sbgcop.mcmc {sbgcop} | R Documentation |
sbgcop.mcmc
is used to semiparametrically estimate the
parameters of a Gaussian copula. It can be used for posterior
inference on the copula parameters, or for imputation of
missing values in matrix-valued data.
sbgcop.mcmc(Y, S0 = diag(dim(Y)[2]), n0 = dim(Y)[2] + 2, nsamp = 100, odens = max(1, round(nsamp/1000)), seed = 1, verb = TRUE)
Y |
an n x p matrix. Missing values are allowed. |
S0 |
a p x p positive definite matrix |
n0 |
a positive integer |
nsamp |
number of iterations of the Markov chain. |
odens |
output density: number of iterations between saved samples. |
seed |
an integer for the random seed |
verb |
print progress of MCMC(TRUE/FALSE)? |
This function produces MCMC samples from the posterior distribution of a correlation matrix, using a scaled inverse-Wishart prior distribution and an extended rank likelihood. It also provides imputation for missing values in a multivariate dataset.
An object of class psgc
containing the following components:
C.psamp |
an array of size p x p x nsamp/odens ,
consisting of posterior samples of the correlation matrix. |
Y.pmean |
the original datamatrix with imputed values replacing missing data |
LPC |
the log-probability of the latent variables at each saved sample. Used for diagnostic purposes. |
Peter Hoff
http://www.stat.washington.edu/hoff/
fit<-sbgcop.mcmc(swiss) summary(fit) plot(fit)