[r-cran-mnp] 06/51: Import Upstream version 2.2-1
Andreas Tille
tille at debian.org
Fri Sep 8 14:14:44 UTC 2017
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-cran-mnp.
commit 03139736abe351bbb71bdb71a159f3f6d515e075
Author: Andreas Tille <tille at debian.org>
Date: Fri Sep 8 15:54:42 2017 +0200
Import Upstream version 2.2-1
---
DESCRIPTION | 6 +++---
R/mnp.R | 22 ++++++++++++++++++++--
R/predict.mnp.R | 18 +++++++++++++-----
R/ymatrix.mnp.R | 2 +-
man/mnp.Rd | 21 ++++++++++++++++-----
5 files changed, 53 insertions(+), 16 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index ca79af7..f717112 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: MNP
-Version: 2.1-2
-Date: 2005-03-22
+Version: 2.2-1
+Date: 2005-05-01
Title: R Package for the Fitting the Multinomial Probit Model
Author: Kosuke Imai <kimai at princeton.edu>,
David A. van Dyk <dvd at uci.edu>.
@@ -22,4 +22,4 @@ Description: MNP is a publicly available R package that fits the Bayesian
of Econometrics, Vol. 124, No. 2 (February), pp. 311-334.
License: GPL (version 2 or later)
URL: http://www.princeton.edu/~kimai/research/MNP.html
-Packaged: Tue Mar 22 19:38:10 2005; kimai
+Packaged: Sun May 1 21:44:10 2005; kimai
diff --git a/R/mnp.R b/R/mnp.R
index fb9da2b..f5bb133 100644
--- a/R/mnp.R
+++ b/R/mnp.R
@@ -38,10 +38,28 @@ mnp <- function(formula, data = parent.frame(), choiceX = NULL,
X <- tmp$X
coefnames <- tmp$coefnames
n.cov <- ncol(X) / n.dim
+
+ ## listwise deletion for X
+ na.ind <- apply(is.na(X), 1, sum)
+ if (ncol(Y) == 1)
+ na.ind <- na.ind + is.na(Y)
+ Y <- Y[na.ind==0,]
+ X <- X[na.ind==0,]
n.obs <- nrow(X)
- if (verbose)
+
+ if (verbose) {
cat("The dimension of beta is ", n.cov, ".\n\n", sep="")
-
+ cat("The number of observations is ", n.obs, ".\n\n", sep="")
+ if (sum(na.ind>0)>0) {
+ if (sum(na.ind>0)==1)
+ cat("The observation ", (1:length(na.ind))[na.ind>0], " is dropped due to missing values.\n\n", sep="")
+ else {
+ cat("The following ", sum(na.ind>0), " observations are dropped due to missing values:\n", sep="")
+ cat((1:length(na.ind))[na.ind>0], "\n\n")
+ }
+ }
+ }
+
## checking the prior for beta
p.imp <- FALSE
if (p.var == Inf) {
diff --git a/R/predict.mnp.R b/R/predict.mnp.R
index 958b4a8..a2af611 100644
--- a/R/predict.mnp.R
+++ b/R/predict.mnp.R
@@ -25,13 +25,21 @@ predict.mnp <- function(object, newdata = NULL, newdraw = NULL,
cXnames = eval(call$cXnames),
base = object$base, n.dim = p-1,
lev = object$alt, MoP = is.matrix(object$y),
- verbose = FALSE, extra = FALSE)
+ verbose = FALSE, extra = FALSE)
+ if (nrow(x) > 1)
+ x <- as.matrix(x[apply(is.na(x), 1, sum)==0,])
+ else if (sum(is.na(x))>0)
+ stop("Invalid input for `newdata'.")
}
-
+
n.obs <- nrow(x)
- if (verbose)
- cat("There are", n.obs, "observations to predict. Please wait...\n")
-
+ if (verbose) {
+ if (n.obs == 1)
+ cat("There is one observation to predict. Please wait...\n")
+ else
+ cat("There are", n.obs, "observations to predict. Please wait...\n")
+ }
+
alt <- object$alt
if (object$base != alt[1])
alt <- c(object$base, alt[1:(length(alt)-1)])
diff --git a/R/ymatrix.mnp.R b/R/ymatrix.mnp.R
index 635b252..7c1e2f2 100644
--- a/R/ymatrix.mnp.R
+++ b/R/ymatrix.mnp.R
@@ -29,7 +29,7 @@ ymatrix.mnp <- function(data, base=NULL, extra=FALSE, verbose=verbose) {
lev <- lev[counts > 0]
}
p <- length(lev)
- Y <- unclass(Y) - 1
+ Y <- as.matrix(unclass(Y)) - 1
MoP <- FALSE
}
if(extra)
diff --git a/man/mnp.Rd b/man/mnp.Rd
index 4f6de23..3edd1cf 100644
--- a/man/mnp.Rd
+++ b/man/mnp.Rd
@@ -11,7 +11,7 @@
with different choice sets for each observation, and complete or
partial ordering of all the available alternatives. The computation
uses the efficient marginal data augmentation algorithm that is
- developed by Imai and van Dyk (2005). }
+ developed by Imai and van Dyk (2005a). }
\usage{
mnp(formula, data = parent.frame(), choiceX = NULL, cXnames = NULL,
@@ -140,6 +140,12 @@ mnp(formula, data = parent.frame(), choiceX = NULL, cXnames = NULL,
}
\examples{
+###
+### NOTE: this example is not fully analyzed. In particular, the
+### convergence has not been assessed. A full analysis of these data
+### sets appear in Imai and van Dyk (2005b).
+###
+
## load the detergent data
data(detergent)
## run the standard multinomial probit model with intercepts and the price
@@ -190,10 +196,15 @@ predict(res2, newdata = japan[10,], type = "prob")
}
\references{
- Imai, Kosuke and David A. van Dyk. (2005) \dQuote{A Bayesian Analysis of the
- Multinomial Probit Model Using the Marginal Data Augmentation,}
- \emph{Journal of Econometrics}, Vol. 124, No. 2 (February),
- pp.311-334.
+ Imai, Kosuke and David A. van Dyk. (2005a) \dQuote{A Bayesian Analysis
+ of the Multinomial Probit Model Using the Marginal Data
+ Augmentation,} \emph{Journal of Econometrics}, Vol. 124, No. 2
+ (February), pp.311-334.
+
+ Imai, Kosuke and David A. van Dyk. (2005b) \dQuote{MNP: R Package for
+ Fitting the Multinomial Probit Models,} \emph{Working Paper,
+ Department of Politics, Princeton University}, available at
+ \url{http://www.princeton.edu/~kimai/research/MNP.html}
}
\author{
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