Here goes [General Statistics]
Hello Maestro, hello all!
As if echoing you... Don't deny right away, at least in an hour... Provocational post
All thoughts about S2BR, s2BT, s2BTR separately is erroneous or incomplete. This all is part of V. All model notations also explain nothing. You can get some variance component, but without other this nothing matter. This is my very very very humble opinion. But why we need V except to get some specific component for special reason? First of all we need to calculate SE for vector of fixed effect. It can be done simple: sqrt(LCL') where L is "contrast vector" and C is variance-covariance matrix of fixed effect. C can be found like this: SUMi(Xi'Vi-1Xi)-1
There are misunderstanding in mixed models. Many people thoughts, that each line of data is observation. It really is in models without repeating. But in models with repeating all data for each subject is statistically independent observation for multivariate normal random variable with variance-covariance matrix V. V is indivisible and all attempts to obtain components are meaningless, if only because the structure of this matrix itself is just an assumption. If we consider V holistically, then it makes some sense.
As if echoing you... Don't deny right away, at least in an hour... Provocational post
All thoughts about S2BR, s2BT, s2BTR separately is erroneous or incomplete. This all is part of V. All model notations also explain nothing. You can get some variance component, but without other this nothing matter. This is my very very very humble opinion. But why we need V except to get some specific component for special reason? First of all we need to calculate SE for vector of fixed effect. It can be done simple: sqrt(LCL') where L is "contrast vector" and C is variance-covariance matrix of fixed effect. C can be found like this: SUMi(Xi'Vi-1Xi)-1
There are misunderstanding in mixed models. Many people thoughts, that each line of data is observation. It really is in models without repeating. But in models with repeating all data for each subject is statistically independent observation for multivariate normal random variable with variance-covariance matrix V. V is indivisible and all attempts to obtain components are meaningless, if only because the structure of this matrix itself is just an assumption. If we consider V holistically, then it makes some sense.
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 goesPharmCat 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