+ theme readable, toc, code link

idhx · Jun 4, 2020 · 60de48a · 60de48a
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diff --git a/AgePeriodCohort/births.R b/AgePeriodCohort/births.R
@@ -2,6 +2,12 @@
 #' title: "Regression and Other Stories: AgePeriodCohort"
 #' author: "Andrew Gelman, Jennifer Hill, Aki Vehtari"
 #' date: "`r format(Sys.Date())`"
+#' output:
+#'   html_document:
+#'     theme: readable
+#'     toc: true
+#'     toc_depth: 2
+#'     toc_float: true
 #' ---
 
 #' Age-Period-Cohort - Demonstration of age adjustment to estimate
@@ -16,11 +22,11 @@ knitr::opts_chunk$set(message=FALSE, error=FALSE, warning=FALSE, comment=NA)
 # switch this to TRUE to save figures in separate files
 savefigs <- FALSE
 
-#' **Load packages**
+#' #### Load packages
 library("rprojroot")
 root<-has_dirname("ROS-Examples")$make_fix_file()
 
-#' **Load data**
+#' #### Load data
 births <- read.table(root("AgePeriodCohort/data","births.txt"), header=TRUE)
 mean_age_45_54 <- function(yr){
   ages <- 45:54
@@ -29,15 +35,15 @@ mean_age_45_54 <- function(yr){
 }
 for (yr in 1989:2015) print(mean_age_45_54(yr), digits=3)
 
-#' **Calculation**
+#' #### Calculation
 print((.5/10)* (.006423 - .003064)/.003064, digits=3)
 
-#' **From life table**
+#' #### From life table
 deathpr_by_age <- c(.003064, .003322, .003589, .003863, .004148, .004458, .004800, .005165, .005554, .005971)
 deathpr_male <- c(.003244, .003571, .003926, .004309, .004719, .005156, .005622, .006121, .006656, .007222)
 deathpr_female <- c(.002069, .002270, .002486, .002716, .002960, .003226, .003505, .003779, .004040, .004301)
 
-#' **Sum it up**
+#' #### Sum it up
 pop <- read.csv(root("AgePeriodCohort/data","US-EST00INT-ALLDATA.csv"))
 years <- 1989:2013
 deathpr_1 <- rep(NA, length(years))

diff --git a/AgePeriodCohort/births.Rmd b/AgePeriodCohort/births.Rmd
@@ -2,6 +2,12 @@
 title: "Regression and Other Stories: AgePeriodCohort"
 author: "Andrew Gelman, Jennifer Hill, Aki Vehtari"
 date: "`r format(Sys.Date())`"
+output:
+  html_document:
+    theme: readable
+    toc: true
+    toc_depth: 2
+    toc_float: true
 ---
 Age-Period-Cohort - Demonstration of age adjustment to estimate
 trends in mortality rates. See Chapter 3 in Regression and Other
@@ -16,14 +22,14 @@ knitr::opts_chunk$set(message=FALSE, error=FALSE, warning=FALSE, comment=NA)
 savefigs <- FALSE
 ```
 
-**Load packages**
+#### Load packages
 
 ```{r }
 library("rprojroot")
 root<-has_dirname("ROS-Examples")$make_fix_file()
 ```
 
-**Load data**
+#### Load data
 
 ```{r }
 births <- read.table(root("AgePeriodCohort/data","births.txt"), header=TRUE)
@@ -35,21 +41,21 @@ mean_age_45_54 <- function(yr){
 for (yr in 1989:2015) print(mean_age_45_54(yr), digits=3)
 ```
 
-**Calculation**
+#### Calculation
 
 ```{r }
 print((.5/10)* (.006423 - .003064)/.003064, digits=3)
 ```
 
-**From life table**
+#### From life table
 
 ```{r }
 deathpr_by_age <- c(.003064, .003322, .003589, .003863, .004148, .004458, .004800, .005165, .005554, .005971)
 deathpr_male <- c(.003244, .003571, .003926, .004309, .004719, .005156, .005622, .006121, .006656, .007222)
 deathpr_female <- c(.002069, .002270, .002486, .002716, .002960, .003226, .003505, .003779, .004040, .004301)
 ```
 
-**Sum it up**
+#### Sum it up
 
 ```{r }
 pop <- read.csv(root("AgePeriodCohort/data","US-EST00INT-ALLDATA.csv"))

diff --git a/AgePeriodCohort/births_letter.R b/AgePeriodCohort/births_letter.R
@@ -2,6 +2,13 @@
 #' title: "Regression and Other Stories: AgePeriodCohort"
 #' author: "Andrew Gelman, Jennifer Hill, Aki Vehtari"
 #' date: "`r format(Sys.Date())`"
+#' output:
+#'   html_document:
+#'     theme: readable
+#'     toc: true
+#'     toc_depth: 2
+#'     toc_float: true
+#'     code_download: true
 #' ---
 
 #' Age-Period-Cohort - Age adjustment: additional plots.  See Chapter

diff --git a/AgePeriodCohort/births_letter.Rmd b/AgePeriodCohort/births_letter.Rmd
@@ -2,6 +2,13 @@
 title: "Regression and Other Stories: AgePeriodCohort"
 author: "Andrew Gelman, Jennifer Hill, Aki Vehtari"
 date: "`r format(Sys.Date())`"
+output:
+  html_document:
+    theme: readable
+    toc: true
+    toc_depth: 2
+    toc_float: true
+    code_download: true
 ---
 Age-Period-Cohort - Age adjustment: additional plots.  See Chapter
 3 in Regression and Other Stories.

diff --git a/Arsenic/arsenic_logistic_apc.R b/Arsenic/arsenic_logistic_apc.R
@@ -2,6 +2,13 @@
 #' title: "Regression and Other Stories: Arsenic"
 #' author: "Andrew Gelman, Jennifer Hill, Aki Vehtari"
 #' date: "`r format(Sys.Date())`"
+#' output:
+#'   html_document:
+#'     theme: readable
+#'     toc: true
+#'     toc_depth: 2
+#'     toc_float: true
+#'     code_download: true
 #' ---
 
 #' Average predictive comparisons for a logistic regression model:
@@ -13,53 +20,53 @@
 #+ setup, include=FALSE
 knitr::opts_chunk$set(message=FALSE, error=FALSE, warning=FALSE, comment=NA)
 
-#' **Load packages**
+#' #### Load packages
 library("rprojroot")
 root<-has_dirname("ROS-Examples")$make_fix_file()
 library("rstanarm")
 library("loo")
 invlogit <- plogis
 
-#' **Load data**
+#' #### Load data
 wells <- read.csv(root("Arsenic/data","wells.csv"))
 head(wells)
 n <- nrow(wells)
 
-#' **Predict switching with distance, arsenic, and education**
+#' #### Predict switching with distance, arsenic, and education
 #+ results='hide'
 fit_7 <- stan_glm(switch ~ dist100 + arsenic + educ4,
                   family = binomial(link="logit"), data = wells)
 #'
 print(fit_7, digits=2)
 
-#' **Average predictive difference in probability of switching,
-#' comparing households that are next to, or 100 meters from, the nearest safe well**
+#' #### Average predictive difference in probability of switching 1)
+#' comparing households that are next to, or 100 meters from, the nearest safe well
 b <- coef(fit_7)
 hi <- 1
 lo <- 0
 delta <- invlogit (b[1] + b[2]*hi + b[3]*wells$arsenic + b[4]*wells$educ4) -
          invlogit (b[1] + b[2]*lo + b[3]*wells$arsenic + b[4]*wells$educ4)
 round(mean(delta), 2)
 
-#' **Average predictive difference in probability of switching,
-#' comparing households with existing arsenic levels of 0.5 and 1.0**
+#' #### Average predictive difference in probability of switching 2)
+#' comparing households with existing arsenic levels of 0.5 and 1.0
 b <- coef(fit_7)
 hi <- 1.0
 lo <- 0.5
 delta <- invlogit (b[1] + b[2]*wells$dist100 + b[3]*hi + b[4]*wells$educ4) -
          invlogit (b[1] + b[2]*wells$dist100 + b[3]*lo + b[4]*wells$educ4)
 round(mean(delta), 2)
 
-#' **Average predictive difference in probability of switching,
-#' comparing householders with 0 and 12 years of education**
+#' #### Average predictive difference in probability of switching 3)
+#' comparing householders with 0 and 12 years of education
 b <- coef(fit_7)
 hi <- 3
 lo <- 0
 delta <- invlogit (b[1]+b[2]*wells$dist100+b[3]*wells$arsenic+b[4]*hi) -
          invlogit (b[1]+b[2]*wells$dist100+b[3]*wells$arsenic+b[4]*lo)
 round(mean(delta), 2)
 
-#' **Predict switching with distance, arsenic, education and interactions**
+#' #### Predict switching with distance, arsenic, education and interactions
 #+ results='hide'
 wells$c_dist100 <- wells$dist100 - mean(wells$dist100)
 wells$c_arsenic <- wells$arsenic - mean(wells$arsenic)
@@ -70,8 +77,8 @@ fit_8 <- stan_glm(switch ~ c_dist100 + c_arsenic + c_educ4 +
 #'
 print(fit_8, digits=2)
 
-#' **Average predictive difference in probability of switching,
-#' comparing households that are next to, or 100 meters from, the nearest safe well**
+#' ### Average predictive difference in probability of switching 4)
+#' comparing households that are next to, or 100 meters from, the nearest safe well
 b <- coef(fit_8)
 hi <- 1
 lo <- 0

diff --git a/Arsenic/arsenic_logistic_apc.Rmd b/Arsenic/arsenic_logistic_apc.Rmd
@@ -2,6 +2,13 @@
 title: "Regression and Other Stories: Arsenic"
 author: "Andrew Gelman, Jennifer Hill, Aki Vehtari"
 date: "`r format(Sys.Date())`"
+output:
+  html_document:
+    theme: readable
+    toc: true
+    toc_depth: 2
+    toc_float: true
+    code_download: true
 ---
 Average predictive comparisons for a logistic regression model:
 wells in Bangladesh. See Chapter 14 in Regression and Other Stories.
@@ -13,7 +20,7 @@ wells in Bangladesh. See Chapter 14 in Regression and Other Stories.
 knitr::opts_chunk$set(message=FALSE, error=FALSE, warning=FALSE, comment=NA)
 ```
 
-**Load packages**
+#### Load packages
 
 ```{r }
 library("rprojroot")
@@ -23,15 +30,15 @@ library("loo")
 invlogit <- plogis
 ```
 
-**Load data**
+#### Load data
 
 ```{r }
 wells <- read.csv(root("Arsenic/data","wells.csv"))
 head(wells)
 n <- nrow(wells)
 ```
 
-**Predict switching with distance, arsenic, and education**
+#### Predict switching with distance, arsenic, and education
 
 ```{r results='hide'}
 fit_7 <- stan_glm(switch ~ dist100 + arsenic + educ4,
@@ -44,8 +51,8 @@ fit_7 <- stan_glm(switch ~ dist100 + arsenic + educ4,
 print(fit_7, digits=2)
 ```
 
-**Average predictive difference in probability of switching,
-comparing households that are next to, or 100 meters from, the nearest safe well**
+#### Average predictive difference in probability of switching 1)
+comparing households that are next to, or 100 meters from, the nearest safe well
 
 ```{r }
 b <- coef(fit_7)
@@ -56,8 +63,8 @@ delta <- invlogit (b[1] + b[2]*hi + b[3]*wells$arsenic + b[4]*wells$educ4) -
 round(mean(delta), 2)
 ```
 
-**Average predictive difference in probability of switching,
-comparing households with existing arsenic levels of 0.5 and 1.0**
+#### Average predictive difference in probability of switching 2)
+comparing households with existing arsenic levels of 0.5 and 1.0
 
 ```{r }
 b <- coef(fit_7)
@@ -68,8 +75,8 @@ delta <- invlogit (b[1] + b[2]*wells$dist100 + b[3]*hi + b[4]*wells$educ4) -
 round(mean(delta), 2)
 ```
 
-**Average predictive difference in probability of switching,
-comparing householders with 0 and 12 years of education**
+#### Average predictive difference in probability of switching 3)
+comparing householders with 0 and 12 years of education
 
 ```{r }
 b <- coef(fit_7)
@@ -80,7 +87,7 @@ delta <- invlogit (b[1]+b[2]*wells$dist100+b[3]*wells$arsenic+b[4]*hi) -
 round(mean(delta), 2)
 ```
 
-**Predict switching with distance, arsenic, education and interactions**
+#### Predict switching with distance, arsenic, education and interactions
 
 ```{r results='hide'}
 wells$c_dist100 <- wells$dist100 - mean(wells$dist100)
@@ -97,8 +104,8 @@ fit_8 <- stan_glm(switch ~ c_dist100 + c_arsenic + c_educ4 +
 print(fit_8, digits=2)
 ```
 
-**Average predictive difference in probability of switching,
-comparing households that are next to, or 100 meters from, the nearest safe well**
+### Average predictive difference in probability of switching 4)
+comparing households that are next to, or 100 meters from, the nearest safe well
 
 ```{r }
 b <- coef(fit_8)