added MH in tech dataset, CC from https://www.kaggle.com/osmi/mental-…

…health-in-tech-survey/data

_bookdown.yml

@@ -1,64 +1,89 @@
+bibliography: [bibliography.bib]
+biblio-style: apalike
+link-citations: yes
+
 book_filename: "Just enough R"
 new_session: yes
 output_dir: "docs"
-
+delete_merged_file: true
 rmd_files:
 
-  [ "index.Rmd",
+  [
+    # GETTING STARTED
+    "index.Rmd",
     "start_here.Rmd",
 
-    "datasets.Rmd",
+
+    # PART DATA
+    "DATASETS.Rmd",
     "working-with-dataframes.Rmd",
+
     "real-data.Rmd",
+    "import-export.Rmd",
+    "column-types-and-missing.Rmd",
+    "tidy-data.Rmd",
+    "reshaping.Rmd",
     "summarising-data.Rmd",
-    "graphics.Rmd",
-    "graphics-ggplot-extras.Rmd",
+"fancy-reshaping.Rmd",
+
+
+"graphics.Rmd",
+"graphics-ggplot-extras.Rmd",
+
+# "example.datasets.Rmd",
+
 
+# PART MODELS
+"MODELS.Rmd",
+"basic-statistics.Rmd",
+"crosstabulation.Rmd",
+"correlations.Rmd",
+"t-tests.Rmd",
+"linear-models.Rmd",
+"anova.Rmd",
+"general-linear-models.Rmd",
+"multilevel-models.Rmd",
+"mediation-and-covariance-models.Rmd",
+"cfa-sem.Rmd",
 
-    "basic-statistics.Rmd",
-    "crosstabulation.Rmd",
-    "correlations.Rmd",
-    "t-tests.Rmd",
+# "additional-stubs.Rmd",
+"bayes-mcmc.Rmd",
+"power-analysis.Rmd",
 
+# PART Patterns
+"PATTERNS.Rmd",
+"interactions.Rmd",
+"predictions-and-margins.Rmd",
+"models-are-data.Rmd",
+"simplifying-and-reusing.Rmd",
 
-    "linear-models.Rmd",
-    "anova.Rmd",
 
-    "understanding-interactions.Rmd",
-    "predictions-and-margins.Rmd",
+# PART explanations
+"EXPLANATIONS.Rmd",
+"confidence-and-intervals.Rmd",
+"multiple-comparisons.Rmd",
+"clustering.Rmd",
+"fixed-or-random.Rmd",
+"link-functions.Rmd",
+"over-fitting.Rmd",
 
-    "general-linear-models.Rmd",
-    "mediation.Rmd",
-    "multilevel-models.Rmd",
-    "cfa-sem.Rmd",
-    "power-analysis.Rmd",
-
+# PART everyday R
+"EVERYDAY.Rmd",
+"installation.Rmd",
+"packages.Rmd",
 
-    # "additional-stubs.Rmd",
-    "bayes-mcmc.Rmd",
+"quirks.Rmd",
+"string-handling.Rmd",
+"colours.Rmd",
 
-
-    "statistical-explanations.Rmd",
-    "confidence-vs-prediction-intervals.Rmd",
-    "multiple-comparisons.Rmd",
-    "clustering.Rmd",
-    "fixed-or-random.Rmd",
-    "link-functions.Rmd",
-    "over-fitting.Rmd",
-    "gof.Rmd",
+"help.Rmd",
 
+"sharing-and-publishing.Rmd",
 
-    "loose-ends.Rmd",
-    "installation.Rmd",
-    "packages.Rmd",
-    "rownames.Rmd",
-    "colours.Rmd",
-    "string-handling.Rmd",
-    "help.Rmd",
-    "functions.Rmd",
-    "example.datasets.Rmd",    
+"writing-a-paper.Rmd",
+"cleaning-up-your-mess.Rmd",
+"making-table-1.Rmd",
 
-    "sharing-and-publishing.Rmd",
 
-    "references.Rmd"
+"references.Rmd"
   ]

_output.yml

@@ -16,4 +16,5 @@ bookdown::pdf_book:
   latex_engine: xelatex
   citation_package: natbib
   keep_tex: yes
-bookdown::epub_book: default
+
+# bookdown::epub_book: default

additional-stubs.Rmd

@@ -1,16 +1,14 @@
 ---
 title: 'Bayes factors'
-output: bookdown::tufte_html2
+
 ---
 
 
 ```{r, include=FALSE}
-# ignore all this for the moment
 knitr::opts_chunk$set(echo = TRUE, collapse=TRUE, cache=TRUE)
 library(tidyverse)
 library(pander)
 library(lmerTest)
-
 ```
 
 

airquality-r-values.csv

@@ -1,7 +1,7 @@
 "","Ozone","Solar.R","Wind","Temp","Month","Day"
-"Ozone",0,0.00197241942881554,1.2981615782337e-11,0,0.561787014869315,1
-"Solar.R",0.000179310857165049,0,1,0.00751772924010297,1,0.561787014869315
-"Wind",9.27258270166931e-13,0.495955206815127,0,3.43407631220316e-08,0.247105980658337,1
-"Temp",0,0.000751772924010297,2.6415971632332e-09,0,7.23144291114863e-07,0.645698579067462
-"Month",0.0776000963996033,0.366353350873138,0.0274562200731485,6.02620242595719e-08,0,1
-"Day",0.887942543669527,0.0702233768586644,0.738746589753025,0.107616429844577,0.92218998575754,0
+"Ozone",1,0.348341692993603,-0.60154652988895,0.698360342150932,0.164519314380413,-0.013225646554047
+"Solar.R",0.348341692993603,1,-0.0567916657698467,0.275840271340805,-0.0753007638859408,-0.150274979240985
+"Wind",-0.60154652988895,-0.0567916657698467,1,-0.457987879104833,-0.17829257921769,0.027180902809146
+"Temp",0.698360342150932,0.275840271340805,-0.457987879104833,1,0.420947252266222,-0.130593175159278
+"Month",0.164519314380413,-0.0753007638859408,-0.17829257921769,0.420947252266222,1,-0.00796176260045312
+"Day",-0.013225646554047,-0.150274979240985,0.027180902809146,-0.130593175159278,-0.00796176260045312,1

anova.Rmd

@@ -1,6 +1,6 @@
 ---
 title: 'Anova'
-output: bookdown::tufte_html2
+
 ---
 
 

basic-statistics.Rmd

@@ -1,22 +1,18 @@
 ---
 title: 'Basic statistics'
-output:
-  bookdown::tufte_html2
 ---
 
 ```{r, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE, collapse=TRUE, cache=TRUE)
 ```
 
-# (PART) Analysis {-}
 
-
-# Basic inferential statistics
+# Common inferential statistics {#common-inferential-stats}
 
 
 R has simple functions for common inferential statistics like Chi^2^, t-tests, correlations and many more. This section is by no means exhaustive, but covers [statistics for crosstabulations](#crosstabs), [differences in means](#t-tests), and [linear correlation](#correlations).
 
-For non-parametric statistics [this page on the statmethods site](http://www.statmethods.net/stats/nonparametric.html) is a useful guide.
+For more on non-parametric statistics [this page on the statmethods site](http://www.statmethods.net/stats/nonparametric.html) is a useful guide.
 
-The [`coin::` package](http://finzi.psych.upenn.edu/R/library/coin/doc/coin.pdf) implements many resampling tests, which can be useful when assumptions of parametric tests are not valid. [See this intro to resampling statistics](http://www.statmethods.net/stats/resampling.html). 
+The [`coin::` package](http://finzi.psych.upenn.edu/R/library/coin/doc/coin.pdf) implements many resampling tests, which can also be useful when assumptions of parametric tests are not valid. [See this intro to resampling statistics](http://www.statmethods.net/stats/resampling.html). 
 

bayes-mcmc.Rmd

@@ -1,11 +1,11 @@
 ---
 title: 'Bayesian linear modelling via MCMC'
-output: bookdown::tufte_html2
+
 ---
 
 
 ```{r, include=F}
-knitr::opts_chunk$set(echo = TRUE, collapse=TRUE, cache=TRUE)
+knitr::opts_chunk$set(echo = TRUE, collapse=TRUE, cache=TRUE, message=F, warning=F)
 
 library(tidyverse)
 library(pander)
@@ -15,11 +15,12 @@ library(lmerTest)
 
 
 
-# Baysian linear model fitting with MCMC {#bayes-mcmc}
+# Baysian model fitting {#bayes-mcmc}
 
 
-This is a minimal guide to fitting and interpreting regression and multilevel models via MCMC. For _much_ more detail, and a much more comprehensive introduction to modern Bayesian analysis see [Jon Kruschke's *Doing Bayesian Data Analysis*](http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/).
+### Baysian fitting of linear models via MCMC methods {-}
 
+This is a minimal guide to fitting and interpreting regression and multilevel models via MCMC. For _much_ more detail, and a much more comprehensive introduction to modern Bayesian analysis see [Jon Kruschke's *Doing Bayesian Data Analysis*](http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/).
 
 
 Let's revisit our [previous example which investigated the effect of familiar and liked music on pain perception](#pain-music-data):
@@ -40,41 +41,47 @@ painmusic %>%
 ```{r}
 # set sum contrasts
 options(contrasts = c("contr.sum", "contr.poly"))
-
-pain.model <- lm(with.music ~ no.music + familiar*liked , data=painmusic)
+pain.model <- lm(with.music ~ 
+                   no.music + familiar * liked, 
+                 data=painmusic)
 summary(pain.model)
 ```
 
 
-```{r}
-library(rstanarm)
 
+Do the same thing again, but with with MCMC using Stan:
+
+```{r, echo=T, results="hide"}
+library(rstanarm)
 options(contrasts = c("contr.sum", "contr.poly"))
-pain.model.mcmc <- stan_lm(with.music ~ no.music + familiar*liked , data=painmusic, prior=NULL)
+pain.model.mcmc <- stan_lm(with.music ~ no.music + familiar * liked,
+                          data=painmusic, prior=NULL)
+```
+
+```{r}
 summary(pain.model.mcmc)
 ```
 
 
+
+
 ### Posterior probabilities for parameters {-}
 
 ```{r}
-params.of.interest <- 
-  pain.model.mcmc %>% 
-  as_data_frame() %>%
-  select(familiar1, liked1, `familiar1:liked1`) %>% 
-  reshape2::melt() 
+library(bayesplot)
 
-params.of.interest %>% 
-    ggplot(aes(value, color=variable)) + 
-    geom_density() + 
-    geom_vline(xintercept = 0) +
-    scale_color_discrete("") +
-    xlab("Parameter value") + 
-    ylab("Posterior density") + 
-    theme(aspect.ratio = .5)
+mcmc_areas(as.matrix(pain.model.mcmc), regex_pars = 'familiar|liked', prob = .9)
+```
+
+
+```{r}
+mcmc_intervals(as.matrix(pain.model.mcmc), regex_pars = 'familiar|liked', prob_outer = .9)
 ```
 
 
+
+
+
 ### Credible intervals {- #credible-intervals}
 
 
@@ -92,17 +99,24 @@ http://doingbayesiandataanalysis.blogspot.co.uk/2012/04/why-to-use-highest-densi
 -->
 
 ```{r}
-get_HPDI <- function(l){
+mHPDI <- function(l){
+  # median and HPDI
+  # this utility function used to return a dataframe, which is required when using 
+  # dplyr::do() below
   ci = rethinking::HPDI(l, prob=.95)
   data_frame(median=median(l), lower=ci[1], upper=ci[2])
 }
 
+params.of.interest <- 
+  pain.model.mcmc %>% 
+  as_tibble %>% 
+  reshape2::melt() %>% 
+  filter(stringr::str_detect(variable, "famil|liked")) %>% 
+  group_by(variable)
 
 params.of.interest %>% 
-  group_by(variable) %>% 
-  do(., get_HPDI(.$value)) %>% 
-  rename(Estimate=median) %>% 
-  pander::pandoc.table(caption="Estimates and 95% credible intervals for the effect of group 2 at months 6 and 12")
+  do(., mHPDI(.$value)) %>% 
+  pander::pandoc.table(caption="Estimates and 95% credible intervals for the parameters of interest")
 ```
 
 
@@ -111,16 +125,37 @@ params.of.interest %>%
 
 ### Bayesian 'p values' for parameters {-}
 
+We can do simple arithmetic with the posterior draws to calculate the probability a parameter is greater than (or less than) zero:
 
 ```{r}
-params.of.interest %>% 
-  group_by(variable) %>% 
-  summarise(`p (x<0)` = mean(value < 0))
+params.of.interest %>%  
+  summarise(estimate=mean(value),
+            `p (x<0)` = mean(value < 0),
+            `p (x>0)` = mean(value > 0))
+```
+
+
+Or if you'd like the Bayes Factor (evidence ratio) for one hypotheses vs another, for example comparing the hypotheses that a parameter is > vs. <= 0, then you can use the `hypothesis` function in the `brms` package:
+
+```{r}
+pain.model.mcmc.df <- 
+  pain.model.mcmc %>% 
+  as_tibble
+
+brms::hypothesis(pain.model.mcmc.df,  
+                 c("familiar1 > 0",
+                   "liked1 > 0",
+                   "familiar1:liked1 < 0"))
 ```
 
+Here although we only have a 'significant' p value for one of the parameters, we can also see there is "very strong" evidence that familiarity also influences pain, and "strong" evidence for the interaction of familiarity and liking, according to [conventional rules of thumb when interpreting Bayes Factors](https://en.wikipedia.org/wiki/Bayes_factor#Interpretation).
+
+
+
 
 TODO - add a fuller explanation of why [multiple comparisons](#mutiple-comparisons) are not an issue for Bayesian analysis [@gelman2012we], because *p* values do not have the same interpretation in terms of long run frequencies of replication; they are a representation of the weight of the evidence in favour of a hypothesis.
 
+TODO: Also reference Zoltan Dienes Bayes paper.