| This is my first attempt asking question in this forum and I do it because
thorough research on the web didn't give me any answer.I am trying to
accomodate a conditional regression in a Bayesian generalized linear mixed
model using Monte Carlo Markov Chain. I am using the package {MCMCglmm} in R. I
already did such analysis with the {coxme} package and it worked well, but as I
want to access random slope, it seems that I must use such approach to decrease
the bias of estimates (Hadfield 2010). I (partly) understand how to code for
binomial regression (link logit) in MCMCglmm but I cannot find any indication
for paired design. Here an example of my dataset.data(bear)
Id Strata Site Real_rand Spruce Fir Road
Adele Ade-1 Ade-1 1 3 60 100.49
Adele Ade-1 Ade-1A 0 5 58 89.22
Adele Ade-1 Ade-1B 0 2 37 109.79
Adele Ade-2 Ade-2 1 1 103 198.48
Adele Ade-2 Ade-2A 0 0 192 199.26
Adele Ade-2 Ade-2B 0 0 53 201.61
Sally Sal-7 Sal-7 1 0 2 7.02
Sally Sal-7 Sal-7A 0 40 0 94.40
Sally Sal-7 Sal-7B 0 2 3 16.58
Sally Sal-8 Sal-8 1 2 21 48.74
Sally Sal-8 Sal-8A 0 8 17 112.75
Sally Sal-8 Sal-8B 0 63 0 205.04It is a black bear habitat
selection analysis, with used sites (variable Real_rand coded 1) compared with
available sites (coded 0). It is a paired design because available sites were
randomly drawn within a buffer zone around used site. I wonder if strata,
nested in Id, could used as random factor and provide similar result than cox
regression. Here an example of what it could looks like:prior <- list(R =
list(V = 1, nu = 0.002), G = list(G1 = list(V = 1, nu=0.002),
(G2 = list(V = 1, nu=0.002)))
mod1 <- MCMCglmm(Real_rand ~ Spruce + Fir + Road, random = ~Strata:Id +
us(Road):Id,
family = "categorical", data = bear, prior = prior, verbose = FALSE, pr
= TRUE)I added a random slope (us(Road):Id) to have individual coefficient for
road selection. There is probably many errors, both in the prior formula and
the glm call, but if anyone can help me to find a way to code it more
correctly, I would be grateful! |
Rémi L.
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