## generate random adjancency matrix
## radjmat <- function (n) {
## adjmat <- matrix(0L, n, n, dimnames=list(letters[1:n],letters[1:n]))
## adjmat[lower.tri(adjmat)] <- sample(0:1, n*(n-1)/2, replace=TRUE)
## adjmat + t(adjmat)
## }
## set.seed(3); adjmat <- radjmat(5)
adjmat <- structure(
c(0L, 0L, 1L, 0L, 0L,
0L, 0L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 1L,
0L, 1L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 0L),
.Dim = c(5L, 5L),
.Dimnames = rep.int(list(c("a", "b", "c", "d", "e")), 2L)
)
## validated matrix of neighbourhood orders
nbmat <- structure(
c(0L, 2L, 1L, 3L, 2L,
2L, 0L, 1L, 1L, 2L,
1L, 1L, 0L, 2L, 1L,
3L, 1L, 2L, 0L, 1L,
2L, 2L, 1L, 1L, 0L),
.Dim = c(5L, 5L),
.Dimnames = rep.int(list(c("a", "b", "c", "d", "e")), 2L)
)
test_that("nbOrder() returns the validated matrix", if (requireNamespace("spdep")) {
expect_identical(suppressMessages(nbOrder(adjmat, maxlag=Inf)),
nbmat)
})
test_that("zetaweights(.,maxlag=1,normalize=FALSE) is inverse of nbOrder", {
expect_identical(zetaweights(nbmat, maxlag=1, normalize=FALSE),
1*adjmat)
})
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