MCMCpostPsample2D: MCMC posterior sampling for 2D landmark data (in Pre-shape...

In debashischatterjee111/BPviGM1: Novel Bayesian Procrustes Variance-based Inferences in Geometric Morphometrics

MCMC posterior sampling for 2D landmark data (in Pre-shape space) (Gaussian likelihood with Isotropic Error Variance)

Description

Draws posterior from 5 parameter "Sigma" from Whole Data with pre-shape spaced landmarks

Usage

MCMCpostPsample2D(initial, tune, myData, Nsample)

Arguments

initial	The start value of parameter sigma for MCMC run
tune	Tuning value of MCMC sampler
myData	3D array containing 2 dimensional landmark
Nsample	Number of MCMC sample desired

Details

"Sigma" = Isotropic error variation parameter Note that, here we are assuming Isotropy of error variance

Value

matrix containing samples from posterior density of parameter

Examples

## Not run: 
require(shapes)
data(apes)
myData = apes$x
for(i in 1:dim(myData)[3])
{myData[,,i]=
Morpho::rotonto(myData[,,1],myData[,,i])$yrot}
ape5000=MCMCpostPsample2D(1.5,
rep(0.1,1),myData,5000)
head(ape5000)

## End(Not run)

debashischatterjee111/BPviGM1 documentation built on April 8, 2023, 7:28 p.m.