Description Usage Arguments Details Value Author(s) References Examples
sbgcop.mcmc
is used to semiparametrically estimate the parameters of
a Gaussian copula. It can be used for posterior inference on the copula
parameters, and for imputation of missing values in a matrix of ordinal
and/or continuous values.
1 2 3 4 |
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. |
impute |
save posterior predictive values of missing data(TRUE/FALSE)? |
plugin.threshold |
if the number of unique values of a variable exceeds this integer, then plug-in the empirical distribution as the marginal. |
plugin.marginal |
a logical of length p. Gives finer control over which margins to use the empirical distribution for. |
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 |
Y.pmean |
the original datamatrix with imputed values replacing missing data |
Y.impute |
an
array of size n x p x |
LPC |
the log-probability of the latent variables at each saved sample. Used for diagnostic purposes. |
Peter Hoff
http://www.stat.washington.edu/hoff/
1 2 3 | fit<-sbgcop.mcmc(swiss)
summary(fit)
plot(fit)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.