[R-gregmisc-users] SF.net SVN: r-gregmisc: [946] trunk/PathwayModeling/thesispaper

[<r_b...@us...>]

Revision: 946
Author:   r_burrows
Date:     2006-03-20 09:29:20 -0800 (Mon, 20 Mar 2006)
ViewCVS:  http://svn.sourceforge.net/r-gregmisc/?rev=946&view=rev

Log Message:
-----------
set eval=T for code chunks

Modified Paths:
--------------
    trunk/PathwayModeling/thesispaper/methods.Snw
    trunk/PathwayModeling/thesispaper/results.Snw
Modified: trunk/PathwayModeling/thesispaper/methods.Snw
===================================================================
--- trunk/PathwayModeling/thesispaper/methods.Snw	2006-03-20 17:10:18 UTC (rev 945)
+++ trunk/PathwayModeling/thesispaper/methods.Snw	2006-03-20 17:29:20 UTC (rev 946)
@@ -11,7 +11,7 @@
     \cite{Gillespie77}. In this paper we present the results for a 
     5-reaction sequence that is sampled at 12, 16, or 25 time points 
     with 3 replicates at each time point. 
-<<data, echo=F, eval=F>>=1
+<<data, echo=F, eval=T>>=1
 # gillespie.out - output from the Gillespie simulation
 source('R/getPaperData.R')
 source('R/getPaperRates.R')
@@ -59,7 +59,7 @@
     the perturbation of $R1$ at $time = 20$ results in the time
     courses plotted in Figure~\ref{pulse}.
 
-<<echo=F,eval=F>>=2
+<<echo=F,eval=T>>=2
 source("R/pulse.R")
 rawdata <- read.table("data/rawdata.dat",header=T)
 attach(rawdata)

Modified: trunk/PathwayModeling/thesispaper/results.Snw
===================================================================
--- trunk/PathwayModeling/thesispaper/results.Snw	2006-03-20 17:10:18 UTC (rev 945)
+++ trunk/PathwayModeling/thesispaper/results.Snw	2006-03-20 17:29:20 UTC (rev 946)
@@ -20,7 +20,7 @@
 (e.g., $d_1$ --$d_2$ and $d_3$ --$d_4$, Figure~\ref{scatter}) were similar relative distances from their 
 modes. The significance of all this is not clear at this point.}
 
-<<fig4,echo=F,eval=F>>=3
+<<fig4,echo=F,eval=T>>=3
 source("R/paperSSQ.R")
 source("R/getModes.R")
 source("R/do.optim.R")
@@ -63,7 +63,7 @@
 The effect of the number of data points on the parameter distributions
 can be seen in Figure~\ref{converged}.
 
-<<fig5,echo=F,eval=F>>=4
+<<fig5,echo=F,eval=T>>=4
 source("R/plotDensity.R")
 source("R/plotConverged.R")
 output12 <- read.table("data/output12.AllComp.thinned")
@@ -94,7 +94,7 @@
 distributions. There is correlation between some pairs of parameters, e.g.,
 $d_1$ -- $d_2$, but no evidence of multi-modality.
 
-<<fig6, echo=F,eval=F>>=5
+<<fig6, echo=F,eval=T>>=5
 source("R/paperPairs.R")
 mcmcML <-scan("figures/tempDir/mcmcML.dat")
 output16 <- read.table("data/output16.AllComp.thinned")
@@ -127,7 +127,7 @@
 is shown in Figure~\ref{fits}. Quantitative measures of the fits for
 all the algorithms are given in Table~\ref{MSq}.
 
-<<fig7, echo=F, eval=F>>=6
+<<fig7, echo=F, eval=T>>=6
 source("R/paperSSQ.R")
 source("R/getModes.R")
 source("R/do.optim.R")
@@ -186,7 +186,7 @@
 This is not a problem with the all-component Metropolis and NKC 
 algorithms since they update all the parameters at each iteration}.
 
-<<fig8, echo=F,eval=F>>=7
+<<fig8, echo=F,eval=T>>=7
 source("R/getMSDvsEvals.R")
 source("R/plotMSD16.R")
 source('R/paperSSQ.R')


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