TMCMCpostsample2D: MCMC posterior sampling for 2D landmark data (Gaussian...

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

MCMC posterior sampling for 2D landmark data (Gaussian likelihood with Isotropic Error Variance)

Description

Draws posterior from 5 parameters "c1","c2", "b", "theta", "Sigma"

Usage

TMCMCpostsample2D(myData, choice, Nlandmark, tune, Nsample)

Arguments

myData	3D array containing 2 dimensional landmark
choice	Which to compare with 1st object or mean obj
Nlandmark	How many landmarks are there for each object? (usually nrow(myData,,1))
tune	Tuning value of MCMC sampler
Nsample	Number of MCMC sample desired

Details

"c1","c2" = Location parameter "b" = dilation parameter "theta" =Rotation angle "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$x
ape_ress_10000=MCMCpostsample2D(apes$x,10,nrow(myData[,,1]), tune=1,10)

## End(Not run)

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