[Depmix-commits] r329 - trunk

[noreply at r-forge.r-project.org]

Author: ingmarvisser
Date: 2010-01-28 17:06:19 +0100 (Thu, 28 Jan 2010)
New Revision: 329

Modified:
   trunk/DESCRIPTION
   trunk/NEWS
   trunk/depmixNew-test5.R
Log:
upped version nr to 0.3-0 and related stuff

Modified: trunk/DESCRIPTION
===================================================================
--- trunk/DESCRIPTION	2010-01-28 16:02:54 UTC (rev 328)
+++ trunk/DESCRIPTION	2010-01-28 16:06:19 UTC (rev 329)
@@ -1,10 +1,10 @@
 Package: depmixS4
-Version: 0.3
+Version: 0.3-0
 Date: 2010-01-19
 Title: Dependent Mixture Models
 Author: Ingmar Visser <i.visser at uva.nl>, Maarten Speekenbrink <m.speekenbrink at ucl.ac.uk>
 Maintainer: Ingmar Visser <i.visser at uva.nl>
-Depends: R (>= 2.9.1), nnet, methods, MASS
+Depends: R (>= 2.9.1), nnet, methods, MASS, MCMCpack
 Suggests: Rdonlp2
 Description: Fit latent (hidden) Markov models on mixed categorical and continuous (timeseries)
    data, otherwise known as dependent mixture models

Modified: trunk/NEWS
===================================================================
--- trunk/NEWS	2010-01-28 16:02:54 UTC (rev 328)
+++ trunk/NEWS	2010-01-28 16:06:19 UTC (rev 329)
@@ -7,7 +7,9 @@
     can be used for optimization, or alternatively, if and when Rdonlp2
     is used, sum constraints need to be added when fitting the model.
 
-  o 
+  o added an example of how to specify a model with multivariate normal
+    responses (and fixed a bug in MVNresponse that prevented such models
+    from being specified in the first place). 
 
 Changes in depmixS4 version 0.2-2
 

Modified: trunk/depmixNew-test5.R
===================================================================
--- trunk/depmixNew-test5.R	2010-01-28 16:02:54 UTC (rev 328)
+++ trunk/depmixNew-test5.R	2010-01-28 16:06:19 UTC (rev 329)
@@ -93,16 +93,16 @@
 
 
 library(depmixS4)
-
 # use function xpnd and vech from MCMCpack to convert from lower.tri to square matrix and back
 
-
 # multivariate normal response model
 mn <- c(1,2,3)
 sig <- matrix(c(1,.5,0,.5,1,0,0,0,2),3,3)
 y <- mvrnorm(1000,mn,sig)
 mod <- MVNresponse(y~rnorm(1000))
 
+y <- simulate(mod)
+
 head(dens(mod,log=T))
 
 head(predict(mod))
@@ -128,6 +128,18 @@
 
 y <- rbind(y1,y2)
 
+m1 <- MVNresponse(y~1,pst=c(0,.1,1,0.1,1))
+
+m2 <- MVNresponse(y~1)
+
+m1 
+
+m1 at parameters
+
+m2 
+
+m2 at parameters
+
 rModels <- list(
 	list(
 		MVNresponse(y~1)
@@ -152,9 +164,17 @@
 
 
 fm <- fit(mod)
+fmd <- fit(mod,meth="donlp")
 
-fm <- fit(mod,meth="donlp")
+pem <- getpars(fm)[7:16]
+pdon <- getpars(fmd)[7:16]
 
+all.equal(pem,pdon)
+
+fm <- simulate(fm)
+
+fm <- fit(fm)
+
 fm 
 
 summary(fm)