[R-sig-ME] Time-varying random effects
marc.jacobs012 at gmail.com
Fri Nov 25 08:05:12 CET 2016
thnx tor the replies but in the answers I have not found what I was looking
The pigs are separated from their litter in the nursery phase being placed
in pens based on a blocking factor (Bodyweight). This happens again in the
growth-finish fase. Thus yes, they are moved around at least two times, all
Hence, although the genetic similarity remains across the entire study
(pigs nested in sows), there are crossed effects with blocks, rooms, and
pen, because it changes. Since pigs are social animals, the pen effect
should matter and hence should be taken into account. The Blocking effect
speaks for itself I think.
Normally, this data set would be analyzed three times - once for the
farrowing phase, once for the nursery phase, and once for the growth finish
fase. This way, you have no time-varying RANDOM effects, but I want to
model the entire growth curve, whilst taking into account random factors
that change over time.
2016-11-24 15:43 GMT+01:00 Thierry Onkelinx <thierry.onkelinx at inbo.be>:
> Hi Mark,
> I have some questions on the design.
> - Can you identify the individual pigs in the data?
> - How is the grouping of the pigs? Is it constant (e.g. all pigs from the
> same litter stay together)? Or does the grouping changes over time?
> - Do expect any effect of the pens itself? Or are the pens rather a just
> group of pigs.
> Best regards,
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
> 2016-11-23 15:58 GMT+01:00 Marc Jacobs <marc.jacobs012 at gmail.com>:
>> By request of Prof. Bolker, i am posting my question here.
>> I am currently in the process of analyzing a growth model in pigs. Due to
>> the confidentiality of the data, I cannot add any data which is of course
>> the preferred course, but I hope to gain some insight here. I apologize in
>> advance if the description is unclear.
>> The data shows growth in 300+ pigs over 168 days, measured on 11
>> time-points. These 168 days can be divided in three separate phases:
>> farrowing/mom (2 timepoints), nursery (4 timepoints), and growth-finish (5
>> During each of these phases, the animals are placed in different rooms and
>> pens (nested in the rooms), which by definition are random factors. Also,
>> there is a genetic dependency of pigs (litter) nested in moms, which would
>> be a crossed effect, since the effect takes place across the entire
>> dataset, separate from the room/pen (pigs are separated from the litter
>> after the farrowing/mom phase).
>> As such, from my point of view, the room/pen are now time-varying random
>> effects. Since I wish to model the entire growth curve, I was wondering if
>> anybody knows how to incorporate time-varying random effects?
>> My gut feeling tells me this is quite easy, but my models do not converge.
>> If you need more information, please let me know.
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>> R-sig-mixed-models at r-project.org mailing list
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