Creates a square matrix suitable for spatial statistics models.
diag_expand(...) # S3 method for list diag_expand( graph, self = getOption("diffnet.self"), valued = getOption("diffnet.valued"), ... ) # S3 method for diffnet diag_expand( graph, self = getOption("diffnet.self"), valued = getOption("diffnet.valued"), ... ) # S3 method for matrix diag_expand( graph, nper, self = getOption("diffnet.self"), valued = getOption("diffnet.valued"), ... ) # S3 method for array diag_expand( graph, self = getOption("diffnet.self"), valued = getOption("diffnet.valued"), ... ) # S3 method for dgCMatrix diag_expand( graph, nper, self = getOption("diffnet.self"), valued = getOption("diffnet.valued"), ... )
... | Further arguments to be passed to the method. |
---|---|
graph | Any class of accepted graph format (see |
self | Logical scalar. When |
valued | Logical scalar. When |
nper | Integer scalar. Number of time periods of the graph. |
A square matrix of class dgCMatrix
of
size (nnode(g)*nper)^2
# Simple example ------------------------------------------------------------ set.seed(23) g <- rgraph_er(n=10, p=.5, t=2,undirected=TRUE) # What we've done: A list with 2 bernoulli graphs g#> $`1` #> 10 x 10 sparse Matrix of class "dgCMatrix"#>#> #> 1 . 1 . 1 1 1 . . . . #> 2 1 . . 1 1 1 1 . 1 1 #> 3 . . . . 1 . 1 1 1 1 #> 4 1 1 . . 1 . 1 . . . #> 5 1 1 1 1 . 1 1 1 . 1 #> 6 1 1 . . 1 . 1 1 . . #> 7 . 1 1 1 1 1 . 1 . . #> 8 . . 1 . 1 1 1 . 1 1 #> 9 . 1 1 . . . . 1 . 1 #> 10 . 1 1 . 1 . . 1 1 . #> #> $`2` #> 10 x 10 sparse Matrix of class "dgCMatrix"#>#> #> 1 . 1 . . 1 . . . 1 . #> 2 1 . . 1 . . . 1 . 1 #> 3 . . . . 1 1 1 . . 1 #> 4 . 1 . . 1 1 . 1 1 1 #> 5 1 . 1 1 . 1 . 1 . . #> 6 . . 1 1 1 . . 1 1 . #> 7 . . 1 . . . . 1 1 1 #> 8 . 1 . 1 1 1 1 . 1 . #> 9 1 . . 1 . 1 1 1 . . #> 10 . 1 1 1 . . 1 . . . #> #> attr(,"undirected") #> [1] TRUE# Expanding to a 20*20 matrix with structural zeros on the diagonal # and on cell 'off' adjacency matrix diag_expand(g)#> 20 x 20 sparse Matrix of class "dgCMatrix" #> #> [1,] . 1 . 1 1 1 . . . . . . . . . . . . . . #> [2,] 1 . . 1 1 1 1 . 1 1 . . . . . . . . . . #> [3,] . . . . 1 . 1 1 1 1 . . . . . . . . . . #> [4,] 1 1 . . 1 . 1 . . . . . . . . . . . . . #> [5,] 1 1 1 1 . 1 1 1 . 1 . . . . . . . . . . #> [6,] 1 1 . . 1 . 1 1 . . . . . . . . . . . . #> [7,] . 1 1 1 1 1 . 1 . . . . . . . . . . . . #> [8,] . . 1 . 1 1 1 . 1 1 . . . . . . . . . . #> [9,] . 1 1 . . . . 1 . 1 . . . . . . . . . . #> [10,] . 1 1 . 1 . . 1 1 . . . . . . . . . . . #> [11,] . . . . . . . . . . . 1 . . 1 . . . 1 . #> [12,] . . . . . . . . . . 1 . . 1 . . . 1 . 1 #> [13,] . . . . . . . . . . . . . . 1 1 1 . . 1 #> [14,] . . . . . . . . . . . 1 . . 1 1 . 1 1 1 #> [15,] . . . . . . . . . . 1 . 1 1 . 1 . 1 . . #> [16,] . . . . . . . . . . . . 1 1 1 . . 1 1 . #> [17,] . . . . . . . . . . . . 1 . . . . 1 1 1 #> [18,] . . . . . . . . . . . 1 . 1 1 1 1 . 1 . #> [19,] . . . . . . . . . . 1 . . 1 . 1 1 1 . . #> [20,] . . . . . . . . . . . 1 1 1 . . 1 . . .