[r-cran-mcmcpack] 40/90: Imported Upstream version 1.0-4
Andreas Tille
tille at debian.org
Fri Dec 16 09:07:38 UTC 2016
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-cran-mcmcpack.
commit 012836f78f227c1a4f35bab6d61f3cc0bf109912
Author: Andreas Tille <tille at debian.org>
Date: Fri Dec 16 08:07:15 2016 +0100
Imported Upstream version 1.0-4
---
DESCRIPTION | 8 ++++----
HISTORY | 5 ++++-
R/MCMCbinaryChange.R | 0
R/MCMCquantreg.R | 12 ++++++------
man/MCMCfactanal.Rd | 2 +-
man/MCMClogit.Rd | 3 +--
man/MCMCmixfactanal.Rd | 2 +-
man/MCMCordfactanal.Rd | 2 +-
man/MCMCpoisson.Rd | 2 +-
man/MCMCprobit.Rd | 2 +-
man/MCMCquantreg.Rd | 26 +++++++++++++-------------
src/MCMCfcds.h | 21 ++++++++-------------
src/MCMCquantreg.cc | 10 +++++-----
13 files changed, 46 insertions(+), 49 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 88533af..63c905f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: MCMCpack
-Version: 1.0-3
-Date: 2009-7-27
+Version: 1.0-4
+Date: 2009-09-07
Title: Markov chain Monte Carlo (MCMC) Package
Author: Andrew D. Martin <admartin at wustl.edu>, Kevin M. Quinn
<kevin_quinn at harvard.edu>, Jong Hee Park <jhp at uchicago.edu>
@@ -19,6 +19,6 @@ Description: This package contains functions to perform Bayesian
License: GPL-2
SystemRequirements: gcc (>= 4.0)
URL: http://mcmcpack.wustl.edu
-Packaged: 2009-07-28 17:41:00 UTC; adm
+Packaged: 2009-09-08 00:08:21 UTC; adm
Repository: CRAN
-Date/Publication: 2009-07-30 12:51:08
+Date/Publication: 2009-09-08 05:04:49
diff --git a/HISTORY b/HISTORY
index 984aa75..ceedbee 100644
--- a/HISTORY
+++ b/HISTORY
@@ -2,8 +2,11 @@
// Changes and Bug Fixes
//
+1.0-3 to 1.0-4
+ * little change to parameterization of BQR [contributed by Craig Reed]
+
1.0-2 to 1.0-3
- * Mix fix to hierarchical IRT documentation [written by Mike Malecki]
+ * fix to hierarchical IRT documentation [written by Mike Malecki]
* added Bayesian quantile regression [contributed by Craig Reed]
1.0-1 to 1.0-2
diff --git a/R/MCMCbinaryChange.R b/R/MCMCbinaryChange.R
old mode 100644
new mode 100755
diff --git a/R/MCMCquantreg.R b/R/MCMCquantreg.R
index 865af80..c6f1d66 100644
--- a/R/MCMCquantreg.R
+++ b/R/MCMCquantreg.R
@@ -1,5 +1,5 @@
"MCMCquantreg" <-
- function(formula, p=0.5, data=NULL, burnin = 1000, mcmc = 10000,
+ function(formula, tau=0.5, data=NULL, burnin = 1000, mcmc = 10000,
thin=1, verbose = 0, seed = NA, beta.start = NA,
b0 = 0, B0 = 0, c0 = 0.001, d0 = 0.001,
...) {
@@ -9,8 +9,8 @@
check.mcmc.parameters(burnin, mcmc, thin)
cl <- match.call()
- if (p<=0 || p>=1){
- stop("p must be in (0,1).\n Please respecify and call again.\n")
+ if (tau<=0 || tau>=1){
+ stop("tau must be in (0,1).\n Please respecify and call again.\n")
}
## seeds
@@ -29,7 +29,7 @@
## starting values and priors
ols.fit <- lm(formula)
defaults <- matrix(coef(ols.fit),K,1)
- defaults[1] <- defaults[1]+summary(ols.fit)$sigma*qnorm(p)
+ defaults[1] <- defaults[1]+summary(ols.fit)$sigma*qnorm(tau)
beta.start <- coef.start(beta.start, K, formula, family=gaussian, data, defaults=defaults)
mvn.prior <- form.mvn.prior(b0, B0, K)
b0 <- mvn.prior[[1]]
@@ -48,7 +48,7 @@
sample <- matrix(data=0, mcmc/thin, K+1)
posterior <- NULL
- if (p==0.5) {
+ if (tau==0.5) {
## call C++/Scythe function "MCMCmedreg" to draw samples
auto.Scythe.call(output.object="posterior", cc.fun.name="MCMCmedreg",
sample.nonconst=sample, Y=Y, X=X, burnin=as.integer(burnin),
@@ -64,7 +64,7 @@
## call C++/Scythe function "MCMCquantreg" to draw samples
auto.Scythe.call(output.object="posterior", cc.fun.name="MCMCquantreg",
- sample.nonconst=sample, p=as.double(p), Y=Y, X=X,
+ sample.nonconst=sample, tau=as.double(tau), Y=Y, X=X,
burnin=as.integer(burnin),
mcmc=as.integer(mcmc), thin=as.integer(thin),
lecuyer=as.integer(lecuyer),
diff --git a/man/MCMCfactanal.Rd b/man/MCMCfactanal.Rd
index 6b41536..967ad91 100644
--- a/man/MCMCfactanal.Rd
+++ b/man/MCMCfactanal.Rd
@@ -197,5 +197,5 @@ MCMCfactanal(x, factors, lambda.constraints=list(),
\keyword{models}
-\seealso{\code{\link[coda]{plot.mcmc}},\code{\link[coda]{summary.mcmc}},\code{\link[mva]{factanal}}}
+\seealso{\code{\link[coda]{plot.mcmc}},\code{\link[coda]{summary.mcmc}},\code{\link[stats]{factanal}}}
diff --git a/man/MCMClogit.Rd b/man/MCMClogit.Rd
index 64d9d36..c17b218 100644
--- a/man/MCMClogit.Rd
+++ b/man/MCMClogit.Rd
@@ -191,5 +191,4 @@ MCMClogit(formula, data=NULL, burnin = 1000, mcmc = 10000,
\keyword{models}
-\seealso{\code{\link[coda]{plot.mcmc}},\code{\link[coda]{summary.mcmc}}, \code{\link[base]{glm}}}
-
+\seealso{\code{\link[coda]{plot.mcmc}},\code{\link[coda]{summary.mcmc}}, \code{\link[stats]{glm}}}
diff --git a/man/MCMCmixfactanal.Rd b/man/MCMCmixfactanal.Rd
index a800392..c674d4e 100644
--- a/man/MCMCmixfactanal.Rd
+++ b/man/MCMCmixfactanal.Rd
@@ -298,7 +298,7 @@ summary(post)
\keyword{models}
\seealso{\code{\link[coda]{plot.mcmc}}, \code{\link[coda]{summary.mcmc}},
- \code{\link[mva]{factanal}}, \code{\link[MCMCpack]{MCMCfactanal}},
+ \code{\link[stats]{factanal}}, \code{\link[MCMCpack]{MCMCfactanal}},
\code{\link[MCMCpack]{MCMCordfactanal}},
\code{\link[MCMCpack]{MCMCirt1d}}, \code{\link[MCMCpack]{MCMCirtKd}}}
diff --git a/man/MCMCordfactanal.Rd b/man/MCMCordfactanal.Rd
index 3f8f931..3b934ac 100644
--- a/man/MCMCordfactanal.Rd
+++ b/man/MCMCordfactanal.Rd
@@ -227,6 +227,6 @@ MCMCordfactanal(x, factors, lambda.constraints=list(),
\keyword{models}
\seealso{\code{\link[coda]{plot.mcmc}}, \code{\link[coda]{summary.mcmc}},
- \code{\link[mva]{factanal}}, \code{\link[MCMCpack]{MCMCfactanal}},
+ \code{\link[stats]{factanal}}, \code{\link[MCMCpack]{MCMCfactanal}},
\code{\link[MCMCpack]{MCMCirt1d}}, \code{\link[MCMCpack]{MCMCirtKd}}}
diff --git a/man/MCMCpoisson.Rd b/man/MCMCpoisson.Rd
index d51ace3..78a3a59 100644
--- a/man/MCMCpoisson.Rd
+++ b/man/MCMCpoisson.Rd
@@ -132,5 +132,5 @@ MCMCpoisson(formula, data = NULL, burnin = 1000, mcmc = 10000,
\keyword{models}
-\seealso{\code{\link[coda]{plot.mcmc}},\code{\link[coda]{summary.mcmc}}, \code{\link[base]{glm}}}
+\seealso{\code{\link[coda]{plot.mcmc}},\code{\link[coda]{summary.mcmc}}, \code{\link[stats]{glm}}}
diff --git a/man/MCMCprobit.Rd b/man/MCMCprobit.Rd
index 52287b5..fbdb03c 100644
--- a/man/MCMCprobit.Rd
+++ b/man/MCMCprobit.Rd
@@ -130,5 +130,5 @@ MCMCprobit(formula, data = NULL, burnin = 1000, mcmc = 10000,
\keyword{models}
-\seealso{\code{\link[coda]{plot.mcmc}},\code{\link[coda]{summary.mcmc}}, \code{\link[base]{glm}}}
+\seealso{\code{\link[coda]{plot.mcmc}},\code{\link[coda]{summary.mcmc}}, \code{\link[stats]{glm}}}
diff --git a/man/MCMCquantreg.Rd b/man/MCMCquantreg.Rd
index ca7a8cf..e9bdddb 100644
--- a/man/MCMCquantreg.Rd
+++ b/man/MCMCquantreg.Rd
@@ -2,7 +2,7 @@
\alias{MCMCquantreg}
-\title{Bayesian quantile regression using Gibbs sampling}
+\title{ Bayesian quantile regression using Gibbs sampling }
\description{
This function fits quantile regression models under Bayesian inference.
@@ -14,7 +14,7 @@
}
\usage{
-MCMCquantreg(formula, p=0.5, data = NULL, burnin = 1000,
+MCMCquantreg(formula, tau=0.5, data = NULL, burnin = 1000,
mcmc = 10000, thin = 1, verbose = 0, seed = NA,
beta.start = NA, b0 = 0, B0 = 0, c0 = 0.001, d0 = 0.001, ...)
}
@@ -23,7 +23,7 @@ MCMCquantreg(formula, p=0.5, data = NULL, burnin = 1000,
\item{formula}{ Model formula. }
- \item{p}{The quantile of interest. Must be between 0 and 1. The default value of 0.5 corresponds to median regression.}
+ \item{tau}{The quantile of interest. Must be between 0 and 1. The default value of 0.5 corresponds to median regression.}
\item{data}{ Data frame. }
@@ -56,10 +56,10 @@ MCMCquantreg(formula, p=0.5, data = NULL, burnin = 1000,
column vector with dimension equal to the dimension of \eqn{\beta}{beta}.
The default value of NA will use the OLS
estimate \eqn{\hat{\beta}}{beta^hat} with
- \eqn{\hat{\sigma}\Phi^{-1}(p)}{sigma^hat*Phi^(-1)(p)} added on to the first element of \eqn{\hat{\beta}}{beta^hat} as the starting value.
+ \eqn{\hat{\sigma}\Phi^{-1}(\tau)}{sigma^hat*Phi^(-1)(tau)} added on to the first element of \eqn{\hat{\beta}}{beta^hat} as the starting value.
(\eqn{\hat{\sigma}^2}{(sigma^hat)^2} denotes the usual unbiased estimator of
\eqn{\sigma^2}{sigma^2} under ordinary mean regression and
- \eqn{\Phi^{-1}(p)}{Phi^(-1)(p)} denotes the inverse of the
+ \eqn{\Phi^{-1}(\tau)}{Phi^(-1)(tau)} denotes the inverse of the
cumulative density function of the standard normal distribution.)
Note that the default value assume that an intercept is included in the model.
If a scalar is given, that value will serve as the starting value
@@ -99,11 +99,11 @@ MCMCquantreg(formula, p=0.5, data = NULL, burnin = 1000,
The model takes the following form:
\deqn{y_i = x_i ' \beta + \varepsilon_i}{y_i = x_i'beta + epsilon_i}
- The errors are assumed to have an Asymmetric Laplace distribution with pth quantile equal to zero
+ The errors are assumed to have an Asymmetric Laplace distribution with \eqn{\tau}{tau}th quantile equal to zero
and shape parameter equal to \eqn{\sigma}{sigma}:
- \deqn{\varepsilon_i(p) \sim \mathcal{AL}(0, \sigma, p)}{epsilon_i(p) ~ AL(0,
- sigma, p),}
- where \eqn{\beta}{beta} and \eqn{\sigma}{sigma} depend on p.
+ \deqn{\varepsilon_i(\tau) \sim \mathcal{AL}(0, \sigma, \tau)}{epsilon_i(tau) ~ AL(0,
+ sigma, tau),}
+ where \eqn{\beta}{beta} and \eqn{\sigma}{sigma} depend on \eqn{\tau}{tau}.
We assume standard, semi-conjugate priors:
\deqn{\beta \sim \mathcal{N}(b_0,B_0^{-1})}{beta ~ N(b0,B0^(-1)),}
and
@@ -122,16 +122,16 @@ MCMCquantreg(formula, p=0.5, data = NULL, burnin = 1000,
\references{ Daniel Pemstein, Kevin M. Quinn, and Andrew D. Martin. 2007.
\emph{Scythe Statistical Library 1.2.} \url{http://scythe.wustl.edu}.
- Craig Reed and Keming Yu. 2009. ``A Partially Collapsed Gibbs Sampler for Bayesian Quantile Regression." Technical Report.
+ Craig Reed and Keming Yu. 2009. "A Partially Collapsed Gibbs Sampler for Bayesian Quantile Regression." Technical Report.
- Keming Yu and Jin Zhang. 2005. ``A Three Parameter Asymmetric Laplace Distribution and it's extensions."
+ Keming Yu and Jin Zhang. 2005. "A Three Parameter Asymmetric Laplace Distribution and it's extensions."
\emph{Communications in Statistics - Theory and Methods}, 34, 1867-1879.
Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. 2002.
\emph{Output Analysis and Diagnostics for MCMC (CODA)}.
\url{http://www-fis.iarc.fr/coda/}.}
-\author{Craig Reed}
+\author{ Craig Reed}
\seealso{ \code{\link[MCMCpack]{MCMCregress}}, \code{\link[coda]{plot.mcmc}},
\code{\link[coda]{summary.mcmc}}, \code{\link[stats]{lm}}, \code{\link[quantreg]{rq}} }
@@ -142,7 +142,7 @@ MCMCquantreg(formula, p=0.5, data = NULL, burnin = 1000,
x<-rep(1:10,5)
y<-rnorm(50,mean=x)
posterior_50 <- MCMCquantreg(y~x)
-posterior_95 <- MCMCquantreg(y~x, p=0.95, verbose=10000,
+posterior_95 <- MCMCquantreg(y~x, tau=0.95, verbose=10000,
mcmc=50000, thin=10, seed=2)
plot(posterior_50)
plot(posterior_95)
diff --git a/src/MCMCfcds.h b/src/MCMCfcds.h
index b55ea66..bcfe162 100644
--- a/src/MCMCfcds.h
+++ b/src/MCMCfcds.h
@@ -19,14 +19,14 @@
//
// KQ 6/10/2004
// DBP 7/01/2007 [ported to scythe 1.0.x (partial)]
-// ADM 7/28/2009 [added some functions from Craig Reed for quantile
-// regression]
//
// Copyright (C) 2003-2007 Andrew D. Martin and Kevin M. Quinn
// Copyright (C) 2007-present Andrew D. Martin, Kevin M. Quinn,
// and Jong Hee Park
//////////////////////////////////////////////////////////////////////////
+
+
#ifndef MCMCFCDS_H
#define MCMCFCDS_H
@@ -87,8 +87,6 @@ NormIGregress_sigma2_draw (const Matrix <> &X, const Matrix <> &Y,
return stream.rigamma (c_post, d_post);
}
-////////////// New Additions ////////////////////////
-
// linear regression with Laplace errors beta draw
// (multivariate Normal prior)
// regression model is y = X * beta + epsilon, epsilon ~ Laplace(0,sigma)
@@ -135,7 +133,6 @@ LaplaceNormregress_beta_draw (const Matrix<>& X, const Matrix<>& Y, const Matrix
return( gaxpy(C, stream.rnorm(k,1, 0, 1), betahat) );
}
-
// linear regression with Laplace errors sigma draw
// (inverse-Gamma prior)
@@ -157,12 +154,12 @@ LaplaceIGammaregress_sigma_draw (const Matrix <> &abse, double c0, double d0,
// linear regression with Asymmetric Laplace errors beta draw
// (multivariate Normal prior)
-// regression model is y = X * beta + epsilon, epsilon ~ ALaplace(0,sigma,p)
+// regression model is y = X * beta + epsilon, epsilon ~ ALaplace(0,sigma,tau)
// b0 is the prior mean of beta
// B0 is the prior precision (the inverse variance) of beta
template <typename RNGTYPE>
Matrix<double>
-ALaplaceNormregress_beta_draw (double p, const Matrix<>& X, const Matrix<>& Y, const Matrix<>& weights,
+ALaplaceNormregress_beta_draw (double tau, const Matrix<>& X, const Matrix<>& Y, const Matrix<>& weights,
const Matrix<>& b0, const Matrix<>& B0, double sigma,
rng<RNGTYPE>& stream)
{
@@ -170,7 +167,7 @@ ALaplaceNormregress_beta_draw (double p, const Matrix<>& X, const Matrix<>& Y, c
const unsigned int k = X.cols();
const unsigned int n_obs = X.rows();
const double one_over_two_sigma = 1.0/(2.0*sigma);
- const Matrix<> U = Y - (1.0-2.0*p)*pow(weights,-1.0);
+ const Matrix<> U = Y - (1.0-2.0*tau)*pow(weights,-1.0);
Matrix<> XtwX(k,k,false);
Matrix<> XtwU(k,1,false);
double temp_x = 0.0;
@@ -205,17 +202,17 @@ ALaplaceNormregress_beta_draw (double p, const Matrix<>& X, const Matrix<>& Y, c
// linear regression with Asymmetric Laplace errors sigma draw
// (inverse-Gamma prior)
-// regression model is y = X * beta + epsilon, epsilon ~ ALaplace(0,sigma, p)
+// regression model is y = X * beta + epsilon, epsilon ~ ALaplace(0,sigma,tau)
// c0/2 is the prior shape parameter for sigma
// d0/2 is the prior scale parameter for sigma
template <typename RNGTYPE>
double
-ALaplaceIGammaregress_sigma_draw (double p, const Matrix<> &e, const Matrix <> &abse, double c0, double d0,
+ALaplaceIGammaregress_sigma_draw (double tau, const Matrix<> &e, const Matrix <> &abse, double c0, double d0,
rng<RNGTYPE>& stream)
{
const double c_post = 0.5*c0 + abse.rows();
- const double d_post = 0.5*(d0 + sum(abse)+(2.0*p-1.0)*sum(e));
+ const double d_post = 0.5*(d0 + sum(abse)+(2.0*tau-1.0)*sum(e));
return stream.rigamma (c_post, d_post);
@@ -251,8 +248,6 @@ ALaplaceIGaussregress_weights_draw (const Matrix <> &abse, double sigma,
return w;
}
-////////////////////////////////////////////////////
-
// update latent data for standard item response models
// only works for 1 dimensional case
template <typename RNGTYPE>
diff --git a/src/MCMCquantreg.cc b/src/MCMCquantreg.cc
index 1b81884..35ecab3 100644
--- a/src/MCMCquantreg.cc
+++ b/src/MCMCquantreg.cc
@@ -56,7 +56,7 @@ using namespace scythe;
* fills with the posterior.
*/
template <typename RNGTYPE>
-void MCMCquantreg_impl (rng<RNGTYPE>& stream, double p, const Matrix<>& Y,
+void MCMCquantreg_impl (rng<RNGTYPE>& stream, double tau, const Matrix<>& Y,
const Matrix<>& X, Matrix<>& beta, const Matrix<>& b0,
const Matrix<>& B0, double c0, double d0,
unsigned int burnin, unsigned int mcmc, unsigned int thin,
@@ -78,9 +78,9 @@ void MCMCquantreg_impl (rng<RNGTYPE>& stream, double p, const Matrix<>& Y,
for (unsigned int iter = 0; iter < tot_iter; ++iter) {
Matrix<> e = gaxpy(X, (-1*beta), Y);
Matrix<> abse = fabs(e);
- double sigma = ALaplaceIGammaregress_sigma_draw (p, e, abse, c0, d0, stream);
+ double sigma = ALaplaceIGammaregress_sigma_draw (tau, e, abse, c0, d0, stream);
Matrix<> weights = ALaplaceIGaussregress_weights_draw (abse, sigma, stream);
- beta = ALaplaceNormregress_beta_draw (p, X, Y, weights, b0, B0, sigma, stream);
+ beta = ALaplaceNormregress_beta_draw (tau, X, Y, weights, b0, B0, sigma, stream);
// store draws in storage matrix (or matrices)
if (iter >= burnin && (iter % thin == 0)) {
@@ -108,7 +108,7 @@ void MCMCquantreg_impl (rng<RNGTYPE>& stream, double p, const Matrix<>& Y,
extern "C" {
void MCMCquantreg(double *sampledata, const int *samplerow,
- const int *samplecol, const double *p, const double *Ydata, const int *Yrow,
+ const int *samplecol, const double *tau, const double *Ydata, const int *Yrow,
const int *Ycol, const double *Xdata, const int *Xrow,
const int *Xcol, const int *burnin, const int *mcmc,
const int *thin, const int *uselecuyer, const int *seedarray,
@@ -127,7 +127,7 @@ extern "C" {
Matrix<> B0(*B0row, *B0col, B0data);
Matrix<> storagematrix;
- MCMCPACK_PASSRNG2MODEL(MCMCquantreg_impl, *p, Y, X, betastart, b0, B0,
+ MCMCPACK_PASSRNG2MODEL(MCMCquantreg_impl, *tau, Y, X, betastart, b0, B0,
*c0, *d0, *burnin, *mcmc, *thin, *verbose,
storagematrix);
--
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