| sbgcop.mcmc {sbgcop} | R Documentation |
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)
Results
R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(sbgcop)
> png(filename="sbgcop.mcmc_%03d_med.png", width=480, height=480)
> ### Name: sbgcop.mcmc
> ### Title: Semiparametric Bayesian Gaussian copula estimation and
> ### imputation
> ### Aliases: sbgcop.mcmc plot.psgc summary.psgc print.sum.psgc
> ### Keywords: multivariate models
>
> ### ** Examples
>
> fit<-sbgcop.mcmc(swiss)
10 percent done Thu Oct 14 22:51:27 2010
20 percent done Thu Oct 14 22:51:27 2010
30 percent done Thu Oct 14 22:51:28 2010
40 percent done Thu Oct 14 22:51:28 2010
50 percent done Thu Oct 14 22:51:28 2010
60 percent done Thu Oct 14 22:51:28 2010
70 percent done Thu Oct 14 22:51:29 2010
80 percent done Thu Oct 14 22:51:29 2010
90 percent done Thu Oct 14 22:51:29 2010
100 percent done Thu Oct 14 22:51:29 2010
> summary(fit)
#### MCMC details ####
number of saved samples: 100
average effective sample size: 94.51049
effective sample sizes
Fertility*Agriculture Fertility*Examination
126.70 158.88
Fertility*Education Fertility*Catholic
106.27 74.17
Fertility*Infant.Mortality Agriculture*Examination
114.38 75.09
Agriculture*Education Agriculture*Catholic
81.68 69.64
Agriculture*Infant.Mortality Examination*Education
75.75 41.05
Examination*Catholic Examination*Infant.Mortality
80.47 137.03
Education*Catholic Education*Infant.Mortality
129.33 79.29
Catholic*Infant.Mortality
67.93
#### Parameter estimation ####
Posterior quantiles of correlation coefficients:
2.5% quantile 50% quantile 97.5% quantile
Fertility*Agriculture 0.05 0.27 0.50
Fertility*Examination -0.74 -0.58 -0.39
Fertility*Education -0.63 -0.44 -0.20
Fertility*Catholic 0.13 0.32 0.57
Fertility*Infant.Mortality 0.17 0.38 0.56
Agriculture*Examination -0.73 -0.60 -0.42
Agriculture*Education -0.73 -0.60 -0.42
Agriculture*Catholic 0.10 0.35 0.58
Agriculture*Infant.Mortality -0.37 -0.11 0.16
Examination*Education 0.47 0.63 0.78
Examination*Catholic -0.59 -0.41 -0.18
Examination*Infant.Mortality -0.25 0.02 0.22
Education*Catholic -0.39 -0.14 0.11
Education*Infant.Mortality -0.29 0.00 0.20
Catholic*Infant.Mortality -0.15 0.08 0.33
Posterior quantiles of regression coefficients:
2.5% quantile 50% quantile 97.5% quantile
Fertility~Agriculture -0.41 -0.11 0.19
Fertility~Examination -0.73 -0.50 -0.27
Fertility~Education -0.46 -0.17 0.09
Fertility~Catholic -0.10 0.12 0.36
Fertility~Infant.Mortality 0.18 0.35 0.54
Agriculture~Fertility -0.36 -0.13 0.20
Agriculture~Examination -0.52 -0.30 -0.03
Agriculture~Education -0.63 -0.44 -0.21
Agriculture~Catholic -0.03 0.21 0.45
Agriculture~Infant.Mortality -0.31 -0.10 0.12
Examination~Fertility -0.59 -0.38 -0.17
Examination~Agriculture -0.44 -0.22 -0.03
Examination~Education 0.11 0.30 0.53
Examination~Catholic -0.34 -0.20 -0.02
Examination~Infant.Mortality -0.05 0.16 0.37
Education~Fertility -0.38 -0.17 0.07
Education~Agriculture -0.62 -0.41 -0.20
Education~Examination 0.17 0.38 0.65
Education~Catholic 0.03 0.24 0.43
Education~Infant.Mortality -0.25 -0.04 0.13
Catholic~Fertility -0.13 0.18 0.48
Catholic~Agriculture -0.04 0.34 0.66
Catholic~Examination -0.72 -0.37 -0.04
Catholic~Education 0.05 0.36 0.69
Catholic~Infant.Mortality -0.21 0.09 0.35
Infant.Mortality~Fertility 0.27 0.52 0.77
Infant.Mortality~Agriculture -0.46 -0.17 0.19
Infant.Mortality~Examination -0.10 0.32 0.66
Infant.Mortality~Education -0.44 -0.07 0.25
Infant.Mortality~Catholic -0.17 0.10 0.37
> plot(fit)
>
>
>
>
> dev.off()
null device
1
>
>
Image(s)
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