Print and summary function for neighbours and weights lists

The function prints summary measures for links in a neighbours list. If a matrix of coordinates is given as well, summary descriptive measures for the link lengths are also printed. Print and summary functions are also available for "listw" weights list objects, also reporting constants (S0, S1, S2) used in inference for global spatial autocorrelation statistics such as Moran's I, Geary's C, join-count tests and Getis-Ord G.

Usage

# S3 method for class 'nb'
summary(object, coords=NULL, longlat = NULL, scale = 1, ...)
# S3 method for class 'nb'
print(x, ...)
# S3 method for class 'listw'
summary(object, coords, longlat, zero.policy = attr(object, "zero.policy"), 
 scale = 1, adjust.n=TRUE, ...)
# S3 method for class 'listw'
print(x, zero.policy = attr(x, "zero.policy"), ...)

Arguments

object: an object of class nb
coords: matrix of region point coordinates or a SpatialPoints object or an sfc points object
longlat: TRUE if point coordinates are longitude-latitude decimal degrees, in which case distances are measured in kilometers; if coords is a SpatialPoints object, the value is taken from the object itself
...: additional arguments affecting the output produced
x: an object of class nb
zero.policy: default attr(listw, "zero.policy") as set when listw was created, if attribute not set, use global option value; if FALSE stop with error for any empty neighbour sets
scale: passed through to stem() for control of plot length
adjust.n: default TRUE, if FALSE the number of observations is not adjusted for no-neighbour observations, if TRUE, the number of observations in spweights.constants is adjusted

Author

Roger Bivand Roger.Bivand@nhh.no

Examples

columbus <- st_read(system.file("shapes/columbus.gpkg", package="spData")[1], quiet=TRUE)
col.gal.nb <- read.gal(system.file("weights/columbus.gal", package="spData")[1])
coords <- st_centroid(st_geometry(columbus), of_largest_polygon=TRUE)
col.gal.nb
#> Neighbour list object:
#> Number of regions: 49 
#> Number of nonzero links: 230 
#> Percentage nonzero weights: 9.579342 
#> Average number of links: 4.693878 
summary(col.gal.nb, coords)
#> Neighbour list object:
#> Number of regions: 49 
#> Number of nonzero links: 230 
#> Percentage nonzero weights: 9.579342 
#> Average number of links: 4.693878 
#> Link number distribution:
#> 
#>  2  3  4  5  6  7  8  9 10 
#>  7  7 13  4  9  6  1  1  1 
#> 7 least connected regions:
#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
#> 20 with 10 links
col.listw <- nb2listw(col.gal.nb, style="W")
col.listw
#> Characteristics of weights list object:
#> Neighbour list object:
#> Number of regions: 49 
#> Number of nonzero links: 230 
#> Percentage nonzero weights: 9.579342 
#> Average number of links: 4.693878 
#> 
#> Weights style: W 
#> Weights constants summary:
#>    n   nn S0       S1       S2
#> W 49 2401 49 23.48489 204.6687
summary(col.listw)
#> Characteristics of weights list object:
#> Neighbour list object:
#> Number of regions: 49 
#> Number of nonzero links: 230 
#> Percentage nonzero weights: 9.579342 
#> Average number of links: 4.693878 
#> Link number distribution:
#> 
#>  2  3  4  5  6  7  8  9 10 
#>  7  7 13  4  9  6  1  1  1 
#> 7 least connected regions:
#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
#> 20 with 10 links
#> 
#> Weights style: W 
#> Weights constants summary:
#>    n   nn S0       S1       S2
#> W 49 2401 49 23.48489 204.6687
col_geoms <- st_geometry(columbus)
col_geoms[21] <- st_buffer(col_geoms[21], dist=-0.05)
st_geometry(columbus) <- col_geoms
nb <- poly2nb(columbus)
#> Warning: some observations have no neighbours;
#> if this seems unexpected, try increasing the snap argument.
#> Warning: neighbour object has 3 sub-graphs;
#> if this sub-graph count seems unexpected, try increasing the snap argument.
summary(nb)
#> Neighbour list object:
#> Number of regions: 49 
#> Number of nonzero links: 230 
#> Percentage nonzero weights: 9.579342 
#> Average number of links: 4.693878 
#> 1 region with no links:
#> 21
#> 3 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  0  2  3  4  5  6  7  8  9 10 
#>  1  5  9 12  4 10  2  4  1  1 
#> 5 least connected regions:
#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
#> 20 with 10 links
try(nb2listw(nb, style="W"))
#> Error in nb2listw(nb, style = "W") : 
#>   Empty neighbour sets found (zero.policy: FALSE)
summary(nb2listw(nb, style="W", zero.policy=TRUE))
#> Characteristics of weights list object:
#> Neighbour list object:
#> Number of regions: 49 
#> Number of nonzero links: 230 
#> Percentage nonzero weights: 9.579342 
#> Average number of links: 4.693878 
#> 1 region with no links:
#> 21
#> 3 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  0  2  3  4  5  6  7  8  9 10 
#>  1  5  9 12  4 10  2  4  1  1 
#> 5 least connected regions:
#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
#> 20 with 10 links
#> 
#> Weights style: W 
#> Weights constants summary:
#>    n   nn S0       S1       S2
#> W 48 2304 48 22.46811 199.4398
summary(nb2listw(nb, style="W", zero.policy=TRUE), adjust.n=FALSE)
#> Characteristics of weights list object:
#> Neighbour list object:
#> Number of regions: 49 
#> Number of nonzero links: 230 
#> Percentage nonzero weights: 9.579342 
#> Average number of links: 4.693878 
#> 1 region with no links:
#> 21
#> 3 disjoint connected subgraphs
#> Link number distribution:
#> 
#>  0  2  3  4  5  6  7  8  9 10 
#>  1  5  9 12  4 10  2  4  1  1 
#> 5 least connected regions:
#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
#> 20 with 10 links
#> 
#> Weights style: W 
#> Weights constants summary:
#>    n   nn S0       S1       S2
#> W 49 2401 48 22.46811 199.4398

Print and summary function for neighbours and weights lists

Usage

Arguments

Author

See also

Examples