Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
knitr::opts_knit$set(global.par = TRUE)
## ---- echo = FALSE------------------------------------------------------------
set.seed(5)
oldmar <- par()$mar
par(mar = c(0, 0, 0, 0) + 0.1)
## ----setup--------------------------------------------------------------------
library(backbone)
## -----------------------------------------------------------------------------
dat <- matrix(runif(100),10,10) #Some data
backbone.suggest(dat) #What should I do?
backbone <- backbone.suggest(dat, s = 0.05) #Or, just do it
## -----------------------------------------------------------------------------
W <- matrix(c(0,10,10,10,10,75,0,0,0,0,
10,0,1,1,1,0,0,0,0,0,
10,1,0,1,1,0,0,0,0,0,
10,1,1,0,1,0,0,0,0,0,
10,1,1,1,0,0,0,0,0,0,
75,0,0,0,0,0,100,100,100,100,
0,0,0,0,0,100,0,10,10,10,
0,0,0,0,0,100,10,0,10,10,
0,0,0,0,0,100,10,10,0,10,
0,0,0,0,0,100,10,10,10,0),10)
## -----------------------------------------------------------------------------
weighted <- igraph::graph_from_adjacency_matrix(W, mode = "undirected", weighted = TRUE, diag = FALSE)
plot(weighted, edge.width = sqrt(igraph::E(weighted)$weight), vertex.label = NA)
## -----------------------------------------------------------------------------
bb <- global(W, upper = 0, class = "igraph")
plot(bb, vertex.label = NA)
## -----------------------------------------------------------------------------
bb <- global(W, upper = function(x)mean(x), class = "igraph")
plot(bb, vertex.label = NA)
## -----------------------------------------------------------------------------
bb <- disparity(W, alpha = 0.05, narrative = TRUE, class = "igraph")
plot(bb, vertex.label = NA)
## -----------------------------------------------------------------------------
B <- rbind(cbind(matrix(rbinom(250,1,.8),10),
matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.2),10)),
cbind(matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.8),10),
matrix(rbinom(250,1,.2),10)),
cbind(matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.8),10)))
## -----------------------------------------------------------------------------
B[1:5,1:5]
## -----------------------------------------------------------------------------
rowSums(B)
colSums(B)
## -----------------------------------------------------------------------------
P <- B%*%t(B)
plot(igraph::graph_from_adjacency_matrix(P, mode = "undirected", diag = FALSE, weighted = TRUE), vertex.label = NA)
## -----------------------------------------------------------------------------
bb <- sdsm(B, alpha = 0.075, narrative = TRUE, class = "igraph")
## -----------------------------------------------------------------------------
plot(bb, vertex.label = NA)
## -----------------------------------------------------------------------------
U.with.communities <- igraph::sbm.game(60, matrix(c(.75,.25,.25,.25,.75,.25,.25,.25,.75),3,3), c(20,20,20))
plot(U.with.communities, vertex.label = NA)
## -----------------------------------------------------------------------------
bb <- sparsify.with.lspar(U.with.communities, s = 0.6, narrative = TRUE)
plot(bb, vertex.label = NA)
## -----------------------------------------------------------------------------
U.with.hubs <- igraph::as.undirected(igraph::sample_pa(60, m = 3), mode = "collapse")
plot(U.with.hubs, vertex.size = igraph::degree(bb), vertex.label = NA) #A hairball
## -----------------------------------------------------------------------------
bb <- sparsify.with.localdegree(U.with.hubs, s = 0.3, narrative = TRUE)
plot(bb, vertex.size = igraph::degree(bb), vertex.label = NA)
## ---- echo = TRUE, results = 'hide', warning = FALSE--------------------------
B <- rbind(cbind(matrix(rbinom(250,1,.8),10),
matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.2),10)),
cbind(matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.8),10),
matrix(rbinom(250,1,.2),10)),
cbind(matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.8),10)))
bb.object <- fdsm(B, alpha = NULL, trials = 1000) #Backbone object containing edgewise p-values
## -----------------------------------------------------------------------------
bb1 <- backbone.extract(bb.object, alpha = 0.5, class = "igraph") #Backbone extracted at alpha = 0.5
plot(bb1, vertex.label = NA)
## -----------------------------------------------------------------------------
bb2 <- backbone.extract(bb.object, alpha = 0.05, class = "igraph") #Backbone extracted at alpha = 0.05
plot(bb2, vertex.label = NA)
## -----------------------------------------------------------------------------
mat <- rbind(c(1,0,0), c(0,0,1), c(0,1,1))
mat
fastball(mat)
## -----------------------------------------------------------------------------
mat <- rbind(c(1,0,0), c(0,0,1), c(0,1,1))
bicm(mat)
## ---- echo = FALSE------------------------------------------------------------
par(mar = oldmar)
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