MCMCglmm phylogenetic model with polymorphic binary outcome

I'm using MCMCglmm to construct phylogenetic mixed models with binary
outcomes (see code below). I have 106 species and between n=3 and n=110
individuals per species. My predictor variable (ratio of incisor size to
molar size) is split, using the van der Pol and Wright (2009) method, into
species-mean and within-species terms. I have several "binary.outcome"
variables. Some of these outcome variables are at the species level (i.e.,
all individuals within a species have the same outcome), while others
exhibit polymorphism (i.e., individuals within a species have different
outcomes).

My question is, is the model specification below appropriate for both
species-specific outcomes and those that vary within species? If not, what
would be appropriate for these two types of binary outcome? I'm ultimately
concerned with interpreting the between species slope of my predictor
variable.


prior1 <- list(
    B = list(mu = rep(0, 3), V = diag(3) * (1 + pi^2/3)),
    G = list(G1 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000),
             G2 = list(V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000)),
    R = list(V = 1, fix = 1)
)

inv_phylo_mat <- inverseA(tree, nodes = "TIPS", scale = TRUE)

set.seed(1234)

fit <- MCMCglmm(
    fixed = binary.outcome ~ I2.M1.species.mean + I2.M1.within.species,
    random = ~ phylo + species,
    rcov = ~ units,
    data = incisor.dat,
    family = "categorical",
    ginverse = list(phylo = inv_phylo_mat$Ainv),
    prior = prior1,
    pr = TRUE,
    pl = TRUE,
    nitt = 1.1e+7, thin = 2000, burnin = 1e+5,
    verbose = FALSE,
    slice = TRUE
)

best,
Alberto