sbgcop.mcmc: Semiparametric Bayesian Gaussian copula estimation and...

In pdhoff/sbgcop: Semiparametric Bayesian Gaussian Copula Estimation and Imputation

Description

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.

Usage

sbgcop.mcmc(Y, S0 = diag(dim(Y)[2]), n0 = dim(Y)[2] + 2, nsamp = 100,
  odens = max(1, round(nsamp/1000)), impute = any(is.na(Y)),
  plugin.threshold = 100, plugin.marginal = (apply(Y, 2, function(x) {    
  length(unique(x)) }) > plugin.threshold), seed = 1, verb = TRUE)

Arguments

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)?

Details

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.

Value

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
Y.impute	an array of size n x p x nsamp/odens, consisting of copies of the original data matrix, with posterior samples of missing values included.
LPC	the log-probability of the latent variables at each saved sample. Used for diagnostic purposes.

Author(s)

Peter Hoff

References

http://www.stat.washington.edu/hoff/

Examples

fit<-sbgcop.mcmc(swiss)
summary(fit)
plot(fit)

pdhoff/sbgcop documentation built on May 31, 2019, 12:12 p.m.