bmdsMCMC {bayMDS} | R Documentation |
run MCMC algorithm given in Oh and Raftery (2001) and return posterior samples of parameters as well as object configuration and other parameter estimates, for a given number of dimensions p
bmdsMCMC(DIST,p,nwarm = 1000,niter = 5000)
DIST |
symmetric matrix of dissimilarity measures between objects |
p |
number of dimensions of object configuration |
nwarm |
number of iterations for burn-in period in MCMC (default=1000) |
niter |
number of MCMC iterations after burn-in period (default=5000) |
A list of MCMC results
n by p matrix of object configuration that minimizes the sum of squares of residuals(SSR), where n is the number of objects, i.e., n=nrow(DIST)
n by p matrix of object configuration from the classical multidimensional scaling of Togerson(1952)
minimum of sum of squares of residuals between the observed dissimilarities and the estimated Euclidean distances for pairs of objects
index of the iteration corresponding to minimum SSR
STRESS computed from minSSR
posterior mean of \sigma^2
posterior variance of \sigma^2
niter dimensional vector of posterior samples of SSR
niter by p matrix of posterior samples of elements of \Lambda
niter dimensional vector of posterior samples of \sigma^2
niter by n(n-1)/2
matrix of posterior samples of \delta
, p-dimensional Euclidean distances
between pairs of objects
Oh, M-S., Raftery A.E. (2001). Bayesian Multidimensional Scaling and Choice of Dimension, Journal of the American Statistical Association, 96, 1031-1044.
data(cityDIST)
result=bmdsMCMC(cityDIST,p=3)