From d9146d93c846938cc80eb13babad37bfc3d5a543 Mon Sep 17 00:00:00 2001
From: David <david_hodgson1991@hotmail.co.uk>
Date: Sun, 20 Oct 2024 17:02:33 +0100
Subject: [PATCH] vignette ex1

---
 .DS_Store              | Bin 0 -> 6148 bytes
 vignettes/Example1.Rmd |  13 ++++++++++---
 2 files changed, 10 insertions(+), 3 deletions(-)
 create mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..3c7a831e039444d2d00d75e6eaa0d0a607001fc6
GIT binary patch
literal 6148
zcmeHK%}&BV7@P$XDO@;uG3gVK_yz^q1BoI5A3!OD7!eYFJooN9_zFIsC!KG1L)t|X
zPsW(<Yj$V*b!N9aZMt0mrhk}U0v!NN7QtG?>Wh%;Qcj}gGcBUAeJruX1_!J$KVfU)
zFFGL4u7w%yk>e4i=Qmt!^4V%L<8^sCdMw=!8*Bk>q{uME022%`#x3g`i~`kLplaZO
z@5T=MD$iU5yfa*}zX3-Waf~crzXa?$o_PQ3fb|}*BKFn8wY}>xTkO56s4WA9!KBKt
zF1(gph&cO<+0^#i<$cTpg*Z@F)H(Yt@+!;Dzs!p8OsAIgYJokE1$Kc#JSZ!8s-X_3
z1M0w!9pIWR(QG@^S{+aa)PbV|@_mR|1QU;iL;LAqWw!vt2Gcg!mfuCu#2yomg+pFp
z7$+q<sqtG3<K)c8o|kwm96C9S-+UN9vhh0<V@GHIv8BT#4z*SX)Pbr4b$8m4^*{T5
z|6dj9nL3~j{3{1cBb}ywPRXyWCzF%4HfFhFkq}<t@F-!!F2&@^Qhd&0gZWq*#KdFa
RkQs*l2v{4mQU`w3flt28h)Dnd

literal 0
HcmV?d00001

diff --git a/vignettes/Example1.Rmd b/vignettes/Example1.Rmd
index 4ea17be..97700ce 100644
--- a/vignettes/Example1.Rmd
+++ b/vignettes/Example1.Rmd
@@ -18,7 +18,8 @@ knitr::opts_chunk$set(echo = TRUE)
 ```{r setup}
 #devtools::install("..") #install if needed
 library(ptmc)
-library(tidyverse) 
+library(dplyr)
+library(tidyr) 
 library(coda)
 
 ```
@@ -183,6 +184,7 @@ post <- ptmc_func(model=model, data=data_t, settings=settings)
 `ptmc_func` returns a list of length two. The first entry is `post$mcmc` a mcmc or mcmc.list object (from the coda package). I can plot these and calculate convergence diagnostics using coda functions:
 
 ```{r plot outcomes}
+
 library(posterior)
 library(coda)
 library(bayesplot)
@@ -198,6 +200,9 @@ gelman.diag(post$mcmc)
 
 The second entry is `post$lpost` and is long table dataframe of the log-posterior values. These values can be easily plotted using ggplot2:
 ```{r}
+
+library(ggplot2)
+
 # Plot of the logposterior for the three chains
 lpost_conv <- post$lpost %>% filter(sample_no>250)
 logpostplot <- ggplot(lpost_conv, aes(x = sample_no, y = lpost)) + 
@@ -209,10 +214,12 @@ logpostplot
 
 The third entry is `post$temp` and is long table dataframe of the adaptive temperature values. These values can be easily plotted using ggplot2:
 ```{r}
-# Plot of the logposterior for the three chains
-#temp_conv <- post$temp %>% filter(sample_no>5000)
+
+
 tempplot <- ggplot(post$temp, aes(x = sample_no, y = temperature)) + 
   geom_line(aes(color = chain_no), size = 0.2, alpha=0.8) +
   theme_minimal()
+
 tempplot
+
 ```
\ No newline at end of file