.0.395 / 6.4.0.511 yjlee168 2014-02-02 07:54
- PHX build 6.3.0.395 / 6.4.0.511 Helmut 2014-02-02 02:04
- Example data set yjlee168 2014-02-01 17:40
- Example data set Helmut 2014-02-01 16:03
- Negative variance component ElMaestro 2014-02-01 16:31
- Negative variance component yjlee168 2014-02-01 17:47
- Just thinking loudElMaestro 2014-02-01 19:02
- All models are wrong… Helmut 2014-02-02 02:31
- another book for linear model yjlee168 2014-02-02 08:07
- All models are wrong… Helmut 2014-02-02 02:31
- Just thinking loudElMaestro 2014-02-01 19:02
- References Helmut 2014-02-02 02:19
- References ElMaestro 2014-02-02 09:56
- Negative variance component yjlee168 2014-02-01 17:47
- Negative variance component – Chow/Liu d_labes 2014-02-03 09:02
- Negative variance component – Chow/Liu ElMaestro 2014-02-03 10:22
- Variance components – Proc mixed d_labes 2014-02-03 11:58
- Variance components – Proc mixed ElMaestro 2014-02-03 12:58
- Variance components – Proc mixed 90% CIs d_labes 2014-02-03 13:16
- Variance components – Proc mixed Helmut 2014-02-03 14:14
- FDA code for non-replicate crossover? d_labes 2014-02-03 15:54
- Proc GLM rulez Helmut 2014-02-03 16:16
- Variance components – Proc mixed yjlee168 2014-02-03 20:43
- NOBOUND Helmut 2014-02-03 22:08
- FDA code for non-replicate crossover? d_labes 2014-02-03 15:54
- Variance components – Proc mixed ElMaestro 2014-02-03 12:58
- Variance components – Proc mixed d_labes 2014-02-03 11:58
- lm() or lme() for 2x2x2 study design? yjlee168 2014-02-03 20:22
- lm() or lme() for 2x2x2 study design? ElMaestro 2014-02-03 22:11
- lm() or lme() for 2x2x2 study design? yjlee168 2014-02-04 13:09
- bear for 2x2x2 study with negative variance components yjlee168 2014-02-05 19:12
- lm() or lme() for 2x2x2 study design? yjlee168 2014-02-04 13:09
- lm() or lme() for 2x2x2 study design? ElMaestro 2014-02-03 22:11
- Negative variance component – Chow/Liu ElMaestro 2014-02-03 10:22
- Negative variance component Helmut 2014-01-30 01:16