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RegisterR1 vs R2 [RSABE / ABEL]
Dear d_labes,
The standard formula applies, doesn't it? The df's are easy from the model or anova, the critical t-value is obtainable at your chosen alpha and the derived df's, the PE (R1 - R2) is extracted via from the model effects, the sigma thorugh the residual, and you have the harmonic mean of no's of sequences under the square root sign.
I wouldn't personally think of it as a 90% CI for R vs. R, but rather refer to it as a 90% CI for the "first R vs. second R". Quite clear that we also violate some assumptions for the normal linear model here, but I guess we must live with that.
We could perhaps alternatively think along the lines of a mixed model with a covariance matrix having both a within- and between-sigma2 (the betweens would be off diagonal, the within on the diagonal) and optimise it by REML? I have no idea how to actually do this in R or any other package. Have I been sniffing too much glue? My understanding of covariance matrices is still a bit backward.
❝ Ok, now I have the LSMeans (in SASophylistic way as standard or in R using (...blah blah blah...)
❝ And now? How to get a 90% CI for R vs. R?
❝ Simple use R1-R2 of the recoded data?
❝ Or anything else?
The standard formula applies, doesn't it? The df's are easy from the model or anova, the critical t-value is obtainable at your chosen alpha and the derived df's, the PE (R1 - R2) is extracted via from the model effects, the sigma thorugh the residual, and you have the harmonic mean of no's of sequences under the square root sign.
I wouldn't personally think of it as a 90% CI for R vs. R, but rather refer to it as a 90% CI for the "first R vs. second R". Quite clear that we also violate some assumptions for the normal linear model here, but I guess we must live with that.
We could perhaps alternatively think along the lines of a mixed model with a covariance matrix having both a within- and between-sigma2 (the betweens would be off diagonal, the within on the diagonal) and optimise it by REML? I have no idea how to actually do this in R or any other package. Have I been sniffing too much glue? My understanding of covariance matrices is still a bit backward.
—
Pass or fail!
ElMaestro
Pass or fail!
ElMaestro
Complete thread:
- Ref. vs. Ref. in partial replicate design d_labes 2012-04-25 15:52 [RSABE / ABEL]
![[Mix]](https://static.bebac.at/img/mix.png "open in Mix view")
- Ref. vs. Ref. in partial replicate design again d_labes 2012-10-10 10:56
- Ref. vs. Ref. in partial replicate design ElMaestro 2012-10-10 12:39
- Ref. vs. Ref. in partial replicate design d_labes 2012-10-10 16:26
- Ref. vs. Ref. in partial replicate design ElMaestro 2012-10-10 17:16
- R vs. R in partial replicate design d_labes 2012-10-11 09:43
- R1 vs R2ElMaestro 2012-10-11 10:26
- R1 vs R2 d_labes 2012-10-11 12:01
- R1 vs R2 ElMaestro 2012-10-11 12:09
- R1 vs R2 d_labes 2012-10-11 12:46
- treatment df ElMaestro 2012-10-11 13:38
- Missunderstanding d_labes 2012-10-11 16:34
- Missunderstanding, crippled ElMaestro 2012-10-11 18:12
- EMA crippled method - R peculiarities d_labes 2012-10-12 14:54
- All is good, just forget ANOVA - you don't need it! ElMaestro 2012-10-12 16:25
- Just forget ANOVA. What next? d_labes 2012-10-15 08:35
- Just forget ANOVA. What next? ElMaestro 2012-10-15 10:04
- Just forget ANOVA. What next? d_labes 2012-10-15 08:35
- All is good, just forget ANOVA - you don't need it! ElMaestro 2012-10-12 16:25
- EMA crippled method - R peculiarities d_labes 2012-10-12 14:54
- Missunderstanding, crippled ElMaestro 2012-10-11 18:12
- Missunderstanding d_labes 2012-10-11 16:34
- treatment df ElMaestro 2012-10-11 13:38
- R1 vs R2 d_labes 2012-10-11 12:46
- R1 vs R2 ElMaestro 2012-10-11 12:09
- R1 vs R2 d_labes 2012-10-11 12:01
- R1 vs R2ElMaestro 2012-10-11 10:26
- R vs. R in partial replicate design d_labes 2012-10-11 09:43
- Ref. vs. Ref. in partial replicate design ElMaestro 2012-10-10 17:16
- Ref. vs. Ref. in partial replicate design d_labes 2012-10-10 16:26
Ing. Helmut Schütz