[R-sig-ME] Unclear output from MCMCglmm with categorical predictors
roim@or m@ili@g off post@t@u@@c@il
roim@or m@ili@g off post@t@u@@c@il
Wed Nov 7 14:51:40 CET 2018
Dear list members,
I have a model with categorical response and categorical + continuous
predictors.
My model has two categorical predictors: "diet" (3 levels) and
"habitat" (6 levels):
THRE1 <- MCMCglmm(Activity ~ -1 + Habitat + Diet + log(Mass) + Max.Temp,
prior = list(R = list(V = 1, fix = 1)),
ginverse = list(Binomial=INphylo$Ainv),
family = "threshold",
data = Tdata)
If I understand correctly, in this configuration the algorithm
shouldn't return estimated values for the effect of each level of a
categorical predictor, instead, it returns a contrast between that
level and another level which was arbitrarily chosen as the base
level. Each species (data point) has a value for each of these traits,
so I would expect them to be estimated independently, meaning that one
level of each predictor should be the 'baseline' and absorbed into the
global intercept. In that case I expect 2 contrasts to be returned for
diet categories and 5 contrasts for habitat.
However, I get 2 estimates (presumably contrasts) for diet categories,
and 6 for habitat categories, i.e., no habitat category was designated
as baseline, which makes me question whether the estimates are
contrasts or actual effect sizes.
My questions:
- Is the algorithm pooling all the predictor categories as if they
were a single trait with 8 levels?
- If the habitat effect estimates are contrasts - what are they compared to?
- If they are effect sizes - what did I do to not get the contrasts as
I expected?
Any help would be much appreciated!
Thanks,
--
Roi Maor
PhD candidate
School of Zoology, Tel Aviv University
Centre for Biodiversity and Environment Research, UCL
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