[R-sig-ME] Modelling non-negative non-zero continuous data
v|ctor|@@w||||t@ @end|ng |rom gm@||@com
Fri Apr 29 20:57:49 CEST 2022
I am hoping you can help me. I am trying to model chick tarsi (leg length)
data. Briefly, I have mean measurements of tarsus length from 457 nests.
The data were collected across 31 sites (10 nests in each site) over a
six-year period so I have an *a priori *nested random effects structure:
(1|SITE_ID/BOX_NUMBER) + (1|YEAR). (Although I have had to remove the
nested nest_box term due to convergence issues - there is a lot of variance
between nests within sites.)
The problem that I am running into is that the data is bound between the
values 12.73 and 20.12 mm. Both the data itself and the residuals from a
lmer model are left-skewed because the data is non-negative and non-zero.
The initial suite of models I have tried follows the below: I am running
models using both glmmTMB and lme (lmer).
(Also I have run the same models using the same length data for a bunch of
other response variables with no issues including various breeding outcomes
and chick measurements). Fixed covariates are scaled and centred: (sc.)
data=DF_CHICK_TARSUS, family = gaussian)
I am a little stumped as to what to do - I have run the same model using
reflected and log (and/or square root) transformed data - which does seem
to resolve the residual issues. However, I know that this is not the best
resolution and is rarely done, and transforming data even for the more
commonly found right-skewed data is increasingly discouraged. However, I am
not finding (and this may be me not using the correct terms in my search!)
any other options to overcome the issue of non-negative non-zero data -
plenty of advice for ecological data that is right-skewed or left-skewed
If anyone can help me I would really appreciate it. Thank you all so much
as always in advance for your time and knowledge sharing. I am gradually
building up my competence in R and mixed modelling and this forum has been
really helpful on this steep learning curve! I am hoping I am just missing
something obvious! Please let me know if you need any other information
from me. Thank you!
Very best wishes.
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