Formatting headers and lists

04-overview.Rmd

@@ -1,4 +1,4 @@
-# (PART\*) DIMENSIONALITY REDUCTION OVERVIEW {-}
+# Introduction
 
 ## The difficulties of high-dimensional datasets
 

05-student-guide.Rmd

@@ -70,11 +70,16 @@ str(pca.obj)
 ```
 
 The `prcomp()` command will create a list containing multiple dataframes and vectors: 
-  -_sdev_, a vector of the standard deviations for each PC
-  -_rotation_, a matrix of the eigenvectors for each variable
-  -_center_, a vector of the center value for each variable
-  -_scale_, a vector of the scaling factor for each variable
-  -_x_, a matrix of the individual PC values for each flower
+
+  *_sdev_, a vector of the standard deviations for each PC
+
+  *_rotation_, a matrix of the eigenvectors for each variable
+
+  *_center_, a vector of the center value for each variable
+
+  *_scale_, a vector of the scaling factor for each variable
+
+  *_x_, a matrix of the individual PC values for each flower
 
 
 ## Choosing the number of PCs
@@ -140,14 +145,17 @@ fviz_pca_var(pca.obj, col.var = "black", repel=T)
 
 The size and position of the vectors (arrows) in the correlation circle can tell us quite a lot about the variables.
 
-  -Positively correlated variables are grouped together.
-  -Negatively correlated variables are positioned on opposite sides of the plot origin (opposed quadrants) .
+  *Positively correlated variables are grouped together.
+
+  *Negatively correlated variables are positioned on opposite sides of the plot origin (opposed quadrants) .
 
-  -Vectors that end close to the circle outline greatly contribute to a given PC.
-  -Shorter vectors contribute very little.
+  *Vectors that end close to the circle outline greatly contribute to a given PC.
+
+  *Shorter vectors contribute very little.
 
-  -Vectors that are primarily horizontal mostly contribute to the first PC (the x-axis). 
-  -Vectors that are more vertical contribute primarily to the second PC (the y-axis).
+  *Vectors that are primarily horizontal mostly contribute to the first PC (the x-axis). 
+
+  *Vectors that are more vertical contribute primarily to the second PC (the y-axis).
 
 
 ## Visualizing structure using PCs