MCMCSO3: MCMC for rotation data

In rotations: Working with Rotation Data

MCMC for rotation data

Description

Use non-informative Bayesian methods to infer about the central orientation and concentration parameter for a sample of rotations.

Usage

MCMCSO3(x, type, S0, kappa0, tuneS, tuneK, burn_in, m = 5000)

## S3 method for class 'SO3'
MCMCSO3(x, type, S0, kappa0, tuneS, tuneK, burn_in, m = 5000)

## S3 method for class 'Q4'
MCMCSO3(x, type, S0, kappa0, tuneS, tuneK, burn_in, m = 5000)

Arguments

x	n-by-p matrix where each row corresponds to a random rotation in matrix (p=9) or quaternion (p=4) form.
type	Angular distribution assumed on R. Options are Cayley, Fisher or Mises
S0	initial estimate of central orientation
kappa0	initial estimate of concentration parameter
tuneS	central orientation tuning parameter, concentration of proposal distribution
tuneK	concentration tuning parameter, standard deviation of proposal distribution
burn_in	number of draws to use as burn-in
m	number of draws to keep from posterior distribution

Details

The procedures detailed in bingham2009b and bingham2010 are implemented to obtain draws from the posterior distribution for the central orientation and concentration parameters for a sample of 3D rotations. A uniform prior on SO(3) is used for the central orientation and the Jeffreys prior determined by type is used for the concentration parameter.

bingham2009b bingham2010

Value

list of

• S Draws from the posterior distribution for central orientation S

• kappa Draws from the posterior distribution for concentration parameter kappa

• Saccept Acceptance rate for central orientation draws

• Kaccept Acceptance rate for concentration draws

Examples

#Not run due to time constraints

Rs <- ruars(20, rfisher, kappa = 10)
draws <- MCMCSO3(Rs, type = "Fisher", S0 = mean(Rs), kappa0 = 10, tuneS = 5000,
                 tuneK = 1,burn_in = 1000, m = 5000)

rotations documentation built on June 25, 2022, 1:06 a.m.