Restructuring

_includes/package-nav.html

@@ -4,7 +4,9 @@
 <li><a href="visualize.html">Visualize</a></li>
 <li><a href="transform.html">Transform</a></li>
 <li><a href="tidy.html">Tidy</a></li>
+<li><a href="model.html">Model</a></li>
 <li><a href="import.html">Import</a></li>
+<li><a href="eda.html">Exploratory data analysis</a></li>
 
 <li class="dropdown-header">Communication</li>
 <li><a href="rmarkdown.html">R Markdown</a></li>
@@ -12,13 +14,11 @@
 
 <li class="dropdown-header">Programming</li>
 <li><a href="data-structures.html">Data structures</a></li>
+<li><a href="functions.html">Expressing yourself with code</a></li>
 <li><a href="strings.html">Strings</a></li>
 <li><a href="datetimes.html">Dates and times</a></li>
-<li><a href="functions.html">Expressing yourself with code</a></li>
 <li><a href="lists.html">Lists</a></li>
 
 <li class="dropdown-header">Modelling</li>
-<li><a href="model-linear.html">Linear models</a></li>
 <li><a href="model-vis.html">Models and visualisation</a></li>
-<li><a href="model-assess.html">Model assesment</a></li>
-<li><a href="model-other.html">Other models</a></li>
+<li><a href="model-assess.html">Model assessment</a></li>

eda.Rmd

@@ -0,0 +1,5 @@
+---
+layout: default
+title: Exploratory data analysis
+output: bookdown::html_chapter
+---

model-assess.Rmd

@@ -10,6 +10,9 @@ set.seed(1014)
 options(digits = 3)
 ```
 
+* Some discussion of p-values.
+* Bootstrapping to understand uncertainty in parameters.
+* Cross-validation to understand predictive quality.
 
 ## Multiple models
 

model-other.Rmd

@@ -1,24 +0,0 @@
----
-layout: default
-title: Other model familes
-output: bookdown::html_chapter
----
-
-
-[Applied Predictive Modeling](http://amzn.com/1461468485). 
-[An Introduction to Statistical Learning](http://amzn.com/1461471370)
-
-
-## Extensions of linear models
-
-* Generalised linear models: logistic, ...
-
-* Hierarchical models
-
-## Non-linear
-
-* Random forrests
-
-## Clustering
-
-Show example of clustering babynames by year.

model.Rmd

@@ -18,3 +18,19 @@ Review caret and mlr.
 * Interactions
 * Splines
 * Log transform
+
+
+[Applied Predictive Modeling](http://amzn.com/1461468485). 
+[An Introduction to Statistical Learning](http://amzn.com/1461471370)
+
+# Other model families
+
+List of important classes on modelling; paragraph about why it's useful/different to lm, and a pointer to where to learn more.
+
+* Generalised linear models: logistic, ...
+
+* Hierarchical models
+
+* Random forrests
+
+* Unsupervised techniques