[Pomp-commits] r634 - in pkg: . inst/doc

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

Author: kingaa
Date: 2012-03-27 23:17:56 +0200 (Tue, 27 Mar 2012)
New Revision: 634

Modified:
   pkg/.Rinstignore
   pkg/inst/doc/bsmc-ricker-flat-prior.rda
   pkg/inst/doc/intro_to_pomp.Rnw
   pkg/inst/doc/intro_to_pomp.pdf
   pkg/inst/doc/manual.pdf
Log:
- put inst/doc/*.rda files into .Rinstignore
- improve 'bsmc' section in intro vignette


Modified: pkg/.Rinstignore
===================================================================
--- pkg/.Rinstignore	2012-03-27 20:57:54 UTC (rev 633)
+++ pkg/.Rinstignore	2012-03-27 21:17:56 UTC (rev 634)
@@ -1,2 +1,3 @@
 inst/doc/Makefile
 inst/doc/fullnat.bst
+inst/doc/(.+?)\.rda$

Modified: pkg/inst/doc/bsmc-ricker-flat-prior.rda
===================================================================
(Binary files differ)

Modified: pkg/inst/doc/intro_to_pomp.Rnw
===================================================================
--- pkg/inst/doc/intro_to_pomp.Rnw	2012-03-27 20:57:54 UTC (rev 633)
+++ pkg/inst/doc/intro_to_pomp.Rnw	2012-03-27 21:17:56 UTC (rev 634)
@@ -1386,24 +1386,24 @@
 
 First, we draw a sample from our prior distribution.
 Here, we'll use uniform priors on two model parameters ($\log{r}$ and $\sigma$), leaving the others fixed at their true values.
-<<bsmc-example-flat-prior-1,echo=F,eval=F>>=
+<<bsmc-example-flat-prior-1,echo=F,eval=T>>=
 require(pomp)
 data(ricker)
-set.seed(136872209L)
 @ 
 We'll use 10,000 particles, so we'll need that many samples from the prior distribution.
 <<bsmc-example-flat-prior-2,eval=F>>=
+set.seed(136872209L)
 Np <- 10000
-params <- parmat(coef(ricker),nrep=Np)
-params["log.r",] <- runif(n=Np,min=2,max=5)
-params["sigma",] <- runif(n=Np,min=0.1,max=1)
+prior1 <- parmat(coef(ricker),nrep=Np)
+prior1["log.r",] <- runif(n=Np,min=2,max=5)
+prior1["sigma",] <- runif(n=Np,min=0.1,max=1)
 @ 
 Now \code{params} holds these 10,000 samples.
 The following runs the sequential Monte Carlo algorithm.
 Note that, by specifying \code{transform=TRUE}, we cause the estimation to proceed on the transformed scale.
 <<bsmc-example-flat-prior-3,eval=F>>=
-fit <- bsmc(ricker,params=params,transform=TRUE,
-            est=c("log.r","log.sigma"),smooth=0.2)
+  fit1 <- bsmc(ricker,params=prior1,transform=TRUE,
+               est=c("log.r","log.sigma"),smooth=0.2)
 @ 
 <<bsmc-example-flat-prior-eval,eval=T,echo=F>>=
 binary.file <- "bsmc-ricker-flat-prior.rda"
@@ -1413,40 +1413,42 @@
 <<bsmc-example-flat-prior-1>>
 <<bsmc-example-flat-prior-2>>
 <<bsmc-example-flat-prior-3>>
-  save(fit,file=binary.file,compress="xz")
+  save(fit1,file=binary.file,compress="xz")
 }
 @ 
 Fig.~\ref{fig:bsmc-example-flat-prior} shows the results of this computation.
 Obtain the posterior means of the parameters by doing
 <<bsmc-example-flat-prior-coef>>=
-signif(coef(fit),digits=2)
+signif(coef(fit1),digits=2)
 @ 
 Note that these are reported on the natural (i.e., untransformed) scale.
 
 \begin{figure}
 <<bsmc-example-flat-prior-plot,fig=T,pdf=F,png=T,height=6,width=6,echo=F>>=
-  plot(fit,thin=5000)
+  plot(fit1,thin=5000)
 @ 
 \caption{
   Results of \code{bsmc} on the Ricker model.
   The off-diagonal panels show 5000 samples from the prior (grey) and posterior (blue) distributions.
   The diagonal panels show histograms of the marginal posterior distributions for each of the parameters.
   Note that these are shown on the transformed scale.
-  This plot was produced by executing \code{plot(fit,thin=5000)}.
+  This plot was produced by executing \code{plot(fit1,thin=5000)}.
   \label{fig:bsmc-example-flat-prior}
 }
 \end{figure}
 
 To repeat the procedure with log-normal priors on $r$ and $\sigma$, one might do the following.
 <<bsmc-example-normal-prior>>=
+set.seed(90348704L)
+Np <- 10000
+prior2 <- parmat(coef(ricker),nrep=Np)
 ## normal prior on log.r
-params["log.r",] <- rnorm(n=Np,mean=coef(ricker,"log.r"),sd=3)
+prior2["log.r",] <- rnorm(n=Np,mean=4,sd=3)
 ## log-normal prior on sigma
-params["sigma",] <- rlnorm(n=Np,meanlog=log(coef(ricker,"sigma")),
-                           sdlog=5)
-fit <- bsmc(ricker,params=params,transform=TRUE,
+prior2["sigma",] <- rlnorm(n=Np,meanlog=log(0.5),sdlog=5)
+fit2 <- bsmc(ricker,params=prior2,transform=TRUE,
             est=c("log.r","log.sigma"),smooth=0.2)
-signif(coef(fit),digits=2)
+signif(coef(fit2),digits=2)
 @ 
 
 

Modified: pkg/inst/doc/intro_to_pomp.pdf
===================================================================
(Binary files differ)

Modified: pkg/inst/doc/manual.pdf
===================================================================
--- pkg/inst/doc/manual.pdf	2012-03-27 20:57:54 UTC (rev 633)
+++ pkg/inst/doc/manual.pdf	2012-03-27 21:17:56 UTC (rev 634)
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[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/pomp -r 634