From 5b6ba6a28a538434daf048d3ff7a830b5fd5a5e8 Mon Sep 17 00:00:00 2001
From: davkoh <david.kohns94@googlemail.com>
Date: Tue, 29 Aug 2023 12:12:27 +0300
Subject: [PATCH] Fixes and reminders

---
 .DS_Store                     | Bin 0 -> 6148 bytes
 README.md                     |   2 +-
 demos_ch12/demo12_1.R         |   2 +-
 demos_ch12/demo12_1.Rmd       |   2 +-
 demos_ch5/demo5_2.R           |   8 ++++----
 demos_rstan/README.md         |   2 ++
 demos_rstan/rstanarm_demo.R   |   4 ++--
 demos_rstan/rstanarm_demo.Rmd |   4 ++--
 8 files changed, 13 insertions(+), 11 deletions(-)
 create mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
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diff --git a/README.md b/README.md
index b900ffa..46c9b77 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ Currently there are demos for BDA3 Chapters 2, 3, 4, 5, 6, 10, 11 and 12.
 Furthermore there are demos for
 [CmdStanR](https://github.com/stan-dev/cmdstanr),
 [RStan](https://github.com/stan-dev/rstan)
-[RStanARM](https://github.com/stan-dev/rstanarm).
+[RStanARM](https://github.com/stan-dev/rstanarm). When running the R-scripts from Aalto Jupyter notebook, please make sure to set the parent directory to "~/notebooks/BDA_R_demos".
 
 The initial demos were originally written for Matlab by [Aki
 Vehtari](http://users.aalto.fi/~ave/) and translated to R by [Markus
diff --git a/demos_ch12/demo12_1.R b/demos_ch12/demo12_1.R
index 55a0961..078c764 100644
--- a/demos_ch12/demo12_1.R
+++ b/demos_ch12/demo12_1.R
@@ -54,7 +54,7 @@ tt[1,] <- c(t1, t2)    # Save starting point
 # For demonstration load pre-computed values
 # Replace this with your algorithm!
 # tt is a M x 2 array, with M samples of both theta_1 and theta_2
-load(root("demos_ch11","demo12_1b.RData"))
+load(root("demos_ch12","demo12_1b.RData"))
 # Here we have intentionally used a very small step size for smooth
 # simulations, but for more efficient simulations larger step size
 # could be used
diff --git a/demos_ch12/demo12_1.Rmd b/demos_ch12/demo12_1.Rmd
index 72e0baf..3fb1041 100644
--- a/demos_ch12/demo12_1.Rmd
+++ b/demos_ch12/demo12_1.Rmd
@@ -69,7 +69,7 @@ tt[1,] <- c(t1, t2)    # Save starting point
 # For demonstration load pre-computed values
 # Replace this with your algorithm!
 # tt is a M x 2 array, with M samples of both theta_1 and theta_2
-load(root("demos_ch11","demo12_1b.RData"))
+load(root("demos_ch12","demo12_1b.RData"))
 # Here we have intentionally used a very small step size for smooth
 # simulations, but for more efficient simulations larger step size
 # could be used
diff --git a/demos_ch5/demo5_2.R b/demos_ch5/demo5_2.R
index 375dbe5..d5f5076 100644
--- a/demos_ch5/demo5_2.R
+++ b/demos_ch5/demo5_2.R
@@ -20,7 +20,7 @@ library(gridExtra)
 library(tidyr)
 library(latex2exp)
 library(rprojroot)
-root<-has_file(".BDA_R_demos_root")$make_fix_file()
+root<-has_file(".BDA_R_demos_root")$make_fix_file() # Make sure to set parent directory to ~/notebooks/BDA_R_demos
 
 #' Data 
 y <- c(28,8,-3,7,-1,1,18,12)
@@ -81,7 +81,7 @@ plot_margpost <-
   geom_line(aes(x = x, y = p), color='forestgreen') +
   labs(x = expression(tau), y = TeX('$p(\\tau | y)$'), title = title1) +
     scale_y_continuous(breaks = 0) +
-    xlim(c(0, 35))
+    xlim(c(0, 40))
 
 
 df_condmeans <- as.data.frame(t(tm)) %>%
@@ -93,7 +93,7 @@ yl <- c(-5, 40)
 title2 <- TeX('Conditional means E\\[$\\theta_j | \\tau , y $\\]')
 plot_condmeans <- ggplot(data = df_condmeans) +
   geom_line(aes(x = x, y = p, color = (school=='A'), group = school)) +
-  coord_cartesian(ylim = yl, xlim = c(0, 35)) +
+  coord_cartesian(ylim = yl, xlim = c(0, 40)) +
   labs(x = expression(tau), y = TeX('E\\[$\\theta_j | \\tau , y $\\]'), title = title2, color = '') +
   scale_color_manual(values = c('blue','red'), labels = labs1) +
   theme(legend.background = element_blank(), legend.position = c(0.8,0.9))
@@ -107,7 +107,7 @@ yl <- c(0, 25)
 title3 <- TeX('Conditional standard deviations sd\\[$\\theta_j | \\tau , y $\\]')
 plot_condsds <- ggplot(data = df_condsds) +
   geom_line(aes(x = x, y = p, color = (school=='A'), group = school)) +
-  coord_cartesian(ylim = yl, xlim = c(0,35)) +
+  coord_cartesian(ylim = yl, xlim = c(0,40)) +
   labs(x = expression(tau), y = TeX('sd\\[$\\theta_j | \\tau , y $\\]'), title = title3, color = '') +
   scale_color_manual(values = c('blue','red'), labels = labs1) +
   theme(legend.background = element_blank(), legend.position = c(0.8,0.9))
diff --git a/demos_rstan/README.md b/demos_rstan/README.md
index 22efce0..a4307f0 100644
--- a/demos_rstan/README.md
+++ b/demos_rstan/README.md
@@ -1,5 +1,7 @@
 # Links to html versions of demos
 
+If running the CMDStanR demos on Aalto Jupyter, please make sure to have specified the location for CMDStanR: set_cmdstan_path('/coursedata/cmdstan').
+
 - [rstan_demo: RStan demo](http://avehtari.github.io/BDA_R_demos/demos_rstan/rstan_demo.html)
 - [cmdstanr_demo: CmdStanR demo](http://avehtari.github.io/BDA_R_demos/demos_rstan/cmdstanr_demo.html)
 - [rstanarm_demo: RStanARM demo](http://avehtari.github.io/BDA_R_demos/demos_rstan/rstanarm_demo.html)
diff --git a/demos_rstan/rstanarm_demo.R b/demos_rstan/rstanarm_demo.R
index cc96292..8a393b7 100644
--- a/demos_rstan/rstanarm_demo.R
+++ b/demos_rstan/rstanarm_demo.R
@@ -194,7 +194,7 @@ mcmc_hist(draws_bin2, pars='oddsratio')
 #' # Linear Gaussian model
 #' 
 #' The following file has Kilpisjärvi summer month temperatures 1952-2013:
-data_kilpis <- read.delim('kilpisjarvi-summer-temp.csv', sep = ';')
+data_kilpis <- read.delim('~/BDA_R_demos/demos_rstan/kilpisjarvi-summer-temp.csv', sep = ';')
 data_lin <-data.frame(year = data_kilpis$year,
                    temp = data_kilpis[,5])
 
@@ -284,7 +284,7 @@ loo_compare(loo1, loo2)
 #' # Comparison of k groups with hierarchical models
 #' 
 #' Let's compare the temperatures in three summer months.
-data_kilpis <- read.delim('kilpisjarvi-summer-temp.csv', sep = ';')
+data_kilpis <- read.delim('~/BDA_R_demos/demos_rstan/kilpisjarvi-summer-temp.csv', sep = ';')
 data_grp <- data.frame(month = rep(6:8, nrow(data_kilpis)),
               temp = c(t(data_kilpis[,2:4])))
 
diff --git a/demos_rstan/rstanarm_demo.Rmd b/demos_rstan/rstanarm_demo.Rmd
index e090987..f54f173 100644
--- a/demos_rstan/rstanarm_demo.Rmd
+++ b/demos_rstan/rstanarm_demo.Rmd
@@ -239,7 +239,7 @@ mcmc_hist(draws_bin2, pars='oddsratio')
 The following file has Kilpisjärvi summer month temperatures 1952-2013:
 
 ```{r }
-data_kilpis <- read.delim('kilpisjarvi-summer-temp.csv', sep = ';')
+data_kilpis <- read.delim('~/BDA_R_demos/demos_rstan/kilpisjarvi-summer-temp.csv', sep = ';')
 data_lin <-data.frame(year = data_kilpis$year,
                    temp = data_kilpis[,5])
 ```
@@ -348,7 +348,7 @@ There is no practical difference between Gaussian and Student's t models.
 Let's compare the temperatures in three summer months.
 
 ```{r }
-data_kilpis <- read.delim('kilpisjarvi-summer-temp.csv', sep = ';')
+data_kilpis <- read.delim('~/BDA_R_demos/demos_rstan/kilpisjarvi-summer-temp.csv', sep = ';')
 data_grp <- data.frame(month = rep(6:8, nrow(data_kilpis)),
               temp = c(t(data_kilpis[,2:4])))
 ```