Comparisons at dyadic level
vertex_covariate_compare(graph, X, funname)
| graph | A matrix of size \(n\times n\) of class |
|---|---|
| X | A numeric vector of length \(n\). |
| funname | Character scalar. Comparison to make (see details). |
A matrix dgCMatrix of size \(n\times n\) with values in
the form of \(funname(x_i,x_j)\).
This auxiliary function takes advantage of the sparseness of graph and
applies a function in the form of \(funname(x_i,x_j)\)
only to \((i,j)\) that have no empty entry. In other words, applies a compares
elements of X only between vertices that have a link; making
nlinks(graph) comparisons instead of looping through \(n\times n\),
which is much faster.
funname can take any of the following values:
"distance", "^2" or "quaddistance", ">" or "greater",
"<" or "smaller", ">=" or "greaterequal",
"<=" or "smallerequal", "==" or "equal".
Other dyadic-level comparison functions:
matrix_compare(),
vertex_covariate_dist()
# Basic example ------------------------------------------------------------ set.seed(1313) G <- rgraph_ws(10, 4, .2) x <- rnorm(10) vertex_covariate_compare(G, x, "distance")#> 10 x 10 sparse Matrix of class "dgCMatrix" #> #> [1,] . 1.0641842 . . 1.160060 . . #> [2,] 1.0641842 . 0.4867249 0.2437569 . . . #> [3,] 0.5774593 0.4867249 . 0.2429680 1.737519 . . #> [4,] . 0.2437569 0.2429680 . 1.980487 0.3777802 . #> [5,] . . 1.7375191 1.9804871 . 2.3582672 2.7931946 #> [6,] . . . 0.3777802 2.358267 . 0.4349274 #> [7,] . . . . 2.793195 0.4349274 . #> [8,] . . . 0.5029751 1.477512 0.8807552 1.3156826 #> [9,] 1.3807050 . 0.8032456 . . . 0.2524298 #> [10,] 1.2314124 0.1672282 . 0.4109851 . . . #> #> [1,] . 1.3807050 1.2314124 #> [2,] . . 0.1672282 #> [3,] . . . #> [4,] . . . #> [5,] . . . #> [6,] 0.8807552 . . #> [7,] 1.3156826 0.2524298 . #> [8,] . . . #> [9,] . . 0.1492926 #> [10,] 0.9139601 . .vertex_covariate_compare(G, x, "^2")#> 10 x 10 sparse Matrix of class "dgCMatrix" #> #> [1,] . 1.13248802 . . 1.345739 . . #> [2,] 1.1324880 . 0.23690109 0.05941743 . . . #> [3,] 0.3334593 0.23690109 . 0.05903343 3.018973 . . #> [4,] . 0.05941743 0.05903343 . 3.922329 0.1427178 . #> [5,] . . 3.01897268 3.92232904 . 5.5614243 7.80193599 #> [6,] . . . 0.14271785 5.561424 . 0.18916181 #> [7,] . . . . 7.801936 0.1891618 . #> [8,] . . . 0.25298393 2.183042 0.7757298 1.73102068 #> [9,] 1.9063463 . 0.64520356 . . . 0.06372082 #> [10,] 1.5163764 0.02796526 . 0.16890871 . . . #> #> [1,] . 1.90634627 1.51637640 #> [2,] . . 0.02796526 #> [3,] . . . #> [4,] . . . #> [5,] . . . #> [6,] 0.7757298 . . #> [7,] 1.7310207 0.06372082 . #> [8,] . . . #> [9,] . . 0.02228829 #> [10,] 0.8353231 . .vertex_covariate_compare(G, x, ">=")#> 10 x 10 sparse Matrix of class "dgCMatrix" #> #> [1,] . 1 . . . . . . 1 1 #> [2,] . . . . . . . . . 1 #> [3,] . 1 . 1 . . . . . . #> [4,] . 1 . . . 1 . . . . #> [5,] . . 1 1 . 1 1 . . . #> [6,] . . . . . . 1 . . . #> [7,] . . . . . . . . . . #> [8,] . . . 1 . 1 1 . . . #> [9,] . . . . . . 1 . . . #> [10,] . . . . . . . . . .vertex_covariate_compare(G, x, "<=")#> 10 x 10 sparse Matrix of class "dgCMatrix" #> #> [1,] . . . . 1 . . . . . #> [2,] 1 . 1 1 . . . . . . #> [3,] 1 . . . 1 . . . . . #> [4,] . . 1 . 1 . . . . . #> [5,] . . . . . . . . . . #> [6,] . . . 1 1 . . 1 . . #> [7,] . . . . 1 1 . 1 1 . #> [8,] . . . . 1 . . . . . #> [9,] 1 . 1 . . . . . . 1 #> [10,] 1 1 . 1 . . . 1 . .