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Bayesian phylogenetic regression incorporating phylogenetic uncertainty by sampling from multiple trees (work in progress)
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require(rstan) | |
require(geiger) | |
require(MCMCglmm) | |
# load data | |
data(geospiza) | |
dat <- geospiza$geospiza.data | |
# create fake sample of trees | |
tr <- drop.tip(geospiza$geospiza.tree, 'olivacea') | |
trees <- vector('list', 15) | |
for(i in 1:15) trees[[i]] <- tr | |
# get the inverse of the phylovcv, order all to match data | |
invA <- lapply(trees, function(x) solve(vcv.phylo(x))[row.names(dat), row.names(dat)]) | |
stdat <- list( | |
N=dim(dat)[1], | |
y=dat$wingL, | |
x=dat$tarsusL, | |
invA=invA, | |
Ntree=length(invA), #number of trees | |
p=rep(1/length(invA), length(invA)) #probability of sampling each tree | |
) | |
phymodel <- " | |
data { | |
int<lower=0> N; //species | |
int<lower=0> Ntree; //number of trees | |
vector[N] y; | |
real x[N]; | |
matrix[N,N] invA[Ntree]; //array of inverse of phylovcv? | |
} | |
parameters { | |
real alpha; //intercept | |
real beta; //slope | |
real<lower=0> tau; // scaling factor | |
simplex[Ntree] theta; //mixture sampling? | |
} | |
transformed parameters{ | |
real sigma; //regression error | |
sigma <- 1/sqrt(tau); | |
} | |
model { | |
vector[N] mu; //multivariate normal mean | |
//priors | |
alpha ~ normal(0, 10); | |
beta ~ normal(0, 10); | |
sigma ~ uniform(0,1000); | |
tau ~ gamma(1,1); | |
theta ~ uniform(0,1); | |
for(n in 1:N){ | |
mu[n] <- alpha+beta*x[n]; | |
} | |
y ~ multi_normal_prec(mu, tau*invA[Ntree]); | |
} | |
" | |
phystan = stan(model_name="phylogenetic regression", model_code = phymodel, data=stdat , iter = 1000, chains = 2) |
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