BBMV (version 2.1)

posterior_vs_prior: Posterior vs. prior comparison for the FPK model.

Description

Plots the posterior vs. prior distributions of parameters of the FPK model.

Usage

posterior_vs_prior(chain,param='a',burnin=0.2,type_prior='Normal',shape_prior=c(0,10))

Arguments

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.

See Also

MH_MCMC_FPK

Examples

# 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))
# }