Yeah but, no but, yeah but… [General Statistics]
Hi ElMaestro,
I must confess that I don’t have the slightest idea what you have done here. Not surprising. Hazelnut-sized brain < walnut-sized brain. However:
» I was happy to look up how Chow & Liu see it; see e.g. formula 2.5.1 and 9.1.1 in the 3rd edition
$$Y_{ijk}=\mu+S_{ik}+P_j+F_{(j,k)}+{\color{Red}{C_{(j-1,k)}}}+e_{ijk} \tag{2.5.1=9.1.1}$$Did you implement the first-order carryover as well? If yes, try it without. Might explain the differences below.
» Here's my result with EMA's dataset II, if it is of interest:
»
»
OK, you start with the EMA’s approach.
Anything goes. (© Paul Feyerabend)
With your previous REML-code I got
-code). Hence, I see two problems:
I must confess that I don’t have the slightest idea what you have done here. Not surprising. Hazelnut-sized brain < walnut-sized brain. However:
» I was happy to look up how Chow & Liu see it; see e.g. formula 2.5.1 and 9.1.1 in the 3rd edition
$$Y_{ijk}=\mu+S_{ik}+P_j+F_{(j,k)}+{\color{Red}{C_{(j-1,k)}}}+e_{ijk} \tag{2.5.1=9.1.1}$$Did you implement the first-order carryover as well? If yes, try it without. Might explain the differences below.
» Here's my result with EMA's dataset II, if it is of interest:
»
Var.Component Ini.value Value
»
varWR 0.01240137 0.01211072
OK, you start with the EMA’s approach.
library(replicateBE)
CV.wR <- 0.01*method.A(data = rds02, print = FALSE, details = TRUE)[["CVwR(%)"]]
cat("varwR", signif(log(CV.wR^2+1), 7), "\n")
varwR 0.01240137
Anything goes. (© Paul Feyerabend)
With your previous REML-code I got
0.01324648
and following the FDA’s approach (intra-subject contrasts) I got 0.01298984
(in Phoenix, SAS, and by my - Your result does not match the FDA’s approach. Your previous result is even closer (+2.0%) than the new one (–6.8%).
- Even if it would match, how would you code the FDA’s mixed model for ABE in
?
That’s the most important question.
—
Dif-tor heh smusma 🖖![[image]](https://static.bebac.at/pics/Blue_and_yellow_ribbon_UA.png)
Helmut Schütz
![[image]](https://static.bebac.at/img/CC by.png)
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Science Quotes
Dif-tor heh smusma 🖖
![[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:
- The grandiose shocker of 2020 ElMaestro 2020-07-22 13:17 [General Statistics]
- The most obscure post ever Helmut 2020-07-22 13:51
- Here goes ElMaestro 2020-07-22 14:34
- Yeah but, no but, yeah but…Helmut 2020-07-22 15:37
- Yeah but, no but, yeah but… ElMaestro 2020-07-22 17:53
- Here goes PharmCat 2020-07-29 21:11
- Yeah but, no but, yeah but…Helmut 2020-07-22 15:37
- Here goes ElMaestro 2020-07-22 14:34
- The grandiose shocker of 2020 jag009 2020-07-24 18:52
- The grandiose shocker of 2020 ElMaestro 2020-07-24 19:01
- The most obscure post ever Helmut 2020-07-22 13:51