fixes #11

vignettes/indirect_relations.Rmd

@@ -61,39 +61,46 @@ the `type` parameter.
 data("dbces11")
 g <- dbces11
 
-#adjacency
-A <- indirect_relations(g,type = "identity")
-#shortest path distances
-D <- indirect_relations(g,type="dist_sp")
-#dyadic dependencies (as used in betweenness centrality)
-B <- indirect_relations(g,type = "depend_sp")
-#resistance distance (as used in information centrality)
-R <- indirect_relations(g,type="dist_resist")
-#Logarithmic forest distance (parametrized family of distances)
-LF <- indirect_relations(g, type = "dist_lf",lfparam = 1) 
-#Walk distance (parametrized family of distances)
-WD <- indirect_relations(g, type = "dist_walk",dwparam = 0.001) 
-#Random walk distance
-WD <- indirect_relations(g, type = "dist_rwalk") 
-#See ?indirect_relations for further options
+# adjacency
+A <- indirect_relations(g, type = "adjacency")
+# shortest path distances
+D <- indirect_relations(g, type = "dist_sp")
+# dyadic dependencies (as used in betweenness centrality)
+B <- indirect_relations(g, type = "depend_sp")
+# resistance distance (as used in information centrality)
+R <- indirect_relations(g, type = "dist_resist")
+# Logarithmic forest distance (parametrized family of distances)
+LF <- indirect_relations(g, type = "dist_lf", lfparam = 1)
+# Walk distance (parametrized family of distances)
+WD <- indirect_relations(g, type = "dist_walk", dwparam = 0.001)
+# Random walk distance
+WD <- indirect_relations(g, type = "dist_rwalk")
+# See ?indirect_relations for further options
 ```
 
+Indirect relations are represented as matrices, similar to the adjacency matrix. The below matrices show 
+the distance matrix based on sahortest paths, and the pairwise dependencies (used for e.g. betweenness).  
+```{r example_mat}
+D
+B
+``` 
+
 The function takes an additional parameter `FUN` which can be used to pass a function
 to further transform relations. The main use is to obtain indirect relations based on walk counts.
 
 ```{r indirwalks}
-#count the limit proportion of walks (used for eigenvector centrality)
-W <-  indirect_relations(g,type = "walks",FUN = walks_limit_prop)
-#count the number of walks of arbitrary length between nodes, weighted by 
-#the inverse factorial of their length (used for subgraph centrality)
-S <-  indirect_relations(g,type = "walks",FUN = walks_exp)
+# count the limit proportion of walks (used for eigenvector centrality)
+W <- indirect_relations(g, type = "walks", FUN = walks_limit_prop)
+# count the number of walks of arbitrary length between nodes, weighted by
+# the inverse factorial of their length (used for subgraph centrality)
+S <- indirect_relations(g, type = "walks", FUN = walks_exp)
 ```
 
 Additional parameters can also be passed to calculate parameterized versions of
 relations.
 ```{r indirparam}
-#Calculate dist(s,t)^-alpha
-D <- indirect_relations(g,type="dist_sp",FUN=dist_dpow,alpha = 2)
+# Calculate dist(s,t)^-alpha
+D <- indirect_relations(g, type = "dist_sp", FUN = dist_dpow, alpha = 2)
 ```
 
 To view all predefined transformation functions see `?transform_relations`. The 
@@ -104,10 +111,10 @@ The predefined functions are not exhaustive and just constitute the most common
 transformations. It is, however, straightforward to pass your own transformation function. 
 
 ```{r own_func}
-dist_integration <- function(x){
-  x <- 1 - (x - 1)/max(x)
+dist_integration <- function(x) {
+    x <- 1 - (x - 1) / max(x)
 }
-D <- indirect_relations(g,type="dist_sp",FUN=dist_integration)
+D <- indirect_relations(g, type = "dist_sp", FUN = dist_integration)
 ```
 
 The function `dist_integration()` computes