fixed typos in diffusion and rerendered whole book

03-exploratory-analysis.Rmd

@@ -263,7 +263,7 @@ names <- c("003", "012", "102", "021D", "021U", "021C",
 for (i in 0:15) {
   g_temp <- graph_from_isomorphism_class(size = 3,
                                          number = i,
-                                         directed = T)
+                                         directed = TRUE)
   g[[i + 1]] <- ggraph(g_temp,
                        layout = "manual",
                        x = xy[, 1],

10-diffusion.Rmd

@@ -85,7 +85,7 @@ We can also plot the adoption curve across all time steps using the `plot_adopte
 plot_adopters(net_test1)
 ```
 
-This result shows the classic S-shaped curve for cumulative adoption with the parameters we've provided where adoption is at first slow, followed by a period of rapid adoption, and then a gradual slowdown as adoptions reaches saturation.
+These results show the classic S-shaped curve for cumulative adoption with the parameters we've provided where adoption is at first slow, followed by a period of rapid adoption, and then a gradual slowdown as adoptions reaches saturation.
 
 We can also plot a network that shows the time step at which each node adopted the contagion:
 
@@ -270,7 +270,7 @@ ggmap(my_map) +
   theme_void()
 ```
 
-As this map illustrates, and as we would expect, nodes closest to the initial adopters are the earliest adopters. Further the area in the southern portion of the study area shows a dense collection of nodes colored gray indicating they did not adopt in the 20 time steps we assessed.
+As this map illustrates, nodes closest to the initial adopters are the earliest adopters. Further the area in the southern portion of the study area shows a dense collection of nodes colored gray indicating they did not adopt in the 20 time steps we assessed.
 
 Let's now create another adopter plot and map color coded by time of adoption. In the next example, we leave everything alone but this time set the initial adopters as nodes 6 and 7 in the northwestern portion of the study area:
 
@@ -444,6 +444,6 @@ ggplot(data = df) +
   theme_bw()
 ```
 
-As this boxplot illustrates, sites that were in the "Early Adopter" or "Early Majority" category generally have earlier starting dates than those in the other categories. This may suggest that network distance from Chaco Canyon (where we originated our "contagion" and where the earliest Great Houses are found) may have been a factor in the establishment of Chacoan complexes outside of Chaco. Of Course, if wanted to take this further we would need to assess the variable roles of spatial distance, network distance, and perhaps could even consider material cultural similarity data. At this point, however, this brief example at least points out that there is an interesting pattern worth investigation. Further, this example demonstrates one simple approach that could be used to compare diffusion models to other archaeological data.
+As this boxplot illustrates, sites that were in the "Early Adopter" or "Early Majority" category include the vast majority of sites that have earlier starting dates though the median is the same across groups. This may suggest that network distance from Chaco Canyon (where we originated our "contagion" and where the earliest Great Houses are found) may have been a factor in the establishment of Chacoan complexes outside of Chaco. Of Course, if wanted to take this further we would need to assess the variable roles of spatial distance, network distance, and perhaps could even consider material cultural similarity data. At this point, however, this brief example at least points out that there is an interesting pattern worth investigation. Further, this example demonstrates one simple approach that could be used to compare diffusion models to other archaeological data.
 
 We have only scratched the surface on the network methods that can be used to study diffusion here. There are many other advanced models that may be relevant for archaeological analysis including many interesting [Epidemiological Models](http://www.epimodel.org/tut.html) that would likely work well in archaeological context for considerations of all sorts of contagions (social or biological). We hope these brief examples will promote further exploration of such approaches. 

DESCRIPTION

@@ -67,6 +67,10 @@ Suggests:
   rmarkdown,
   netdiffuseR,
   ca,
+  Matrix,
+  SparseM,
+  pracma,
+  graphkernels,
   xml2
 Encoding: UTF-8
 LazyData: true

_book/01-data.md

@@ -255,16 +255,19 @@ This version of the book was built with R version 4.2.0 (2022-04-22 ucrt) and th
 |ggrepel        |0.9.1   |CRAN (R 4.2.0) |
 |ggsn           |0.5.0   |CRAN (R 4.2.0) |
 |GISTools       |0.7-4   |CRAN (R 4.2.0) |
+|graphkernels   |1.6.1   |CRAN (R 4.2.0) |
 |igraph         |1.3.1   |CRAN (R 4.2.0) |
 |intergraph     |2.0-2   |CRAN (R 4.2.0) |
 |knitr          |1.39    |CRAN (R 4.2.0) |
 |latticeExtra   |0.6-29  |CRAN (R 4.2.0) |
 |maptools       |1.1-4   |CRAN (R 4.2.0) |
+|Matrix         |1.4-1   |CRAN (R 4.2.0) |
 |multinet       |4.0.1   |CRAN (R 4.2.0) |
 |netdiffuseR    |1.22.3  |CRAN (R 4.2.0) |
 |networkD3      |0.4     |CRAN (R 4.2.0) |
 |networkDynamic |0.11.2  |CRAN (R 4.2.0) |
 |patchwork      |1.1.1   |CRAN (R 4.2.0) |
+|pracma         |2.3.8   |CRAN (R 4.2.0) |
 |RColorBrewer   |1.1-3   |CRAN (R 4.2.0) |
 |Rcpp           |1.0.8.3 |CRAN (R 4.2.0) |
 |reshape2       |1.4.4   |CRAN (R 4.2.0) |
@@ -274,6 +277,7 @@ This version of the book was built with R version 4.2.0 (2022-04-22 ucrt) and th
 |rmarkdown      |2.14    |CRAN (R 4.2.0) |
 |scatterplot3d  |0.3-41  |CRAN (R 4.2.0) |
 |sf             |1.0-7   |CRAN (R 4.2.0) |
+|SparseM        |1.81    |CRAN (R 4.2.0) |
 |statnet        |2019.6  |CRAN (R 4.2.0) |
 |superheat      |0.1.0   |CRAN (R 4.2.0) |
 |tidyverse      |1.3.1   |CRAN (R 4.2.0) |

_book/02-network-data-formats.md

@@ -100,9 +100,9 @@ cibola_net
 ```
 
 ```
-## IGRAPH 7daf077 UN-- 30 167 -- 
+## IGRAPH 8dc4fdd UN-- 30 167 -- 
 ## + attr: name (v/c)
-## + edges from 7daf077 (vertex names):
+## + edges from 8dc4fdd (vertex names):
 ##  [1] Apache Creek--Casa Malpais          Apache Creek--Coyote Creek         
 ##  [3] Apache Creek--Hooper Ranch          Apache Creek--Horse Camp Mill      
 ##  [5] Apache Creek--Hubble Corner         Apache Creek--Mineral Creek Pueblo 
@@ -138,7 +138,7 @@ adj_list$`Apache Creek`
 ```
 
 ```
-## + 11/167 edges from 7daf077 (vertex names):
+## + 11/167 edges from 8dc4fdd (vertex names):
 ##  [1] Apache Creek--Casa Malpais         Apache Creek--Coyote Creek        
 ##  [3] Apache Creek--Hooper Ranch         Apache Creek--Horse Camp Mill     
 ##  [5] Apache Creek--Hubble Corner        Apache Creek--Mineral Creek Pueblo
@@ -154,7 +154,7 @@ adj_list[[2]]
 ```
 
 ```
-## + 11/167 edges from 7daf077 (vertex names):
+## + 11/167 edges from 8dc4fdd (vertex names):
 ##  [1] Apache Creek--Casa Malpais    Casa Malpais--Coyote Creek   
 ##  [3] Casa Malpais--Hooper Ranch    Casa Malpais--Horse Camp Mill
 ##  [5] Casa Malpais--Hubble Corner   Casa Malpais--Rudd Creek Ruin
@@ -351,9 +351,9 @@ cibola_net2
 ```
 
 ```
-## IGRAPH 7de07b9 UN-- 31 167 -- 
+## IGRAPH 8df9547 UN-- 31 167 -- 
 ## + attr: name (v/c), region (v/c)
-## + edges from 7de07b9 (vertex names):
+## + edges from 8df9547 (vertex names):
 ##  [1] Apache.Creek--Casa.Malpais          Apache.Creek--Coyote.Creek         
 ##  [3] Apache.Creek--Hooper.Ranch          Apache.Creek--Horse.Camp.Mill      
 ##  [5] Apache.Creek--Hubble.Corner         Apache.Creek--Mineral.Creek.Pueblo 
@@ -409,9 +409,9 @@ simple_net_i
 ```
 
 ```
-## IGRAPH 7edae9c UN-- 31 167 -- 
+## IGRAPH 8eed013 UN-- 31 167 -- 
 ## + attr: name (v/c)
-## + edges from 7edae9c (vertex names):
+## + edges from 8eed013 (vertex names):
 ##  [1] Apache.Creek--Casa.Malpais          Apache.Creek--Coyote.Creek         
 ##  [3] Apache.Creek--Hooper.Ranch          Apache.Creek--Horse.Camp.Mill      
 ##  [5] Apache.Creek--Hubble.Corner         Apache.Creek--Mineral.Creek.Pueblo 
@@ -476,9 +476,9 @@ directed_net
 ```
 
 ```
-## IGRAPH 7ee54ae DN-- 30 125 -- 
+## IGRAPH 8ef4e9b DN-- 30 125 -- 
 ## + attr: name (v/c)
-## + edges from 7ee54ae (vertex names):
+## + edges from 8ef4e9b (vertex names):
 ##  [1] Coyote Creek   ->Techado Springs      
 ##  [2] Hubble Corner  ->Tri-R Pueblo         
 ##  [3] Hubble Corner  ->Techado Springs      
@@ -611,9 +611,9 @@ cibola_inc
 ```
 
 ```
-## IGRAPH 7f2ff90 UN-B 41 2214 -- 
+## IGRAPH 8f3b690 UN-B 41 2214 -- 
 ## + attr: type (v/l), name (v/c)
-## + edges from 7f2ff90 (vertex names):
+## + edges from 8f3b690 (vertex names):
 ##  [1] Apache Creek--Clust1 Apache Creek--Clust1 Apache Creek--Clust1
 ##  [4] Apache Creek--Clust1 Apache Creek--Clust1 Apache Creek--Clust1
 ##  [7] Apache Creek--Clust1 Apache Creek--Clust2 Apache Creek--Clust2
@@ -1158,9 +1158,9 @@ ego_nets[[1]]
 ```
 
 ```
-## IGRAPH 8190296 UN-- 12 59 -- 
+## IGRAPH 919a9da UN-- 12 59 -- 
 ## + attr: name (v/c)
-## + edges from 8190296 (vertex names):
+## + edges from 919a9da (vertex names):
 ##  [1] Apache Creek   --Casa Malpais         Apache Creek   --Coyote Creek        
 ##  [3] Casa Malpais   --Coyote Creek         Apache Creek   --Hooper Ranch        
 ##  [5] Casa Malpais   --Hooper Ranch         Coyote Creek   --Hooper Ranch        
@@ -1250,7 +1250,7 @@ multinet::degree_ml(florentine)
 ```
 
 ```
-##  [1]  3  4  4 11  3  7  2  6  5  6  3  6  3  6  1
+##  [1]  3  7  2 11  4  4  3  6  5  6  6  3  1  3  6
 ```
 
 ```r
@@ -1261,30 +1261,30 @@ multinet::glouvain_ml(florentine)
 
 ```
 ##           actor    layer cid
-## 1        Ginori business   0
-## 2        Ginori marriage   0
-## 3       Albizzi marriage   0
-## 4     Barbadori business   0
-## 5     Barbadori marriage   0
-## 6       Ridolfi marriage   1
-## 7    Tornabuoni business   1
-## 8    Tornabuoni marriage   1
-## 9        Medici business   1
-## 10       Medici marriage   1
-## 11     Salviati business   1
-## 12     Salviati marriage   1
-## 13        Pazzi business   1
-## 14        Pazzi marriage   1
-## 15   Acciaiuoli marriage   1
-## 16      Strozzi marriage   2
-## 17      Peruzzi business   2
-## 18      Peruzzi marriage   2
-## 19     Guadagni business   2
-## 20     Guadagni marriage   2
-## 21 Lamberteschi business   2
-## 22 Lamberteschi marriage   2
-## 23   Castellani business   2
-## 24   Castellani marriage   2
+## 1      Salviati business   0
+## 2      Salviati marriage   0
+## 3         Pazzi business   0
+## 4         Pazzi marriage   0
+## 5        Medici business   0
+## 6        Medici marriage   0
+## 7    Tornabuoni business   0
+## 8    Tornabuoni marriage   0
+## 9       Ridolfi marriage   0
+## 10      Albizzi marriage   0
+## 11   Acciaiuoli marriage   0
+## 12       Ginori business   0
+## 13       Ginori marriage   0
+## 14      Peruzzi business   1
+## 15      Peruzzi marriage   1
+## 16      Strozzi marriage   1
+## 17    Barbadori business   1
+## 18    Barbadori marriage   1
+## 19   Castellani business   1
+## 20   Castellani marriage   1
+## 21     Guadagni business   2
+## 22     Guadagni marriage   2
+## 23 Lamberteschi business   2
+## 24 Lamberteschi marriage   2
 ## 25     Bischeri business   2
 ## 26     Bischeri marriage   2
 ```
@@ -1317,9 +1317,9 @@ mor_wt_i
 ```
 
 ```
-## IGRAPH 822f31f U-W- 31 465 -- 
+## IGRAPH 9210d54 U-W- 31 465 -- 
 ## + attr: na (v/l), vertex.names (v/c), na (e/l), weight (e/n)
-## + edges from 822f31f:
+## + edges from 9210d54:
 ##   [1] 1-- 2 1-- 3 1-- 4 1-- 5 1-- 6 1-- 7 1-- 8 1-- 9 1--10 1--11 1--12 1--13
 ##  [13] 1--14 1--15 1--16 1--17 1--18 1--19 1--20 1--21 1--22 1--23 1--24 1--25
 ##  [25] 1--26 1--27 1--28 1--29 1--30 1--31 2-- 3 2-- 4 2-- 5 2-- 6 2-- 7 2-- 8

_book/03-exploratory-analysis.md

@@ -482,7 +482,7 @@ names <- c("003", "012", "102", "021D", "021U", "021C",
 for (i in 0:15) {
   g_temp <- graph_from_isomorphism_class(size = 3,
                                          number = i,
-                                         directed = T)
+                                         directed = TRUE)
   g[[i + 1]] <- ggraph(g_temp,
                        layout = "manual",
                        x = xy[, 1],
@@ -588,7 +588,7 @@ igraph::shortest_paths(simple_net, from = 1, to = 21)
 ```
 ## $vpath
 ## $vpath[[1]]
-## + 5/31 vertices, named, from 85cf5f8:
+## + 5/31 vertices, named, from 959baa5:
 ## [1] Apache.Creek          Casa.Malpais          Garcia.Ranch         
 ## [4] Heshotauthla          Pueblo.de.los.Muertos
 ## 
@@ -624,7 +624,7 @@ igraph::farthest_vertices(directed_net, directed = TRUE)
 
 ```
 ## $vertices
-## + 2/30 vertices, named, from 85d04f6:
+## + 2/30 vertices, named, from 959cc79:
 ## [1] Apache Creek          Pueblo de los Muertos
 ## 
 ## $distance
@@ -676,9 +676,9 @@ components
 
 ```
 ## [[1]]
-## IGRAPH 884630f UN-- 30 167 -- 
+## IGRAPH 980a918 UN-- 30 167 -- 
 ## + attr: name (v/c)
-## + edges from 884630f (vertex names):
+## + edges from 980a918 (vertex names):
 ##  [1] Apache.Creek--Casa.Malpais          Apache.Creek--Coyote.Creek         
 ##  [3] Apache.Creek--Hooper.Ranch          Apache.Creek--Horse.Camp.Mill      
 ##  [5] Apache.Creek--Hubble.Corner         Apache.Creek--Mineral.Creek.Pueblo 
@@ -690,9 +690,9 @@ components
 ## + ... omitted several edges
 ## 
 ## [[2]]
-## IGRAPH 8846335 UN-- 1 0 -- 
+## IGRAPH 980a93a UN-- 1 0 -- 
 ## + attr: name (v/c)
-## + edges from 8846335 (vertex names):
+## + edges from 980a93a (vertex names):
 ```
 
 ```r
@@ -752,15 +752,15 @@ min_cut(simple_net_noiso, value.only = FALSE)
 ## [1] 1
 ## 
 ## $cut
-## + 1/167 edge from 85cfbd4 (vertex names):
+## + 1/167 edge from 959c0f2 (vertex names):
 ## [1] Ojo Bonito--Baca Pueblo
 ## 
 ## $partition1
-## + 1/30 vertex, named, from 85cfbd4:
+## + 1/30 vertex, named, from 959c0f2:
 ## [1] Baca Pueblo
 ## 
 ## $partition2
-## + 29/30 vertices, named, from 85cfbd4:
+## + 29/30 vertices, named, from 959c0f2:
 ##  [1] Apache Creek          Casa Malpais          Coyote Creek         
 ##  [4] Hooper Ranch          Horse Camp Mill       Hubble Corner        
 ##  [7] Mineral Creek Pueblo  Rudd Creek Ruin       Techado Springs      
@@ -789,7 +789,7 @@ max_cliques(simple_net, min = 1)[[24]]
 ```
 
 ```
-## + 9/31 vertices, named, from 85cf5f8:
+## + 9/31 vertices, named, from 959baa5:
 ## [1] Los.Gigantes    Cienega         Tinaja          Spier.170      
 ## [5] Scribe.S        Pescado.Cluster Mirabal         Heshotauthla   
 ## [9] Yellowhouse

_book/05-visualization.md

@@ -56,9 +56,9 @@ cibola_i
 ```
 
 ```
-## IGRAPH 934c5c1 UN-- 31 167 -- 
+## IGRAPH a2ecb43 UN-- 31 167 -- 
 ## + attr: name (v/c)
-## + edges from 934c5c1 (vertex names):
+## + edges from a2ecb43 (vertex names):
 ##  [1] Apache.Creek--Casa.Malpais          Apache.Creek--Coyote.Creek         
 ##  [3] Apache.Creek--Hooper.Ranch          Apache.Creek--Horse.Camp.Mill      
 ##  [5] Apache.Creek--Hubble.Corner         Apache.Creek--Mineral.Creek.Pueblo 

_book/06-spatial-networks.md

@@ -277,9 +277,9 @@ tree1
 ```
 
 ```
-## IGRAPH c036ecc U--- 50 49 -- Tree
+## IGRAPH ce8790e U--- 50 49 -- Tree
 ## + attr: name (g/c), children (g/n), mode (g/c)
-## + edges from c036ecc:
+## + edges from ce8790e:
 ##  [1]  1-- 2  1-- 3  1-- 4  1-- 5  1-- 6  2-- 7  2-- 8  2-- 9  2--10  2--11
 ## [11]  3--12  3--13  3--14  3--15  3--16  4--17  4--18  4--19  4--20  4--21
 ## [21]  5--22  5--23  5--24  5--25  5--26  6--27  6--28  6--29  6--30  6--31

_book/10-diffusion.md

@@ -134,7 +134,7 @@ plot_adopters(net_test1)
 
 <img src="10-diffusion_files/figure-html/unnamed-chunk-4-1.png" width="672" />
 
-This result shows the classic S-shaped curve for cumulative adoption with the parameters we've provided where adoption is at first slow, followed by a period of rapid adoption, and then a gradual slowdown as adoptions reaches saturation.
+These results show the classic S-shaped curve for cumulative adoption with the parameters we've provided where adoption is at first slow, followed by a period of rapid adoption, and then a gradual slowdown as adoptions reaches saturation.
 
 We can also plot a network that shows the time step at which each node adopted the contagion:
 
@@ -479,7 +479,7 @@ ggmap(my_map) +
 
 <img src="10-diffusion_files/figure-html/unnamed-chunk-10-1.png" width="672" />
 
-As this map illustrates, and as we would expect, nodes closest to the initial adopters are the earliest adopters. Further the area in the southern portion of the study area shows a dense collection of nodes colored gray indicating they did not adopt in the 20 time steps we assessed.
+As this map illustrates, nodes closest to the initial adopters are the earliest adopters. Further the area in the southern portion of the study area shows a dense collection of nodes colored gray indicating they did not adopt in the 20 time steps we assessed.
 
 Let's now create another adopter plot and map color coded by time of adoption. In the next example, we leave everything alone but this time set the initial adopters as nodes 6 and 7 in the northwestern portion of the study area:
 
@@ -715,6 +715,6 @@ ggplot(data = df) +
 
 <img src="10-diffusion_files/figure-html/unnamed-chunk-16-1.png" width="672" />
 
-As this boxplot illustrates, sites that were in the "Early Adopter" or "Early Majority" category generally have earlier starting dates than those in the other categories. This may suggest that network distance from Chaco Canyon (where we originated our "contagion" and where the earliest Great Houses are found) may have been a factor in the establishment of Chacoan complexes outside of Chaco. Of Course, if wanted to take this further we would need to assess the variable roles of spatial distance, network distance, and perhaps could even consider material cultural similarity data. At this point, however, this brief example at least points out that there is an interesting pattern worth investigation. Further, this example demonstrates one simple approach that could be used to compare diffusion models to other archaeological data.
+As this boxplot illustrates, sites that were in the "Early Adopter" or "Early Majority" category include the vast majority of sites that have earlier starting dates though the median is the same across groups. This may suggest that network distance from Chaco Canyon (where we originated our "contagion" and where the earliest Great Houses are found) may have been a factor in the establishment of Chacoan complexes outside of Chaco. Of Course, if wanted to take this further we would need to assess the variable roles of spatial distance, network distance, and perhaps could even consider material cultural similarity data. At this point, however, this brief example at least points out that there is an interesting pattern worth investigation. Further, this example demonstrates one simple approach that could be used to compare diffusion models to other archaeological data.
 
 We have only scratched the surface on the network methods that can be used to study diffusion here. There are many other advanced models that may be relevant for archaeological analysis including many interesting [Epidemiological Models](http://www.epimodel.org/tut.html) that would likely work well in archaeological context for considerations of all sorts of contagions (social or biological). We hope these brief examples will promote further exploration of such approaches. 

_book/404.html

@@ -128,7 +128,7 @@ <h1>Page not found<a class="anchor" aria-label="anchor" href="#page-not-found"><
 <footer class="bg-primary text-light mt-5"><div class="container"><div class="row">
 
   <div class="col-12 col-md-6 mt-3">
-    <p>"<strong>Online Companion to <em>Archaeological Network Science</em></strong>" was written by Matthew A. Peeples and Tom Brughmans. It was last built on 2022-06-26.</p>
+    <p>"<strong>Online Companion to <em>Archaeological Network Science</em></strong>" was written by Matthew A. Peeples and Tom Brughmans. It was last built on 2022-07-05.</p>
   </div>
 
   <div class="col-12 col-md-6 mt-3">