[Pomp-commits] r1185 - www/vignettes

Fri Jun 5 15:18:40 CEST 2015

Author: kingaa
Date: 2015-06-05 15:18:39 +0200 (Fri, 05 Jun 2015)
New Revision: 1185

Modified:
   www/vignettes/getting_started.R
   www/vignettes/getting_started.Rmd
   www/vignettes/getting_started.html
   www/vignettes/pomp.pdf
Log:
- modify "Getting Started" vignette to use 'mif2'

Modified: www/vignettes/getting_started.R
===================================================================

--- www/vignettes/getting_started.R	2015-06-05 11:31:44 UTC (rev 1184)
+++ www/vignettes/getting_started.R	2015-06-05 13:18:39 UTC (rev 1185)
@@ -205,9 +205,9 @@
   geom_line()
 
 ## ----parus-mif,cache=TRUE------------------------------------------------
-mf <- mif(parus,Nmif=50,Np=1000,method="mif2",cooling.fraction=0.8,
-          rw.sd=c(r=0.02,K=0.02,phi=0.02,sigma=0.02),transform=TRUE)
-mf <- mif(mf)
+mf <- mif2(parus,Nmif=50,Np=1000,cooling.fraction=0.8,
+           rw.sd=rw.sd(r=0.02,K=0.02,phi=0.02,sigma=0.02),transform=TRUE)
+mf <- mif2(mf)
 mle <- coef(mf); mle
 logmeanexp(replicate(5,logLik(pfilter(mf))),se=TRUE)
 sim2 <- simulate(mf,nsim=10,as.data.frame=TRUE,include.data=TRUE)

Modified: www/vignettes/getting_started.Rmd
===================================================================
--- www/vignettes/getting_started.Rmd	2015-06-05 11:31:44 UTC (rev 1184)
+++ www/vignettes/getting_started.Rmd	2015-06-05 13:18:39 UTC (rev 1185)
@@ -453,9 +453,9 @@
 
 Iterated filtering [@Ionides2015; @Ionides2006] is a method for maximizing the likelihood by repeatedly applying a particle filter.  The following codes apply the IF2 algorithm [@Ionides2015].
 ```{r parus-mif,cache=TRUE}
-mf <- mif(parus,Nmif=50,Np=1000,method="mif2",cooling.fraction=0.8,
-          rw.sd=c(r=0.02,K=0.02,phi=0.02,sigma=0.02),transform=TRUE)
-mf <- mif(mf)
+mf <- mif2(parus,Nmif=50,Np=1000,cooling.fraction=0.8,
+           rw.sd=rw.sd(r=0.02,K=0.02,phi=0.02,sigma=0.02),transform=TRUE)
+mf <- mif2(mf)
 mle <- coef(mf); mle
 logmeanexp(replicate(5,logLik(pfilter(mf))),se=TRUE)
 sim2 <- simulate(mf,nsim=10,as.data.frame=TRUE,include.data=TRUE)
@@ -465,7 +465,7 @@
   geom_line()
 ```
 
-The first command runs 50 iterations of the algorithm; the second re-runs the algorithm from where the first run ended up.
+The first command runs 50 iterations of the algorithm; the second re-runs the algorithm, starting from where the first run ended up.
 The next line extracts and displays the MLE. 
 The fourth command runs 5 replicate particle filters to compute the log likelihood at the estimated parameters and averages these appropriately to get an estimate of this likelihood and of the standard Monte Carlo error.
 Finally, the above plots the data and 10 simulated realizations of the model process on the same axes.

Modified: www/vignettes/getting_started.html
===================================================================
--- www/vignettes/getting_started.html	2015-06-05 11:31:44 UTC (rev 1184)
+++ www/vignettes/getting_started.html	2015-06-05 13:18:39 UTC (rev 1185)
@@ -90,7 +90,7 @@
 </div>
 
 <p>Licensed under the <a href="http://creativecommons.org/licenses/by-nc/3.0">Creative Commons attribution-noncommercial license</a>. Please share and remix noncommercially, mentioning its origin.<br /><img src="data:image/png;base64,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" alt="CC-BY_NC" /></p>
-<p>This document was produced using <code>pomp</code> version 0.65.1.</p>
+<p>This document was produced using <code>pomp</code> version 0.66.4.</p>
 <div id="introduction" class="section level2">
 <h2>Introduction</h2>
 <p>This tutorial aims to help you get started using <code>pomp</code> as a suite of tools for analysis of time series data based on dynamical systems models. First, we give some conceptual background regarding the class of models—partially observed Markov processes—that <code>pomp</code> handles. We then discuss some preliminaries: installing the package and so on. Next, using a basic question about ecological population regulation as an example, we load some data and implement some models as <code>R</code> objects of class <code>pomp</code>. Finally, we illustrate some of the package’s capabilities by using its algorithms to fit and compare the models using various inference methods.</p>
@@ -174,7 +174,7 @@
   geom_line()+geom_point()+
   expand_limits(y=0)+
   theme_classic()</code></pre>
[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/pomp -r 1185
