[R-sig-ME] R-sig-mixed-models Digest, Vol 119, Issue 26

[Cole, Tim]

Hi Marc,

Ken Beath described a growth curve model with time-varying covariates in Statist Med 2007; 26:2547-2564, where his example was the effect of breastfeeding on infant weight. His appendix includes the relevant R code.

My CRAN sitar library implements a simplified version of his model without the time-varying element.

Best wishes,
Tim
---
tim.cole at ucl.ac.uk<mailto:tim.cole at ucl.ac.uk> Phone +44(0)20 7905 2666 Fax +44(0)20 7905 2381
Population Policy and Practice Programme
UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK

Date: Wed, 23 Nov 2016 15:58:18 +0100
From: Marc Jacobs <marc.jacobs012 at gmail.com<mailto:marc.jacobs012 at gmail.com>>
To: r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
Subject: [R-sig-ME] Time-varying random effects

Hi,

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
timepoints).

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.

Marc


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