Dear List,

Would anyone be able to advise on how to analyse an incomplete repeated measures experiment?

I looked at photosynthesis rates at two time points during a field season (herafter period, a factor) and I was interested in the effect of the altitude of plant origin (hereafter 'treatment',  continuous independent variable) on photosynthesis rate and if there was an interaction between treatment and period. I studied 6 plants per treatment, making 48 plants, which were in 6 different blocks (1 treatment per block).
I initially used this model (in lmer)
Photosynthesis~Treatment*Period+(1|Block)
and there is an insignificant interaction, so I used the model
Photosynthesis~Treatment+Period+(1|Block)
However, thinking about this more, I realised that about 50% of the measures during the second season were made on the same plants as in the first season, and 50% werent (because plants died in between). So it is an incomplete repeated measures design.

I could account for the repeated measures part by this model:

Photosynthesis~Treatment+Period+(1|Block)+(1|Plant)

However, this model will not converge as some plants are not repeated (I think - and the 'plant' variance becomes 0). The model will converge if I use only the plants which were repeated, but then I lose half my data. This also seems 'wrong' as the plants which were not repeated are still 'good' data points.



Could anyone advise if I can code a model that will account for this correlation structure? The main thing I am interested in is the treatment effect, the interaction between treatment and period is less important for the question.



Thank you,



Rebecca Ross

Department of Plant Sciences,

University of Oxford







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