dsimpostppt: Posterior projected Polya Tree distribution

In PPTcirc: Projected Polya Tree for Circular Data

Posterior projected Polya Tree distribution

Description

Performs posterior inference for a given a circular dataset with the Projected Polya Tree via a MCMC algorithm.

Usage

dsimpostppt(datafile,units = c("radians", "degrees", "hours"),
mm = 4, mu = c(0, 0), sig = 1, aa = 1, delta = 1.1,
it = 500, bi = 50, ti = 2, kapa = 0.5, ha = 0, hm = 0,
c0 = 1, c1 = 2, iota = 6, mu0 = 0, taum = 1, control.circular = list())

Arguments

datafile	the data from which the estimate is to be computed. The object is circular or will be coerced to circular.
units	units of the support: "radians", "degrees" or "hours".
mm	number of finite levels of the Polya tree
mu	mean vector of the projected bivariate normal centering distribution.
sig	precision of the projected bivariate normal centering distribution.
aa	alpha. Standard deviation parameter of the projected Polya tree.
delta	controls of the speed at which the variances of the branching probabilities move down in the tree, rho(m)=m^delta.
it	number of iterations for MCMC.
bi	number of burn in iterations for MCMC.
ti	thinning parameter of the MCMC chain.
kapa	tunning parameter in the MH proposal distribution for the latent resultants R.
ha	logical. If TRUE alpha will be assigned Ga(c0,c1) hyper-prior distribution.
hm	logical. If TRUE mu will be assigned N(mu0,taum) independent hyper-prior distributions for each coordinate.
c0, c1	shape and rate hyper-parameters of the gamma prior distribution for alpha. These will be used only when ha=1.
iota	tunning parameter in the MH proposal distribution for alpha.
mu0, taum	mean and precision hyper-parameters of the independent normal prior distribution for each coordinate of mu. These will be used only when hm=1.
control.circular	the attribute used to coerced the resulting. object. See circular.

Value

An object of class postppt.circ whose underlying structure is a list containing the following components:

x	points where the density is evaluated.
predictive	predicitive density estimated with the projected Polya tree.
quantile2.5 quantile97.5	lower and upper 95% credible interval limits.
stats	descriptive statistics: mean direction and concentration of each MCMC density.
cpo	conditional predictive ordinate statistic for the data.
LMPL	logarithm of the pseudo marginal likelihood statistic.
aa.sims	vector of simulated alphas when ha=1.
mu.sims	matrix of simulated bivariate means when hm=1.
acceptancerate	Acceptance rate of MH step for the latent resultants.
acceptancerate_aa	Acceptance rate of MH step for alpha.
data	original dataset.

References

Nieto-Barajas, L.E. & Nunez-Antonio, G. (2019). Projected Polya tree. https://arxiv.org/pdf/1902.06020.pdf

See Also

postppt.plot, postppt.summary

Examples

data(tapir)
#It is advised to increase the number of iterations for a better fitting
z1 <- dsimpostppt(tapir, units = "radians", it = 5, ti =1, bi=0, ha = 1, hm =1)
class(z1)
length(z1$acceptancerate)
z1$acceptancerate

postppt.summary(z1)
postppt.plot(z1, plot.type= "line" , ylim = c(0,0.8))

PPTcirc documentation built on Aug. 30, 2022, 9:06 a.m.