Merge pull request #2 from abmantz/plotting

plotting options

R/rgw/DESCRIPTION

@@ -1,8 +1,8 @@
 Package: rgw
 Type: Package
 Title: Goodman-Weare Affine-Invariant Sampling
-Version: 0.1.0
-Date: 2016-10-10
+Version: 0.2.0
+Date: 2017-11-16
 Author: Adam Mantz
 Maintainer: Adam Mantz <[email protected]>
 Description: Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.

R/rgw/R/rgw.R

@@ -41,16 +41,26 @@ GoodmanWeare = function(ensemble, lnpost, Nsteps, current.lnP=NULL, mc.cores=get
   list(ensemble=ensemble, current.lnP=current.lnP)
 }
 
-GoodmanWeare.rem = function(post, lnpost, thin=1, mention.every=NA, save.every=NA, save.file=NA, ...) {
+GoodmanWeare.rem = function(post, lnpost, thin=1, mention.every=NA, save.every=NA, save.file=NA, show.every=NA, show.params=1:dim(post)[1], show.walkers=min(dim(post)[2],8), show.pch1=1, show.pch2='.', show.pch.switch=500, ...) {
   ## post should be an Nparam*Nwalkers*Nsteps array with the initial walker positions
   ## set in post[,,1]. The ensemble will be saved in post[,,i] every thin
   ## iterations until post is filled.
   res = list()
+  if (!is.na(show.every)) {
+    par(mfrow=c(length(show.params), 1))
+  }
   for (i in 2:dim(post)[3]) {
     res = GoodmanWeare(post[,,i-1], lnpost, thin, res$current.lnP, ...)
     post[,,i] = res$ensemble
     if (!is.na(mention.every) & i %% mention.every == 0) message(paste('Finished iteration', i))
     if (!is.na(save.every) & !is.na(save.file) & i %% save.every == 0) save(post, file=save.file)
+    if (!is.na(show.every) & i %% show.every == 0) {
+      if (i < show.pch.switch) pch = show.pch1 else pch = show.pch2
+      for (j in show.params) {
+        plot(post[j,1,1:i], ylab=paste('parameter', j), pch=pch, col=1+1)
+        if (show.walkers > 1) for (k in 2:show.walkers) points(post[j,k,1:i], pch=pch, col=1+k)
+      }
+    }
   }
   post
 }

R/rgw/man/GoodmanWeare.rem.Rd

@@ -9,7 +9,9 @@ Produces a Monte-Carlo Markov ensemble using the affine-invariant method of Good
 }
 \usage{
 GoodmanWeare.rem(post, lnpost, thin=1, mention.every=NA,
- save.every=NA, save.file=NA, ...)
+ save.every=NA, save.file=NA, show.every=NA,
+ show.params=1:dim(post)[1], show.walkers=min(dim(post)[2],8),
+ show.pch1=1, show.pch2='.', show.pch.switch=500, ...)
 }
 %- maybe also 'usage' for other objects documented here.
 \arguments{
@@ -30,9 +32,27 @@ save the accumulated Markov ensemble to disk every time this many iterations are
 }
   \item{save.file}{
 filename for saving progress.
+}
+  \item{show.every}{
+plot parameter traces so far to the active graphics device periodically.
+}
+  \item{show.params}{
+(sub)set of parameter traces to plot (default is to show all).
+}
+  \item{show.walkers}{
+which walkers to plot traces of (default is first 8).
+}
+  \item{show.pch1}{
+plot symbol to use for short chains.
+}
+  \item{show.pch2}{
+plot symbol to use for long chains.
+}
+  \item{show.pch.switch}{
+chain length that distinguishes "short" and "long" chains for plotting purposes.
 }
   \item{...}{
-additional arguments to pass to GoodmanWeare or lnpost.
+additional named arguments to pass to GoodmanWeare or lnpost.
 }
 }
 %\details{
@@ -47,7 +67,12 @@ See help for GoodmanWeare.
 Adam Mantz
 }
 \note{
-By default, the code will attempt to run in parallel (see the `parallel' package). To prevent this, pass mc.cores=1.
+By default, the code will attempt to run in parallel (see the `parallel'
+package). To prevent this, pass mc.cores=1.
+
+If traces are being plotted (show.every not NA),
+par(mfrow=c(length(show.params), 1)) is called on the current graphics
+device.
 }
 
 %% ~Make other sections like Warning with \section{Warning }{....} ~

R/rgw/man/rgw-package.Rd

@@ -12,8 +12,8 @@ This package implements the affine-invariant method of Goodman & Weare (2010) <D
 \tabular{ll}{
 Package: \tab rgw\cr
 Type: \tab Package\cr
-Version: \tab 0.1.0\cr
-Date: \tab 2016-10-10\cr
+Version: \tab 0.2.0\cr
+Date: \tab 2017-11-16\cr
 License: \tab MIT\cr
 LazyLoad: \tab yes\cr
 }

README.md

@@ -1,3 +1,7 @@
+<a href="http://ascl.net/1711.006"><img src="https://img.shields.io/badge/ascl-1711.006-blue.svg?colorB=262255" alt="ascl:1711.006" /></a>
+<a href="https://cran.r-project.org/package=rgw"><img src="https://img.shields.io/cran/v/rgw.svg" alt="CRAN" /></a>
+<a href="https://raw.githubusercontent.com/abmantz/rgw/master/LICENSE"><img src="https://img.shields.io/cran/l/rgw.svg" alt="MIT License" /></a>
+
 # rgw
 
 This package implements in [R](https://www.r-project.org/)  the affine-invariant sampling method of [Goodman & Weare (2010)](http://dx.doi.org/10.2140/camcos.2010.5.65). This is a way of producing Monte-Carlo samples from a target distribution, which can be used for statistical inference.
@@ -45,4 +49,11 @@ plot(post[2,1,])
 
 ## Help
 
-Use the [issues](https://github.com/abmantz/rgw/issues).
+Open an [issue](https://github.com/abmantz/rgw/issues).
+
+## History
+
+Version | Comments
+:-----: | --------
+0.2.0   | Added options for plotting traces periodically during the run
+0.1.0   | First release --> CRAN