Description Usage Arguments Author(s) See Also Examples
View source: R/posterior_vs_prior.R
Plots the posterior vs. prior distributions of parameters of the FPK model.
1 | posterior_vs_prior(chain,param='a',burnin=0.2,type_prior='Normal',shape_prior=c(0,10))
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chain |
An data.frame object representing the output of an MCMC chain, as obtained by MH_MCMC_FPK. |
param |
The parameter of the model for which we want to compare prior and posterior. |
burnin |
The percentage of generations discarded as burnin. |
type_prior |
A character specifying the type of prior used for this parameter. |
shape_prior |
A numeric vector giving the shape of the prior used for this parameter. |
F.C. Boucher
MH_MCMC_FPK
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Not run:
# Simulate data: tree + continuous trait
library(geiger)
tree=sim.bdtree(stop='taxa',n=10) # tree with few tips for quick tests
tree$edge.length=100*tree$edge.length/max(branching.times(tree)) # rescale the tree
# Simulate trait evolving on a macroevolutionary landscape with two peaks of equal heights
x=seq(from=-1.5,to=1.5,length.out=100)
bounds=c(min(x),max(x)) # the bounds we use for simulating: for technical purposes only
V6=10*(x^4-0.5*(x^2)+0.*x) # this is the evolutionary potential: it has two wells
TRAIT= Sim_FPK(tree,x0=0,V=V6,sigma=10,bounds=c(-5, 5))
# Run a MCMC chain to fit the FPK model
MCMC=MH_MCMC_FPK(tree,trait=TRAIT,bounds=c(5,5),Nsteps=10000,record_every=100,
plot_every=100,Npts=20,pars_init=c(0,-4,-4,0,1),prob_update=c(0.2,0.25,0.25,0.25,0.05),
verbose=TRUE,plot=TRUE,save_to='MCMC_FPK_test.Rdata',save_every=100,
type_priors=c(rep('Normal',4),'Uniform'),
shape_priors=list(c(0,10),c(0,10),c(0,10),c(0,10),NA),proposal_type='Uniform',
proposal_sensitivity=c(0.1,0.1,0.1,0.1,1),prior.only=F)
get.landscape.FPK.MCMC(chain=MCMC,bounds=c(5,5),Npts=100,burnin=0.1,
probs.CI=c(0.025,0.975),COLOR_MEDIAN='red',COLOR_FILL='red',transparency=0.3,
main='Macroevolutionary landscapes MCMC',ylab='N.exp(-V)',xlab='Trait',
xlim=NULL,ylim=NULL)
posterior_vs_prior(chain=MCMC,param='a',Npts=100,burnin=0.2,type_prior='Normal',shape_prior=c(0,10))
## End(Not run)
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