[R-sig-ME] Fw: re: Trend in total number of animals

[damian.collins at industry.nsw.gov.au]

Dear Jarrod,

You asked about ASReml's na.include=Y option.

No, this does not do any fancy imputation or augmentation like MCMCglmm
does either.

As you can read on p113 of the user guide (p139 of the pdf)
http://www.vsni.co.uk/downloads/asreml/release3/UserGuide.pdf
!mvinclude just imputes zeros. This obviously assumes centred covariates.
For factors, another factor level is created.

Damian

Damian Collins, Biometrician, I&I NSW
damian.collins at industry.nsw.gov.au

>Dear Doug,

>Perhaps I misunderstand Rubin's missing data theory, and/or perhaps
>its not relevant to Thierry's problem.


>I was under the impression that if the probability of missingness
>depends on the value observed for some other data (MAR), then by
>including this data and structuring the likelihood correctly then
>correct inferences (i.e. in the absence of missingness) could be made.
>Given that the default na.action of lmer seems to deletes other data
>(complete case analysis), it is hard to see how the other data can be
>used to 'correct' for missingness. MCMCglmm uses augmentation for
>missing data. Internally, this is often used just to simplify/speed up
>the matrix operations using dummy data.  However, I had presumed that
>if users really did have MAR data then the augmentation would take
>care of this. I know ASReml has an na.includeY argument so presumably
>there is something to be gained by not reducing the problem to a
>complete-case analysis, but perhaps this function is there just to
>allow users to make predictions for missing data points. I know the
>asreml team read this list, so perhaps they could comment?

>Cheers,
>Jarrod,


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