MCMCSO3 | R Documentation |
Use non-informative Bayesian methods to infer about the central orientation and concentration parameter for a sample of rotations.
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)
x |
|
type |
Angular distribution assumed on R. Options are |
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 |
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
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
#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)
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