[Depmix-commits] r576 - in tags/release-1.2-2: . R
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Jan 29 15:04:41 CET 2013
Author: ingmarvisser
Date: 2013-01-29 15:04:40 +0100 (Tue, 29 Jan 2013)
New Revision: 576
Modified:
tags/release-1.2-2/NEWS
tags/release-1.2-2/R/depmix-class.R
tags/release-1.2-2/R/llratio.R
tags/release-1.2-2/R/stationary.R
tags/release-1.2-2/R/transInit.R
Log:
Some minor changes, see NEWS
Modified: tags/release-1.2-2/NEWS
===================================================================
--- tags/release-1.2-2/NEWS 2013-01-24 21:30:14 UTC (rev 575)
+++ tags/release-1.2-2/NEWS 2013-01-29 14:04:40 UTC (rev 576)
@@ -1,11 +1,10 @@
Changes in depmixS4 version 1.2-2
- o Changed class assignment of depmix.fitted object using as() to make
- this version compatible with future R releases (2.3.0?)
+ o Changed class assignment of depmix.fitted object using as().
o Fixed a bug in the fit method of depmix models: linear inequality
constraints were not passed on to rsolnp (thanks to Peiming Wang for
- bringing this to my attention.)
+ bringing this to my attention).
Changes in depmixS4 version 1.2-1
Modified: tags/release-1.2-2/R/depmix-class.R
===================================================================
--- tags/release-1.2-2/R/depmix-class.R 2013-01-24 21:30:14 UTC (rev 575)
+++ tags/release-1.2-2/R/depmix-class.R 2013-01-29 14:04:40 UTC (rev 576)
@@ -67,7 +67,7 @@
# random generation is slow when done separately for each t, so first draw
# variates for all t, and then determine state sequences iteratively
states <- array(,dim=c(nt,nsim))
- states[bt,] <- simulate(object at prior,n=nsim,is.prior=T)
+ states[bt,] <- simulate(object at prior,nsim=nsim,is.prior=TRUE)
sims <- array(,dim=c(nt,ns,nsim))
states <- as.vector(states)
@@ -82,7 +82,8 @@
}
# generate new mix.sim object
- class(object) <- c("mix.sim")
+
+ object <- as(object,"mix.sim") # class(object) <- "depmix.fitted"
object at states <- as.matrix(states)
object at prior@x <- as.matrix(apply(object at prior@x,2,rep,nsim))
@@ -222,7 +223,7 @@
# random generation is slow when done separately for each t, so first draw
# variates for all t, and then determine state sequences iteratively
states <- array(,dim=c(nt,nsim))
- states[bt,] <- simulate(object at prior,n=nsim,is.prior=T)
+ states[bt,] <- simulate(object at prior,nsim=nsim,is.prior=TRUE)
sims <- array(,dim=c(nt,ns,nsim))
for(i in 1:ns) {
if(is.stationary(object)) {
@@ -251,7 +252,7 @@
}
# generate new depmix.sim object
- class(object) <- c("depmix.sim")
+ object <- as(object,"depmix.sim")
object at states <- as.matrix(states)
object at prior@x <- as.matrix(apply(object at prior@x,2,rep,nsim))
Modified: tags/release-1.2-2/R/llratio.R
===================================================================
--- tags/release-1.2-2/R/llratio.R 2013-01-24 21:30:14 UTC (rev 575)
+++ tags/release-1.2-2/R/llratio.R 2013-01-29 14:04:40 UTC (rev 576)
@@ -19,7 +19,7 @@
setMethod("show","llratio",
function(object) {
- pvalue=round(pchisq(object at value,object at df,lower=FALSE),3)
+ pvalue=round(pchisq(object at value,object at df,lower.tail=FALSE),3)
cat("log Likelihood ratio: ", round(object at value,3), " (df=",object at df,"), p=",pvalue,"\n",sep="")
}
)
Modified: tags/release-1.2-2/R/stationary.R
===================================================================
--- tags/release-1.2-2/R/stationary.R 2013-01-24 21:30:14 UTC (rev 575)
+++ tags/release-1.2-2/R/stationary.R 2013-01-29 14:04:40 UTC (rev 576)
@@ -9,7 +9,7 @@
nr <- nrow(tpm)
rs <- all.equal(rowSums(tpm),rep(1,nr))
if(!(rs==TRUE)) stop("rows of the transition matrix should sum to unity")
- e1 <- as.real(eigen(t(tpm))$vectors[,1])
+ e1 <- as.double(eigen(t(tpm))$vectors[,1])
e1 <- e1/sum(e1)
return(e1)
}
Modified: tags/release-1.2-2/R/transInit.R
===================================================================
--- tags/release-1.2-2/R/transInit.R 2013-01-24 21:30:14 UTC (rev 575)
+++ tags/release-1.2-2/R/transInit.R 2013-01-29 14:04:40 UTC (rev 576)
@@ -36,7 +36,7 @@
fixed <- c(as.logical(fixed))
}
constr <- list(
- lin = matrix(1,nr=1,nc=nstates),
+ lin = matrix(1,nrow=1,ncol=nstates),
linup = 1,
linlow = 1,
parup = rep(1,nstates),
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