## 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:
»  Var.Component  Ini.value      Value
»          varWR 0.01240137 0.01211072

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

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 -code). Hence, I see two problems:
1. Your result does not match the FDA’s approach. Your previous result is even closer (+2.0%) than the new one (–6.8%).
2. 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 🖖
Helmut Schütz

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