The sbgcop Package. March 9, 2007
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1 The sbgcop Package March 9, 2007 Title Semiparametric Bayesian Gaussian copula estimation Version 0.95 Date Author Maintainer This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. License GPL Version 2 or later URL R topics documented: ldmvnorm plotci.sa qm.sm rwish sr.sc sbgcop.mcmc sbgcop-package Index 7 1
2 2 plotci.sa ldmvnorm Log Multivariate Normal Density Computes the log of the multivariate normal density ldmvnorm(y, S) Y S an n x p matrix a p x p positive definite matrix This function computes the log density of the data matrix Y under the model that the rows are independent samples from a mean-zero multivariate normal distribution with covariance matrix S. A real number. Examples Y<-matrix(rnorm(9*7),9,7) ldmvnorm(y,diag(7)) plotci.sa Plot Confidence Bands for Association Parameters Plots 95 parameters plotci.sa(sa, ylabs = colnames(sa[,, 1]), mgp = c(1.75, 0.75, 0))
3 qm.sm 3 sa ylabs mgp a p x p x nsamp array a p x 1 vector of names for plotting labels margin parameters qm.sm Matrix Quantiles Computes quantiles along the third dimension of a 3-d array. qm.sm(sm, quantiles = c(0.025, 0.5, 0.975)) sm quantiles an m x n x s array quantiles to be computed an array of dimension m x n x l, where l is the length of quantiles
4 4 sr.sc rwish Sample from the Wishart Distribution Generate a random sample from the Wishart distribution. rwish(s0, nu) S0 nu a positive definite matrix a positive integer Return the sum of nu i.i.d. rank-one matrices generated as z%*%t(z), where z is a sample from a multivariate normal distribution with covariance S0. The resulting random variable has mean nu*s0. a positive definite matrix. sr.sc Compute Regression Parameters Compute an array of regression parameters from an array of correlation parameters. sr.sc(sc) sc a p x p x nsamp array of, made up of nsamp correlation matrices.
5 sbgcop.mcmc 5 For each of the nsamp correlation matrices C, a matrix of regression parameters is computed via R[j,-j]<- C[j,-j]%*%solve(C[-j,-j]) a p x p x nsamp array of regression parameters. sbgcop.mcmc Semiparametric Bayesian Gaussian copula estimation 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 S0 n0 nsamp odens seed verb an n x p matrix. Missing values are allowed. a p x p positive definite matrix a positive integer number of iterations of the Markov chain. output density: number of iterations between saved samples. an integer for the random seed 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.
6 6 sbgcop-package An object of class psgc containing the following components: C.psamp Y.pmean LPC an array of size p x p x nsamp/odens, consisting of posterior samples of the correlation matrix. the original datamatrix with imputed values replacing missing data the log-probability of the latent variables at each saved sample. Used for diagnostic purposes. References Examples fit<-sbgcop.mcmc(swiss) summary(fit) plot(fit) sbgcop-package Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric imputation procedure for missing multivariate data. Package: sbgcop Type: Package Version: 0.95 Date: License: GPL Version 2 or later 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.
7 sbgcop-package 7 <hoff@stat.washington.edu> References Hoff (2007) Extending the rank likelihood for semiparametric copula estimation Examples fit<-sbgcop.mcmc(swiss) summary(fit) plot(fit)
8 Index Topic array plotci.sa, 2 qm.sm, 3 sr.sc, 4 Topic datagen rwish, 3 Topic distribution ldmvnorm, 1 rwish, 3 Topic models sbgcop.mcmc, 5 Topic multivariate ldmvnorm, 1 qm.sm, 3 rwish, 3 sbgcop-package, 6 sbgcop.mcmc, 5 sr.sc, 4 Topic regression sr.sc, 4 ldmvnorm, 1 plot.psgc (sbgcop.mcmc), 5 plotci.sa, 2 print.sum.psgc (sbgcop.mcmc), 5 qm.sm, 3 rwish, 3 sbgcop (sbgcop-package), 6 sbgcop-package, 6 sbgcop.mcmc, 5 sr.sc, 4 summary.psgc (sbgcop.mcmc), 5 8
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