`testnb.Rd`

Checks a neighbours list for symmetry/transitivity (if i is a neighbour of j,
then j is a neighbour of i). This holds for distance and contiguity based
neighbours, but not for k-nearest neighbours. The helper function
`sym.attr.nb()`

calls `is.symmetric.nb()`

to set the `sym`

attribute if needed, and `make.sym.nb`

makes a non-symmetric list symmetric by adding neighbors. `is.symmetric.glist`

checks a list of general weights corresponding to neighbours for symmetry for symmetric neighbours.

```
is.symmetric.nb(nb, verbose = NULL, force = FALSE)
sym.attr.nb(nb)
make.sym.nb(nb)
old.make.sym.nb(nb)
is.symmetric.glist(nb, glist)
```

- nb
an object of class

`nb`

with a list of integer vectors containing neighbour region number ids.- verbose
default NULL, use global option value; if TRUE prints non-matching pairs

- force
do not respect a neighbours list

`sym`

attribute and test anyway- glist
list of general weights corresponding to neighbours

TRUE if symmetric, FALSE if not; is.symmetric.glist returns a value with an attribute, "d", indicating for failed symmetry the largest failing value.

A new version of `make.sym.nb`

by Bjarke Christensen is now included. The older version has been renamed `old.make.sym.nb`

, and their comparison constitutes a nice demonstration of vectorising speedup using `sapply`

and `lapply`

rather than loops. When any no-neighbour observations are present, `old.make.sym.nb`

is used.

```
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
col.gal.nb <- read.gal(system.file("weights/columbus.gal", package="spData")[1])
coords <- coordinates(as(columbus, "Spatial"))
ind <- row.names(as(columbus, "Spatial"))
print(is.symmetric.nb(col.gal.nb, verbose=TRUE, force=TRUE))
#> [1] TRUE
k4 <- knn2nb(knearneigh(coords, k=4), row.names=ind)
k4 <- sym.attr.nb(k4)
print(is.symmetric.nb(k4))
#> [1] FALSE
k4.sym <- make.sym.nb(k4)
print(is.symmetric.nb(k4.sym))
#> [1] TRUE
```