[r-cran-mnp] 08/51: Import Upstream version 2.2-2
Andreas Tille
tille at debian.org
Fri Sep 8 14:14:45 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 9eba54feaf07ed3e0fcf81cf10f9659eb0ebf381
Author: Andreas Tille <tille at debian.org>
Date: Fri Sep 8 15:54:43 2017 +0200
Import Upstream version 2.2-2
---
DESCRIPTION | 8 +-
data/japan.txt | 838 ++++++++++++++++++++++++++++-----------------------------
man/japan.Rd | 18 +-
man/mnp.Rd | 9 +-
4 files changed, 436 insertions(+), 437 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index f717112..7657218 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,11 +1,11 @@
Package: MNP
-Version: 2.2-1
-Date: 2005-05-01
+Version: 2.2-2
+Date: 2005-05-09
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>.
Maintainer: Kosuke Imai <kimai at princeton.edu>
-Depends: R (>= 2.0.0), MASS
+Depends: R (>= 2.0), MASS
Description: MNP is a publicly available R package that fits the Bayesian
multinomial probit model via Markov chain Monte Carlo. The
multinomial probit model is often used to analyze the discrete
@@ -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: Sun May 1 21:44:10 2005; kimai
+Packaged: Mon May 9 11:13:50 2005; kimai
diff --git a/data/japan.txt b/data/japan.txt
index 19f76ef..20f586a 100644
--- a/data/japan.txt
+++ b/data/japan.txt
@@ -1,419 +1,419 @@
-"LDP" "NFP" "SKG" "JCP" "sex" "education" "age"
-80 75 80 0 1 1 75
-75 80 50 20 2 1 64
-100 25 100 0 1 2 56
-75 50 25 50 1 2 52
-75 50 50 0 1 4 52
-50 75 50 50 2 3 31
-25 50 25 0 1 1 64
-25 25 50 0 1 2 46
-50 50 50 50 1 1 70
-60 90 10 10 1 4 50
-70 25 0 0 1 1 62
-75 50 25 25 2 2 41
-50 50 25 25 1 1 74
-75 0 100 25 1 4 46
-75 100 50 0 2 4 29
-50 50 75 75 2 2 66
-50 40 40 40 1 2 68
-25 50 60 60 1 4 23
-50 40 25 25 1 3 70
-75 25 50 25 2 2 53
-75 40 25 10 1 2 65
-50 25 50 25 1 2 65
-50 30 30 70 1 2 45
-90 0 50 30 1 4 53
-50 25 0 50 1 2 51
-70 20 50 80 1 2 51
-50 50 50 10 1 3 71
-75 0 20 10 2 4 52
-50 25 25 0 1 3 76
-80 25 25 0 1 4 48
-70 70 25 25 1 2 64
-50 25 25 0 1 4 72
-50 30 25 50 2 2 67
-50 25 50 50 2 3 50
-80 50 40 0 1 3 74
-50 100 25 0 1 2 68
-40 80 50 50 1 1 63
-25 80 40 20 2 2 26
-50 100 0 0 2 3 48
-60 60 70 30 2 2 68
-80 70 90 80 1 1 63
-50 90 30 50 1 2 69
-50 50 30 20 2 2 50
-75 50 50 25 2 3 52
-50 75 25 50 1 3 53
-25 75 75 0 1 4 47
-75 75 25 0 1 1 68
-25 75 0 0 1 2 48
-40 80 30 0 2 2 46
-50 75 75 25 2 2 29
-75 50 50 25 1 4 56
-80 80 50 40 2 2 84
-50 100 50 25 1 1 74
-50 25 50 25 2 2 43
-75 25 75 25 1 4 66
-50 75 50 75 1 2 56
-50 25 75 50 1 3 61
-70 30 50 70 1 4 42
-0 25 75 50 1 4 35
-10 80 10 0 2 3 44
-50 50 10 0 1 2 36
-100 60 30 30 1 2 57
-40 40 60 0 1 4 46
-50 40 25 70 1 2 27
-75 50 50 0 1 1 71
-50 30 60 25 1 4 61
-50 10 65 70 1 3 45
-60 10 30 0 1 1 73
-50 50 75 25 2 3 81
-50 30 30 30 2 2 63
-50 25 50 30 1 4 59
-90 60 60 40 1 4 41
-75 25 25 0 1 2 57
-50 75 50 40 2 2 61
-50 10 10 0 2 2 67
-100 50 0 0 1 2 51
-25 75 25 25 2 3 60
-50 50 50 50 1 2 62
-75 50 25 50 1 2 44
-50 25 50 25 2 2 48
-50 75 25 25 1 2 44
-50 25 25 20 1 2 52
-70 60 50 30 1 4 60
-50 75 50 30 1 3 65
-50 40 40 30 1 1 58
-50 40 50 30 1 1 56
-0 0 0 100 1 1 58
-75 50 50 25 1 2 48
-50 50 80 50 2 3 69
-50 50 50 0 1 2 54
-50 75 50 25 1 1 67
-50 75 75 25 2 3 53
-25 25 50 75 1 2 54
-90 40 20 0 1 2 70
-75 100 25 0 1 2 66
-50 50 50 30 1 1 72
-50 75 25 25 1 3 31
-75 75 25 0 2 3 60
-75 25 50 50 1 2 60
-50 50 75 25 1 2 58
-75 50 50 0 2 1 73
-75 25 0 0 1 2 37
-50 75 100 0 2 2 59
-15 15 30 10 1 4 28
-50 75 0 0 1 3 64
-75 75 95 25 1 4 39
-75 25 75 0 1 3 60
-50 25 50 25 1 1 75
-25 100 0 0 1 1 76
-50 50 50 50 2 2 38
-50 10 50 100 1 1 65
-40 40 10 15 1 1 80
-50 50 50 25 1 4 62
-50 75 25 25 1 4 67
-70 70 50 50 1 2 34
-80 90 40 0 1 4 54
-60 60 20 20 1 1 70
-75 50 25 25 1 1 73
-25 0 50 100 1 4 61
-100 75 75 0 1 2 46
-100 25 50 0 1 1 57
-10 100 10 0 2 2 37
-75 50 40 40 1 1 75
-75 50 50 25 1 2 66
-25 50 50 25 1 2 54
-70 50 50 20 2 1 65
-50 60 50 0 1 4 59
-90 70 75 60 2 1 62
-50 50 50 50 1 1 58
-50 50 50 30 1 2 57
-50 50 50 30 2 2 43
-50 25 50 25 1 1 64
-75 25 50 0 1 2 60
-50 50 50 50 2 2 60
-25 75 50 0 1 1 65
-75 50 50 0 1 2 59
-75 25 25 0 1 1 74
-50 50 25 25 2 1 64
-70 50 50 80 1 1 70
-50 70 0 0 1 2 51
-20 20 30 50 1 2 59
-50 50 40 25 1 4 63
-50 30 50 30 1 2 57
-10 90 10 10 2 2 65
-50 50 50 25 2 2 53
-50 50 25 0 2 1 64
-75 75 50 50 1 3 60
-75 25 75 0 2 1 66
-65 60 50 10 2 1 62
-60 55 62 10 2 2 55
-58 50 48 5 2 2 50
-20 70 10 10 1 3 65
-60 40 50 50 2 2 52
-60 20 20 30 1 2 63
-75 75 75 25 2 4 49
-50 50 50 40 1 1 81
-55 55 70 40 1 2 64
-50 50 50 25 1 2 68
-85 55 55 0 1 1 67
-50 75 50 25 2 2 79
-60 60 50 0 2 1 59
-70 70 30 30 1 1 56
-75 50 0 0 1 2 74
-75 75 75 75 1 3 70
-75 0 0 50 1 2 68
-80 60 50 20 1 2 65
-60 40 30 10 1 2 55
-60 55 60 10 2 1 74
-75 75 40 5 2 2 64
-60 50 40 10 2 3 42
-50 80 20 20 1 2 48
-75 80 50 50 2 1 52
-43 40 70 20 1 2 41
-50 70 30 0 2 3 44
-20 100 0 0 2 2 27
-70 30 30 10 1 1 75
-75 50 100 25 1 2 58
-50 50 50 30 1 2 44
-75 50 50 25 2 3 48
-50 50 50 25 2 2 60
-50 50 50 25 2 2 49
-25 25 50 75 2 3 52
-50 50 30 30 1 1 62
-50 50 50 75 1 2 69
-75 50 25 0 1 2 56
-50 50 50 0 1 2 34
-50 80 0 25 1 2 75
-40 50 0 10 2 3 67
-70 50 40 0 1 2 57
-50 25 50 80 1 1 52
-40 90 0 0 2 1 48
-50 60 20 0 1 4 69
-50 50 15 25 1 1 35
-50 50 50 50 1 2 41
-70 50 30 0 2 2 69
-50 40 50 30 1 2 66
-30 40 20 20 1 3 40
-50 75 25 0 1 2 57
-30 30 30 40 2 2 59
-75 60 40 0 1 4 44
-50 75 25 25 1 2 59
-75 50 50 25 2 2 57
-75 50 50 0 1 1 54
-50 25 50 25 1 3 66
-50 75 30 25 1 1 59
-25 50 25 0 1 2 50
-25 75 25 0 2 3 36
-25 50 25 0 1 2 45
-25 75 75 50 2 2 51
-25 75 0 0 1 2 62
-50 75 0 50 1 2 36
-70 50 50 30 1 2 55
-75 50 75 0 1 4 64
-50 75 50 25 2 1 59
-70 60 40 30 2 2 41
-70 30 50 30 1 2 51
-50 40 40 20 1 2 42
-50 75 50 0 2 2 47
-75 40 25 10 2 2 50
-75 75 50 40 2 3 51
-75 50 50 60 2 1 72
-80 40 50 30 2 1 69
-80 60 40 0 2 1 68
-75 25 75 0 2 2 46
-50 50 25 25 1 2 77
-25 25 25 0 1 2 42
-70 10 60 30 1 2 68
-80 50 20 0 2 1 60
-50 50 20 10 2 2 55
-90 50 30 10 1 2 49
-50 10 0 0 2 3 49
-50 80 20 0 2 1 58
-45 60 50 0 1 2 53
-50 50 50 0 1 2 49
-75 50 50 25 1 2 56
-75 100 50 0 2 3 43
-100 75 50 0 1 2 48
-75 75 0 0 2 1 59
-100 75 25 0 2 2 48
-100 75 25 0 1 1 68
-75 50 0 0 2 2 61
-70 30 30 0 2 2 59
-50 50 50 50 2 3 30
-75 50 25 0 1 1 78
-75 50 25 25 1 2 53
-75 50 25 0 2 2 50
-75 25 100 0 1 1 62
-70 50 60 25 2 2 59
-75 50 50 0 1 2 48
-50 50 50 50 1 2 61
-0 90 50 50 2 2 33
-55 45 55 30 1 2 63
-25 25 25 50 2 2 40
-50 25 25 50 1 2 65
-0 100 0 0 2 2 25
-80 0 30 30 1 2 67
-25 80 20 15 1 2 60
-25 75 25 0 2 2 58
-70 70 50 0 1 1 52
-50 25 25 50 1 4 54
-70 40 30 0 1 4 47
-30 0 80 30 2 3 51
-0 50 0 0 2 3 54
-50 100 25 0 2 2 61
-75 70 40 0 2 2 67
-25 75 25 25 1 4 61
-50 70 20 10 1 2 37
-25 100 0 0 2 3 54
-75 75 50 0 1 4 46
-60 70 40 40 2 2 47
-45 50 30 30 2 2 37
-60 40 50 30 1 1 62
-25 100 0 0 2 2 43
-25 25 55 20 1 2 65
-0 0 0 85 1 4 64
-75 50 78 10 1 1 68
-50 25 75 50 1 4 67
-25 75 25 0 1 4 76
-5 0 10 90 1 2 62
-50 70 50 45 1 2 61
-40 50 40 50 2 2 41
-50 25 50 75 1 2 61
-25 100 0 0 2 2 58
-70 30 60 30 1 2 54
-40 30 50 75 1 4 35
-55 55 45 40 1 1 50
-50 75 25 25 1 2 64
-25 25 25 25 1 4 50
-70 40 60 10 1 2 53
-70 60 50 40 1 2 55
-25 100 0 75 2 1 54
-40 90 60 10 1 3 69
-25 50 20 0 1 1 60
-75 75 25 0 1 4 71
-50 50 50 0 1 2 44
-60 50 70 50 1 4 40
-40 40 80 20 2 1 48
-40 50 70 30 2 2 57
-70 50 80 20 2 1 43
-70 60 40 20 2 2 46
-40 40 80 30 1 1 49
-30 30 60 60 1 4 41
-60 70 40 30 2 2 28
-80 60 50 10 1 1 62
-75 75 50 0 1 2 57
-60 60 40 10 1 4 51
-60 30 70 60 1 4 58
-60 70 50 0 2 4 52
-60 50 70 50 1 4 44
-60 70 50 50 1 1 74
-50 80 50 30 1 1 32
-30 30 30 70 2 1 56
-50 0 50 0 1 2 42
-80 25 25 0 1 2 67
-90 70 50 10 1 2 59
-50 50 50 50 1 1 58
-70 80 55 25 1 2 45
-75 90 25 10 1 4 57
-50 50 50 30 1 3 60
-100 50 30 0 1 3 31
-50 100 25 0 1 2 31
-25 75 25 25 2 2 34
-70 60 40 0 1 2 62
-50 60 20 10 1 3 74
-40 60 40 30 2 2 71
-50 70 30 30 1 1 64
-70 40 30 60 1 1 72
-50 50 50 50 2 3 30
-100 40 50 30 1 1 65
-70 50 70 50 1 4 63
-75 50 25 25 1 2 62
-75 0 100 90 2 3 61
-25 75 20 20 1 2 51
-75 0 75 25 1 4 53
-70 40 60 0 1 2 52
-30 80 20 20 1 2 49
-70 50 50 0 2 2 43
-50 50 50 25 2 2 48
-40 75 40 40 1 2 53
-70 0 80 50 1 4 31
-25 25 50 25 1 2 50
-70 70 75 20 2 3 60
-75 25 20 25 2 1 27
-75 50 50 50 1 2 40
-60 40 50 20 1 4 51
-50 75 50 0 2 2 66
-60 70 50 50 1 4 63
-50 50 50 50 2 2 27
-50 50 60 40 2 1 62
-50 70 30 0 2 2 70
-50 50 30 0 1 2 70
-80 80 80 20 1 3 66
-50 80 30 30 1 2 65
-80 80 10 0 1 4 35
-90 50 50 30 1 1 77
-80 50 80 80 1 2 58
-50 60 40 40 2 2 68
-70 50 50 40 1 2 63
-50 0 0 0 1 2 60
-50 50 75 50 1 2 68
-25 90 10 25 2 2 32
-30 20 25 50 2 2 46
-50 90 20 10 2 2 59
-90 50 50 0 1 2 73
-40 25 25 50 2 3 63
-60 60 60 50 2 2 60
-50 50 50 75 2 2 39
-25 25 25 75 1 2 44
-35 60 0 0 1 1 55
-70 50 50 40 2 2 31
-75 75 50 0 1 2 66
-75 75 75 0 1 2 72
-50 75 50 25 2 2 47
-25 75 50 25 2 2 37
-30 80 50 50 1 4 56
-60 40 40 10 1 2 57
-80 50 50 50 1 3 56
-50 75 25 25 2 4 44
-100 75 75 25 1 4 46
-80 50 40 10 2 4 31
-50 25 75 25 1 4 43
-75 60 70 0 2 1 48
-50 75 50 25 1 2 62
-75 50 50 0 2 2 42
-50 50 50 0 2 2 47
-75 0 25 25 2 2 54
-80 75 40 0 1 2 63
-60 70 0 0 1 2 72
-50 60 30 0 1 2 47
-50 75 0 0 2 2 42
-50 100 25 0 1 1 60
-75 50 0 0 1 2 67
-80 70 40 20 2 2 70
-70 80 50 10 1 1 70
-60 80 20 30 1 4 64
-50 60 10 10 1 2 67
-50 70 60 30 1 2 58
-25 75 5 0 2 2 56
-50 50 50 50 2 2 68
-50 50 50 0 1 1 69
-50 25 50 25 2 1 66
-50 80 50 0 1 2 49
-30 0 90 10 2 1 63
-40 0 0 30 2 4 37
-75 25 50 25 2 2 54
-80 0 70 0 2 1 71
-50 30 25 0 2 2 51
-80 20 25 0 2 3 31
-25 50 0 0 1 2 66
-50 25 75 25 1 2 70
-10 5 10 1 1 2 39
-10 7 7 1 2 1 63
-75 50 50 25 2 2 76
-50 75 75 25 2 2 56
-50 100 50 25 2 2 65
-50 75 25 50 1 4 29
-50 50 40 20 2 1 77
-50 40 30 50 1 1 65
+"LDP" "NFP" "SKG" "JCP" "gender" "education" "age"
+80 75 80 0 "male" 1 75
+75 80 50 20 "female" 1 64
+100 25 100 0 "male" 2 56
+75 50 25 50 "male" 2 52
+75 50 50 0 "male" 4 52
+50 75 50 50 "female" 3 31
+25 50 25 0 "male" 1 64
+25 25 50 0 "male" 2 46
+50 50 50 50 "male" 1 70
+60 90 10 10 "male" 4 50
+70 25 0 0 "male" 1 62
+75 50 25 25 "female" 2 41
+50 50 25 25 "male" 1 74
+75 0 100 25 "male" 4 46
+75 100 50 0 "female" 4 29
+50 50 75 75 "female" 2 66
+50 40 40 40 "male" 2 68
+25 50 60 60 "male" 4 23
+50 40 25 25 "male" 3 70
+75 25 50 25 "female" 2 53
+75 40 25 10 "male" 2 65
+50 25 50 25 "male" 2 65
+50 30 30 70 "male" 2 45
+90 0 50 30 "male" 4 53
+50 25 0 50 "male" 2 51
+70 20 50 80 "male" 2 51
+50 50 50 10 "male" 3 71
+75 0 20 10 "female" 4 52
+50 25 25 0 "male" 3 76
+80 25 25 0 "male" 4 48
+70 70 25 25 "male" 2 64
+50 25 25 0 "male" 4 72
+50 30 25 50 "female" 2 67
+50 25 50 50 "female" 3 50
+80 50 40 0 "male" 3 74
+50 100 25 0 "male" 2 68
+40 80 50 50 "male" 1 63
+25 80 40 20 "female" 2 26
+50 100 0 0 "female" 3 48
+60 60 70 30 "female" 2 68
+80 70 90 80 "male" 1 63
+50 90 30 50 "male" 2 69
+50 50 30 20 "female" 2 50
+75 50 50 25 "female" 3 52
+50 75 25 50 "male" 3 53
+25 75 75 0 "male" 4 47
+75 75 25 0 "male" 1 68
+25 75 0 0 "male" 2 48
+40 80 30 0 "female" 2 46
+50 75 75 25 "female" 2 29
+75 50 50 25 "male" 4 56
+80 80 50 40 "female" 2 84
+50 100 50 25 "male" 1 74
+50 25 50 25 "female" 2 43
+75 25 75 25 "male" 4 66
+50 75 50 75 "male" 2 56
+50 25 75 50 "male" 3 61
+70 30 50 70 "male" 4 42
+0 25 75 50 "male" 4 35
+10 80 10 0 "female" 3 44
+50 50 10 0 "male" 2 36
+100 60 30 30 "male" 2 57
+40 40 60 0 "male" 4 46
+50 40 25 70 "male" 2 27
+75 50 50 0 "male" 1 71
+50 30 60 25 "male" 4 61
+50 10 65 70 "male" 3 45
+60 10 30 0 "male" 1 73
+50 50 75 25 "female" 3 81
+50 30 30 30 "female" 2 63
+50 25 50 30 "male" 4 59
+90 60 60 40 "male" 4 41
+75 25 25 0 "male" 2 57
+50 75 50 40 "female" 2 61
+50 10 10 0 "female" 2 67
+100 50 0 0 "male" 2 51
+25 75 25 25 "female" 3 60
+50 50 50 50 "male" 2 62
+75 50 25 50 "male" 2 44
+50 25 50 25 "female" 2 48
+50 75 25 25 "male" 2 44
+50 25 25 20 "male" 2 52
+70 60 50 30 "male" 4 60
+50 75 50 30 "male" 3 65
+50 40 40 30 "male" 1 58
+50 40 50 30 "male" 1 56
+0 0 0 100 "male" 1 58
+75 50 50 25 "male" 2 48
+50 50 80 50 "female" 3 69
+50 50 50 0 "male" 2 54
+50 75 50 25 "male" 1 67
+50 75 75 25 "female" 3 53
+25 25 50 75 "male" 2 54
+90 40 20 0 "male" 2 70
+75 100 25 0 "male" 2 66
+50 50 50 30 "male" 1 72
+50 75 25 25 "male" 3 31
+75 75 25 0 "female" 3 60
+75 25 50 50 "male" 2 60
+50 50 75 25 "male" 2 58
+75 50 50 0 "female" 1 73
+75 25 0 0 "male" 2 37
+50 75 100 0 "female" 2 59
+15 15 30 10 "male" 4 28
+50 75 0 0 "male" 3 64
+75 75 95 25 "male" 4 39
+75 25 75 0 "male" 3 60
+50 25 50 25 "male" 1 75
+25 100 0 0 "male" 1 76
+50 50 50 50 "female" 2 38
+50 10 50 100 "male" 1 65
+40 40 10 15 "male" 1 80
+50 50 50 25 "male" 4 62
+50 75 25 25 "male" 4 67
+70 70 50 50 "male" 2 34
+80 90 40 0 "male" 4 54
+60 60 20 20 "male" 1 70
+75 50 25 25 "male" 1 73
+25 0 50 100 "male" 4 61
+100 75 75 0 "male" 2 46
+100 25 50 0 "male" 1 57
+10 100 10 0 "female" 2 37
+75 50 40 40 "male" 1 75
+75 50 50 25 "male" 2 66
+25 50 50 25 "male" 2 54
+70 50 50 20 "female" 1 65
+50 60 50 0 "male" 4 59
+90 70 75 60 "female" 1 62
+50 50 50 50 "male" 1 58
+50 50 50 30 "male" 2 57
+50 50 50 30 "female" 2 43
+50 25 50 25 "male" 1 64
+75 25 50 0 "male" 2 60
+50 50 50 50 "female" 2 60
+25 75 50 0 "male" 1 65
+75 50 50 0 "male" 2 59
+75 25 25 0 "male" 1 74
+50 50 25 25 "female" 1 64
+70 50 50 80 "male" 1 70
+50 70 0 0 "male" 2 51
+20 20 30 50 "male" 2 59
+50 50 40 25 "male" 4 63
+50 30 50 30 "male" 2 57
+10 90 10 10 "female" 2 65
+50 50 50 25 "female" 2 53
+50 50 25 0 "female" 1 64
+75 75 50 50 "male" 3 60
+75 25 75 0 "female" 1 66
+65 60 50 10 "female" 1 62
+60 55 62 10 "female" 2 55
+58 50 48 5 "female" 2 50
+20 70 10 10 "male" 3 65
+60 40 50 50 "female" 2 52
+60 20 20 30 "male" 2 63
+75 75 75 25 "female" 4 49
+50 50 50 40 "male" 1 81
+55 55 70 40 "male" 2 64
+50 50 50 25 "male" 2 68
+85 55 55 0 "male" 1 67
+50 75 50 25 "female" 2 79
+60 60 50 0 "female" 1 59
+70 70 30 30 "male" 1 56
+75 50 0 0 "male" 2 74
+75 75 75 75 "male" 3 70
+75 0 0 50 "male" 2 68
+80 60 50 20 "male" 2 65
+60 40 30 10 "male" 2 55
+60 55 60 10 "female" 1 74
+75 75 40 5 "female" 2 64
+60 50 40 10 "female" 3 42
+50 80 20 20 "male" 2 48
+75 80 50 50 "female" 1 52
+43 40 70 20 "male" 2 41
+50 70 30 0 "female" 3 44
+20 100 0 0 "female" 2 27
+70 30 30 10 "male" 1 75
+75 50 100 25 "male" 2 58
+50 50 50 30 "male" 2 44
+75 50 50 25 "female" 3 48
+50 50 50 25 "female" 2 60
+50 50 50 25 "female" 2 49
+25 25 50 75 "female" 3 52
+50 50 30 30 "male" 1 62
+50 50 50 75 "male" 2 69
+75 50 25 0 "male" 2 56
+50 50 50 0 "male" 2 34
+50 80 0 25 "male" 2 75
+40 50 0 10 "female" 3 67
+70 50 40 0 "male" 2 57
+50 25 50 80 "male" 1 52
+40 90 0 0 "female" 1 48
+50 60 20 0 "male" 4 69
+50 50 15 25 "male" 1 35
+50 50 50 50 "male" 2 41
+70 50 30 0 "female" 2 69
+50 40 50 30 "male" 2 66
+30 40 20 20 "male" 3 40
+50 75 25 0 "male" 2 57
+30 30 30 40 "female" 2 59
+75 60 40 0 "male" 4 44
+50 75 25 25 "male" 2 59
+75 50 50 25 "female" 2 57
+75 50 50 0 "male" 1 54
+50 25 50 25 "male" 3 66
+50 75 30 25 "male" 1 59
+25 50 25 0 "male" 2 50
+25 75 25 0 "female" 3 36
+25 50 25 0 "male" 2 45
+25 75 75 50 "female" 2 51
+25 75 0 0 "male" 2 62
+50 75 0 50 "male" 2 36
+70 50 50 30 "male" 2 55
+75 50 75 0 "male" 4 64
+50 75 50 25 "female" 1 59
+70 60 40 30 "female" 2 41
+70 30 50 30 "male" 2 51
+50 40 40 20 "male" 2 42
+50 75 50 0 "female" 2 47
+75 40 25 10 "female" 2 50
+75 75 50 40 "female" 3 51
+75 50 50 60 "female" 1 72
+80 40 50 30 "female" 1 69
+80 60 40 0 "female" 1 68
+75 25 75 0 "female" 2 46
+50 50 25 25 "male" 2 77
+25 25 25 0 "male" 2 42
+70 10 60 30 "male" 2 68
+80 50 20 0 "female" 1 60
+50 50 20 10 "female" 2 55
+90 50 30 10 "male" 2 49
+50 10 0 0 "female" 3 49
+50 80 20 0 "female" 1 58
+45 60 50 0 "male" 2 53
+50 50 50 0 "male" 2 49
+75 50 50 25 "male" 2 56
+75 100 50 0 "female" 3 43
+100 75 50 0 "male" 2 48
+75 75 0 0 "female" 1 59
+100 75 25 0 "female" 2 48
+100 75 25 0 "male" 1 68
+75 50 0 0 "female" 2 61
+70 30 30 0 "female" 2 59
+50 50 50 50 "female" 3 30
+75 50 25 0 "male" 1 78
+75 50 25 25 "male" 2 53
+75 50 25 0 "female" 2 50
+75 25 100 0 "male" 1 62
+70 50 60 25 "female" 2 59
+75 50 50 0 "male" 2 48
+50 50 50 50 "male" 2 61
+0 90 50 50 "female" 2 33
+55 45 55 30 "male" 2 63
+25 25 25 50 "female" 2 40
+50 25 25 50 "male" 2 65
+0 100 0 0 "female" 2 25
+80 0 30 30 "male" 2 67
+25 80 20 15 "male" 2 60
+25 75 25 0 "female" 2 58
+70 70 50 0 "male" 1 52
+50 25 25 50 "male" 4 54
+70 40 30 0 "male" 4 47
+30 0 80 30 "female" 3 51
+0 50 0 0 "female" 3 54
+50 100 25 0 "female" 2 61
+75 70 40 0 "female" 2 67
+25 75 25 25 "male" 4 61
+50 70 20 10 "male" 2 37
+25 100 0 0 "female" 3 54
+75 75 50 0 "male" 4 46
+60 70 40 40 "female" 2 47
+45 50 30 30 "female" 2 37
+60 40 50 30 "male" 1 62
+25 100 0 0 "female" 2 43
+25 25 55 20 "male" 2 65
+0 0 0 85 "male" 4 64
+75 50 78 10 "male" 1 68
+50 25 75 50 "male" 4 67
+25 75 25 0 "male" 4 76
+5 0 10 90 "male" 2 62
+50 70 50 45 "male" 2 61
+40 50 40 50 "female" 2 41
+50 25 50 75 "male" 2 61
+25 100 0 0 "female" 2 58
+70 30 60 30 "male" 2 54
+40 30 50 75 "male" 4 35
+55 55 45 40 "male" 1 50
+50 75 25 25 "male" 2 64
+25 25 25 25 "male" 4 50
+70 40 60 10 "male" 2 53
+70 60 50 40 "male" 2 55
+25 100 0 75 "female" 1 54
+40 90 60 10 "male" 3 69
+25 50 20 0 "male" 1 60
+75 75 25 0 "male" 4 71
+50 50 50 0 "male" 2 44
+60 50 70 50 "male" 4 40
+40 40 80 20 "female" 1 48
+40 50 70 30 "female" 2 57
+70 50 80 20 "female" 1 43
+70 60 40 20 "female" 2 46
+40 40 80 30 "male" 1 49
+30 30 60 60 "male" 4 41
+60 70 40 30 "female" 2 28
+80 60 50 10 "male" 1 62
+75 75 50 0 "male" 2 57
+60 60 40 10 "male" 4 51
+60 30 70 60 "male" 4 58
+60 70 50 0 "female" 4 52
+60 50 70 50 "male" 4 44
+60 70 50 50 "male" 1 74
+50 80 50 30 "male" 1 32
+30 30 30 70 "female" 1 56
+50 0 50 0 "male" 2 42
+80 25 25 0 "male" 2 67
+90 70 50 10 "male" 2 59
+50 50 50 50 "male" 1 58
+70 80 55 25 "male" 2 45
+75 90 25 10 "male" 4 57
+50 50 50 30 "male" 3 60
+100 50 30 0 "male" 3 31
+50 100 25 0 "male" 2 31
+25 75 25 25 "female" 2 34
+70 60 40 0 "male" 2 62
+50 60 20 10 "male" 3 74
+40 60 40 30 "female" 2 71
+50 70 30 30 "male" 1 64
+70 40 30 60 "male" 1 72
+50 50 50 50 "female" 3 30
+100 40 50 30 "male" 1 65
+70 50 70 50 "male" 4 63
+75 50 25 25 "male" 2 62
+75 0 100 90 "female" 3 61
+25 75 20 20 "male" 2 51
+75 0 75 25 "male" 4 53
+70 40 60 0 "male" 2 52
+30 80 20 20 "male" 2 49
+70 50 50 0 "female" 2 43
+50 50 50 25 "female" 2 48
+40 75 40 40 "male" 2 53
+70 0 80 50 "male" 4 31
+25 25 50 25 "male" 2 50
+70 70 75 20 "female" 3 60
+75 25 20 25 "female" 1 27
+75 50 50 50 "male" 2 40
+60 40 50 20 "male" 4 51
+50 75 50 0 "female" 2 66
+60 70 50 50 "male" 4 63
+50 50 50 50 "female" 2 27
+50 50 60 40 "female" 1 62
+50 70 30 0 "female" 2 70
+50 50 30 0 "male" 2 70
+80 80 80 20 "male" 3 66
+50 80 30 30 "male" 2 65
+80 80 10 0 "male" 4 35
+90 50 50 30 "male" 1 77
+80 50 80 80 "male" 2 58
+50 60 40 40 "female" 2 68
+70 50 50 40 "male" 2 63
+50 0 0 0 "male" 2 60
+50 50 75 50 "male" 2 68
+25 90 10 25 "female" 2 32
+30 20 25 50 "female" 2 46
+50 90 20 10 "female" 2 59
+90 50 50 0 "male" 2 73
+40 25 25 50 "female" 3 63
+60 60 60 50 "female" 2 60
+50 50 50 75 "female" 2 39
+25 25 25 75 "male" 2 44
+35 60 0 0 "male" 1 55
+70 50 50 40 "female" 2 31
+75 75 50 0 "male" 2 66
+75 75 75 0 "male" 2 72
+50 75 50 25 "female" 2 47
+25 75 50 25 "female" 2 37
+30 80 50 50 "male" 4 56
+60 40 40 10 "male" 2 57
+80 50 50 50 "male" 3 56
+50 75 25 25 "female" 4 44
+100 75 75 25 "male" 4 46
+80 50 40 10 "female" 4 31
+50 25 75 25 "male" 4 43
+75 60 70 0 "female" 1 48
+50 75 50 25 "male" 2 62
+75 50 50 0 "female" 2 42
+50 50 50 0 "female" 2 47
+75 0 25 25 "female" 2 54
+80 75 40 0 "male" 2 63
+60 70 0 0 "male" 2 72
+50 60 30 0 "male" 2 47
+50 75 0 0 "female" 2 42
+50 100 25 0 "male" 1 60
+75 50 0 0 "male" 2 67
+80 70 40 20 "female" 2 70
+70 80 50 10 "male" 1 70
+60 80 20 30 "male" 4 64
+50 60 10 10 "male" 2 67
+50 70 60 30 "male" 2 58
+25 75 5 0 "female" 2 56
+50 50 50 50 "female" 2 68
+50 50 50 0 "male" 1 69
+50 25 50 25 "female" 1 66
+50 80 50 0 "male" 2 49
+30 0 90 10 "female" 1 63
+40 0 0 30 "female" 4 37
+75 25 50 25 "female" 2 54
+80 0 70 0 "female" 1 71
+50 30 25 0 "female" 2 51
+80 20 25 0 "female" 3 31
+25 50 0 0 "male" 2 66
+50 25 75 25 "male" 2 70
+10 5 10 1 "male" 2 39
+10 7 7 1 "female" 1 63
+75 50 50 25 "female" 2 76
+50 75 75 25 "female" 2 56
+50 100 50 25 "female" 2 65
+50 75 25 50 "male" 4 29
+50 50 40 20 "female" 1 77
+50 40 30 50 "male" 1 65
diff --git a/man/japan.Rd b/man/japan.Rd
index fbd1194..87af369 100644
--- a/man/japan.Rd
+++ b/man/japan.Rd
@@ -7,7 +7,7 @@
in Japan on the 0 (least preferred) - 100 (most preferred) scale.
It is based on the 1995 survey data of 418 individual voters.
The data also include the sex, education level, and age of
- the voters. The survey allowed voters to chose among four parties:
+ the voters. The survey allowed voters to choose among four parties:
Liberal Democratic Party (LDP), New Frontier Party (NFP), Sakigake
(SKG), and Japanese Communist Party (JCP).
}
@@ -16,14 +16,14 @@
\format{A data frame containing the following 7 variables for 418 observations.
- \tabular{lll}{
- LDP \tab preference for Liberal Democratic Party \tab 0 - 100 \cr
- NFP \tab preference for New Frontier Party \tab 0 - 100 \cr
- SKG \tab preference for Sakigake \tab 0 - 100 \cr
- JCP \tab preference for Japanse Communist Party \tab 0 - 100 \cr
- sex \tab sex of each voter \tab 1 = male, 2 = female \cr
- education \tab levels of education for each voter \tab \cr
- age \tab age of each voter \tab
+ \tabular{llll}{
+ LDP \tab numeric \tab preference for Liberal Democratic Party \tab 0 - 100 \cr
+ NFP \tab numeric \tab preference for New Frontier Party \tab 0 - 100 \cr
+ SKG \tab numeric \tab preference for Sakigake \tab 0 - 100 \cr
+ JCP \tab numeric \tab preference for Japanese Communist Party \tab 0 - 100 \cr
+ gender \tab factor \tab gender of each voter \tab \code{male} or \code{female} \cr
+ education \tab numeric \tab levels of education for each voter \tab \cr
+ age \tab numeric \tab age of each voter \tab
}
}
\keyword{datasets}
diff --git a/man/mnp.Rd b/man/mnp.Rd
index 3edd1cf..669281e 100644
--- a/man/mnp.Rd
+++ b/man/mnp.Rd
@@ -109,7 +109,7 @@ mnp(formula, data = parent.frame(), choiceX = NULL, cXnames = NULL,
names of the response variable, and \code{z1} and \code{z2} are each
vectors of length \eqn{n} that record the values of the two
choice-specific covariates for each individual for choice A, likewise
- for \code{z3}, ..., \code{z6}. The corresponding variable names via
+ for \code{z3}, \eqn{\ldots}, \code{z6}. The corresponding variable names via
\code{cXnames=c("price", "quantity")}
need to be specified, where \code{price} refers to the coefficient
name for \code{z1}, \code{z3}, and \code{z5}, and \code{quantity}
@@ -162,7 +162,7 @@ predict(res1, newdata = detergent[1:3,], type="prob", verbose = TRUE)
## load the Japanese election data
data(japan)
## run the multinomial probit model with ordered preferences
-res2 <- mnp(cbind(LDP, NFP, SKG, JCP) ~ sex + education + age, data = japan,
+res2 <- mnp(cbind(LDP, NFP, SKG, JCP) ~ gender + education + age, data = japan,
verbose = TRUE)
## summarize the results
summary(res2)
@@ -202,9 +202,8 @@ predict(res2, newdata = japan[10,], type = "prob")
(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}
+ Fitting the Multinomial Probit Models,} \emph{Journal of Statistical
+ Software}, Vol. 14, No. 2.
}
\author{
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-science/packages/r-cran-mnp.git
More information about the debian-science-commits
mailing list