mat2listw.RdThe function converts a square spatial weights matrix, optionally a sparse matrix to a weights list object, optionally adding region IDs from the row names of the matrix, as a sequence of numbers 1:nrow(x), or as given as an argument. The style can be imposed by rebuilting the weights list object internally.
mat2listw(x, row.names = NULL, style=NULL, zero.policy = NULL)A square non-negative matrix with no NAs representing spatial weights; may be a matrix of class “sparseMatrix”
row names to use for region IDs
default NULL, missing, set to "M" and warning given; if not "M", passed to nb2listw to re-build the object
default NULL, use global option value; if FALSE stop with error for any empty neighbour sets, if TRUE permit the weights list to be formed with zero-length weights vectors
A listw object with the following members:
"M", meaning matrix style, underlying style unknown, or assigned style argument in rebuilt object
the derived neighbours list
the weights for the neighbours derived from the matrix
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
col005 <- dnearneigh(st_coordinates(st_centroid(st_geometry(columbus),
of_largest_polygon=TRUE)), 0, 0.5, as.character(columbus$NEIGNO))
summary(col005)
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 170
#> Percentage nonzero weights: 7.080383
#> Average number of links: 3.469388
#> 4 regions with no links:
#> 1005 1006 1008 1043
#> 8 disjoint connected subgraphs
#> Link number distribution:
#>
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
#> 1038 1036 with 9 links
col005.w.mat <- nb2mat(col005, style="W", zero.policy=TRUE)
try(col005.w.b <- mat2listw(col005.w.mat, style="W"))
#> Warning: no-neighbour observations found, set zero.policy to TRUE;
#> this warning will soon become an error
#> Error in nb2listw(res$neighbours, glist = res$weights, style = style, :
#> Empty neighbour sets found
col005.w.b <- mat2listw(col005.w.mat, style="W", zero.policy=TRUE)
summary(col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 170
#> Percentage nonzero weights: 7.080383
#> Average number of links: 3.469388
#> 4 regions with no links:
#> 1005 1006 1008 1043
#> 8 disjoint connected subgraphs
#> Link number distribution:
#>
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
#> 1038 1036 with 9 links
diffnb(col005, col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 0
#> Percentage nonzero weights: 0
#> Average number of links: 0
#> 49 regions with no links:
#> 1005 1001 1006 1002 1007 1008 1004 1003 1018 1010 1038 1037 1039 1040
#> 1009 1036 1011 1042 1041 1017 1043 1019 1012 1035 1032 1020 1021 1031
#> 1033 1034 1045 1013 1022 1044 1023 1046 1030 1024 1047 1016 1014 1049
#> 1029 1025 1028 1048 1015 1027 1026
#> 49 disjoint connected subgraphs
col005.w.mat.3T <- kronecker(diag(3), col005.w.mat)
col005.w.b.3T <- mat2listw(col005.w.mat.3T, style="W", zero.policy=TRUE)
summary(col005.w.b.3T$neighbours)
#> Neighbour list object:
#> Number of regions: 147
#> Number of nonzero links: 510
#> Percentage nonzero weights: 2.360128
#> Average number of links: 3.469388
#> 12 regions with no links:
#> 1 3 6 21 50 52 55 70 99 101 104 119
#> 24 disjoint connected subgraphs
#> Link number distribution:
#>
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
#> 2 5 9 10 31 34 36 39 42 46 47 51 54 58 59 80 83 85 88 91 95 96 100 103 107 108 129 132 134 137 140 144 145 with 1 link
#> 6 most connected regions:
#> 11 16 60 65 109 114 with 9 links
run <- FALSE
if (require("spatialreg", quiet=TRUE)) run <- TRUE
if (run) {
W <- as(nb2listw(col005, style="W", zero.policy=TRUE), "CsparseMatrix")
try(col005.spM <- mat2listw(W))
col005.spM <- mat2listw(W, style="W", zero.policy=TRUE)
summary(col005.spM$neighbours)
}
#> Warning: style is M (missing); style should be set to a valid value
#> Warning: style is M (missing); style should be set to a valid value
#> Warning: 1005, 1006, 1008, 1043 are not origins
#> Warning: no-neighbour observations found, set zero.policy to TRUE;
#> this warning will soon become an error
#> Warning: no-neighbour observations found, set zero.policy to TRUE;
#> this warning will soon become an error
#> Warning: no-neighbour observations found, set zero.policy to TRUE;
#> this warning will soon become an error
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 170
#> Percentage nonzero weights: 7.080383
#> Average number of links: 3.469388
#> 4 regions with no links:
#> 1005 1006 1008 1043
#> 8 disjoint connected subgraphs
#> Link number distribution:
#>
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
#> 1038 1036 with 9 links
if (run) {
diffnb(col005, col005.spM$neighbours)
}
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 0
#> Percentage nonzero weights: 0
#> Average number of links: 0
#> 49 regions with no links:
#> 1005 1001 1006 1002 1007 1008 1004 1003 1018 1010 1038 1037 1039 1040
#> 1009 1036 1011 1042 1041 1017 1043 1019 1012 1035 1032 1020 1021 1031
#> 1033 1034 1045 1013 1022 1044 1023 1046 1030 1024 1047 1016 1014 1049
#> 1029 1025 1028 1048 1015 1027 1026
#> 49 disjoint connected subgraphs
if (run && require("Matrix", quiet=TRUE)) {
IW <- kronecker(Diagonal(3), W)
col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W", zero.policy=TRUE)
summary(col005.spM.3T$neighbours)
}
#> Warning: no-neighbour observations found, set zero.policy to TRUE;
#> this warning will soon become an error
#> Neighbour list object:
#> Number of regions: 147
#> Number of nonzero links: 510
#> Percentage nonzero weights: 2.360128
#> Average number of links: 3.469388
#> 12 regions with no links:
#> 1 3 6 21 50 52 55 70 99 101 104 119
#> 24 disjoint connected subgraphs
#> Link number distribution:
#>
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
#> 2 5 9 10 31 34 36 39 42 46 47 51 54 58 59 80 83 85 88 91 95 96 100 103 107 108 129 132 134 137 140 144 145 with 1 link
#> 6 most connected regions:
#> 11 16 60 65 109 114 with 9 links