Spatial weights## 1 2 3 4 5 6 7 8
## 1 2 5 5 8 1 2 1
## 1 least connected region:
-## with 1 link
+## E with 1 link
## 1 most connected region:
-## with 8 links
+## V with 8 links
##
## Weights style: B
## Weights constants summary:
@@ -565,7 +565,7 @@ Measures of spatial autocorrelation
Prop_stdR <- lapply(vars, function(x) moran.test(eire_ge1[[x]], listw=lw_std, randomisation=TRUE))
})
## user system elapsed
-## 0.121 0.000 0.121
+## 0.118 0.000 0.119
res <- sapply(c("MoranN", "MoranR", "GearyN", "GearyR", "Prop_unstdN", "Prop_unstdR", "Prop_stdN", "Prop_stdR"), function(x) sapply(get(x), "[[", "statistic"))
rownames(res) <- vars
@@ -839,7 +839,7 @@ Simulating measures of s
Prop_stdEx <- lapply(lm_objs, function(x) lm.morantest.exact(x, listw=lw_std))
})
## user system elapsed
-## 0.083 0.000 0.083
+## 0.085 0.000 0.085
res <- sapply(c("MoranEx", "Prop_unstdEx", "Prop_stdEx"), function(x) sapply(get(x), "[[", "statistic"))
rownames(res) <- vars
diff --git a/docs/articles/nb.html b/docs/articles/nb.html
index ad9ea526..7167ee8a 100644
--- a/docs/articles/nb.html
+++ b/docs/articles/nb.html
@@ -168,9 +168,9 @@ Introduction## 1 2 3 4 5 6 7 8 9
## 1 1 5 9 14 17 9 6 1
## 1 least connected region:
-## with 1 link
+## 164 with 1 link
## 1 most connected region:
-## with 9 links
+## 136 with 9 links
Creating Contiguity Neighbours
diff --git a/docs/articles/nb_sf.html b/docs/articles/nb_sf.html
index 3a020026..ca1a7cdd 100644
--- a/docs/articles/nb_sf.html
+++ b/docs/articles/nb_sf.html
@@ -258,7 +258,7 @@ Contiguity neighbours for pol
eps <- sqrt(.Machine$double.eps)
system.time(for(i in 1:reps) NY8_sf_1_nb <- poly2nb(NY8_sf, queen=TRUE, snap=eps))/reps
## user system elapsed
-## 0.0925 0.0038 0.0965
+## 0.0938 0.0042 0.0984
Using spatial indices to check intersection of polygons is much
faster than the legacy method in poly2nb. From spdep
1.1-7, use is made of GEOS through sf to find candidate
@@ -427,12 +427,12 @@
K-nearest neighbours
system.time(for (i in 1:reps) NY88_nb_sf <- knn2nb(knearneigh(NY8_ct_sf, k=1)))/reps
## user system elapsed
-## 0.0174 0.0009 0.0183
+## 0.0179 0.0011 0.0191
Legacy code may be used omitting the kd-tree:
system.time(for (i in 1:reps) NY89_nb_sf <- knn2nb(knearneigh(NY8_ct_sf, k=1, use_kd_tree=FALSE)))/reps
## user system elapsed
-## 0.0174 0.0013 0.0188
+## 0.0182 0.0016 0.0199
Distance neighbours
@@ -451,7 +451,7 @@ Distance neighbours
system.time(for (i in 1:reps) NY810_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn))/reps
## user system elapsed
-## 0.0315 0.0011 0.0327
+## 0.0319 0.0015 0.0337
By default, the function uses dbscan::frNN() to build a
kd-tree in 2D or 3D which is then used to find distance neighbours. For
small n, the argument use_kd_tree=FALSE may speed up
@@ -461,7 +461,7 @@
Distance neighbours
system.time(for (i in 1:reps) NY811_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn, use_kd_tree=FALSE))/reps
## user system elapsed
-## 0.0163 0.0015 0.0178
+## 0.0166 0.0017 0.0183
@@ -491,7 +491,7 @@ K-nearest neighbourssf_use_s2(TRUE)
system.time(for (i in 1:reps) pts_ll1_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps
## user system elapsed
-## 0.0252 0.0000 0.0252
+## 0.0248 0.0000 0.0248
For this smaller data set, the legacy approach without spatial
indexing is adequate, but slows down as the number of observations
increases:
@@ -501,7 +501,7 @@ K-nearest neighbours
system.time(for (i in 1:reps) pts_ll2_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps
## user system elapsed
-## 0.0191 0.0000 0.0191
+## 0.0194 0.0000 0.0194
The WGS84 ellipsoid Great Circle distances differ a very little from
the s2 spherical distances, yielding output that here
diverges for two tract centroids:
@@ -554,7 +554,7 @@ Distance neighbours=0.75*max_1nn_ll))/(reps/5)
}
## user system elapsed
-## 0.0440 0.0000 0.0445
+## 0.0445 0.0000 0.0445
Alternatively, spherical distances can be used with
dwithin=FALSE and s2::s2_closest_edges();
although running in similar time, s2::s2_closest_edges()
@@ -563,7 +563,7 @@
Distance neighbours
system.time(for (i in 1:(reps/5)) pts_ll5_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, dwithin=FALSE))/(reps/5)
## user system elapsed
-## 0.027 0.000 0.027
+## 0.0270 0.0005 0.0275
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3_nb, pts_ll5_nb, check.attributes=FALSE)
## [1] TRUE
@@ -586,7 +586,7 @@ Distance neighbours=0.75*max_1nn_ll))/(reps/5)
}
## user system elapsed
-## 0.0725 0.0000 0.0725
+## 0.0730 0.0000 0.0735
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3a_nb, pts_ll5a_nb, check.attributes=FALSE)
## [1] TRUE
@@ -595,7 +595,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0097 0.0000 0.0098
Minor differences may occur between the legacy ellipsoid and
s2 spherical approaches:
@@ -625,7 +625,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6a_nb <- dnearneigh(pts_ll, d1=5, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0098 0.0000 0.0097
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll5a_nb, pts_ll6a_nb, check.attributes=FALSE)
## [1] "Component 20: Numeric: lengths (6, 5) differ"
@@ -676,7 +676,7 @@ Contiguity neighbours for spherical polygon support
system.time(for (i in 1:reps) NY8_sf_1_nb_ll <- poly2nb(NY8_sf_ll, queen=TRUE, snap=eps))/reps
## user system elapsed
-## 0.1553 0.0013 0.1572
+## 0.1524 0.0019 0.1548
all.equal(NY8_sf_1_nb, NY8_sf_1_nb_ll, check.attributes=FALSE)
## [1] TRUE
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 5f78f8ad..a8b299ea 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,5 +6,5 @@ articles:
nb: nb.html
nb_sf: nb_sf.html
sids: sids.html
-last_built: 2023-11-04T17:52Z
+last_built: 2023-11-06T13:06Z
diff --git a/docs/reference/EBImoran.mc.html b/docs/reference/EBImoran.mc.html
index db555f47..4886f83f 100644
--- a/docs/reference/EBImoran.mc.html
+++ b/docs/reference/EBImoran.mc.html
@@ -184,7 +184,7 @@ Examples
#> weights: nb2listw(ncCC89_nb, style = "B", zero.policy = TRUE)
#> number of simulations + 1: 1000
#>
-#> statistic = 0.25789, observed rank = 998, p-value = 0.004
+#> statistic = 0.25789, observed rank = 1000, p-value < 2.2e-16
#> alternative hypothesis: two.sided
#>
sids.p <- nc.sids$SID74 / nc.sids$BIR74
diff --git a/docs/reference/LOSH.mc-1.png b/docs/reference/LOSH.mc-1.png
index 5a905795..3b4cabfc 100644
Binary files a/docs/reference/LOSH.mc-1.png and b/docs/reference/LOSH.mc-1.png differ
diff --git a/docs/reference/LOSH.mc.html b/docs/reference/LOSH.mc.html
index c0a6763a..30a4b3ed 100644
--- a/docs/reference/LOSH.mc.html
+++ b/docs/reference/LOSH.mc.html
@@ -154,10 +154,10 @@ Examples
summary(resLOSH_mc)
#> Hi x_bar_i ei Pr()
#> Min. :0.03438 Min. :13.85 Min. : 0.0298 Min. :0.009901
-#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.118812
-#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.702970
-#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.542332
-#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.891089
+#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.079208
+#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.712871
+#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.554860
+#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.910891
#> Max. :4.68765 Max. :54.91 Max. :2455.2201 Max. :0.990099
resLOSH_cs <- LOSH.cs(columbus$CRIME, nb2listw(col.gal.nb))
summary(resLOSH_cs)
diff --git a/docs/reference/bhicv.html b/docs/reference/bhicv.html
index 2e1f9102..072425ba 100644
--- a/docs/reference/bhicv.html
+++ b/docs/reference/bhicv.html
@@ -105,7 +105,7 @@ Examples
bh <- st_read(system.file("etc/shapes/bhicv.shp",
package="spdep")[1])
#> Reading layer `bhicv' from data source
-#> `/tmp/Rtmpu0jdcX/temp_libpath70612704761e5/spdep/etc/shapes/bhicv.shp'
+#> `/tmp/RtmpU193Yl/temp_libpath9f2c94443f9d4/spdep/etc/shapes/bhicv.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 98 features and 8 fields
#> Geometry type: POLYGON
diff --git a/docs/reference/cell2nb.html b/docs/reference/cell2nb.html
index 9b67215a..e3badaf1 100644
--- a/docs/reference/cell2nb.html
+++ b/docs/reference/cell2nb.html
@@ -137,9 +137,9 @@ Examples
#> 2 3 4
#> 4 20 25
#> 4 least connected regions:
-#> with 2 links
+#> 1:1 7:1 1:7 7:7 with 2 links
#> 25 most connected regions:
-#> with 4 links
+#> 2:2 3:2 4:2 5:2 6:2 2:3 3:3 4:3 5:3 6:3 2:4 3:4 4:4 5:4 6:4 2:5 3:5 4:5 5:5 6:5 2:6 3:6 4:6 5:6 6:6 with 4 links
xyc <- attr(nb7rt, "region.id")
xy <- matrix(as.integer(unlist(strsplit(xyc, ":"))), ncol=2, byrow=TRUE)
plot(nb7rt, xy)
@@ -156,9 +156,9 @@ Examples
#> 4
#> 49
#> 49 least connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
#> 49 most connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
run <- FALSE
if (require("sp", quietly=TRUE)) run <- TRUE
if (run) {
diff --git a/docs/reference/dnearneigh.html b/docs/reference/dnearneigh.html
index afbf4662..937dd9d6 100644
--- a/docs/reference/dnearneigh.html
+++ b/docs/reference/dnearneigh.html
@@ -149,9 +149,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11
#> 4 8 6 2 5 8 6 2 6 1 1
#> 4 least connected regions:
-#> with 1 link
+#> 6 10 21 47 with 1 link
#> 1 most connected region:
-#> with 11 links
+#> 28 with 11 links
opar <- par(no.readonly=TRUE)
plot(st_geometry(columbus), border="grey", reset=FALSE,
main=paste("Distance based neighbours 0-", format(all.linked), sep=""))
@@ -198,9 +198,9 @@ Examples
#> 1 2 3 4 5 7 8 9 10
#> 11 9 4 8 4 4 4 3 1
#> 11 least connected regions:
-#> with 1 link
+#> 2 4 8 10 24 26 29 32 35 41 45 with 1 link
#> 1 most connected region:
-#> with 10 links
+#> 28 with 10 links
gck1 <- knn2nb(knearneigh(xy, k=1, longlat=TRUE))
(all.linked <- max(unlist(nbdists(gck1, xy, longlat=TRUE))))
#> [1] 523.5819
@@ -217,9 +217,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
plot(ll.nb, xy)
plot(diffnb(ll.nb, gc.nb), xy, add=TRUE, col="red", lty=2)
title(main="Differences Euclidean/Great Circle")
@@ -239,13 +239,13 @@ Examples
gck1b <- knn2nb(knearneigh(xy1, k=1))
system.time(o <- nbdists(gck1b, xy1))
#> user system elapsed
-#> 0.007 0.000 0.006
+#> 0.006 0.000 0.006
(all.linked <- max(unlist(o)))
#> [1] 522.4464
# use s2 brute-force dwithin_matrix approach for s2 <= 1.0.7
system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE))
#> user system elapsed
-#> 0.01 0.00 0.01
+#> 0.009 0.000 0.010
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -258,9 +258,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use s2 closest_edges approach s2 > 1.0.7
if (packageVersion("s2") > "1.0.7") {
(system.time(gc.nb.closest <- dnearneigh(xy1, 0, all.linked, dwithin=FALSE)))
@@ -288,9 +288,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") {
summary(gc.nb.closest, xy1, scale=0.5)
}
@@ -307,13 +307,13 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use legacy symmetric brute-force approach
system.time(gc.nb.legacy <- dnearneigh(xy1, 0, all.linked, use_s2=FALSE))
#> user system elapsed
-#> 0.003 0.000 0.003
+#> 0.003 0.000 0.004
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -326,9 +326,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") all.equal(gc.nb.closest, gc.nb.dwithin, check.attributes=FALSE)
#> [1] TRUE
# legacy is ellipsoidal, s2 spherical, so minor differences expected
diff --git a/docs/reference/edit.nb.html b/docs/reference/edit.nb.html
index 44773c64..d59c98e1 100644
--- a/docs/reference/edit.nb.html
+++ b/docs/reference/edit.nb.html
@@ -121,12 +121,11 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
class(columbus)
-#> [1] "sf" "data.frame"
if (FALSE) nnb1 <- edit.nb(col.gal.nb, polys=as(columbus, "Spatial"))
-# }
+}
diff --git a/docs/reference/geary.mc.html b/docs/reference/geary.mc.html
index 236e80b0..3164b159 100644
--- a/docs/reference/geary.mc.html
+++ b/docs/reference/geary.mc.html
@@ -160,12 +160,12 @@ Examples
#> alternative hypothesis: less
#>
mean(sim1$res)
-#> [1] 1.012612
+#> [1] 0.9890232
var(sim1$res)
-#> [1] 0.0119779
+#> [1] 0.01235098
summary(sim1$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.5299 0.9401 1.0218 1.0126 1.0906 1.2388
+#> 0.5299 0.9288 1.0029 0.9890 1.0515 1.3024
colold.lags <- nblag(COL.nb, 3)
sim2 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[2]],
style="W"), nsim=99)
@@ -177,12 +177,12 @@ Examples
#> weights: nb2listw(colold.lags[[2]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 0.81129, observed rank = 1, p-value = 0.01
+#> statistic = 0.81129, observed rank = 5, p-value = 0.05
#> alternative hypothesis: greater
#>
summary(sim2$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8113 0.9541 1.0113 1.0014 1.0569 1.1619
+#> 0.7211 0.9490 0.9896 0.9948 1.0550 1.1807
sim3 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[3]],
style="W"), nsim=99)
sim3
@@ -193,12 +193,12 @@ Examples
#> weights: nb2listw(colold.lags[[3]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 1.1303, observed rank = 92, p-value = 0.92
+#> statistic = 1.1303, observed rank = 95, p-value = 0.95
#> alternative hypothesis: greater
#>
summary(sim3$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8264 0.9586 1.0079 1.0099 1.0562 1.2047
+#> 0.8034 0.9550 1.0098 1.0076 1.0474 1.2499
diff --git a/docs/reference/graphneigh.html b/docs/reference/graphneigh.html
index f41bced1..008fcf4f 100644
--- a/docs/reference/graphneigh.html
+++ b/docs/reference/graphneigh.html
@@ -285,9 +285,9 @@ Examples
#> 0 1 2 3 4 5
#> 1 8 10 18 55 8
#> 8 least connected regions:
-#> with 1 link
+#> 46 47 48 49 96 97 98 99 with 1 link
#> 8 most connected regions:
-#> with 5 links
+#> 10 15 20 25 30 35 40 45 with 5 links
grd <- as.matrix(expand.grid(x=1:5, y=1:5)) #gridded data
r2 <- gabrielneigh(grd)
set.seed(1)
diff --git a/docs/reference/grid2nb.html b/docs/reference/grid2nb.html
index 27f554ac..eb1e9527 100644
--- a/docs/reference/grid2nb.html
+++ b/docs/reference/grid2nb.html
@@ -150,9 +150,9 @@ Examples
#> 7 11 17 26
#> 8 36 54 27
#> 8 least connected regions:
-#> with 7 links
+#> 1 5 21 25 101 105 121 125 with 7 links
#> 27 most connected regions:
-#> with 26 links
+#> 32 33 34 37 38 39 42 43 44 57 58 59 62 63 64 67 68 69 82 83 84 87 88 89 92 93 94 with 26 links
if (require("sp", quietly=TRUE)) {
gt <- GridTopology(c(.125,.1), c(.25,.2), c(4L, 5L))
nb1 <- grid2nb(gt, queen = FALSE)
diff --git a/docs/reference/include.self.html b/docs/reference/include.self.html
index 1b8fad9a..287bd036 100644
--- a/docs/reference/include.self.html
+++ b/docs/reference/include.self.html
@@ -114,9 +114,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
summary(include.self(col.gal.nb), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -128,9 +128,9 @@ Examples
#> 3 4 5 6 7 8 9 10 11
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 3 links
+#> 1 6 31 39 42 46 47 with 3 links
#> 1 most connected region:
-#> with 11 links
+#> 20 with 11 links
summary(remove.self(include.self(col.gal.nb)), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -142,9 +142,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
diff --git a/docs/reference/joincount.multi.html b/docs/reference/joincount.multi.html
index 528fbbcd..8415a202 100644
--- a/docs/reference/joincount.multi.html
+++ b/docs/reference/joincount.multi.html
@@ -161,95 +161,30 @@ Examples
#> high:high 52.000 28.138 18.342 5.5716
#> high:low 28.000 61.170 25.882 -6.5200
#> Jtot 28.000 61.170 33.190 -5.7577
-# \dontrun{
+if (FALSE) {
data(oldcol)
HICRIME <- cut(COL.OLD$CRIME, breaks=c(0,35,80), labels=c("low","high"))
names(HICRIME) <- rownames(COL.OLD)
joincount.multi(HICRIME, nb2listw(COL.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> low:low 34.000 29.592 18.895 1.0141
-#> high:high 54.000 27.224 17.888 6.3307
-#> high:low 28.000 59.184 26.233 -6.0884
-#> Jtot 28.000 59.184 26.233 -6.0884
data(hopkins, package="spData")
image(1:32, 1:32, hopkins[5:36,36:5], breaks=c(-0.5, 3.5, 20),
col=c("white", "black"))
box()
-
hopkins.rook.nb <- cell2nb(32, 32, type="rook")
unlist(spweights.constants(nb2listw(hopkins.rook.nb, style="B")))
-#> n n1 n2 n3 nn S0 S1 S2
-#> 1024 1023 1022 1021 1048576 3968 7936 61984
hopkins.queen.nb <- cell2nb(32, 32, type="queen")
hopkins.bishop.nb <- diffnb(hopkins.rook.nb, hopkins.queen.nb, verbose=FALSE)
hopkins4 <- hopkins[5:36,36:5]
hopkins4[which(hopkins4 > 3, arr.ind=TRUE)] <- 4
hopkins4.f <- factor(hopkins4)
table(hopkins4.f)
-#> hopkins4.f
-#> 0 1 2 3 4
-#> 657 215 98 30 24
joincount.multi(hopkins4.f, nb2listw(hopkins.rook.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 864.00000 816.27273 116.05233 4.4304
-#> 1:1 94.00000 87.14015 55.25216 0.9229
-#> 2:2 18.00000 18.00379 14.81562 -0.0010
-#> 3:3 2.00000 1.64773 1.55539 0.2825
-#> 4:4 5.00000 1.04545 0.99845 3.9576
-#> 1:0 503.00000 535.05682 227.76750 -2.1241
-#> 2:0 213.00000 243.88636 97.21769 -3.1325
-#> 2:1 99.00000 79.81061 59.01930 2.4978
-#> 3:0 61.00000 74.65909 28.58592 -2.5547
-#> 3:1 28.00000 24.43182 18.99976 0.8186
-#> 3:2 15.00000 11.13636 9.82411 1.2327
-#> 4:0 40.00000 59.72727 22.78583 -4.1327
-#> 4:1 23.00000 19.54545 15.26564 0.8842
-#> 4:2 14.00000 8.90909 7.90051 1.8112
-#> 4:3 5.00000 2.72727 2.58616 1.4133
-#> Jtot 1001.00000 1059.89015 273.78610 -3.5591
cat("replicates Upton & Fingleton table 3.4 (p. 166)\n")
-#> replicates Upton & Fingleton table 3.4 (p. 166)
joincount.multi(hopkins4.f, nb2listw(hopkins.bishop.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 823.00000 790.76420 144.44877 2.6821
-#> 1:1 101.00000 84.41702 55.98143 2.2164
-#> 2:2 19.00000 17.44117 14.61542 0.4077
-#> 3:3 3.00000 1.59624 1.51444 1.1407
-#> 4:4 3.00000 1.01278 0.97111 2.0166
-#> 1:0 497.00000 518.33629 234.93545 -1.3920
-#> 2:0 216.00000 236.26491 104.42142 -1.9831
-#> 2:1 81.00000 77.31652 58.70829 0.4807
-#> 3:0 58.00000 72.32599 31.49151 -2.5529
-#> 3:1 21.00000 23.66832 18.85316 -0.6145
-#> 3:2 17.00000 10.78835 9.62487 2.0022
-#> 4:0 48.00000 57.86080 25.15973 -1.9659
-#> 4:1 21.00000 18.93466 15.14473 0.5307
-#> 4:2 10.00000 8.63068 7.73708 0.4923
-#> 4:3 4.00000 2.64205 2.51686 0.8560
-#> Jtot 973.00000 1026.76858 284.51030 -3.1877
cat("replicates Upton & Fingleton table 3.6 (p. 168)\n")
-#> replicates Upton & Fingleton table 3.6 (p. 168)
joincount.multi(hopkins4.f, nb2listw(hopkins.queen.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 1687.0000 1607.0369 303.8034 4.5877
-#> 1:1 195.0000 171.5572 114.2057 2.1936
-#> 2:2 37.0000 35.4450 29.6821 0.2854
-#> 3:3 5.0000 3.2440 3.0687 1.0024
-#> 4:4 8.0000 2.0582 1.9674 4.2361
-#> 1:0 1000.0000 1053.3931 480.6959 -2.4353
-#> 2:0 429.0000 480.1513 215.0360 -3.4882
-#> 2:1 180.0000 157.1271 119.3987 2.0932
-#> 3:0 119.0000 146.9851 65.1029 -3.4684
-#> 3:1 49.0000 48.1001 38.3268 0.1454
-#> 3:2 32.0000 21.9247 19.5237 2.2802
-#> 4:0 88.0000 117.5881 52.0312 -4.1019
-#> 4:1 44.0000 38.4801 30.7868 0.9948
-#> 4:2 24.0000 17.5398 15.6933 1.6308
-#> 4:3 9.0000 5.3693 5.0994 1.6078
-#> Jtot 1974.0000 2086.6587 582.8326 -4.6665
cat("replicates Upton & Fingleton table 3.7 (p. 169)\n")
-#> replicates Upton & Fingleton table 3.7 (p. 169)
-# }
+}
diff --git a/docs/reference/knearneigh.html b/docs/reference/knearneigh.html
index f5a9e742..5f4466fa 100644
--- a/docs/reference/knearneigh.html
+++ b/docs/reference/knearneigh.html
@@ -161,9 +161,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
summary(gck4.nb, xy, longlat=TRUE, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -176,9 +176,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#xy1 <- SpatialPoints((as.data.frame(state.center))[m50.48,],
# proj4string=CRS("+proj=longlat +ellps=GRS80"))
@@ -191,7 +191,7 @@ Examples
sf_use_s2(TRUE)
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.008 0.000 0.009
+#> 0.009 0.000 0.010
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -204,14 +204,14 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(FALSE)
#> Spherical geometry (s2) switched off
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.005 0.000 0.006
+#> 0.005 0.000 0.005
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -224,9 +224,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(old_use_s2)
#> Spherical geometry (s2) switched on
diff --git a/docs/reference/localC.html b/docs/reference/localC.html
index 1efd5e5b..03cfd45b 100644
--- a/docs/reference/localC.html
+++ b/docs/reference/localC.html
@@ -245,11 +245,9 @@ Examples
}
#> [1] 0.985611
# pseudo-p values probably wrongly folded https://github.com/GeoDaCenter/rgeoda/issues/28
-# \dontrun{
+if (FALSE) {
tmap_ok <- FALSE
if (require(tmap, quietly=TRUE)) tmap_ok <- TRUE
-#> Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
-#> remotes::install_github('r-tmap/tmap')
if (run) {
# doi: 10.1111/gean.12164
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
@@ -263,25 +261,10 @@ Examples
moran(g$Infants, lw, n=nrow(g), S0=Szero(lw))$I
moran(g$Suicids, lw, n=nrow(g), S0=Szero(lw))$I
}
-#> Reading layer `Guerry' from data source
-#> `/home/rsb/lib/r_libs/rgeoda/extdata/Guerry.shp' using driver `ESRI Shapefile'
-#> Simple feature collection with 85 features and 29 fields
-#> Geometry type: MULTIPOLYGON
-#> Dimension: XY
-#> Bounding box: xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
-#> Projected CRS: NTF (Paris) / Lambert zone II
-#> [1] 0.4016812
if (run) {
o <- prcomp(st_drop_geometry(g), scale.=TRUE)
cor(st_drop_geometry(g), o$x[,1:2])^2 #(Tab. 2)
}
-#> PC1 PC2
-#> Crm_prs 0.009286863 0.418851254
-#> Crm_prp 0.561825664 0.009377232
-#> Litercy 0.560570048 0.020095011
-#> Donatns 0.024138865 0.586720363
-#> Infants 0.436025658 0.012817054
-#> Suicids 0.548623327 0.152958961
if (run) {
g$PC1 <- o$x[, "PC1"]
brks <- c(min(g$PC1), natural_breaks(k=6, g["PC1"]), max(g$PC1))
@@ -289,7 +272,6 @@ Examples
tm_borders() # Fig. 1
else pplot(g["PC1"], breaks=brks)
}
-
if (run) {
g$PC2 <- -1*o$x[, "PC2"] # eigenvalue sign arbitrary
brks <- c(min(g$PC2), natural_breaks(k=6, g["PC2"]), max(g$PC2))
@@ -297,7 +279,6 @@ Examples
tm_borders() # Fig. 2
else plot(g["PC2"], breaks=brks)
}
-
if (run) {
w <- queen_weights(g)
lm_PC1 <- local_moran(w, g["PC1"], significance_cutoff=0.01,
@@ -310,7 +291,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 3
else plot(g["lm_PC1"])
}
-
if (run) {
set.seed(1)
lm_PC1_spdep <- localmoran_perm(g$PC1, lw, nsim=9999)
@@ -322,7 +302,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 3
else plot(g["lm_PC1_spdep"])
}
-
if (run) {
lg_PC1 <- local_g(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -340,7 +319,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 4 (guess)
else plot(g["lg_PC1"])
}
-
if (run) {
lc_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -352,14 +330,11 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 5
else plot(g["lc_PC1"])
}
-
if (run) {
set.seed(1)
system.time(lc_PC1_spdep <- localC_perm(g$PC1, lw, nsim=9999,
alternative="two.sided"))
}
-#> user system elapsed
-#> 0.376 0.001 0.377
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -372,7 +347,6 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
g$lc_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.01
@@ -381,7 +355,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 5
else plot(g["lc_PC1_spdep"])
}
-
if (run) {
g$both_PC1 <- interaction(g$lc_PC1, g$lm_PC1)
g$both_PC1 <- droplevels(g$both_PC1)
@@ -389,7 +362,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 6
else plot(g["both_PC1"])
}
-
if (run) {
lc005_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.005,
permutations=99999)
@@ -401,7 +373,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 7
else plot(g["lc005_PC1"])
}
-
if (run) {
g$lc005_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc005_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.005
@@ -410,7 +381,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 7
else plot(g["lc005_PC1_spdep"])
}
-
if (run) {
lc001_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.001,
permutations=99999)
@@ -430,7 +400,6 @@ Examples
else plot(g["lc001_PC1_spdep"])
}
}
-
if (run) {
lc_PC2 <- local_geary(w, g["PC2"], significance_cutoff=0.01,
permutations=99999)
@@ -442,7 +411,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 9
else plot(g["lc_PC2"])
}
-
if (run) {
lmc_PC <- local_multigeary(w, g[c("PC1","PC2")], significance_cutoff=0.00247,
permutations=99999)
@@ -452,25 +420,19 @@ Examples
g$lmc_PC <- droplevels(g$lmc_PC)
table(interaction((p.adjust(lisa_pvalues(lmc_PC), "fdr") < 0.01), g$lmc_PC))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_PC", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 10
else plot(g["lmc_PC"])
}
-
if (run) {
set.seed(1)
lmc_PC_spdep <- localC_perm(g[c("PC1","PC2")], lw, nsim=9999, alternative="two.sided")
all.equal(lisa_values(lmc_PC), c(lmc_PC_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_PC_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_PC))
}
-#> [1] 0.99052
if (run) {
g$lmc_PC_spdep <- attr(lmc_PC_spdep, "cluster")
is.na(g$lmc_PC_spdep) <- p.adjust(attr(lmc_PC_spdep, "pseudo-p")[,6], "fdr") > 0.01
@@ -479,7 +441,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 10
else plot(g["lmc_PC_spdep"])
}
-
if (run) {
lmc_vars <- local_multigeary(w, st_drop_geometry(g)[, 1:6],
significance_cutoff=0.00247, permutations=99999)
@@ -490,30 +451,22 @@ Examples
table(interaction((p.adjust(lisa_pvalues(lmc_vars), "fdr") < 0.01),
g$lmc_vars))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_vars", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 11
else plot(g["lmc_vars"])
}
-
if (run) {
set.seed(1)
system.time(lmc_vars_spdep <- localC_perm(st_drop_geometry(g)[, 1:6], lw,
nsim=9999, alternative="two.sided"))
}
-#> user system elapsed
-#> 0.824 0.002 0.828
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -526,15 +479,12 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep1))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep1, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
g$lmc_vars_spdep <- attr(lmc_vars_spdep1, "cluster")
is.na(g$lmc_vars_spdep) <- p.adjust(attr(lmc_vars_spdep1, "pseudo-p")[,6], "fdr") > 0.01
@@ -543,10 +493,9 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 11
else plot(g["lmc_vars_spdep"])
}
-
-# }
-# \dontrun{
+}
+if (FALSE) {
library(reticulate)
use_python("/usr/bin/python", required = TRUE)
gp <- import("geopandas")
@@ -557,7 +506,6 @@ Examples
esda <- import("esda")
lM <- esda$Moran_Local(x, w)
all.equal(unname(localmoran(x, listw, mlvar=FALSE)[,1]), c(lM$Is))
-#> [1] TRUE
# confirm x and w the same
lC <- esda$Geary_Local(connectivity=w)$fit(scale(x))
# np$std missing ddof=1
@@ -566,19 +514,10 @@ Examples
# lC components probably wrongly ordered https://github.com/pysal/esda/issues/192
o <- match(round(D0, 6), round(lC$localG, 6))
all.equal(c(lC$localG)[o], D0)
-#> [1] TRUE
# simulation order not retained
lC$p_sim[o]
-#> [1] 0.278 0.460 0.112 0.019 0.121 0.038 0.330 0.412 0.147 0.001 0.113 0.141
-#> [13] 0.188 0.246 0.450 0.172 0.102 0.298 0.001 0.027 0.038 0.091 0.055 0.297
-#> [25] 0.153 0.031 0.107 0.280 0.450 0.001 0.066 0.104 0.080 0.231 0.306 0.001
-#> [37] 0.326 0.482 0.321 0.216 0.235 0.288
attr(C, "pseudo-p")[,6]
-#> [1] 0.408 0.374 0.144 0.056 0.010 0.284 0.032 0.264 0.186 0.004 0.150 0.224
-#> [13] 0.022 0.150 0.438 0.084 0.126 0.184 0.002 0.238 0.126 0.100 0.068 0.372
-#> [25] 0.124 0.234 0.230 0.016 0.318 0.176 0.272 0.164 0.164 0.206 0.402 0.250
-#> [37] 0.410 0.402 0.328 0.294 0.438 0.248
-# }
+}
diff --git a/docs/reference/mat2listw.html b/docs/reference/mat2listw.html
index 096c6e5d..656b612c 100644
--- a/docs/reference/mat2listw.html
+++ b/docs/reference/mat2listw.html
@@ -140,13 +140,14 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
-col005.w.mat <- nb2mat(col005, zero.policy=TRUE)
-col005.w.b <- mat2listw(col005.w.mat)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> 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"))
+#> Error in mat2listw(col005.w.mat, style = "W") :
+#> no-neighbour observations found, set zero.policy to TRUE
+col005.w.b <- mat2listw(col005.w.mat, style="W", zero.policy=TRUE)
summary(col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -161,9 +162,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
diffnb(col005, col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -177,9 +178,7 @@ Examples
#> 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")
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
+col005.w.b.3T <- mat2listw(col005.w.mat.3T, style="W", zero.policy=TRUE)
summary(col005.w.b.3T$neighbours)
#> Neighbour list object:
#> Number of regions: 147
@@ -194,21 +193,22 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 2 5 9 10 31 34 36 39 42 46 47 51 54 58 59 80 83 85 88 91 95 96 100 103 107 108 129 132 134 137 140 144 145 with 1 link
#> 6 most connected regions:
-#> with 9 links
+#> 11 16 60 65 109 114 with 9 links
run <- FALSE
if (require("spatialreg", quiet=TRUE)) run <- TRUE
if (run) {
W <- as(nb2listw(col005, style="W", zero.policy=TRUE), "CsparseMatrix")
-col005.spM <- mat2listw(W)
+try(col005.spM <- mat2listw(W))
+col005.spM <- mat2listw(W, style="W", zero.policy=TRUE)
summary(col005.spM$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, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> Error in sn2listw(df, style = style, zero.policy = zero.policy) :
+#> no-neighbour observations found, set zero.policy to TRUE
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 170
@@ -222,9 +222,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
if (run) {
diffnb(col005, col005.spM$neighbours)
}
@@ -241,14 +241,9 @@ Examples
#> 49 disjoint connected subgraphs
if (run && require("Matrix", quiet=TRUE)) {
IW <- kronecker(Diagonal(3), W)
-col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W")
+col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W", zero.policy=TRUE)
summary(col005.spM.3T$neighbours)
}
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: 1, 3, 6, 21, 50, 52, 55, 70, 99, 101, 104, 119 are not origins
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
#> Neighbour list object:
#> Number of regions: 147
#> Number of nonzero links: 510
@@ -262,9 +257,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 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:
-#> with 9 links
+#> 11 16 60 65 109 114 with 9 links
diff --git a/docs/reference/nb2blocknb.html b/docs/reference/nb2blocknb.html
index c2276d23..460ced1b 100644
--- a/docs/reference/nb2blocknb.html
+++ b/docs/reference/nb2blocknb.html
@@ -127,96 +127,31 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
data(boston, package="spData")
summary(as.vector(table(boston.c$TOWN)))
-#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 1.0 2.0 4.0 5.5 7.0 30.0
townaggr <- aggregate(boston.utm, list(town=boston.c$TOWN), mean)
block.rel <- graph2nb(relativeneigh(as.matrix(townaggr[,2:3])),
as.character(townaggr[,1]), sym=TRUE)
block.rel
-#> Neighbour list object:
-#> Number of regions: 92
-#> Number of nonzero links: 240
-#> Percentage nonzero weights: 2.835539
-#> Average number of links: 2.608696
print(is.symmetric.nb(block.rel))
-#> [1] TRUE
plot(block.rel, as.matrix(townaggr[,2:3]))
points(boston.utm, pch=18, col="lightgreen")
-
block.nb <- nb2blocknb(block.rel, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 15234
-#> Percentage nonzero weights: 5.949945
-#> Average number of links: 30.10672
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
points(boston.utm, pch=18, col="lightgreen")
-
n.comp.nb(block.nb)$nc
-#> [1] 1
moran.test(boston.c$CMEDV, nb2listw(boston.soi))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(boston.soi)
-#>
-#> Moran I statistic standard deviate = 21.786, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.690285059 -0.001980198 0.001009685
-#>
moran.test(boston.c$CMEDV, nb2listw(block.nb))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb)
-#>
-#> Moran I statistic standard deviate = 22.455, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.3122905961 -0.0019801980 0.0001958827
-#>
block.nb <- nb2blocknb(NULL, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 4868
-#> Percentage nonzero weights: 1.901295
-#> Average number of links: 9.620553
-#> 17 regions with no links:
-#> 1 55 56 57 58 65 196 257 284 285 286 287 342 343 348 349 354
-#> 92 disjoint connected subgraphs
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
-
n.comp.nb(block.nb)$nc
-#> [1] 92
moran.test(boston.c$CMEDV, nb2listw(block.nb, zero.policy=TRUE), zero.policy=TRUE)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb, zero.policy = TRUE) n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 21.145, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.6188830566 -0.0020491803 0.0008623116
-#>
-# }
+}
diff --git a/docs/reference/nb2lines.html b/docs/reference/nb2lines.html
index 38b287ec..1b49a033 100644
--- a/docs/reference/nb2lines.html
+++ b/docs/reference/nb2lines.html
@@ -158,12 +158,12 @@ Examples
tf <- paste0(tempfile(), ".gpkg")
st_write(res, dsn=tf, driver="GPKG")
#> writing: substituting ENGCRS["Undefined Cartesian SRS with unknown unit"] for missing CRS
-#> Writing layer `file706d874b42afe' to data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Writing layer `file9f36c3d2d676e' to data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Writing 230 features with 5 fields and geometry type Line String.
inMap <- st_read(tf)
-#> Reading layer `file706d874b42afe' from data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Reading layer `file9f36c3d2d676e' from data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Simple feature collection with 230 features and 5 fields
#> Geometry type: LINESTRING
#> Dimension: XY
diff --git a/docs/reference/nb2listw.html b/docs/reference/nb2listw.html
index fd9ebeee..ef19145f 100644
--- a/docs/reference/nb2listw.html
+++ b/docs/reference/nb2listw.html
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: minmax
#> Weights constants summary:
diff --git a/docs/reference/nb2listwdist.html b/docs/reference/nb2listwdist.html
index a6ffbf2d..98dcafb5 100644
--- a/docs/reference/nb2listwdist.html
+++ b/docs/reference/nb2listwdist.html
@@ -171,43 +171,15 @@ Examples
#> Moran I statistic Expectation Variance
#> 0.473883371 -0.007042254 0.029994057
#>
-# \dontrun{
+if (FALSE) {
# Moran's I (life expectancy) with IDW with alpha = 2, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "idw",
alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 2.7769, p-value = 0.002744
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.473883371 -0.007042254 0.029994057
-#>
# Moran's I (life expectancy), DPD, alpha = 2, dmax = 1000 km, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "dpd",
dmax = 1000000, alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 8.8557, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.601498970 -0.007042254 0.004722063
-#>
# Boston examples
data(boston, package="spData")
boston_coords <- data.frame(x = boston.utm[,1], y = boston.utm[,2])
@@ -217,37 +189,11 @@ Examples
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "exp", alpha = 2,
style="raw", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 51.34, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.8845114518 -0.0019960080 0.0002981586
-#>
# Moran's I (crime) with idw weights with alpha = 2, coding scheme = W
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "idw", alpha = 2,
style="W", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 18.976, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.4392852330 -0.0019960080 0.0005408065
-#>
-# }
+}
diff --git a/docs/reference/nb2mat.html b/docs/reference/nb2mat.html
index 7f17e8bb..39ec4504 100644
--- a/docs/reference/nb2mat.html
+++ b/docs/reference/nb2mat.html
@@ -137,9 +137,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
col005.w.mat <- nb2mat(col005, style="B", zero.policy=TRUE)
table(round(rowSums(col005.w.mat)))
#>
diff --git a/docs/reference/nblag.html b/docs/reference/nblag.html
index 08180e37..bd822ea5 100644
--- a/docs/reference/nblag.html
+++ b/docs/reference/nblag.html
@@ -122,9 +122,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.lags <- nblag(col.gal.nb, 2)
print(col.lags)
#> [[1]]
@@ -154,9 +154,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#> 1 2 5 4 2 3 5 4 2 3 7 3 4 1 1 1 1
#> 1 least connected region:
-#> with 1 link
+#> 36 with 1 link
#> 1 most connected region:
-#> with 17 links
+#> 25 with 17 links
plot(st_geometry(columbus), border="grey")
plot(col.gal.nb, coords, add=TRUE)
title(main="GAL order 1 (black) and 2 (red) links")
diff --git a/docs/reference/poly2nb.html b/docs/reference/poly2nb.html
index 1cad8008..79b13c61 100644
--- a/docs/reference/poly2nb.html
+++ b/docs/reference/poly2nb.html
@@ -174,10 +174,10 @@ Examples
nc.sids <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE)
system.time(xxnb <- poly2nb(nc.sids))
#> user system elapsed
-#> 0.018 0.000 0.018
+#> 0.019 0.000 0.019
system.time(xxnb <- poly2nb(as(nc.sids, "Spatial")))
#> user system elapsed
-#> 0.029 0.000 0.029
+#> 0.034 0.000 0.035
plot(st_geometry(nc.sids))
plot(xxnb, st_coordinates(st_centroid(nc.sids)), add=TRUE, col="blue")
#> Warning: st_centroid assumes attributes are constant over geometries
diff --git a/docs/reference/skater.html b/docs/reference/skater.html
index aefe0190..d2b9c2e6 100644
--- a/docs/reference/skater.html
+++ b/docs/reference/skater.html
@@ -292,22 +292,18 @@ Examples
table(res5d$groups)
#> Error in eval(expr, envir, enclos): object 'res5d' not found
-# \dontrun{
+if (FALSE) {
data(boston, package="spData")
bh.nb <- boston.soi
dpad <- data.frame(scale(boston.c[,c(7:10)]))
### calculating costs
system.time(lcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.042 0.000 0.043
### making listw
nb.w <- nb2listw(bh.nb, lcosts, style="B")
### find a minimum spanning tree
mst.bh <- mstree(nb.w,5)
### three groups with no restriction
system.time(res1 <- skater(mst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
library(parallel)
nc <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
# set nc to 1L here
@@ -321,26 +317,20 @@ Examples
}
### calculating costs
system.time(plcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.04 0.00 0.04
all.equal(lcosts, plcosts, check.attributes=FALSE)
-#> [1] TRUE
### making listw
pnb.w <- nb2listw(bh.nb, plcosts, style="B")
### find a minimum spanning tree
pmst.bh <- mstree(pnb.w,5)
### three groups with no restriction
system.time(pres1 <- skater(pmst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
if(!get.mcOption()) {
set.ClusterOption(NULL)
stopCluster(cl)
}
all.equal(res1, pres1, check.attributes=FALSE)
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'target' in selecting a method for function 'all.equal': object 'res1' not found
invisible(set.coresOption(coresOpt))
-# }
+}
diff --git a/docs/reference/summary.nb.html b/docs/reference/summary.nb.html
index 79407597..674042bf 100644
--- a/docs/reference/summary.nb.html
+++ b/docs/reference/summary.nb.html
@@ -145,9 +145,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.listw <- nb2listw(col.gal.nb, style="W")
col.listw
#> Characteristics of weights list object:
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -199,9 +199,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
try(nb2listw(nb, style="W"))
#> Error in nb2listw(nb, style = "W") : Empty neighbour sets found
summary(nb2listw(nb, style="W", zero.policy=TRUE))
@@ -219,9 +219,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -242,9 +242,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
diff --git a/docs/reference/tolerance.nb.html b/docs/reference/tolerance.nb.html
index 14a72c2e..e793d2ac 100644
--- a/docs/reference/tolerance.nb.html
+++ b/docs/reference/tolerance.nb.html
@@ -130,24 +130,18 @@ Examples
### and a maximum distance of 2 m.
nb.ex<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of 30 degrees
### and a maximum distance of 2 m. The coordinates are rotated at an angle
### of 45 degrees counterclockwise.
nb.ex2<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30, rot.angle = 45)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of pi/8 radians
### and a maximum distance of 1.5 m. The coordinates are rotated at
### an angle of pi/4 radians clockwise.
nb.ex3<-tolerance.nb(ex.data, unit.angle = "radians", max.dist=1.5,
tolerance = pi/8, rot.angle = -pi*2/3)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
par(mfrow=c(1,3))
plot(nb.ex,ex.data,asp=1)
## user system elapsed
-## 0.121 0.000 0.121
res <- sapply(c("MoranN", "MoranR", "GearyN", "GearyR", "Prop_unstdN", "Prop_unstdR", "Prop_stdN", "Prop_stdR"), function(x) sapply(get(x), "[[", "statistic"))
rownames(res) <- vars
@@ -839,7 +839,7 @@ Simulating measures of s
Prop_stdEx <- lapply(lm_objs, function(x) lm.morantest.exact(x, listw=lw_std))
})
## user system elapsed
-## 0.083 0.000 0.083
res <- sapply(c("MoranEx", "Prop_unstdEx", "Prop_stdEx"), function(x) sapply(get(x), "[[", "statistic"))
rownames(res) <- varsIntroduction## 1 2 3 4 5 6 7 8 9
## 1 1 5 9 14 17 9 6 1
## 1 least connected region:
-## with 1 link
+## 164 with 1 link
## 1 most connected region:
-## with 9 links
+## 136 with 9 links
Creating Contiguity Neighbours
diff --git a/docs/articles/nb_sf.html b/docs/articles/nb_sf.html
index 3a020026..ca1a7cdd 100644
--- a/docs/articles/nb_sf.html
+++ b/docs/articles/nb_sf.html
@@ -258,7 +258,7 @@ Contiguity neighbours for pol
eps <- sqrt(.Machine$double.eps)
system.time(for(i in 1:reps) NY8_sf_1_nb <- poly2nb(NY8_sf, queen=TRUE, snap=eps))/reps
## user system elapsed
-## 0.0925 0.0038 0.0965
+## 0.0938 0.0042 0.0984
Using spatial indices to check intersection of polygons is much
faster than the legacy method in poly2nb. From spdep
1.1-7, use is made of GEOS through sf to find candidate
@@ -427,12 +427,12 @@
K-nearest neighbours
system.time(for (i in 1:reps) NY88_nb_sf <- knn2nb(knearneigh(NY8_ct_sf, k=1)))/reps
## user system elapsed
-## 0.0174 0.0009 0.0183
+## 0.0179 0.0011 0.0191
Legacy code may be used omitting the kd-tree:
system.time(for (i in 1:reps) NY89_nb_sf <- knn2nb(knearneigh(NY8_ct_sf, k=1, use_kd_tree=FALSE)))/reps
## user system elapsed
-## 0.0174 0.0013 0.0188
+## 0.0182 0.0016 0.0199
Distance neighbours
@@ -451,7 +451,7 @@ Distance neighbours
system.time(for (i in 1:reps) NY810_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn))/reps
## user system elapsed
-## 0.0315 0.0011 0.0327
+## 0.0319 0.0015 0.0337
By default, the function uses dbscan::frNN() to build a
kd-tree in 2D or 3D which is then used to find distance neighbours. For
small n, the argument use_kd_tree=FALSE may speed up
@@ -461,7 +461,7 @@
Distance neighbours
system.time(for (i in 1:reps) NY811_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn, use_kd_tree=FALSE))/reps
## user system elapsed
-## 0.0163 0.0015 0.0178
+## 0.0166 0.0017 0.0183
@@ -491,7 +491,7 @@ K-nearest neighbourssf_use_s2(TRUE)
system.time(for (i in 1:reps) pts_ll1_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps
## user system elapsed
-## 0.0252 0.0000 0.0252
+## 0.0248 0.0000 0.0248
For this smaller data set, the legacy approach without spatial
indexing is adequate, but slows down as the number of observations
increases:
@@ -501,7 +501,7 @@ K-nearest neighbours
system.time(for (i in 1:reps) pts_ll2_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps
## user system elapsed
-## 0.0191 0.0000 0.0191
+## 0.0194 0.0000 0.0194
The WGS84 ellipsoid Great Circle distances differ a very little from
the s2 spherical distances, yielding output that here
diverges for two tract centroids:
@@ -554,7 +554,7 @@ Distance neighbours=0.75*max_1nn_ll))/(reps/5)
}
## user system elapsed
-## 0.0440 0.0000 0.0445
+## 0.0445 0.0000 0.0445
Alternatively, spherical distances can be used with
dwithin=FALSE and s2::s2_closest_edges();
although running in similar time, s2::s2_closest_edges()
@@ -563,7 +563,7 @@
Distance neighbours
system.time(for (i in 1:(reps/5)) pts_ll5_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, dwithin=FALSE))/(reps/5)
## user system elapsed
-## 0.027 0.000 0.027
+## 0.0270 0.0005 0.0275
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3_nb, pts_ll5_nb, check.attributes=FALSE)
## [1] TRUE
@@ -586,7 +586,7 @@ Distance neighbours=0.75*max_1nn_ll))/(reps/5)
}
## user system elapsed
-## 0.0725 0.0000 0.0725
+## 0.0730 0.0000 0.0735
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3a_nb, pts_ll5a_nb, check.attributes=FALSE)
## [1] TRUE
@@ -595,7 +595,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0097 0.0000 0.0098
Minor differences may occur between the legacy ellipsoid and
s2 spherical approaches:
@@ -625,7 +625,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6a_nb <- dnearneigh(pts_ll, d1=5, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0098 0.0000 0.0097
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll5a_nb, pts_ll6a_nb, check.attributes=FALSE)
## [1] "Component 20: Numeric: lengths (6, 5) differ"
@@ -676,7 +676,7 @@ Contiguity neighbours for spherical polygon support
system.time(for (i in 1:reps) NY8_sf_1_nb_ll <- poly2nb(NY8_sf_ll, queen=TRUE, snap=eps))/reps
## user system elapsed
-## 0.1553 0.0013 0.1572
+## 0.1524 0.0019 0.1548
all.equal(NY8_sf_1_nb, NY8_sf_1_nb_ll, check.attributes=FALSE)
## [1] TRUE
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 5f78f8ad..a8b299ea 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,5 +6,5 @@ articles:
nb: nb.html
nb_sf: nb_sf.html
sids: sids.html
-last_built: 2023-11-04T17:52Z
+last_built: 2023-11-06T13:06Z
diff --git a/docs/reference/EBImoran.mc.html b/docs/reference/EBImoran.mc.html
index db555f47..4886f83f 100644
--- a/docs/reference/EBImoran.mc.html
+++ b/docs/reference/EBImoran.mc.html
@@ -184,7 +184,7 @@ Examples
#> weights: nb2listw(ncCC89_nb, style = "B", zero.policy = TRUE)
#> number of simulations + 1: 1000
#>
-#> statistic = 0.25789, observed rank = 998, p-value = 0.004
+#> statistic = 0.25789, observed rank = 1000, p-value < 2.2e-16
#> alternative hypothesis: two.sided
#>
sids.p <- nc.sids$SID74 / nc.sids$BIR74
diff --git a/docs/reference/LOSH.mc-1.png b/docs/reference/LOSH.mc-1.png
index 5a905795..3b4cabfc 100644
Binary files a/docs/reference/LOSH.mc-1.png and b/docs/reference/LOSH.mc-1.png differ
diff --git a/docs/reference/LOSH.mc.html b/docs/reference/LOSH.mc.html
index c0a6763a..30a4b3ed 100644
--- a/docs/reference/LOSH.mc.html
+++ b/docs/reference/LOSH.mc.html
@@ -154,10 +154,10 @@ Examples
summary(resLOSH_mc)
#> Hi x_bar_i ei Pr()
#> Min. :0.03438 Min. :13.85 Min. : 0.0298 Min. :0.009901
-#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.118812
-#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.702970
-#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.542332
-#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.891089
+#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.079208
+#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.712871
+#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.554860
+#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.910891
#> Max. :4.68765 Max. :54.91 Max. :2455.2201 Max. :0.990099
resLOSH_cs <- LOSH.cs(columbus$CRIME, nb2listw(col.gal.nb))
summary(resLOSH_cs)
diff --git a/docs/reference/bhicv.html b/docs/reference/bhicv.html
index 2e1f9102..072425ba 100644
--- a/docs/reference/bhicv.html
+++ b/docs/reference/bhicv.html
@@ -105,7 +105,7 @@ Examples
bh <- st_read(system.file("etc/shapes/bhicv.shp",
package="spdep")[1])
#> Reading layer `bhicv' from data source
-#> `/tmp/Rtmpu0jdcX/temp_libpath70612704761e5/spdep/etc/shapes/bhicv.shp'
+#> `/tmp/RtmpU193Yl/temp_libpath9f2c94443f9d4/spdep/etc/shapes/bhicv.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 98 features and 8 fields
#> Geometry type: POLYGON
diff --git a/docs/reference/cell2nb.html b/docs/reference/cell2nb.html
index 9b67215a..e3badaf1 100644
--- a/docs/reference/cell2nb.html
+++ b/docs/reference/cell2nb.html
@@ -137,9 +137,9 @@ Examples
#> 2 3 4
#> 4 20 25
#> 4 least connected regions:
-#> with 2 links
+#> 1:1 7:1 1:7 7:7 with 2 links
#> 25 most connected regions:
-#> with 4 links
+#> 2:2 3:2 4:2 5:2 6:2 2:3 3:3 4:3 5:3 6:3 2:4 3:4 4:4 5:4 6:4 2:5 3:5 4:5 5:5 6:5 2:6 3:6 4:6 5:6 6:6 with 4 links
xyc <- attr(nb7rt, "region.id")
xy <- matrix(as.integer(unlist(strsplit(xyc, ":"))), ncol=2, byrow=TRUE)
plot(nb7rt, xy)
@@ -156,9 +156,9 @@ Examples
#> 4
#> 49
#> 49 least connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
#> 49 most connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
run <- FALSE
if (require("sp", quietly=TRUE)) run <- TRUE
if (run) {
diff --git a/docs/reference/dnearneigh.html b/docs/reference/dnearneigh.html
index afbf4662..937dd9d6 100644
--- a/docs/reference/dnearneigh.html
+++ b/docs/reference/dnearneigh.html
@@ -149,9 +149,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11
#> 4 8 6 2 5 8 6 2 6 1 1
#> 4 least connected regions:
-#> with 1 link
+#> 6 10 21 47 with 1 link
#> 1 most connected region:
-#> with 11 links
+#> 28 with 11 links
opar <- par(no.readonly=TRUE)
plot(st_geometry(columbus), border="grey", reset=FALSE,
main=paste("Distance based neighbours 0-", format(all.linked), sep=""))
@@ -198,9 +198,9 @@ Examples
#> 1 2 3 4 5 7 8 9 10
#> 11 9 4 8 4 4 4 3 1
#> 11 least connected regions:
-#> with 1 link
+#> 2 4 8 10 24 26 29 32 35 41 45 with 1 link
#> 1 most connected region:
-#> with 10 links
+#> 28 with 10 links
gck1 <- knn2nb(knearneigh(xy, k=1, longlat=TRUE))
(all.linked <- max(unlist(nbdists(gck1, xy, longlat=TRUE))))
#> [1] 523.5819
@@ -217,9 +217,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
plot(ll.nb, xy)
plot(diffnb(ll.nb, gc.nb), xy, add=TRUE, col="red", lty=2)
title(main="Differences Euclidean/Great Circle")
@@ -239,13 +239,13 @@ Examples
gck1b <- knn2nb(knearneigh(xy1, k=1))
system.time(o <- nbdists(gck1b, xy1))
#> user system elapsed
-#> 0.007 0.000 0.006
+#> 0.006 0.000 0.006
(all.linked <- max(unlist(o)))
#> [1] 522.4464
# use s2 brute-force dwithin_matrix approach for s2 <= 1.0.7
system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE))
#> user system elapsed
-#> 0.01 0.00 0.01
+#> 0.009 0.000 0.010
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -258,9 +258,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use s2 closest_edges approach s2 > 1.0.7
if (packageVersion("s2") > "1.0.7") {
(system.time(gc.nb.closest <- dnearneigh(xy1, 0, all.linked, dwithin=FALSE)))
@@ -288,9 +288,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") {
summary(gc.nb.closest, xy1, scale=0.5)
}
@@ -307,13 +307,13 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use legacy symmetric brute-force approach
system.time(gc.nb.legacy <- dnearneigh(xy1, 0, all.linked, use_s2=FALSE))
#> user system elapsed
-#> 0.003 0.000 0.003
+#> 0.003 0.000 0.004
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -326,9 +326,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") all.equal(gc.nb.closest, gc.nb.dwithin, check.attributes=FALSE)
#> [1] TRUE
# legacy is ellipsoidal, s2 spherical, so minor differences expected
diff --git a/docs/reference/edit.nb.html b/docs/reference/edit.nb.html
index 44773c64..d59c98e1 100644
--- a/docs/reference/edit.nb.html
+++ b/docs/reference/edit.nb.html
@@ -121,12 +121,11 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
class(columbus)
-#> [1] "sf" "data.frame"
if (FALSE) nnb1 <- edit.nb(col.gal.nb, polys=as(columbus, "Spatial"))
-# }
+}
diff --git a/docs/reference/geary.mc.html b/docs/reference/geary.mc.html
index 236e80b0..3164b159 100644
--- a/docs/reference/geary.mc.html
+++ b/docs/reference/geary.mc.html
@@ -160,12 +160,12 @@ Examples
#> alternative hypothesis: less
#>
mean(sim1$res)
-#> [1] 1.012612
+#> [1] 0.9890232
var(sim1$res)
-#> [1] 0.0119779
+#> [1] 0.01235098
summary(sim1$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.5299 0.9401 1.0218 1.0126 1.0906 1.2388
+#> 0.5299 0.9288 1.0029 0.9890 1.0515 1.3024
colold.lags <- nblag(COL.nb, 3)
sim2 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[2]],
style="W"), nsim=99)
@@ -177,12 +177,12 @@ Examples
#> weights: nb2listw(colold.lags[[2]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 0.81129, observed rank = 1, p-value = 0.01
+#> statistic = 0.81129, observed rank = 5, p-value = 0.05
#> alternative hypothesis: greater
#>
summary(sim2$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8113 0.9541 1.0113 1.0014 1.0569 1.1619
+#> 0.7211 0.9490 0.9896 0.9948 1.0550 1.1807
sim3 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[3]],
style="W"), nsim=99)
sim3
@@ -193,12 +193,12 @@ Examples
#> weights: nb2listw(colold.lags[[3]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 1.1303, observed rank = 92, p-value = 0.92
+#> statistic = 1.1303, observed rank = 95, p-value = 0.95
#> alternative hypothesis: greater
#>
summary(sim3$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8264 0.9586 1.0079 1.0099 1.0562 1.2047
+#> 0.8034 0.9550 1.0098 1.0076 1.0474 1.2499
diff --git a/docs/reference/graphneigh.html b/docs/reference/graphneigh.html
index f41bced1..008fcf4f 100644
--- a/docs/reference/graphneigh.html
+++ b/docs/reference/graphneigh.html
@@ -285,9 +285,9 @@ Examples
#> 0 1 2 3 4 5
#> 1 8 10 18 55 8
#> 8 least connected regions:
-#> with 1 link
+#> 46 47 48 49 96 97 98 99 with 1 link
#> 8 most connected regions:
-#> with 5 links
+#> 10 15 20 25 30 35 40 45 with 5 links
grd <- as.matrix(expand.grid(x=1:5, y=1:5)) #gridded data
r2 <- gabrielneigh(grd)
set.seed(1)
diff --git a/docs/reference/grid2nb.html b/docs/reference/grid2nb.html
index 27f554ac..eb1e9527 100644
--- a/docs/reference/grid2nb.html
+++ b/docs/reference/grid2nb.html
@@ -150,9 +150,9 @@ Examples
#> 7 11 17 26
#> 8 36 54 27
#> 8 least connected regions:
-#> with 7 links
+#> 1 5 21 25 101 105 121 125 with 7 links
#> 27 most connected regions:
-#> with 26 links
+#> 32 33 34 37 38 39 42 43 44 57 58 59 62 63 64 67 68 69 82 83 84 87 88 89 92 93 94 with 26 links
if (require("sp", quietly=TRUE)) {
gt <- GridTopology(c(.125,.1), c(.25,.2), c(4L, 5L))
nb1 <- grid2nb(gt, queen = FALSE)
diff --git a/docs/reference/include.self.html b/docs/reference/include.self.html
index 1b8fad9a..287bd036 100644
--- a/docs/reference/include.self.html
+++ b/docs/reference/include.self.html
@@ -114,9 +114,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
summary(include.self(col.gal.nb), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -128,9 +128,9 @@ Examples
#> 3 4 5 6 7 8 9 10 11
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 3 links
+#> 1 6 31 39 42 46 47 with 3 links
#> 1 most connected region:
-#> with 11 links
+#> 20 with 11 links
summary(remove.self(include.self(col.gal.nb)), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -142,9 +142,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
diff --git a/docs/reference/joincount.multi.html b/docs/reference/joincount.multi.html
index 528fbbcd..8415a202 100644
--- a/docs/reference/joincount.multi.html
+++ b/docs/reference/joincount.multi.html
@@ -161,95 +161,30 @@ Examples
#> high:high 52.000 28.138 18.342 5.5716
#> high:low 28.000 61.170 25.882 -6.5200
#> Jtot 28.000 61.170 33.190 -5.7577
-# \dontrun{
+if (FALSE) {
data(oldcol)
HICRIME <- cut(COL.OLD$CRIME, breaks=c(0,35,80), labels=c("low","high"))
names(HICRIME) <- rownames(COL.OLD)
joincount.multi(HICRIME, nb2listw(COL.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> low:low 34.000 29.592 18.895 1.0141
-#> high:high 54.000 27.224 17.888 6.3307
-#> high:low 28.000 59.184 26.233 -6.0884
-#> Jtot 28.000 59.184 26.233 -6.0884
data(hopkins, package="spData")
image(1:32, 1:32, hopkins[5:36,36:5], breaks=c(-0.5, 3.5, 20),
col=c("white", "black"))
box()
-
hopkins.rook.nb <- cell2nb(32, 32, type="rook")
unlist(spweights.constants(nb2listw(hopkins.rook.nb, style="B")))
-#> n n1 n2 n3 nn S0 S1 S2
-#> 1024 1023 1022 1021 1048576 3968 7936 61984
hopkins.queen.nb <- cell2nb(32, 32, type="queen")
hopkins.bishop.nb <- diffnb(hopkins.rook.nb, hopkins.queen.nb, verbose=FALSE)
hopkins4 <- hopkins[5:36,36:5]
hopkins4[which(hopkins4 > 3, arr.ind=TRUE)] <- 4
hopkins4.f <- factor(hopkins4)
table(hopkins4.f)
-#> hopkins4.f
-#> 0 1 2 3 4
-#> 657 215 98 30 24
joincount.multi(hopkins4.f, nb2listw(hopkins.rook.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 864.00000 816.27273 116.05233 4.4304
-#> 1:1 94.00000 87.14015 55.25216 0.9229
-#> 2:2 18.00000 18.00379 14.81562 -0.0010
-#> 3:3 2.00000 1.64773 1.55539 0.2825
-#> 4:4 5.00000 1.04545 0.99845 3.9576
-#> 1:0 503.00000 535.05682 227.76750 -2.1241
-#> 2:0 213.00000 243.88636 97.21769 -3.1325
-#> 2:1 99.00000 79.81061 59.01930 2.4978
-#> 3:0 61.00000 74.65909 28.58592 -2.5547
-#> 3:1 28.00000 24.43182 18.99976 0.8186
-#> 3:2 15.00000 11.13636 9.82411 1.2327
-#> 4:0 40.00000 59.72727 22.78583 -4.1327
-#> 4:1 23.00000 19.54545 15.26564 0.8842
-#> 4:2 14.00000 8.90909 7.90051 1.8112
-#> 4:3 5.00000 2.72727 2.58616 1.4133
-#> Jtot 1001.00000 1059.89015 273.78610 -3.5591
cat("replicates Upton & Fingleton table 3.4 (p. 166)\n")
-#> replicates Upton & Fingleton table 3.4 (p. 166)
joincount.multi(hopkins4.f, nb2listw(hopkins.bishop.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 823.00000 790.76420 144.44877 2.6821
-#> 1:1 101.00000 84.41702 55.98143 2.2164
-#> 2:2 19.00000 17.44117 14.61542 0.4077
-#> 3:3 3.00000 1.59624 1.51444 1.1407
-#> 4:4 3.00000 1.01278 0.97111 2.0166
-#> 1:0 497.00000 518.33629 234.93545 -1.3920
-#> 2:0 216.00000 236.26491 104.42142 -1.9831
-#> 2:1 81.00000 77.31652 58.70829 0.4807
-#> 3:0 58.00000 72.32599 31.49151 -2.5529
-#> 3:1 21.00000 23.66832 18.85316 -0.6145
-#> 3:2 17.00000 10.78835 9.62487 2.0022
-#> 4:0 48.00000 57.86080 25.15973 -1.9659
-#> 4:1 21.00000 18.93466 15.14473 0.5307
-#> 4:2 10.00000 8.63068 7.73708 0.4923
-#> 4:3 4.00000 2.64205 2.51686 0.8560
-#> Jtot 973.00000 1026.76858 284.51030 -3.1877
cat("replicates Upton & Fingleton table 3.6 (p. 168)\n")
-#> replicates Upton & Fingleton table 3.6 (p. 168)
joincount.multi(hopkins4.f, nb2listw(hopkins.queen.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 1687.0000 1607.0369 303.8034 4.5877
-#> 1:1 195.0000 171.5572 114.2057 2.1936
-#> 2:2 37.0000 35.4450 29.6821 0.2854
-#> 3:3 5.0000 3.2440 3.0687 1.0024
-#> 4:4 8.0000 2.0582 1.9674 4.2361
-#> 1:0 1000.0000 1053.3931 480.6959 -2.4353
-#> 2:0 429.0000 480.1513 215.0360 -3.4882
-#> 2:1 180.0000 157.1271 119.3987 2.0932
-#> 3:0 119.0000 146.9851 65.1029 -3.4684
-#> 3:1 49.0000 48.1001 38.3268 0.1454
-#> 3:2 32.0000 21.9247 19.5237 2.2802
-#> 4:0 88.0000 117.5881 52.0312 -4.1019
-#> 4:1 44.0000 38.4801 30.7868 0.9948
-#> 4:2 24.0000 17.5398 15.6933 1.6308
-#> 4:3 9.0000 5.3693 5.0994 1.6078
-#> Jtot 1974.0000 2086.6587 582.8326 -4.6665
cat("replicates Upton & Fingleton table 3.7 (p. 169)\n")
-#> replicates Upton & Fingleton table 3.7 (p. 169)
-# }
+}
diff --git a/docs/reference/knearneigh.html b/docs/reference/knearneigh.html
index f5a9e742..5f4466fa 100644
--- a/docs/reference/knearneigh.html
+++ b/docs/reference/knearneigh.html
@@ -161,9 +161,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
summary(gck4.nb, xy, longlat=TRUE, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -176,9 +176,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#xy1 <- SpatialPoints((as.data.frame(state.center))[m50.48,],
# proj4string=CRS("+proj=longlat +ellps=GRS80"))
@@ -191,7 +191,7 @@ Examples
sf_use_s2(TRUE)
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.008 0.000 0.009
+#> 0.009 0.000 0.010
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -204,14 +204,14 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(FALSE)
#> Spherical geometry (s2) switched off
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.005 0.000 0.006
+#> 0.005 0.000 0.005
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -224,9 +224,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(old_use_s2)
#> Spherical geometry (s2) switched on
diff --git a/docs/reference/localC.html b/docs/reference/localC.html
index 1efd5e5b..03cfd45b 100644
--- a/docs/reference/localC.html
+++ b/docs/reference/localC.html
@@ -245,11 +245,9 @@ Examples
}
#> [1] 0.985611
# pseudo-p values probably wrongly folded https://github.com/GeoDaCenter/rgeoda/issues/28
-# \dontrun{
+if (FALSE) {
tmap_ok <- FALSE
if (require(tmap, quietly=TRUE)) tmap_ok <- TRUE
-#> Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
-#> remotes::install_github('r-tmap/tmap')
if (run) {
# doi: 10.1111/gean.12164
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
@@ -263,25 +261,10 @@ Examples
moran(g$Infants, lw, n=nrow(g), S0=Szero(lw))$I
moran(g$Suicids, lw, n=nrow(g), S0=Szero(lw))$I
}
-#> Reading layer `Guerry' from data source
-#> `/home/rsb/lib/r_libs/rgeoda/extdata/Guerry.shp' using driver `ESRI Shapefile'
-#> Simple feature collection with 85 features and 29 fields
-#> Geometry type: MULTIPOLYGON
-#> Dimension: XY
-#> Bounding box: xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
-#> Projected CRS: NTF (Paris) / Lambert zone II
-#> [1] 0.4016812
if (run) {
o <- prcomp(st_drop_geometry(g), scale.=TRUE)
cor(st_drop_geometry(g), o$x[,1:2])^2 #(Tab. 2)
}
-#> PC1 PC2
-#> Crm_prs 0.009286863 0.418851254
-#> Crm_prp 0.561825664 0.009377232
-#> Litercy 0.560570048 0.020095011
-#> Donatns 0.024138865 0.586720363
-#> Infants 0.436025658 0.012817054
-#> Suicids 0.548623327 0.152958961
if (run) {
g$PC1 <- o$x[, "PC1"]
brks <- c(min(g$PC1), natural_breaks(k=6, g["PC1"]), max(g$PC1))
@@ -289,7 +272,6 @@ Examples
tm_borders() # Fig. 1
else pplot(g["PC1"], breaks=brks)
}
-
if (run) {
g$PC2 <- -1*o$x[, "PC2"] # eigenvalue sign arbitrary
brks <- c(min(g$PC2), natural_breaks(k=6, g["PC2"]), max(g$PC2))
@@ -297,7 +279,6 @@ Examples
tm_borders() # Fig. 2
else plot(g["PC2"], breaks=brks)
}
-
if (run) {
w <- queen_weights(g)
lm_PC1 <- local_moran(w, g["PC1"], significance_cutoff=0.01,
@@ -310,7 +291,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 3
else plot(g["lm_PC1"])
}
-
if (run) {
set.seed(1)
lm_PC1_spdep <- localmoran_perm(g$PC1, lw, nsim=9999)
@@ -322,7 +302,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 3
else plot(g["lm_PC1_spdep"])
}
-
if (run) {
lg_PC1 <- local_g(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -340,7 +319,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 4 (guess)
else plot(g["lg_PC1"])
}
-
if (run) {
lc_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -352,14 +330,11 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 5
else plot(g["lc_PC1"])
}
-
if (run) {
set.seed(1)
system.time(lc_PC1_spdep <- localC_perm(g$PC1, lw, nsim=9999,
alternative="two.sided"))
}
-#> user system elapsed
-#> 0.376 0.001 0.377
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -372,7 +347,6 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
g$lc_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.01
@@ -381,7 +355,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 5
else plot(g["lc_PC1_spdep"])
}
-
if (run) {
g$both_PC1 <- interaction(g$lc_PC1, g$lm_PC1)
g$both_PC1 <- droplevels(g$both_PC1)
@@ -389,7 +362,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 6
else plot(g["both_PC1"])
}
-
if (run) {
lc005_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.005,
permutations=99999)
@@ -401,7 +373,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 7
else plot(g["lc005_PC1"])
}
-
if (run) {
g$lc005_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc005_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.005
@@ -410,7 +381,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 7
else plot(g["lc005_PC1_spdep"])
}
-
if (run) {
lc001_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.001,
permutations=99999)
@@ -430,7 +400,6 @@ Examples
else plot(g["lc001_PC1_spdep"])
}
}
-
if (run) {
lc_PC2 <- local_geary(w, g["PC2"], significance_cutoff=0.01,
permutations=99999)
@@ -442,7 +411,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 9
else plot(g["lc_PC2"])
}
-
if (run) {
lmc_PC <- local_multigeary(w, g[c("PC1","PC2")], significance_cutoff=0.00247,
permutations=99999)
@@ -452,25 +420,19 @@ Examples
g$lmc_PC <- droplevels(g$lmc_PC)
table(interaction((p.adjust(lisa_pvalues(lmc_PC), "fdr") < 0.01), g$lmc_PC))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_PC", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 10
else plot(g["lmc_PC"])
}
-
if (run) {
set.seed(1)
lmc_PC_spdep <- localC_perm(g[c("PC1","PC2")], lw, nsim=9999, alternative="two.sided")
all.equal(lisa_values(lmc_PC), c(lmc_PC_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_PC_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_PC))
}
-#> [1] 0.99052
if (run) {
g$lmc_PC_spdep <- attr(lmc_PC_spdep, "cluster")
is.na(g$lmc_PC_spdep) <- p.adjust(attr(lmc_PC_spdep, "pseudo-p")[,6], "fdr") > 0.01
@@ -479,7 +441,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 10
else plot(g["lmc_PC_spdep"])
}
-
if (run) {
lmc_vars <- local_multigeary(w, st_drop_geometry(g)[, 1:6],
significance_cutoff=0.00247, permutations=99999)
@@ -490,30 +451,22 @@ Examples
table(interaction((p.adjust(lisa_pvalues(lmc_vars), "fdr") < 0.01),
g$lmc_vars))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_vars", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 11
else plot(g["lmc_vars"])
}
-
if (run) {
set.seed(1)
system.time(lmc_vars_spdep <- localC_perm(st_drop_geometry(g)[, 1:6], lw,
nsim=9999, alternative="two.sided"))
}
-#> user system elapsed
-#> 0.824 0.002 0.828
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -526,15 +479,12 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep1))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep1, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
g$lmc_vars_spdep <- attr(lmc_vars_spdep1, "cluster")
is.na(g$lmc_vars_spdep) <- p.adjust(attr(lmc_vars_spdep1, "pseudo-p")[,6], "fdr") > 0.01
@@ -543,10 +493,9 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 11
else plot(g["lmc_vars_spdep"])
}
-
-# }
-# \dontrun{
+}
+if (FALSE) {
library(reticulate)
use_python("/usr/bin/python", required = TRUE)
gp <- import("geopandas")
@@ -557,7 +506,6 @@ Examples
esda <- import("esda")
lM <- esda$Moran_Local(x, w)
all.equal(unname(localmoran(x, listw, mlvar=FALSE)[,1]), c(lM$Is))
-#> [1] TRUE
# confirm x and w the same
lC <- esda$Geary_Local(connectivity=w)$fit(scale(x))
# np$std missing ddof=1
@@ -566,19 +514,10 @@ Examples
# lC components probably wrongly ordered https://github.com/pysal/esda/issues/192
o <- match(round(D0, 6), round(lC$localG, 6))
all.equal(c(lC$localG)[o], D0)
-#> [1] TRUE
# simulation order not retained
lC$p_sim[o]
-#> [1] 0.278 0.460 0.112 0.019 0.121 0.038 0.330 0.412 0.147 0.001 0.113 0.141
-#> [13] 0.188 0.246 0.450 0.172 0.102 0.298 0.001 0.027 0.038 0.091 0.055 0.297
-#> [25] 0.153 0.031 0.107 0.280 0.450 0.001 0.066 0.104 0.080 0.231 0.306 0.001
-#> [37] 0.326 0.482 0.321 0.216 0.235 0.288
attr(C, "pseudo-p")[,6]
-#> [1] 0.408 0.374 0.144 0.056 0.010 0.284 0.032 0.264 0.186 0.004 0.150 0.224
-#> [13] 0.022 0.150 0.438 0.084 0.126 0.184 0.002 0.238 0.126 0.100 0.068 0.372
-#> [25] 0.124 0.234 0.230 0.016 0.318 0.176 0.272 0.164 0.164 0.206 0.402 0.250
-#> [37] 0.410 0.402 0.328 0.294 0.438 0.248
-# }
+}
diff --git a/docs/reference/mat2listw.html b/docs/reference/mat2listw.html
index 096c6e5d..656b612c 100644
--- a/docs/reference/mat2listw.html
+++ b/docs/reference/mat2listw.html
@@ -140,13 +140,14 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
-col005.w.mat <- nb2mat(col005, zero.policy=TRUE)
-col005.w.b <- mat2listw(col005.w.mat)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> 1038 1036 with 9 links
+col005.w.mat <- nb2mat(col005, style="W", zero.policy=TRUE)
+try(col005.w.b <- mat2listw(col005.w.mat, style="W"))
+#> Error in mat2listw(col005.w.mat, style = "W") :
+#> no-neighbour observations found, set zero.policy to TRUE
+col005.w.b <- mat2listw(col005.w.mat, style="W", zero.policy=TRUE)
summary(col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -161,9 +162,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
diffnb(col005, col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -177,9 +178,7 @@ Examples
#> 1029 1025 1028 1048 1015 1027 1026
#> 49 disjoint connected subgraphs
col005.w.mat.3T <- kronecker(diag(3), col005.w.mat)
-col005.w.b.3T <- mat2listw(col005.w.mat.3T, style="W")
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
+col005.w.b.3T <- mat2listw(col005.w.mat.3T, style="W", zero.policy=TRUE)
summary(col005.w.b.3T$neighbours)
#> Neighbour list object:
#> Number of regions: 147
@@ -194,21 +193,22 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 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:
-#> with 9 links
+#> 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")
-col005.spM <- mat2listw(W)
+try(col005.spM <- mat2listw(W))
+col005.spM <- mat2listw(W, style="W", zero.policy=TRUE)
summary(col005.spM$neighbours)
}
#> Warning: style is M (missing); style should be set to a valid value
#> Warning: style is M (missing); style should be set to a valid value
#> Warning: 1005, 1006, 1008, 1043 are not origins
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> Error in sn2listw(df, style = style, zero.policy = zero.policy) :
+#> no-neighbour observations found, set zero.policy to TRUE
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 170
@@ -222,9 +222,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
if (run) {
diffnb(col005, col005.spM$neighbours)
}
@@ -241,14 +241,9 @@ Examples
#> 49 disjoint connected subgraphs
if (run && require("Matrix", quiet=TRUE)) {
IW <- kronecker(Diagonal(3), W)
-col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W")
+col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W", zero.policy=TRUE)
summary(col005.spM.3T$neighbours)
}
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: 1, 3, 6, 21, 50, 52, 55, 70, 99, 101, 104, 119 are not origins
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
#> Neighbour list object:
#> Number of regions: 147
#> Number of nonzero links: 510
@@ -262,9 +257,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 2 5 9 10 31 34 36 39 42 46 47 51 54 58 59 80 83 85 88 91 95 96 100 103 107 108 129 132 134 137 140 144 145 with 1 link
#> 6 most connected regions:
-#> with 9 links
+#> 11 16 60 65 109 114 with 9 links
diff --git a/docs/reference/nb2blocknb.html b/docs/reference/nb2blocknb.html
index c2276d23..460ced1b 100644
--- a/docs/reference/nb2blocknb.html
+++ b/docs/reference/nb2blocknb.html
@@ -127,96 +127,31 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
data(boston, package="spData")
summary(as.vector(table(boston.c$TOWN)))
-#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 1.0 2.0 4.0 5.5 7.0 30.0
townaggr <- aggregate(boston.utm, list(town=boston.c$TOWN), mean)
block.rel <- graph2nb(relativeneigh(as.matrix(townaggr[,2:3])),
as.character(townaggr[,1]), sym=TRUE)
block.rel
-#> Neighbour list object:
-#> Number of regions: 92
-#> Number of nonzero links: 240
-#> Percentage nonzero weights: 2.835539
-#> Average number of links: 2.608696
print(is.symmetric.nb(block.rel))
-#> [1] TRUE
plot(block.rel, as.matrix(townaggr[,2:3]))
points(boston.utm, pch=18, col="lightgreen")
-
block.nb <- nb2blocknb(block.rel, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 15234
-#> Percentage nonzero weights: 5.949945
-#> Average number of links: 30.10672
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
points(boston.utm, pch=18, col="lightgreen")
-
n.comp.nb(block.nb)$nc
-#> [1] 1
moran.test(boston.c$CMEDV, nb2listw(boston.soi))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(boston.soi)
-#>
-#> Moran I statistic standard deviate = 21.786, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.690285059 -0.001980198 0.001009685
-#>
moran.test(boston.c$CMEDV, nb2listw(block.nb))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb)
-#>
-#> Moran I statistic standard deviate = 22.455, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.3122905961 -0.0019801980 0.0001958827
-#>
block.nb <- nb2blocknb(NULL, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 4868
-#> Percentage nonzero weights: 1.901295
-#> Average number of links: 9.620553
-#> 17 regions with no links:
-#> 1 55 56 57 58 65 196 257 284 285 286 287 342 343 348 349 354
-#> 92 disjoint connected subgraphs
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
-
n.comp.nb(block.nb)$nc
-#> [1] 92
moran.test(boston.c$CMEDV, nb2listw(block.nb, zero.policy=TRUE), zero.policy=TRUE)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb, zero.policy = TRUE) n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 21.145, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.6188830566 -0.0020491803 0.0008623116
-#>
-# }
+}
diff --git a/docs/reference/nb2lines.html b/docs/reference/nb2lines.html
index 38b287ec..1b49a033 100644
--- a/docs/reference/nb2lines.html
+++ b/docs/reference/nb2lines.html
@@ -158,12 +158,12 @@ Examples
tf <- paste0(tempfile(), ".gpkg")
st_write(res, dsn=tf, driver="GPKG")
#> writing: substituting ENGCRS["Undefined Cartesian SRS with unknown unit"] for missing CRS
-#> Writing layer `file706d874b42afe' to data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Writing layer `file9f36c3d2d676e' to data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Writing 230 features with 5 fields and geometry type Line String.
inMap <- st_read(tf)
-#> Reading layer `file706d874b42afe' from data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Reading layer `file9f36c3d2d676e' from data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Simple feature collection with 230 features and 5 fields
#> Geometry type: LINESTRING
#> Dimension: XY
diff --git a/docs/reference/nb2listw.html b/docs/reference/nb2listw.html
index fd9ebeee..ef19145f 100644
--- a/docs/reference/nb2listw.html
+++ b/docs/reference/nb2listw.html
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: minmax
#> Weights constants summary:
diff --git a/docs/reference/nb2listwdist.html b/docs/reference/nb2listwdist.html
index a6ffbf2d..98dcafb5 100644
--- a/docs/reference/nb2listwdist.html
+++ b/docs/reference/nb2listwdist.html
@@ -171,43 +171,15 @@ Examples
#> Moran I statistic Expectation Variance
#> 0.473883371 -0.007042254 0.029994057
#>
-# \dontrun{
+if (FALSE) {
# Moran's I (life expectancy) with IDW with alpha = 2, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "idw",
alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 2.7769, p-value = 0.002744
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.473883371 -0.007042254 0.029994057
-#>
# Moran's I (life expectancy), DPD, alpha = 2, dmax = 1000 km, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "dpd",
dmax = 1000000, alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 8.8557, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.601498970 -0.007042254 0.004722063
-#>
# Boston examples
data(boston, package="spData")
boston_coords <- data.frame(x = boston.utm[,1], y = boston.utm[,2])
@@ -217,37 +189,11 @@ Examples
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "exp", alpha = 2,
style="raw", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 51.34, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.8845114518 -0.0019960080 0.0002981586
-#>
# Moran's I (crime) with idw weights with alpha = 2, coding scheme = W
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "idw", alpha = 2,
style="W", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 18.976, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.4392852330 -0.0019960080 0.0005408065
-#>
-# }
+}
diff --git a/docs/reference/nb2mat.html b/docs/reference/nb2mat.html
index 7f17e8bb..39ec4504 100644
--- a/docs/reference/nb2mat.html
+++ b/docs/reference/nb2mat.html
@@ -137,9 +137,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
col005.w.mat <- nb2mat(col005, style="B", zero.policy=TRUE)
table(round(rowSums(col005.w.mat)))
#>
diff --git a/docs/reference/nblag.html b/docs/reference/nblag.html
index 08180e37..bd822ea5 100644
--- a/docs/reference/nblag.html
+++ b/docs/reference/nblag.html
@@ -122,9 +122,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.lags <- nblag(col.gal.nb, 2)
print(col.lags)
#> [[1]]
@@ -154,9 +154,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#> 1 2 5 4 2 3 5 4 2 3 7 3 4 1 1 1 1
#> 1 least connected region:
-#> with 1 link
+#> 36 with 1 link
#> 1 most connected region:
-#> with 17 links
+#> 25 with 17 links
plot(st_geometry(columbus), border="grey")
plot(col.gal.nb, coords, add=TRUE)
title(main="GAL order 1 (black) and 2 (red) links")
diff --git a/docs/reference/poly2nb.html b/docs/reference/poly2nb.html
index 1cad8008..79b13c61 100644
--- a/docs/reference/poly2nb.html
+++ b/docs/reference/poly2nb.html
@@ -174,10 +174,10 @@ Examples
nc.sids <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE)
system.time(xxnb <- poly2nb(nc.sids))
#> user system elapsed
-#> 0.018 0.000 0.018
+#> 0.019 0.000 0.019
system.time(xxnb <- poly2nb(as(nc.sids, "Spatial")))
#> user system elapsed
-#> 0.029 0.000 0.029
+#> 0.034 0.000 0.035
plot(st_geometry(nc.sids))
plot(xxnb, st_coordinates(st_centroid(nc.sids)), add=TRUE, col="blue")
#> Warning: st_centroid assumes attributes are constant over geometries
diff --git a/docs/reference/skater.html b/docs/reference/skater.html
index aefe0190..d2b9c2e6 100644
--- a/docs/reference/skater.html
+++ b/docs/reference/skater.html
@@ -292,22 +292,18 @@ Examples
table(res5d$groups)
#> Error in eval(expr, envir, enclos): object 'res5d' not found
-# \dontrun{
+if (FALSE) {
data(boston, package="spData")
bh.nb <- boston.soi
dpad <- data.frame(scale(boston.c[,c(7:10)]))
### calculating costs
system.time(lcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.042 0.000 0.043
### making listw
nb.w <- nb2listw(bh.nb, lcosts, style="B")
### find a minimum spanning tree
mst.bh <- mstree(nb.w,5)
### three groups with no restriction
system.time(res1 <- skater(mst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
library(parallel)
nc <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
# set nc to 1L here
@@ -321,26 +317,20 @@ Examples
}
### calculating costs
system.time(plcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.04 0.00 0.04
all.equal(lcosts, plcosts, check.attributes=FALSE)
-#> [1] TRUE
### making listw
pnb.w <- nb2listw(bh.nb, plcosts, style="B")
### find a minimum spanning tree
pmst.bh <- mstree(pnb.w,5)
### three groups with no restriction
system.time(pres1 <- skater(pmst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
if(!get.mcOption()) {
set.ClusterOption(NULL)
stopCluster(cl)
}
all.equal(res1, pres1, check.attributes=FALSE)
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'target' in selecting a method for function 'all.equal': object 'res1' not found
invisible(set.coresOption(coresOpt))
-# }
+}
diff --git a/docs/reference/summary.nb.html b/docs/reference/summary.nb.html
index 79407597..674042bf 100644
--- a/docs/reference/summary.nb.html
+++ b/docs/reference/summary.nb.html
@@ -145,9 +145,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.listw <- nb2listw(col.gal.nb, style="W")
col.listw
#> Characteristics of weights list object:
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -199,9 +199,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
try(nb2listw(nb, style="W"))
#> Error in nb2listw(nb, style = "W") : Empty neighbour sets found
summary(nb2listw(nb, style="W", zero.policy=TRUE))
@@ -219,9 +219,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -242,9 +242,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
diff --git a/docs/reference/tolerance.nb.html b/docs/reference/tolerance.nb.html
index 14a72c2e..e793d2ac 100644
--- a/docs/reference/tolerance.nb.html
+++ b/docs/reference/tolerance.nb.html
@@ -130,24 +130,18 @@ Examples
### and a maximum distance of 2 m.
nb.ex<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of 30 degrees
### and a maximum distance of 2 m. The coordinates are rotated at an angle
### of 45 degrees counterclockwise.
nb.ex2<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30, rot.angle = 45)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of pi/8 radians
### and a maximum distance of 1.5 m. The coordinates are rotated at
### an angle of pi/4 radians clockwise.
nb.ex3<-tolerance.nb(ex.data, unit.angle = "radians", max.dist=1.5,
tolerance = pi/8, rot.angle = -pi*2/3)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
par(mfrow=c(1,3))
plot(nb.ex,ex.data,asp=1)
Creating Contiguity Neighbours
diff --git a/docs/articles/nb_sf.html b/docs/articles/nb_sf.html
index 3a020026..ca1a7cdd 100644
--- a/docs/articles/nb_sf.html
+++ b/docs/articles/nb_sf.html
@@ -258,7 +258,7 @@ Contiguity neighbours for pol
eps <- sqrt(.Machine$double.eps)
system.time(for(i in 1:reps) NY8_sf_1_nb <- poly2nb(NY8_sf, queen=TRUE, snap=eps))/reps
## user system elapsed
-## 0.0925 0.0038 0.0965
system.time(for (i in 1:reps) NY88_nb_sf <- knn2nb(knearneigh(NY8_ct_sf, k=1)))/reps
## user system elapsed
-## 0.0174 0.0009 0.0183Legacy code may be used omitting the kd-tree:
system.time(for (i in 1:reps) NY89_nb_sf <- knn2nb(knearneigh(NY8_ct_sf, k=1, use_kd_tree=FALSE)))/reps## user system elapsed
-## 0.0174 0.0013 0.0188Distance neighbours
@@ -451,7 +451,7 @@ Distance neighbours
system.time(for (i in 1:reps) NY810_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn))/reps
system.time(for (i in 1:reps) NY810_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn))/reps## user system elapsed
-## 0.0315 0.0011 0.0327By default, the function uses dbscan::frNN() to build a
kd-tree in 2D or 3D which is then used to find distance neighbours. For
small n, the argument use_kd_tree=FALSE may speed up
@@ -461,7 +461,7 @@
Distance neighbours
system.time(for (i in 1:reps) NY811_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn, use_kd_tree=FALSE))/reps
## user system elapsed
-## 0.0163 0.0015 0.0178
+## 0.0166 0.0017 0.0183
@@ -491,7 +491,7 @@ K-nearest neighbourssf_use_s2(TRUE)
system.time(for (i in 1:reps) pts_ll1_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps
## user system elapsed
-## 0.0252 0.0000 0.0252
+## 0.0248 0.0000 0.0248
For this smaller data set, the legacy approach without spatial
indexing is adequate, but slows down as the number of observations
increases:
@@ -501,7 +501,7 @@ K-nearest neighbours
system.time(for (i in 1:reps) pts_ll2_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps
## user system elapsed
-## 0.0191 0.0000 0.0191
+## 0.0194 0.0000 0.0194
The WGS84 ellipsoid Great Circle distances differ a very little from
the s2 spherical distances, yielding output that here
diverges for two tract centroids:
@@ -554,7 +554,7 @@ Distance neighbours=0.75*max_1nn_ll))/(reps/5)
}
## user system elapsed
-## 0.0440 0.0000 0.0445
+## 0.0445 0.0000 0.0445
Alternatively, spherical distances can be used with
dwithin=FALSE and s2::s2_closest_edges();
although running in similar time, s2::s2_closest_edges()
@@ -563,7 +563,7 @@
Distance neighbours
system.time(for (i in 1:(reps/5)) pts_ll5_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, dwithin=FALSE))/(reps/5)
## user system elapsed
-## 0.027 0.000 0.027
+## 0.0270 0.0005 0.0275
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3_nb, pts_ll5_nb, check.attributes=FALSE)
## [1] TRUE
@@ -586,7 +586,7 @@ Distance neighbours=0.75*max_1nn_ll))/(reps/5)
}
## user system elapsed
-## 0.0725 0.0000 0.0725
+## 0.0730 0.0000 0.0735
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3a_nb, pts_ll5a_nb, check.attributes=FALSE)
## [1] TRUE
@@ -595,7 +595,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0097 0.0000 0.0098
Minor differences may occur between the legacy ellipsoid and
s2 spherical approaches:
@@ -625,7 +625,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6a_nb <- dnearneigh(pts_ll, d1=5, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0098 0.0000 0.0097
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll5a_nb, pts_ll6a_nb, check.attributes=FALSE)
## [1] "Component 20: Numeric: lengths (6, 5) differ"
@@ -676,7 +676,7 @@ Contiguity neighbours for spherical polygon support
system.time(for (i in 1:reps) NY8_sf_1_nb_ll <- poly2nb(NY8_sf_ll, queen=TRUE, snap=eps))/reps
## user system elapsed
-## 0.1553 0.0013 0.1572
+## 0.1524 0.0019 0.1548
all.equal(NY8_sf_1_nb, NY8_sf_1_nb_ll, check.attributes=FALSE)
## [1] TRUE
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 5f78f8ad..a8b299ea 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,5 +6,5 @@ articles:
nb: nb.html
nb_sf: nb_sf.html
sids: sids.html
-last_built: 2023-11-04T17:52Z
+last_built: 2023-11-06T13:06Z
diff --git a/docs/reference/EBImoran.mc.html b/docs/reference/EBImoran.mc.html
index db555f47..4886f83f 100644
--- a/docs/reference/EBImoran.mc.html
+++ b/docs/reference/EBImoran.mc.html
@@ -184,7 +184,7 @@ Examples
#> weights: nb2listw(ncCC89_nb, style = "B", zero.policy = TRUE)
#> number of simulations + 1: 1000
#>
-#> statistic = 0.25789, observed rank = 998, p-value = 0.004
+#> statistic = 0.25789, observed rank = 1000, p-value < 2.2e-16
#> alternative hypothesis: two.sided
#>
sids.p <- nc.sids$SID74 / nc.sids$BIR74
diff --git a/docs/reference/LOSH.mc-1.png b/docs/reference/LOSH.mc-1.png
index 5a905795..3b4cabfc 100644
Binary files a/docs/reference/LOSH.mc-1.png and b/docs/reference/LOSH.mc-1.png differ
diff --git a/docs/reference/LOSH.mc.html b/docs/reference/LOSH.mc.html
index c0a6763a..30a4b3ed 100644
--- a/docs/reference/LOSH.mc.html
+++ b/docs/reference/LOSH.mc.html
@@ -154,10 +154,10 @@ Examples
summary(resLOSH_mc)
#> Hi x_bar_i ei Pr()
#> Min. :0.03438 Min. :13.85 Min. : 0.0298 Min. :0.009901
-#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.118812
-#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.702970
-#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.542332
-#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.891089
+#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.079208
+#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.712871
+#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.554860
+#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.910891
#> Max. :4.68765 Max. :54.91 Max. :2455.2201 Max. :0.990099
resLOSH_cs <- LOSH.cs(columbus$CRIME, nb2listw(col.gal.nb))
summary(resLOSH_cs)
diff --git a/docs/reference/bhicv.html b/docs/reference/bhicv.html
index 2e1f9102..072425ba 100644
--- a/docs/reference/bhicv.html
+++ b/docs/reference/bhicv.html
@@ -105,7 +105,7 @@ Examples
bh <- st_read(system.file("etc/shapes/bhicv.shp",
package="spdep")[1])
#> Reading layer `bhicv' from data source
-#> `/tmp/Rtmpu0jdcX/temp_libpath70612704761e5/spdep/etc/shapes/bhicv.shp'
+#> `/tmp/RtmpU193Yl/temp_libpath9f2c94443f9d4/spdep/etc/shapes/bhicv.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 98 features and 8 fields
#> Geometry type: POLYGON
diff --git a/docs/reference/cell2nb.html b/docs/reference/cell2nb.html
index 9b67215a..e3badaf1 100644
--- a/docs/reference/cell2nb.html
+++ b/docs/reference/cell2nb.html
@@ -137,9 +137,9 @@ Examples
#> 2 3 4
#> 4 20 25
#> 4 least connected regions:
-#> with 2 links
+#> 1:1 7:1 1:7 7:7 with 2 links
#> 25 most connected regions:
-#> with 4 links
+#> 2:2 3:2 4:2 5:2 6:2 2:3 3:3 4:3 5:3 6:3 2:4 3:4 4:4 5:4 6:4 2:5 3:5 4:5 5:5 6:5 2:6 3:6 4:6 5:6 6:6 with 4 links
xyc <- attr(nb7rt, "region.id")
xy <- matrix(as.integer(unlist(strsplit(xyc, ":"))), ncol=2, byrow=TRUE)
plot(nb7rt, xy)
@@ -156,9 +156,9 @@ Examples
#> 4
#> 49
#> 49 least connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
#> 49 most connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
run <- FALSE
if (require("sp", quietly=TRUE)) run <- TRUE
if (run) {
diff --git a/docs/reference/dnearneigh.html b/docs/reference/dnearneigh.html
index afbf4662..937dd9d6 100644
--- a/docs/reference/dnearneigh.html
+++ b/docs/reference/dnearneigh.html
@@ -149,9 +149,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11
#> 4 8 6 2 5 8 6 2 6 1 1
#> 4 least connected regions:
-#> with 1 link
+#> 6 10 21 47 with 1 link
#> 1 most connected region:
-#> with 11 links
+#> 28 with 11 links
opar <- par(no.readonly=TRUE)
plot(st_geometry(columbus), border="grey", reset=FALSE,
main=paste("Distance based neighbours 0-", format(all.linked), sep=""))
@@ -198,9 +198,9 @@ Examples
#> 1 2 3 4 5 7 8 9 10
#> 11 9 4 8 4 4 4 3 1
#> 11 least connected regions:
-#> with 1 link
+#> 2 4 8 10 24 26 29 32 35 41 45 with 1 link
#> 1 most connected region:
-#> with 10 links
+#> 28 with 10 links
gck1 <- knn2nb(knearneigh(xy, k=1, longlat=TRUE))
(all.linked <- max(unlist(nbdists(gck1, xy, longlat=TRUE))))
#> [1] 523.5819
@@ -217,9 +217,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
plot(ll.nb, xy)
plot(diffnb(ll.nb, gc.nb), xy, add=TRUE, col="red", lty=2)
title(main="Differences Euclidean/Great Circle")
@@ -239,13 +239,13 @@ Examples
gck1b <- knn2nb(knearneigh(xy1, k=1))
system.time(o <- nbdists(gck1b, xy1))
#> user system elapsed
-#> 0.007 0.000 0.006
+#> 0.006 0.000 0.006
(all.linked <- max(unlist(o)))
#> [1] 522.4464
# use s2 brute-force dwithin_matrix approach for s2 <= 1.0.7
system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE))
#> user system elapsed
-#> 0.01 0.00 0.01
+#> 0.009 0.000 0.010
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -258,9 +258,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use s2 closest_edges approach s2 > 1.0.7
if (packageVersion("s2") > "1.0.7") {
(system.time(gc.nb.closest <- dnearneigh(xy1, 0, all.linked, dwithin=FALSE)))
@@ -288,9 +288,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") {
summary(gc.nb.closest, xy1, scale=0.5)
}
@@ -307,13 +307,13 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use legacy symmetric brute-force approach
system.time(gc.nb.legacy <- dnearneigh(xy1, 0, all.linked, use_s2=FALSE))
#> user system elapsed
-#> 0.003 0.000 0.003
+#> 0.003 0.000 0.004
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -326,9 +326,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") all.equal(gc.nb.closest, gc.nb.dwithin, check.attributes=FALSE)
#> [1] TRUE
# legacy is ellipsoidal, s2 spherical, so minor differences expected
diff --git a/docs/reference/edit.nb.html b/docs/reference/edit.nb.html
index 44773c64..d59c98e1 100644
--- a/docs/reference/edit.nb.html
+++ b/docs/reference/edit.nb.html
@@ -121,12 +121,11 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
class(columbus)
-#> [1] "sf" "data.frame"
if (FALSE) nnb1 <- edit.nb(col.gal.nb, polys=as(columbus, "Spatial"))
-# }
+}
diff --git a/docs/reference/geary.mc.html b/docs/reference/geary.mc.html
index 236e80b0..3164b159 100644
--- a/docs/reference/geary.mc.html
+++ b/docs/reference/geary.mc.html
@@ -160,12 +160,12 @@ Examples
#> alternative hypothesis: less
#>
mean(sim1$res)
-#> [1] 1.012612
+#> [1] 0.9890232
var(sim1$res)
-#> [1] 0.0119779
+#> [1] 0.01235098
summary(sim1$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.5299 0.9401 1.0218 1.0126 1.0906 1.2388
+#> 0.5299 0.9288 1.0029 0.9890 1.0515 1.3024
colold.lags <- nblag(COL.nb, 3)
sim2 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[2]],
style="W"), nsim=99)
@@ -177,12 +177,12 @@ Examples
#> weights: nb2listw(colold.lags[[2]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 0.81129, observed rank = 1, p-value = 0.01
+#> statistic = 0.81129, observed rank = 5, p-value = 0.05
#> alternative hypothesis: greater
#>
summary(sim2$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8113 0.9541 1.0113 1.0014 1.0569 1.1619
+#> 0.7211 0.9490 0.9896 0.9948 1.0550 1.1807
sim3 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[3]],
style="W"), nsim=99)
sim3
@@ -193,12 +193,12 @@ Examples
#> weights: nb2listw(colold.lags[[3]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 1.1303, observed rank = 92, p-value = 0.92
+#> statistic = 1.1303, observed rank = 95, p-value = 0.95
#> alternative hypothesis: greater
#>
summary(sim3$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8264 0.9586 1.0079 1.0099 1.0562 1.2047
+#> 0.8034 0.9550 1.0098 1.0076 1.0474 1.2499
diff --git a/docs/reference/graphneigh.html b/docs/reference/graphneigh.html
index f41bced1..008fcf4f 100644
--- a/docs/reference/graphneigh.html
+++ b/docs/reference/graphneigh.html
@@ -285,9 +285,9 @@ Examples
#> 0 1 2 3 4 5
#> 1 8 10 18 55 8
#> 8 least connected regions:
-#> with 1 link
+#> 46 47 48 49 96 97 98 99 with 1 link
#> 8 most connected regions:
-#> with 5 links
+#> 10 15 20 25 30 35 40 45 with 5 links
grd <- as.matrix(expand.grid(x=1:5, y=1:5)) #gridded data
r2 <- gabrielneigh(grd)
set.seed(1)
diff --git a/docs/reference/grid2nb.html b/docs/reference/grid2nb.html
index 27f554ac..eb1e9527 100644
--- a/docs/reference/grid2nb.html
+++ b/docs/reference/grid2nb.html
@@ -150,9 +150,9 @@ Examples
#> 7 11 17 26
#> 8 36 54 27
#> 8 least connected regions:
-#> with 7 links
+#> 1 5 21 25 101 105 121 125 with 7 links
#> 27 most connected regions:
-#> with 26 links
+#> 32 33 34 37 38 39 42 43 44 57 58 59 62 63 64 67 68 69 82 83 84 87 88 89 92 93 94 with 26 links
if (require("sp", quietly=TRUE)) {
gt <- GridTopology(c(.125,.1), c(.25,.2), c(4L, 5L))
nb1 <- grid2nb(gt, queen = FALSE)
diff --git a/docs/reference/include.self.html b/docs/reference/include.self.html
index 1b8fad9a..287bd036 100644
--- a/docs/reference/include.self.html
+++ b/docs/reference/include.self.html
@@ -114,9 +114,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
summary(include.self(col.gal.nb), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -128,9 +128,9 @@ Examples
#> 3 4 5 6 7 8 9 10 11
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 3 links
+#> 1 6 31 39 42 46 47 with 3 links
#> 1 most connected region:
-#> with 11 links
+#> 20 with 11 links
summary(remove.self(include.self(col.gal.nb)), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -142,9 +142,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
diff --git a/docs/reference/joincount.multi.html b/docs/reference/joincount.multi.html
index 528fbbcd..8415a202 100644
--- a/docs/reference/joincount.multi.html
+++ b/docs/reference/joincount.multi.html
@@ -161,95 +161,30 @@ Examples
#> high:high 52.000 28.138 18.342 5.5716
#> high:low 28.000 61.170 25.882 -6.5200
#> Jtot 28.000 61.170 33.190 -5.7577
-# \dontrun{
+if (FALSE) {
data(oldcol)
HICRIME <- cut(COL.OLD$CRIME, breaks=c(0,35,80), labels=c("low","high"))
names(HICRIME) <- rownames(COL.OLD)
joincount.multi(HICRIME, nb2listw(COL.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> low:low 34.000 29.592 18.895 1.0141
-#> high:high 54.000 27.224 17.888 6.3307
-#> high:low 28.000 59.184 26.233 -6.0884
-#> Jtot 28.000 59.184 26.233 -6.0884
data(hopkins, package="spData")
image(1:32, 1:32, hopkins[5:36,36:5], breaks=c(-0.5, 3.5, 20),
col=c("white", "black"))
box()
-
hopkins.rook.nb <- cell2nb(32, 32, type="rook")
unlist(spweights.constants(nb2listw(hopkins.rook.nb, style="B")))
-#> n n1 n2 n3 nn S0 S1 S2
-#> 1024 1023 1022 1021 1048576 3968 7936 61984
hopkins.queen.nb <- cell2nb(32, 32, type="queen")
hopkins.bishop.nb <- diffnb(hopkins.rook.nb, hopkins.queen.nb, verbose=FALSE)
hopkins4 <- hopkins[5:36,36:5]
hopkins4[which(hopkins4 > 3, arr.ind=TRUE)] <- 4
hopkins4.f <- factor(hopkins4)
table(hopkins4.f)
-#> hopkins4.f
-#> 0 1 2 3 4
-#> 657 215 98 30 24
joincount.multi(hopkins4.f, nb2listw(hopkins.rook.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 864.00000 816.27273 116.05233 4.4304
-#> 1:1 94.00000 87.14015 55.25216 0.9229
-#> 2:2 18.00000 18.00379 14.81562 -0.0010
-#> 3:3 2.00000 1.64773 1.55539 0.2825
-#> 4:4 5.00000 1.04545 0.99845 3.9576
-#> 1:0 503.00000 535.05682 227.76750 -2.1241
-#> 2:0 213.00000 243.88636 97.21769 -3.1325
-#> 2:1 99.00000 79.81061 59.01930 2.4978
-#> 3:0 61.00000 74.65909 28.58592 -2.5547
-#> 3:1 28.00000 24.43182 18.99976 0.8186
-#> 3:2 15.00000 11.13636 9.82411 1.2327
-#> 4:0 40.00000 59.72727 22.78583 -4.1327
-#> 4:1 23.00000 19.54545 15.26564 0.8842
-#> 4:2 14.00000 8.90909 7.90051 1.8112
-#> 4:3 5.00000 2.72727 2.58616 1.4133
-#> Jtot 1001.00000 1059.89015 273.78610 -3.5591
cat("replicates Upton & Fingleton table 3.4 (p. 166)\n")
-#> replicates Upton & Fingleton table 3.4 (p. 166)
joincount.multi(hopkins4.f, nb2listw(hopkins.bishop.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 823.00000 790.76420 144.44877 2.6821
-#> 1:1 101.00000 84.41702 55.98143 2.2164
-#> 2:2 19.00000 17.44117 14.61542 0.4077
-#> 3:3 3.00000 1.59624 1.51444 1.1407
-#> 4:4 3.00000 1.01278 0.97111 2.0166
-#> 1:0 497.00000 518.33629 234.93545 -1.3920
-#> 2:0 216.00000 236.26491 104.42142 -1.9831
-#> 2:1 81.00000 77.31652 58.70829 0.4807
-#> 3:0 58.00000 72.32599 31.49151 -2.5529
-#> 3:1 21.00000 23.66832 18.85316 -0.6145
-#> 3:2 17.00000 10.78835 9.62487 2.0022
-#> 4:0 48.00000 57.86080 25.15973 -1.9659
-#> 4:1 21.00000 18.93466 15.14473 0.5307
-#> 4:2 10.00000 8.63068 7.73708 0.4923
-#> 4:3 4.00000 2.64205 2.51686 0.8560
-#> Jtot 973.00000 1026.76858 284.51030 -3.1877
cat("replicates Upton & Fingleton table 3.6 (p. 168)\n")
-#> replicates Upton & Fingleton table 3.6 (p. 168)
joincount.multi(hopkins4.f, nb2listw(hopkins.queen.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 1687.0000 1607.0369 303.8034 4.5877
-#> 1:1 195.0000 171.5572 114.2057 2.1936
-#> 2:2 37.0000 35.4450 29.6821 0.2854
-#> 3:3 5.0000 3.2440 3.0687 1.0024
-#> 4:4 8.0000 2.0582 1.9674 4.2361
-#> 1:0 1000.0000 1053.3931 480.6959 -2.4353
-#> 2:0 429.0000 480.1513 215.0360 -3.4882
-#> 2:1 180.0000 157.1271 119.3987 2.0932
-#> 3:0 119.0000 146.9851 65.1029 -3.4684
-#> 3:1 49.0000 48.1001 38.3268 0.1454
-#> 3:2 32.0000 21.9247 19.5237 2.2802
-#> 4:0 88.0000 117.5881 52.0312 -4.1019
-#> 4:1 44.0000 38.4801 30.7868 0.9948
-#> 4:2 24.0000 17.5398 15.6933 1.6308
-#> 4:3 9.0000 5.3693 5.0994 1.6078
-#> Jtot 1974.0000 2086.6587 582.8326 -4.6665
cat("replicates Upton & Fingleton table 3.7 (p. 169)\n")
-#> replicates Upton & Fingleton table 3.7 (p. 169)
-# }
+}
diff --git a/docs/reference/knearneigh.html b/docs/reference/knearneigh.html
index f5a9e742..5f4466fa 100644
--- a/docs/reference/knearneigh.html
+++ b/docs/reference/knearneigh.html
@@ -161,9 +161,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
summary(gck4.nb, xy, longlat=TRUE, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -176,9 +176,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#xy1 <- SpatialPoints((as.data.frame(state.center))[m50.48,],
# proj4string=CRS("+proj=longlat +ellps=GRS80"))
@@ -191,7 +191,7 @@ Examples
sf_use_s2(TRUE)
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.008 0.000 0.009
+#> 0.009 0.000 0.010
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -204,14 +204,14 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(FALSE)
#> Spherical geometry (s2) switched off
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.005 0.000 0.006
+#> 0.005 0.000 0.005
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -224,9 +224,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(old_use_s2)
#> Spherical geometry (s2) switched on
diff --git a/docs/reference/localC.html b/docs/reference/localC.html
index 1efd5e5b..03cfd45b 100644
--- a/docs/reference/localC.html
+++ b/docs/reference/localC.html
@@ -245,11 +245,9 @@ Examples
}
#> [1] 0.985611
# pseudo-p values probably wrongly folded https://github.com/GeoDaCenter/rgeoda/issues/28
-# \dontrun{
+if (FALSE) {
tmap_ok <- FALSE
if (require(tmap, quietly=TRUE)) tmap_ok <- TRUE
-#> Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
-#> remotes::install_github('r-tmap/tmap')
if (run) {
# doi: 10.1111/gean.12164
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
@@ -263,25 +261,10 @@ Examples
moran(g$Infants, lw, n=nrow(g), S0=Szero(lw))$I
moran(g$Suicids, lw, n=nrow(g), S0=Szero(lw))$I
}
-#> Reading layer `Guerry' from data source
-#> `/home/rsb/lib/r_libs/rgeoda/extdata/Guerry.shp' using driver `ESRI Shapefile'
-#> Simple feature collection with 85 features and 29 fields
-#> Geometry type: MULTIPOLYGON
-#> Dimension: XY
-#> Bounding box: xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
-#> Projected CRS: NTF (Paris) / Lambert zone II
-#> [1] 0.4016812
if (run) {
o <- prcomp(st_drop_geometry(g), scale.=TRUE)
cor(st_drop_geometry(g), o$x[,1:2])^2 #(Tab. 2)
}
-#> PC1 PC2
-#> Crm_prs 0.009286863 0.418851254
-#> Crm_prp 0.561825664 0.009377232
-#> Litercy 0.560570048 0.020095011
-#> Donatns 0.024138865 0.586720363
-#> Infants 0.436025658 0.012817054
-#> Suicids 0.548623327 0.152958961
if (run) {
g$PC1 <- o$x[, "PC1"]
brks <- c(min(g$PC1), natural_breaks(k=6, g["PC1"]), max(g$PC1))
@@ -289,7 +272,6 @@ Examples
tm_borders() # Fig. 1
else pplot(g["PC1"], breaks=brks)
}
-
if (run) {
g$PC2 <- -1*o$x[, "PC2"] # eigenvalue sign arbitrary
brks <- c(min(g$PC2), natural_breaks(k=6, g["PC2"]), max(g$PC2))
@@ -297,7 +279,6 @@ Examples
tm_borders() # Fig. 2
else plot(g["PC2"], breaks=brks)
}
-
if (run) {
w <- queen_weights(g)
lm_PC1 <- local_moran(w, g["PC1"], significance_cutoff=0.01,
@@ -310,7 +291,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 3
else plot(g["lm_PC1"])
}
-
if (run) {
set.seed(1)
lm_PC1_spdep <- localmoran_perm(g$PC1, lw, nsim=9999)
@@ -322,7 +302,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 3
else plot(g["lm_PC1_spdep"])
}
-
if (run) {
lg_PC1 <- local_g(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -340,7 +319,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 4 (guess)
else plot(g["lg_PC1"])
}
-
if (run) {
lc_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -352,14 +330,11 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 5
else plot(g["lc_PC1"])
}
-
if (run) {
set.seed(1)
system.time(lc_PC1_spdep <- localC_perm(g$PC1, lw, nsim=9999,
alternative="two.sided"))
}
-#> user system elapsed
-#> 0.376 0.001 0.377
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -372,7 +347,6 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
g$lc_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.01
@@ -381,7 +355,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 5
else plot(g["lc_PC1_spdep"])
}
-
if (run) {
g$both_PC1 <- interaction(g$lc_PC1, g$lm_PC1)
g$both_PC1 <- droplevels(g$both_PC1)
@@ -389,7 +362,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 6
else plot(g["both_PC1"])
}
-
if (run) {
lc005_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.005,
permutations=99999)
@@ -401,7 +373,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 7
else plot(g["lc005_PC1"])
}
-
if (run) {
g$lc005_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc005_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.005
@@ -410,7 +381,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 7
else plot(g["lc005_PC1_spdep"])
}
-
if (run) {
lc001_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.001,
permutations=99999)
@@ -430,7 +400,6 @@ Examples
else plot(g["lc001_PC1_spdep"])
}
}
-
if (run) {
lc_PC2 <- local_geary(w, g["PC2"], significance_cutoff=0.01,
permutations=99999)
@@ -442,7 +411,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 9
else plot(g["lc_PC2"])
}
-
if (run) {
lmc_PC <- local_multigeary(w, g[c("PC1","PC2")], significance_cutoff=0.00247,
permutations=99999)
@@ -452,25 +420,19 @@ Examples
g$lmc_PC <- droplevels(g$lmc_PC)
table(interaction((p.adjust(lisa_pvalues(lmc_PC), "fdr") < 0.01), g$lmc_PC))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_PC", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 10
else plot(g["lmc_PC"])
}
-
if (run) {
set.seed(1)
lmc_PC_spdep <- localC_perm(g[c("PC1","PC2")], lw, nsim=9999, alternative="two.sided")
all.equal(lisa_values(lmc_PC), c(lmc_PC_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_PC_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_PC))
}
-#> [1] 0.99052
if (run) {
g$lmc_PC_spdep <- attr(lmc_PC_spdep, "cluster")
is.na(g$lmc_PC_spdep) <- p.adjust(attr(lmc_PC_spdep, "pseudo-p")[,6], "fdr") > 0.01
@@ -479,7 +441,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 10
else plot(g["lmc_PC_spdep"])
}
-
if (run) {
lmc_vars <- local_multigeary(w, st_drop_geometry(g)[, 1:6],
significance_cutoff=0.00247, permutations=99999)
@@ -490,30 +451,22 @@ Examples
table(interaction((p.adjust(lisa_pvalues(lmc_vars), "fdr") < 0.01),
g$lmc_vars))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_vars", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 11
else plot(g["lmc_vars"])
}
-
if (run) {
set.seed(1)
system.time(lmc_vars_spdep <- localC_perm(st_drop_geometry(g)[, 1:6], lw,
nsim=9999, alternative="two.sided"))
}
-#> user system elapsed
-#> 0.824 0.002 0.828
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -526,15 +479,12 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep1))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep1, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
g$lmc_vars_spdep <- attr(lmc_vars_spdep1, "cluster")
is.na(g$lmc_vars_spdep) <- p.adjust(attr(lmc_vars_spdep1, "pseudo-p")[,6], "fdr") > 0.01
@@ -543,10 +493,9 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 11
else plot(g["lmc_vars_spdep"])
}
-
-# }
-# \dontrun{
+}
+if (FALSE) {
library(reticulate)
use_python("/usr/bin/python", required = TRUE)
gp <- import("geopandas")
@@ -557,7 +506,6 @@ Examples
esda <- import("esda")
lM <- esda$Moran_Local(x, w)
all.equal(unname(localmoran(x, listw, mlvar=FALSE)[,1]), c(lM$Is))
-#> [1] TRUE
# confirm x and w the same
lC <- esda$Geary_Local(connectivity=w)$fit(scale(x))
# np$std missing ddof=1
@@ -566,19 +514,10 @@ Examples
# lC components probably wrongly ordered https://github.com/pysal/esda/issues/192
o <- match(round(D0, 6), round(lC$localG, 6))
all.equal(c(lC$localG)[o], D0)
-#> [1] TRUE
# simulation order not retained
lC$p_sim[o]
-#> [1] 0.278 0.460 0.112 0.019 0.121 0.038 0.330 0.412 0.147 0.001 0.113 0.141
-#> [13] 0.188 0.246 0.450 0.172 0.102 0.298 0.001 0.027 0.038 0.091 0.055 0.297
-#> [25] 0.153 0.031 0.107 0.280 0.450 0.001 0.066 0.104 0.080 0.231 0.306 0.001
-#> [37] 0.326 0.482 0.321 0.216 0.235 0.288
attr(C, "pseudo-p")[,6]
-#> [1] 0.408 0.374 0.144 0.056 0.010 0.284 0.032 0.264 0.186 0.004 0.150 0.224
-#> [13] 0.022 0.150 0.438 0.084 0.126 0.184 0.002 0.238 0.126 0.100 0.068 0.372
-#> [25] 0.124 0.234 0.230 0.016 0.318 0.176 0.272 0.164 0.164 0.206 0.402 0.250
-#> [37] 0.410 0.402 0.328 0.294 0.438 0.248
-# }
+}
diff --git a/docs/reference/mat2listw.html b/docs/reference/mat2listw.html
index 096c6e5d..656b612c 100644
--- a/docs/reference/mat2listw.html
+++ b/docs/reference/mat2listw.html
@@ -140,13 +140,14 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
-col005.w.mat <- nb2mat(col005, zero.policy=TRUE)
-col005.w.b <- mat2listw(col005.w.mat)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> 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"))
+#> Error in mat2listw(col005.w.mat, style = "W") :
+#> no-neighbour observations found, set zero.policy to TRUE
+col005.w.b <- mat2listw(col005.w.mat, style="W", zero.policy=TRUE)
summary(col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -161,9 +162,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
diffnb(col005, col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -177,9 +178,7 @@ Examples
#> 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")
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
+col005.w.b.3T <- mat2listw(col005.w.mat.3T, style="W", zero.policy=TRUE)
summary(col005.w.b.3T$neighbours)
#> Neighbour list object:
#> Number of regions: 147
@@ -194,21 +193,22 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 2 5 9 10 31 34 36 39 42 46 47 51 54 58 59 80 83 85 88 91 95 96 100 103 107 108 129 132 134 137 140 144 145 with 1 link
#> 6 most connected regions:
-#> with 9 links
+#> 11 16 60 65 109 114 with 9 links
run <- FALSE
if (require("spatialreg", quiet=TRUE)) run <- TRUE
if (run) {
W <- as(nb2listw(col005, style="W", zero.policy=TRUE), "CsparseMatrix")
-col005.spM <- mat2listw(W)
+try(col005.spM <- mat2listw(W))
+col005.spM <- mat2listw(W, style="W", zero.policy=TRUE)
summary(col005.spM$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, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> Error in sn2listw(df, style = style, zero.policy = zero.policy) :
+#> no-neighbour observations found, set zero.policy to TRUE
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 170
@@ -222,9 +222,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
if (run) {
diffnb(col005, col005.spM$neighbours)
}
@@ -241,14 +241,9 @@ Examples
#> 49 disjoint connected subgraphs
if (run && require("Matrix", quiet=TRUE)) {
IW <- kronecker(Diagonal(3), W)
-col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W")
+col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W", zero.policy=TRUE)
summary(col005.spM.3T$neighbours)
}
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: 1, 3, 6, 21, 50, 52, 55, 70, 99, 101, 104, 119 are not origins
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
#> Neighbour list object:
#> Number of regions: 147
#> Number of nonzero links: 510
@@ -262,9 +257,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 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:
-#> with 9 links
+#> 11 16 60 65 109 114 with 9 links
diff --git a/docs/reference/nb2blocknb.html b/docs/reference/nb2blocknb.html
index c2276d23..460ced1b 100644
--- a/docs/reference/nb2blocknb.html
+++ b/docs/reference/nb2blocknb.html
@@ -127,96 +127,31 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
data(boston, package="spData")
summary(as.vector(table(boston.c$TOWN)))
-#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 1.0 2.0 4.0 5.5 7.0 30.0
townaggr <- aggregate(boston.utm, list(town=boston.c$TOWN), mean)
block.rel <- graph2nb(relativeneigh(as.matrix(townaggr[,2:3])),
as.character(townaggr[,1]), sym=TRUE)
block.rel
-#> Neighbour list object:
-#> Number of regions: 92
-#> Number of nonzero links: 240
-#> Percentage nonzero weights: 2.835539
-#> Average number of links: 2.608696
print(is.symmetric.nb(block.rel))
-#> [1] TRUE
plot(block.rel, as.matrix(townaggr[,2:3]))
points(boston.utm, pch=18, col="lightgreen")
-
block.nb <- nb2blocknb(block.rel, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 15234
-#> Percentage nonzero weights: 5.949945
-#> Average number of links: 30.10672
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
points(boston.utm, pch=18, col="lightgreen")
-
n.comp.nb(block.nb)$nc
-#> [1] 1
moran.test(boston.c$CMEDV, nb2listw(boston.soi))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(boston.soi)
-#>
-#> Moran I statistic standard deviate = 21.786, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.690285059 -0.001980198 0.001009685
-#>
moran.test(boston.c$CMEDV, nb2listw(block.nb))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb)
-#>
-#> Moran I statistic standard deviate = 22.455, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.3122905961 -0.0019801980 0.0001958827
-#>
block.nb <- nb2blocknb(NULL, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 4868
-#> Percentage nonzero weights: 1.901295
-#> Average number of links: 9.620553
-#> 17 regions with no links:
-#> 1 55 56 57 58 65 196 257 284 285 286 287 342 343 348 349 354
-#> 92 disjoint connected subgraphs
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
-
n.comp.nb(block.nb)$nc
-#> [1] 92
moran.test(boston.c$CMEDV, nb2listw(block.nb, zero.policy=TRUE), zero.policy=TRUE)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb, zero.policy = TRUE) n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 21.145, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.6188830566 -0.0020491803 0.0008623116
-#>
-# }
+}
diff --git a/docs/reference/nb2lines.html b/docs/reference/nb2lines.html
index 38b287ec..1b49a033 100644
--- a/docs/reference/nb2lines.html
+++ b/docs/reference/nb2lines.html
@@ -158,12 +158,12 @@ Examples
tf <- paste0(tempfile(), ".gpkg")
st_write(res, dsn=tf, driver="GPKG")
#> writing: substituting ENGCRS["Undefined Cartesian SRS with unknown unit"] for missing CRS
-#> Writing layer `file706d874b42afe' to data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Writing layer `file9f36c3d2d676e' to data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Writing 230 features with 5 fields and geometry type Line String.
inMap <- st_read(tf)
-#> Reading layer `file706d874b42afe' from data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Reading layer `file9f36c3d2d676e' from data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Simple feature collection with 230 features and 5 fields
#> Geometry type: LINESTRING
#> Dimension: XY
diff --git a/docs/reference/nb2listw.html b/docs/reference/nb2listw.html
index fd9ebeee..ef19145f 100644
--- a/docs/reference/nb2listw.html
+++ b/docs/reference/nb2listw.html
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: minmax
#> Weights constants summary:
diff --git a/docs/reference/nb2listwdist.html b/docs/reference/nb2listwdist.html
index a6ffbf2d..98dcafb5 100644
--- a/docs/reference/nb2listwdist.html
+++ b/docs/reference/nb2listwdist.html
@@ -171,43 +171,15 @@ Examples
#> Moran I statistic Expectation Variance
#> 0.473883371 -0.007042254 0.029994057
#>
-# \dontrun{
+if (FALSE) {
# Moran's I (life expectancy) with IDW with alpha = 2, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "idw",
alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 2.7769, p-value = 0.002744
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.473883371 -0.007042254 0.029994057
-#>
# Moran's I (life expectancy), DPD, alpha = 2, dmax = 1000 km, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "dpd",
dmax = 1000000, alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 8.8557, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.601498970 -0.007042254 0.004722063
-#>
# Boston examples
data(boston, package="spData")
boston_coords <- data.frame(x = boston.utm[,1], y = boston.utm[,2])
@@ -217,37 +189,11 @@ Examples
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "exp", alpha = 2,
style="raw", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 51.34, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.8845114518 -0.0019960080 0.0002981586
-#>
# Moran's I (crime) with idw weights with alpha = 2, coding scheme = W
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "idw", alpha = 2,
style="W", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 18.976, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.4392852330 -0.0019960080 0.0005408065
-#>
-# }
+}
diff --git a/docs/reference/nb2mat.html b/docs/reference/nb2mat.html
index 7f17e8bb..39ec4504 100644
--- a/docs/reference/nb2mat.html
+++ b/docs/reference/nb2mat.html
@@ -137,9 +137,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
col005.w.mat <- nb2mat(col005, style="B", zero.policy=TRUE)
table(round(rowSums(col005.w.mat)))
#>
diff --git a/docs/reference/nblag.html b/docs/reference/nblag.html
index 08180e37..bd822ea5 100644
--- a/docs/reference/nblag.html
+++ b/docs/reference/nblag.html
@@ -122,9 +122,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.lags <- nblag(col.gal.nb, 2)
print(col.lags)
#> [[1]]
@@ -154,9 +154,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#> 1 2 5 4 2 3 5 4 2 3 7 3 4 1 1 1 1
#> 1 least connected region:
-#> with 1 link
+#> 36 with 1 link
#> 1 most connected region:
-#> with 17 links
+#> 25 with 17 links
plot(st_geometry(columbus), border="grey")
plot(col.gal.nb, coords, add=TRUE)
title(main="GAL order 1 (black) and 2 (red) links")
diff --git a/docs/reference/poly2nb.html b/docs/reference/poly2nb.html
index 1cad8008..79b13c61 100644
--- a/docs/reference/poly2nb.html
+++ b/docs/reference/poly2nb.html
@@ -174,10 +174,10 @@ Examples
nc.sids <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE)
system.time(xxnb <- poly2nb(nc.sids))
#> user system elapsed
-#> 0.018 0.000 0.018
+#> 0.019 0.000 0.019
system.time(xxnb <- poly2nb(as(nc.sids, "Spatial")))
#> user system elapsed
-#> 0.029 0.000 0.029
+#> 0.034 0.000 0.035
plot(st_geometry(nc.sids))
plot(xxnb, st_coordinates(st_centroid(nc.sids)), add=TRUE, col="blue")
#> Warning: st_centroid assumes attributes are constant over geometries
diff --git a/docs/reference/skater.html b/docs/reference/skater.html
index aefe0190..d2b9c2e6 100644
--- a/docs/reference/skater.html
+++ b/docs/reference/skater.html
@@ -292,22 +292,18 @@ Examples
table(res5d$groups)
#> Error in eval(expr, envir, enclos): object 'res5d' not found
-# \dontrun{
+if (FALSE) {
data(boston, package="spData")
bh.nb <- boston.soi
dpad <- data.frame(scale(boston.c[,c(7:10)]))
### calculating costs
system.time(lcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.042 0.000 0.043
### making listw
nb.w <- nb2listw(bh.nb, lcosts, style="B")
### find a minimum spanning tree
mst.bh <- mstree(nb.w,5)
### three groups with no restriction
system.time(res1 <- skater(mst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
library(parallel)
nc <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
# set nc to 1L here
@@ -321,26 +317,20 @@ Examples
}
### calculating costs
system.time(plcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.04 0.00 0.04
all.equal(lcosts, plcosts, check.attributes=FALSE)
-#> [1] TRUE
### making listw
pnb.w <- nb2listw(bh.nb, plcosts, style="B")
### find a minimum spanning tree
pmst.bh <- mstree(pnb.w,5)
### three groups with no restriction
system.time(pres1 <- skater(pmst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
if(!get.mcOption()) {
set.ClusterOption(NULL)
stopCluster(cl)
}
all.equal(res1, pres1, check.attributes=FALSE)
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'target' in selecting a method for function 'all.equal': object 'res1' not found
invisible(set.coresOption(coresOpt))
-# }
+}
diff --git a/docs/reference/summary.nb.html b/docs/reference/summary.nb.html
index 79407597..674042bf 100644
--- a/docs/reference/summary.nb.html
+++ b/docs/reference/summary.nb.html
@@ -145,9 +145,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.listw <- nb2listw(col.gal.nb, style="W")
col.listw
#> Characteristics of weights list object:
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -199,9 +199,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
try(nb2listw(nb, style="W"))
#> Error in nb2listw(nb, style = "W") : Empty neighbour sets found
summary(nb2listw(nb, style="W", zero.policy=TRUE))
@@ -219,9 +219,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -242,9 +242,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
diff --git a/docs/reference/tolerance.nb.html b/docs/reference/tolerance.nb.html
index 14a72c2e..e793d2ac 100644
--- a/docs/reference/tolerance.nb.html
+++ b/docs/reference/tolerance.nb.html
@@ -130,24 +130,18 @@ Examples
### and a maximum distance of 2 m.
nb.ex<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of 30 degrees
### and a maximum distance of 2 m. The coordinates are rotated at an angle
### of 45 degrees counterclockwise.
nb.ex2<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30, rot.angle = 45)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of pi/8 radians
### and a maximum distance of 1.5 m. The coordinates are rotated at
### an angle of pi/4 radians clockwise.
nb.ex3<-tolerance.nb(ex.data, unit.angle = "radians", max.dist=1.5,
tolerance = pi/8, rot.angle = -pi*2/3)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
par(mfrow=c(1,3))
plot(nb.ex,ex.data,asp=1)
system.time(for (i in 1:reps) NY811_nb <- dnearneigh(NY8_ct_sf, d1=0, d2=0.75*max_1nn, use_kd_tree=FALSE))/reps## user system elapsed
-## 0.0163 0.0015 0.0178K-nearest neighbourssf_use_s2(TRUE) system.time(for (i in 1:reps) pts_ll1_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps
## user system elapsed
-## 0.0252 0.0000 0.0252
system.time(for (i in 1:reps) pts_ll2_nb <- knn2nb(knearneigh(pts_ll, k=6)))/reps
## user system elapsed
-## 0.0191 0.0000 0.0191The WGS84 ellipsoid Great Circle distances differ a very little from the s2 spherical distances, yielding output that here diverges for two tract centroids:
@@ -554,7 +554,7 @@Distance neighbours=0.75*max_1nn_ll))/(reps/5)
}
## user system elapsed
-## 0.0440 0.0000 0.0445
+## 0.0445 0.0000 0.0445
Alternatively, spherical distances can be used with
dwithin=FALSE and s2::s2_closest_edges();
although running in similar time, s2::s2_closest_edges()
@@ -563,7 +563,7 @@
Distance neighbours
system.time(for (i in 1:(reps/5)) pts_ll5_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, dwithin=FALSE))/(reps/5)
## user system elapsed
-## 0.027 0.000 0.027
+## 0.0270 0.0005 0.0275
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3_nb, pts_ll5_nb, check.attributes=FALSE)
## [1] TRUE
@@ -586,7 +586,7 @@ Distance neighbours=0.75*max_1nn_ll))/(reps/5)
}
## user system elapsed
-## 0.0725 0.0000 0.0725
+## 0.0730 0.0000 0.0735
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3a_nb, pts_ll5a_nb, check.attributes=FALSE)
## [1] TRUE
@@ -595,7 +595,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0097 0.0000 0.0098
Minor differences may occur between the legacy ellipsoid and
s2 spherical approaches:
@@ -625,7 +625,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6a_nb <- dnearneigh(pts_ll, d1=5, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0098 0.0000 0.0097
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll5a_nb, pts_ll6a_nb, check.attributes=FALSE)
## [1] "Component 20: Numeric: lengths (6, 5) differ"
@@ -676,7 +676,7 @@ Contiguity neighbours for spherical polygon support
system.time(for (i in 1:reps) NY8_sf_1_nb_ll <- poly2nb(NY8_sf_ll, queen=TRUE, snap=eps))/reps
## user system elapsed
-## 0.1553 0.0013 0.1572
+## 0.1524 0.0019 0.1548
all.equal(NY8_sf_1_nb, NY8_sf_1_nb_ll, check.attributes=FALSE)
## [1] TRUE
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 5f78f8ad..a8b299ea 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,5 +6,5 @@ articles:
nb: nb.html
nb_sf: nb_sf.html
sids: sids.html
-last_built: 2023-11-04T17:52Z
+last_built: 2023-11-06T13:06Z
diff --git a/docs/reference/EBImoran.mc.html b/docs/reference/EBImoran.mc.html
index db555f47..4886f83f 100644
--- a/docs/reference/EBImoran.mc.html
+++ b/docs/reference/EBImoran.mc.html
@@ -184,7 +184,7 @@ Examples
#> weights: nb2listw(ncCC89_nb, style = "B", zero.policy = TRUE)
#> number of simulations + 1: 1000
#>
-#> statistic = 0.25789, observed rank = 998, p-value = 0.004
+#> statistic = 0.25789, observed rank = 1000, p-value < 2.2e-16
#> alternative hypothesis: two.sided
#>
sids.p <- nc.sids$SID74 / nc.sids$BIR74
diff --git a/docs/reference/LOSH.mc-1.png b/docs/reference/LOSH.mc-1.png
index 5a905795..3b4cabfc 100644
Binary files a/docs/reference/LOSH.mc-1.png and b/docs/reference/LOSH.mc-1.png differ
diff --git a/docs/reference/LOSH.mc.html b/docs/reference/LOSH.mc.html
index c0a6763a..30a4b3ed 100644
--- a/docs/reference/LOSH.mc.html
+++ b/docs/reference/LOSH.mc.html
@@ -154,10 +154,10 @@ Examples
summary(resLOSH_mc)
#> Hi x_bar_i ei Pr()
#> Min. :0.03438 Min. :13.85 Min. : 0.0298 Min. :0.009901
-#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.118812
-#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.702970
-#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.542332
-#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.891089
+#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.079208
+#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.712871
+#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.554860
+#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.910891
#> Max. :4.68765 Max. :54.91 Max. :2455.2201 Max. :0.990099
resLOSH_cs <- LOSH.cs(columbus$CRIME, nb2listw(col.gal.nb))
summary(resLOSH_cs)
diff --git a/docs/reference/bhicv.html b/docs/reference/bhicv.html
index 2e1f9102..072425ba 100644
--- a/docs/reference/bhicv.html
+++ b/docs/reference/bhicv.html
@@ -105,7 +105,7 @@ Examples
bh <- st_read(system.file("etc/shapes/bhicv.shp",
package="spdep")[1])
#> Reading layer `bhicv' from data source
-#> `/tmp/Rtmpu0jdcX/temp_libpath70612704761e5/spdep/etc/shapes/bhicv.shp'
+#> `/tmp/RtmpU193Yl/temp_libpath9f2c94443f9d4/spdep/etc/shapes/bhicv.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 98 features and 8 fields
#> Geometry type: POLYGON
diff --git a/docs/reference/cell2nb.html b/docs/reference/cell2nb.html
index 9b67215a..e3badaf1 100644
--- a/docs/reference/cell2nb.html
+++ b/docs/reference/cell2nb.html
@@ -137,9 +137,9 @@ Examples
#> 2 3 4
#> 4 20 25
#> 4 least connected regions:
-#> with 2 links
+#> 1:1 7:1 1:7 7:7 with 2 links
#> 25 most connected regions:
-#> with 4 links
+#> 2:2 3:2 4:2 5:2 6:2 2:3 3:3 4:3 5:3 6:3 2:4 3:4 4:4 5:4 6:4 2:5 3:5 4:5 5:5 6:5 2:6 3:6 4:6 5:6 6:6 with 4 links
xyc <- attr(nb7rt, "region.id")
xy <- matrix(as.integer(unlist(strsplit(xyc, ":"))), ncol=2, byrow=TRUE)
plot(nb7rt, xy)
@@ -156,9 +156,9 @@ Examples
#> 4
#> 49
#> 49 least connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
#> 49 most connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
run <- FALSE
if (require("sp", quietly=TRUE)) run <- TRUE
if (run) {
diff --git a/docs/reference/dnearneigh.html b/docs/reference/dnearneigh.html
index afbf4662..937dd9d6 100644
--- a/docs/reference/dnearneigh.html
+++ b/docs/reference/dnearneigh.html
@@ -149,9 +149,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11
#> 4 8 6 2 5 8 6 2 6 1 1
#> 4 least connected regions:
-#> with 1 link
+#> 6 10 21 47 with 1 link
#> 1 most connected region:
-#> with 11 links
+#> 28 with 11 links
opar <- par(no.readonly=TRUE)
plot(st_geometry(columbus), border="grey", reset=FALSE,
main=paste("Distance based neighbours 0-", format(all.linked), sep=""))
@@ -198,9 +198,9 @@ Examples
#> 1 2 3 4 5 7 8 9 10
#> 11 9 4 8 4 4 4 3 1
#> 11 least connected regions:
-#> with 1 link
+#> 2 4 8 10 24 26 29 32 35 41 45 with 1 link
#> 1 most connected region:
-#> with 10 links
+#> 28 with 10 links
gck1 <- knn2nb(knearneigh(xy, k=1, longlat=TRUE))
(all.linked <- max(unlist(nbdists(gck1, xy, longlat=TRUE))))
#> [1] 523.5819
@@ -217,9 +217,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
plot(ll.nb, xy)
plot(diffnb(ll.nb, gc.nb), xy, add=TRUE, col="red", lty=2)
title(main="Differences Euclidean/Great Circle")
@@ -239,13 +239,13 @@ Examples
gck1b <- knn2nb(knearneigh(xy1, k=1))
system.time(o <- nbdists(gck1b, xy1))
#> user system elapsed
-#> 0.007 0.000 0.006
+#> 0.006 0.000 0.006
(all.linked <- max(unlist(o)))
#> [1] 522.4464
# use s2 brute-force dwithin_matrix approach for s2 <= 1.0.7
system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE))
#> user system elapsed
-#> 0.01 0.00 0.01
+#> 0.009 0.000 0.010
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -258,9 +258,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use s2 closest_edges approach s2 > 1.0.7
if (packageVersion("s2") > "1.0.7") {
(system.time(gc.nb.closest <- dnearneigh(xy1, 0, all.linked, dwithin=FALSE)))
@@ -288,9 +288,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") {
summary(gc.nb.closest, xy1, scale=0.5)
}
@@ -307,13 +307,13 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use legacy symmetric brute-force approach
system.time(gc.nb.legacy <- dnearneigh(xy1, 0, all.linked, use_s2=FALSE))
#> user system elapsed
-#> 0.003 0.000 0.003
+#> 0.003 0.000 0.004
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -326,9 +326,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") all.equal(gc.nb.closest, gc.nb.dwithin, check.attributes=FALSE)
#> [1] TRUE
# legacy is ellipsoidal, s2 spherical, so minor differences expected
diff --git a/docs/reference/edit.nb.html b/docs/reference/edit.nb.html
index 44773c64..d59c98e1 100644
--- a/docs/reference/edit.nb.html
+++ b/docs/reference/edit.nb.html
@@ -121,12 +121,11 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
class(columbus)
-#> [1] "sf" "data.frame"
if (FALSE) nnb1 <- edit.nb(col.gal.nb, polys=as(columbus, "Spatial"))
-# }
+}
diff --git a/docs/reference/geary.mc.html b/docs/reference/geary.mc.html
index 236e80b0..3164b159 100644
--- a/docs/reference/geary.mc.html
+++ b/docs/reference/geary.mc.html
@@ -160,12 +160,12 @@ Examples
#> alternative hypothesis: less
#>
mean(sim1$res)
-#> [1] 1.012612
+#> [1] 0.9890232
var(sim1$res)
-#> [1] 0.0119779
+#> [1] 0.01235098
summary(sim1$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.5299 0.9401 1.0218 1.0126 1.0906 1.2388
+#> 0.5299 0.9288 1.0029 0.9890 1.0515 1.3024
colold.lags <- nblag(COL.nb, 3)
sim2 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[2]],
style="W"), nsim=99)
@@ -177,12 +177,12 @@ Examples
#> weights: nb2listw(colold.lags[[2]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 0.81129, observed rank = 1, p-value = 0.01
+#> statistic = 0.81129, observed rank = 5, p-value = 0.05
#> alternative hypothesis: greater
#>
summary(sim2$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8113 0.9541 1.0113 1.0014 1.0569 1.1619
+#> 0.7211 0.9490 0.9896 0.9948 1.0550 1.1807
sim3 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[3]],
style="W"), nsim=99)
sim3
@@ -193,12 +193,12 @@ Examples
#> weights: nb2listw(colold.lags[[3]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 1.1303, observed rank = 92, p-value = 0.92
+#> statistic = 1.1303, observed rank = 95, p-value = 0.95
#> alternative hypothesis: greater
#>
summary(sim3$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8264 0.9586 1.0079 1.0099 1.0562 1.2047
+#> 0.8034 0.9550 1.0098 1.0076 1.0474 1.2499
diff --git a/docs/reference/graphneigh.html b/docs/reference/graphneigh.html
index f41bced1..008fcf4f 100644
--- a/docs/reference/graphneigh.html
+++ b/docs/reference/graphneigh.html
@@ -285,9 +285,9 @@ Examples
#> 0 1 2 3 4 5
#> 1 8 10 18 55 8
#> 8 least connected regions:
-#> with 1 link
+#> 46 47 48 49 96 97 98 99 with 1 link
#> 8 most connected regions:
-#> with 5 links
+#> 10 15 20 25 30 35 40 45 with 5 links
grd <- as.matrix(expand.grid(x=1:5, y=1:5)) #gridded data
r2 <- gabrielneigh(grd)
set.seed(1)
diff --git a/docs/reference/grid2nb.html b/docs/reference/grid2nb.html
index 27f554ac..eb1e9527 100644
--- a/docs/reference/grid2nb.html
+++ b/docs/reference/grid2nb.html
@@ -150,9 +150,9 @@ Examples
#> 7 11 17 26
#> 8 36 54 27
#> 8 least connected regions:
-#> with 7 links
+#> 1 5 21 25 101 105 121 125 with 7 links
#> 27 most connected regions:
-#> with 26 links
+#> 32 33 34 37 38 39 42 43 44 57 58 59 62 63 64 67 68 69 82 83 84 87 88 89 92 93 94 with 26 links
if (require("sp", quietly=TRUE)) {
gt <- GridTopology(c(.125,.1), c(.25,.2), c(4L, 5L))
nb1 <- grid2nb(gt, queen = FALSE)
diff --git a/docs/reference/include.self.html b/docs/reference/include.self.html
index 1b8fad9a..287bd036 100644
--- a/docs/reference/include.self.html
+++ b/docs/reference/include.self.html
@@ -114,9 +114,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
summary(include.self(col.gal.nb), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -128,9 +128,9 @@ Examples
#> 3 4 5 6 7 8 9 10 11
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 3 links
+#> 1 6 31 39 42 46 47 with 3 links
#> 1 most connected region:
-#> with 11 links
+#> 20 with 11 links
summary(remove.self(include.self(col.gal.nb)), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -142,9 +142,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
diff --git a/docs/reference/joincount.multi.html b/docs/reference/joincount.multi.html
index 528fbbcd..8415a202 100644
--- a/docs/reference/joincount.multi.html
+++ b/docs/reference/joincount.multi.html
@@ -161,95 +161,30 @@ Examples
#> high:high 52.000 28.138 18.342 5.5716
#> high:low 28.000 61.170 25.882 -6.5200
#> Jtot 28.000 61.170 33.190 -5.7577
-# \dontrun{
+if (FALSE) {
data(oldcol)
HICRIME <- cut(COL.OLD$CRIME, breaks=c(0,35,80), labels=c("low","high"))
names(HICRIME) <- rownames(COL.OLD)
joincount.multi(HICRIME, nb2listw(COL.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> low:low 34.000 29.592 18.895 1.0141
-#> high:high 54.000 27.224 17.888 6.3307
-#> high:low 28.000 59.184 26.233 -6.0884
-#> Jtot 28.000 59.184 26.233 -6.0884
data(hopkins, package="spData")
image(1:32, 1:32, hopkins[5:36,36:5], breaks=c(-0.5, 3.5, 20),
col=c("white", "black"))
box()
-
hopkins.rook.nb <- cell2nb(32, 32, type="rook")
unlist(spweights.constants(nb2listw(hopkins.rook.nb, style="B")))
-#> n n1 n2 n3 nn S0 S1 S2
-#> 1024 1023 1022 1021 1048576 3968 7936 61984
hopkins.queen.nb <- cell2nb(32, 32, type="queen")
hopkins.bishop.nb <- diffnb(hopkins.rook.nb, hopkins.queen.nb, verbose=FALSE)
hopkins4 <- hopkins[5:36,36:5]
hopkins4[which(hopkins4 > 3, arr.ind=TRUE)] <- 4
hopkins4.f <- factor(hopkins4)
table(hopkins4.f)
-#> hopkins4.f
-#> 0 1 2 3 4
-#> 657 215 98 30 24
joincount.multi(hopkins4.f, nb2listw(hopkins.rook.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 864.00000 816.27273 116.05233 4.4304
-#> 1:1 94.00000 87.14015 55.25216 0.9229
-#> 2:2 18.00000 18.00379 14.81562 -0.0010
-#> 3:3 2.00000 1.64773 1.55539 0.2825
-#> 4:4 5.00000 1.04545 0.99845 3.9576
-#> 1:0 503.00000 535.05682 227.76750 -2.1241
-#> 2:0 213.00000 243.88636 97.21769 -3.1325
-#> 2:1 99.00000 79.81061 59.01930 2.4978
-#> 3:0 61.00000 74.65909 28.58592 -2.5547
-#> 3:1 28.00000 24.43182 18.99976 0.8186
-#> 3:2 15.00000 11.13636 9.82411 1.2327
-#> 4:0 40.00000 59.72727 22.78583 -4.1327
-#> 4:1 23.00000 19.54545 15.26564 0.8842
-#> 4:2 14.00000 8.90909 7.90051 1.8112
-#> 4:3 5.00000 2.72727 2.58616 1.4133
-#> Jtot 1001.00000 1059.89015 273.78610 -3.5591
cat("replicates Upton & Fingleton table 3.4 (p. 166)\n")
-#> replicates Upton & Fingleton table 3.4 (p. 166)
joincount.multi(hopkins4.f, nb2listw(hopkins.bishop.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 823.00000 790.76420 144.44877 2.6821
-#> 1:1 101.00000 84.41702 55.98143 2.2164
-#> 2:2 19.00000 17.44117 14.61542 0.4077
-#> 3:3 3.00000 1.59624 1.51444 1.1407
-#> 4:4 3.00000 1.01278 0.97111 2.0166
-#> 1:0 497.00000 518.33629 234.93545 -1.3920
-#> 2:0 216.00000 236.26491 104.42142 -1.9831
-#> 2:1 81.00000 77.31652 58.70829 0.4807
-#> 3:0 58.00000 72.32599 31.49151 -2.5529
-#> 3:1 21.00000 23.66832 18.85316 -0.6145
-#> 3:2 17.00000 10.78835 9.62487 2.0022
-#> 4:0 48.00000 57.86080 25.15973 -1.9659
-#> 4:1 21.00000 18.93466 15.14473 0.5307
-#> 4:2 10.00000 8.63068 7.73708 0.4923
-#> 4:3 4.00000 2.64205 2.51686 0.8560
-#> Jtot 973.00000 1026.76858 284.51030 -3.1877
cat("replicates Upton & Fingleton table 3.6 (p. 168)\n")
-#> replicates Upton & Fingleton table 3.6 (p. 168)
joincount.multi(hopkins4.f, nb2listw(hopkins.queen.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 1687.0000 1607.0369 303.8034 4.5877
-#> 1:1 195.0000 171.5572 114.2057 2.1936
-#> 2:2 37.0000 35.4450 29.6821 0.2854
-#> 3:3 5.0000 3.2440 3.0687 1.0024
-#> 4:4 8.0000 2.0582 1.9674 4.2361
-#> 1:0 1000.0000 1053.3931 480.6959 -2.4353
-#> 2:0 429.0000 480.1513 215.0360 -3.4882
-#> 2:1 180.0000 157.1271 119.3987 2.0932
-#> 3:0 119.0000 146.9851 65.1029 -3.4684
-#> 3:1 49.0000 48.1001 38.3268 0.1454
-#> 3:2 32.0000 21.9247 19.5237 2.2802
-#> 4:0 88.0000 117.5881 52.0312 -4.1019
-#> 4:1 44.0000 38.4801 30.7868 0.9948
-#> 4:2 24.0000 17.5398 15.6933 1.6308
-#> 4:3 9.0000 5.3693 5.0994 1.6078
-#> Jtot 1974.0000 2086.6587 582.8326 -4.6665
cat("replicates Upton & Fingleton table 3.7 (p. 169)\n")
-#> replicates Upton & Fingleton table 3.7 (p. 169)
-# }
+}
diff --git a/docs/reference/knearneigh.html b/docs/reference/knearneigh.html
index f5a9e742..5f4466fa 100644
--- a/docs/reference/knearneigh.html
+++ b/docs/reference/knearneigh.html
@@ -161,9 +161,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
summary(gck4.nb, xy, longlat=TRUE, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -176,9 +176,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#xy1 <- SpatialPoints((as.data.frame(state.center))[m50.48,],
# proj4string=CRS("+proj=longlat +ellps=GRS80"))
@@ -191,7 +191,7 @@ Examples
sf_use_s2(TRUE)
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.008 0.000 0.009
+#> 0.009 0.000 0.010
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -204,14 +204,14 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(FALSE)
#> Spherical geometry (s2) switched off
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.005 0.000 0.006
+#> 0.005 0.000 0.005
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -224,9 +224,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(old_use_s2)
#> Spherical geometry (s2) switched on
diff --git a/docs/reference/localC.html b/docs/reference/localC.html
index 1efd5e5b..03cfd45b 100644
--- a/docs/reference/localC.html
+++ b/docs/reference/localC.html
@@ -245,11 +245,9 @@ Examples
}
#> [1] 0.985611
# pseudo-p values probably wrongly folded https://github.com/GeoDaCenter/rgeoda/issues/28
-# \dontrun{
+if (FALSE) {
tmap_ok <- FALSE
if (require(tmap, quietly=TRUE)) tmap_ok <- TRUE
-#> Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
-#> remotes::install_github('r-tmap/tmap')
if (run) {
# doi: 10.1111/gean.12164
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
@@ -263,25 +261,10 @@ Examples
moran(g$Infants, lw, n=nrow(g), S0=Szero(lw))$I
moran(g$Suicids, lw, n=nrow(g), S0=Szero(lw))$I
}
-#> Reading layer `Guerry' from data source
-#> `/home/rsb/lib/r_libs/rgeoda/extdata/Guerry.shp' using driver `ESRI Shapefile'
-#> Simple feature collection with 85 features and 29 fields
-#> Geometry type: MULTIPOLYGON
-#> Dimension: XY
-#> Bounding box: xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
-#> Projected CRS: NTF (Paris) / Lambert zone II
-#> [1] 0.4016812
if (run) {
o <- prcomp(st_drop_geometry(g), scale.=TRUE)
cor(st_drop_geometry(g), o$x[,1:2])^2 #(Tab. 2)
}
-#> PC1 PC2
-#> Crm_prs 0.009286863 0.418851254
-#> Crm_prp 0.561825664 0.009377232
-#> Litercy 0.560570048 0.020095011
-#> Donatns 0.024138865 0.586720363
-#> Infants 0.436025658 0.012817054
-#> Suicids 0.548623327 0.152958961
if (run) {
g$PC1 <- o$x[, "PC1"]
brks <- c(min(g$PC1), natural_breaks(k=6, g["PC1"]), max(g$PC1))
@@ -289,7 +272,6 @@ Examples
tm_borders() # Fig. 1
else pplot(g["PC1"], breaks=brks)
}
-
if (run) {
g$PC2 <- -1*o$x[, "PC2"] # eigenvalue sign arbitrary
brks <- c(min(g$PC2), natural_breaks(k=6, g["PC2"]), max(g$PC2))
@@ -297,7 +279,6 @@ Examples
tm_borders() # Fig. 2
else plot(g["PC2"], breaks=brks)
}
-
if (run) {
w <- queen_weights(g)
lm_PC1 <- local_moran(w, g["PC1"], significance_cutoff=0.01,
@@ -310,7 +291,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 3
else plot(g["lm_PC1"])
}
-
if (run) {
set.seed(1)
lm_PC1_spdep <- localmoran_perm(g$PC1, lw, nsim=9999)
@@ -322,7 +302,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 3
else plot(g["lm_PC1_spdep"])
}
-
if (run) {
lg_PC1 <- local_g(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -340,7 +319,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 4 (guess)
else plot(g["lg_PC1"])
}
-
if (run) {
lc_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -352,14 +330,11 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 5
else plot(g["lc_PC1"])
}
-
if (run) {
set.seed(1)
system.time(lc_PC1_spdep <- localC_perm(g$PC1, lw, nsim=9999,
alternative="two.sided"))
}
-#> user system elapsed
-#> 0.376 0.001 0.377
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -372,7 +347,6 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
g$lc_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.01
@@ -381,7 +355,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 5
else plot(g["lc_PC1_spdep"])
}
-
if (run) {
g$both_PC1 <- interaction(g$lc_PC1, g$lm_PC1)
g$both_PC1 <- droplevels(g$both_PC1)
@@ -389,7 +362,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 6
else plot(g["both_PC1"])
}
-
if (run) {
lc005_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.005,
permutations=99999)
@@ -401,7 +373,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 7
else plot(g["lc005_PC1"])
}
-
if (run) {
g$lc005_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc005_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.005
@@ -410,7 +381,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 7
else plot(g["lc005_PC1_spdep"])
}
-
if (run) {
lc001_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.001,
permutations=99999)
@@ -430,7 +400,6 @@ Examples
else plot(g["lc001_PC1_spdep"])
}
}
-
if (run) {
lc_PC2 <- local_geary(w, g["PC2"], significance_cutoff=0.01,
permutations=99999)
@@ -442,7 +411,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 9
else plot(g["lc_PC2"])
}
-
if (run) {
lmc_PC <- local_multigeary(w, g[c("PC1","PC2")], significance_cutoff=0.00247,
permutations=99999)
@@ -452,25 +420,19 @@ Examples
g$lmc_PC <- droplevels(g$lmc_PC)
table(interaction((p.adjust(lisa_pvalues(lmc_PC), "fdr") < 0.01), g$lmc_PC))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_PC", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 10
else plot(g["lmc_PC"])
}
-
if (run) {
set.seed(1)
lmc_PC_spdep <- localC_perm(g[c("PC1","PC2")], lw, nsim=9999, alternative="two.sided")
all.equal(lisa_values(lmc_PC), c(lmc_PC_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_PC_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_PC))
}
-#> [1] 0.99052
if (run) {
g$lmc_PC_spdep <- attr(lmc_PC_spdep, "cluster")
is.na(g$lmc_PC_spdep) <- p.adjust(attr(lmc_PC_spdep, "pseudo-p")[,6], "fdr") > 0.01
@@ -479,7 +441,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 10
else plot(g["lmc_PC_spdep"])
}
-
if (run) {
lmc_vars <- local_multigeary(w, st_drop_geometry(g)[, 1:6],
significance_cutoff=0.00247, permutations=99999)
@@ -490,30 +451,22 @@ Examples
table(interaction((p.adjust(lisa_pvalues(lmc_vars), "fdr") < 0.01),
g$lmc_vars))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_vars", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 11
else plot(g["lmc_vars"])
}
-
if (run) {
set.seed(1)
system.time(lmc_vars_spdep <- localC_perm(st_drop_geometry(g)[, 1:6], lw,
nsim=9999, alternative="two.sided"))
}
-#> user system elapsed
-#> 0.824 0.002 0.828
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -526,15 +479,12 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep1))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep1, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
g$lmc_vars_spdep <- attr(lmc_vars_spdep1, "cluster")
is.na(g$lmc_vars_spdep) <- p.adjust(attr(lmc_vars_spdep1, "pseudo-p")[,6], "fdr") > 0.01
@@ -543,10 +493,9 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 11
else plot(g["lmc_vars_spdep"])
}
-
-# }
-# \dontrun{
+}
+if (FALSE) {
library(reticulate)
use_python("/usr/bin/python", required = TRUE)
gp <- import("geopandas")
@@ -557,7 +506,6 @@ Examples
esda <- import("esda")
lM <- esda$Moran_Local(x, w)
all.equal(unname(localmoran(x, listw, mlvar=FALSE)[,1]), c(lM$Is))
-#> [1] TRUE
# confirm x and w the same
lC <- esda$Geary_Local(connectivity=w)$fit(scale(x))
# np$std missing ddof=1
@@ -566,19 +514,10 @@ Examples
# lC components probably wrongly ordered https://github.com/pysal/esda/issues/192
o <- match(round(D0, 6), round(lC$localG, 6))
all.equal(c(lC$localG)[o], D0)
-#> [1] TRUE
# simulation order not retained
lC$p_sim[o]
-#> [1] 0.278 0.460 0.112 0.019 0.121 0.038 0.330 0.412 0.147 0.001 0.113 0.141
-#> [13] 0.188 0.246 0.450 0.172 0.102 0.298 0.001 0.027 0.038 0.091 0.055 0.297
-#> [25] 0.153 0.031 0.107 0.280 0.450 0.001 0.066 0.104 0.080 0.231 0.306 0.001
-#> [37] 0.326 0.482 0.321 0.216 0.235 0.288
attr(C, "pseudo-p")[,6]
-#> [1] 0.408 0.374 0.144 0.056 0.010 0.284 0.032 0.264 0.186 0.004 0.150 0.224
-#> [13] 0.022 0.150 0.438 0.084 0.126 0.184 0.002 0.238 0.126 0.100 0.068 0.372
-#> [25] 0.124 0.234 0.230 0.016 0.318 0.176 0.272 0.164 0.164 0.206 0.402 0.250
-#> [37] 0.410 0.402 0.328 0.294 0.438 0.248
-# }
+}
diff --git a/docs/reference/mat2listw.html b/docs/reference/mat2listw.html
index 096c6e5d..656b612c 100644
--- a/docs/reference/mat2listw.html
+++ b/docs/reference/mat2listw.html
@@ -140,13 +140,14 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
-col005.w.mat <- nb2mat(col005, zero.policy=TRUE)
-col005.w.b <- mat2listw(col005.w.mat)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> 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"))
+#> Error in mat2listw(col005.w.mat, style = "W") :
+#> no-neighbour observations found, set zero.policy to TRUE
+col005.w.b <- mat2listw(col005.w.mat, style="W", zero.policy=TRUE)
summary(col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -161,9 +162,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
diffnb(col005, col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -177,9 +178,7 @@ Examples
#> 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")
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
+col005.w.b.3T <- mat2listw(col005.w.mat.3T, style="W", zero.policy=TRUE)
summary(col005.w.b.3T$neighbours)
#> Neighbour list object:
#> Number of regions: 147
@@ -194,21 +193,22 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 2 5 9 10 31 34 36 39 42 46 47 51 54 58 59 80 83 85 88 91 95 96 100 103 107 108 129 132 134 137 140 144 145 with 1 link
#> 6 most connected regions:
-#> with 9 links
+#> 11 16 60 65 109 114 with 9 links
run <- FALSE
if (require("spatialreg", quiet=TRUE)) run <- TRUE
if (run) {
W <- as(nb2listw(col005, style="W", zero.policy=TRUE), "CsparseMatrix")
-col005.spM <- mat2listw(W)
+try(col005.spM <- mat2listw(W))
+col005.spM <- mat2listw(W, style="W", zero.policy=TRUE)
summary(col005.spM$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, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> Error in sn2listw(df, style = style, zero.policy = zero.policy) :
+#> no-neighbour observations found, set zero.policy to TRUE
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 170
@@ -222,9 +222,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
if (run) {
diffnb(col005, col005.spM$neighbours)
}
@@ -241,14 +241,9 @@ Examples
#> 49 disjoint connected subgraphs
if (run && require("Matrix", quiet=TRUE)) {
IW <- kronecker(Diagonal(3), W)
-col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W")
+col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W", zero.policy=TRUE)
summary(col005.spM.3T$neighbours)
}
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: 1, 3, 6, 21, 50, 52, 55, 70, 99, 101, 104, 119 are not origins
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
#> Neighbour list object:
#> Number of regions: 147
#> Number of nonzero links: 510
@@ -262,9 +257,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 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:
-#> with 9 links
+#> 11 16 60 65 109 114 with 9 links
diff --git a/docs/reference/nb2blocknb.html b/docs/reference/nb2blocknb.html
index c2276d23..460ced1b 100644
--- a/docs/reference/nb2blocknb.html
+++ b/docs/reference/nb2blocknb.html
@@ -127,96 +127,31 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
data(boston, package="spData")
summary(as.vector(table(boston.c$TOWN)))
-#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 1.0 2.0 4.0 5.5 7.0 30.0
townaggr <- aggregate(boston.utm, list(town=boston.c$TOWN), mean)
block.rel <- graph2nb(relativeneigh(as.matrix(townaggr[,2:3])),
as.character(townaggr[,1]), sym=TRUE)
block.rel
-#> Neighbour list object:
-#> Number of regions: 92
-#> Number of nonzero links: 240
-#> Percentage nonzero weights: 2.835539
-#> Average number of links: 2.608696
print(is.symmetric.nb(block.rel))
-#> [1] TRUE
plot(block.rel, as.matrix(townaggr[,2:3]))
points(boston.utm, pch=18, col="lightgreen")
-
block.nb <- nb2blocknb(block.rel, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 15234
-#> Percentage nonzero weights: 5.949945
-#> Average number of links: 30.10672
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
points(boston.utm, pch=18, col="lightgreen")
-
n.comp.nb(block.nb)$nc
-#> [1] 1
moran.test(boston.c$CMEDV, nb2listw(boston.soi))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(boston.soi)
-#>
-#> Moran I statistic standard deviate = 21.786, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.690285059 -0.001980198 0.001009685
-#>
moran.test(boston.c$CMEDV, nb2listw(block.nb))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb)
-#>
-#> Moran I statistic standard deviate = 22.455, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.3122905961 -0.0019801980 0.0001958827
-#>
block.nb <- nb2blocknb(NULL, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 4868
-#> Percentage nonzero weights: 1.901295
-#> Average number of links: 9.620553
-#> 17 regions with no links:
-#> 1 55 56 57 58 65 196 257 284 285 286 287 342 343 348 349 354
-#> 92 disjoint connected subgraphs
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
-
n.comp.nb(block.nb)$nc
-#> [1] 92
moran.test(boston.c$CMEDV, nb2listw(block.nb, zero.policy=TRUE), zero.policy=TRUE)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb, zero.policy = TRUE) n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 21.145, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.6188830566 -0.0020491803 0.0008623116
-#>
-# }
+}
diff --git a/docs/reference/nb2lines.html b/docs/reference/nb2lines.html
index 38b287ec..1b49a033 100644
--- a/docs/reference/nb2lines.html
+++ b/docs/reference/nb2lines.html
@@ -158,12 +158,12 @@ Examples
tf <- paste0(tempfile(), ".gpkg")
st_write(res, dsn=tf, driver="GPKG")
#> writing: substituting ENGCRS["Undefined Cartesian SRS with unknown unit"] for missing CRS
-#> Writing layer `file706d874b42afe' to data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Writing layer `file9f36c3d2d676e' to data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Writing 230 features with 5 fields and geometry type Line String.
inMap <- st_read(tf)
-#> Reading layer `file706d874b42afe' from data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Reading layer `file9f36c3d2d676e' from data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Simple feature collection with 230 features and 5 fields
#> Geometry type: LINESTRING
#> Dimension: XY
diff --git a/docs/reference/nb2listw.html b/docs/reference/nb2listw.html
index fd9ebeee..ef19145f 100644
--- a/docs/reference/nb2listw.html
+++ b/docs/reference/nb2listw.html
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: minmax
#> Weights constants summary:
diff --git a/docs/reference/nb2listwdist.html b/docs/reference/nb2listwdist.html
index a6ffbf2d..98dcafb5 100644
--- a/docs/reference/nb2listwdist.html
+++ b/docs/reference/nb2listwdist.html
@@ -171,43 +171,15 @@ Examples
#> Moran I statistic Expectation Variance
#> 0.473883371 -0.007042254 0.029994057
#>
-# \dontrun{
+if (FALSE) {
# Moran's I (life expectancy) with IDW with alpha = 2, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "idw",
alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 2.7769, p-value = 0.002744
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.473883371 -0.007042254 0.029994057
-#>
# Moran's I (life expectancy), DPD, alpha = 2, dmax = 1000 km, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "dpd",
dmax = 1000000, alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 8.8557, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.601498970 -0.007042254 0.004722063
-#>
# Boston examples
data(boston, package="spData")
boston_coords <- data.frame(x = boston.utm[,1], y = boston.utm[,2])
@@ -217,37 +189,11 @@ Examples
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "exp", alpha = 2,
style="raw", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 51.34, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.8845114518 -0.0019960080 0.0002981586
-#>
# Moran's I (crime) with idw weights with alpha = 2, coding scheme = W
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "idw", alpha = 2,
style="W", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 18.976, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.4392852330 -0.0019960080 0.0005408065
-#>
-# }
+}
diff --git a/docs/reference/nb2mat.html b/docs/reference/nb2mat.html
index 7f17e8bb..39ec4504 100644
--- a/docs/reference/nb2mat.html
+++ b/docs/reference/nb2mat.html
@@ -137,9 +137,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
col005.w.mat <- nb2mat(col005, style="B", zero.policy=TRUE)
table(round(rowSums(col005.w.mat)))
#>
diff --git a/docs/reference/nblag.html b/docs/reference/nblag.html
index 08180e37..bd822ea5 100644
--- a/docs/reference/nblag.html
+++ b/docs/reference/nblag.html
@@ -122,9 +122,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.lags <- nblag(col.gal.nb, 2)
print(col.lags)
#> [[1]]
@@ -154,9 +154,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#> 1 2 5 4 2 3 5 4 2 3 7 3 4 1 1 1 1
#> 1 least connected region:
-#> with 1 link
+#> 36 with 1 link
#> 1 most connected region:
-#> with 17 links
+#> 25 with 17 links
plot(st_geometry(columbus), border="grey")
plot(col.gal.nb, coords, add=TRUE)
title(main="GAL order 1 (black) and 2 (red) links")
diff --git a/docs/reference/poly2nb.html b/docs/reference/poly2nb.html
index 1cad8008..79b13c61 100644
--- a/docs/reference/poly2nb.html
+++ b/docs/reference/poly2nb.html
@@ -174,10 +174,10 @@ Examples
nc.sids <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE)
system.time(xxnb <- poly2nb(nc.sids))
#> user system elapsed
-#> 0.018 0.000 0.018
+#> 0.019 0.000 0.019
system.time(xxnb <- poly2nb(as(nc.sids, "Spatial")))
#> user system elapsed
-#> 0.029 0.000 0.029
+#> 0.034 0.000 0.035
plot(st_geometry(nc.sids))
plot(xxnb, st_coordinates(st_centroid(nc.sids)), add=TRUE, col="blue")
#> Warning: st_centroid assumes attributes are constant over geometries
diff --git a/docs/reference/skater.html b/docs/reference/skater.html
index aefe0190..d2b9c2e6 100644
--- a/docs/reference/skater.html
+++ b/docs/reference/skater.html
@@ -292,22 +292,18 @@ Examples
table(res5d$groups)
#> Error in eval(expr, envir, enclos): object 'res5d' not found
-# \dontrun{
+if (FALSE) {
data(boston, package="spData")
bh.nb <- boston.soi
dpad <- data.frame(scale(boston.c[,c(7:10)]))
### calculating costs
system.time(lcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.042 0.000 0.043
### making listw
nb.w <- nb2listw(bh.nb, lcosts, style="B")
### find a minimum spanning tree
mst.bh <- mstree(nb.w,5)
### three groups with no restriction
system.time(res1 <- skater(mst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
library(parallel)
nc <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
# set nc to 1L here
@@ -321,26 +317,20 @@ Examples
}
### calculating costs
system.time(plcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.04 0.00 0.04
all.equal(lcosts, plcosts, check.attributes=FALSE)
-#> [1] TRUE
### making listw
pnb.w <- nb2listw(bh.nb, plcosts, style="B")
### find a minimum spanning tree
pmst.bh <- mstree(pnb.w,5)
### three groups with no restriction
system.time(pres1 <- skater(pmst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
if(!get.mcOption()) {
set.ClusterOption(NULL)
stopCluster(cl)
}
all.equal(res1, pres1, check.attributes=FALSE)
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'target' in selecting a method for function 'all.equal': object 'res1' not found
invisible(set.coresOption(coresOpt))
-# }
+}
diff --git a/docs/reference/summary.nb.html b/docs/reference/summary.nb.html
index 79407597..674042bf 100644
--- a/docs/reference/summary.nb.html
+++ b/docs/reference/summary.nb.html
@@ -145,9 +145,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.listw <- nb2listw(col.gal.nb, style="W")
col.listw
#> Characteristics of weights list object:
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -199,9 +199,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
try(nb2listw(nb, style="W"))
#> Error in nb2listw(nb, style = "W") : Empty neighbour sets found
summary(nb2listw(nb, style="W", zero.policy=TRUE))
@@ -219,9 +219,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -242,9 +242,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
diff --git a/docs/reference/tolerance.nb.html b/docs/reference/tolerance.nb.html
index 14a72c2e..e793d2ac 100644
--- a/docs/reference/tolerance.nb.html
+++ b/docs/reference/tolerance.nb.html
@@ -130,24 +130,18 @@ Examples
### and a maximum distance of 2 m.
nb.ex<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of 30 degrees
### and a maximum distance of 2 m. The coordinates are rotated at an angle
### of 45 degrees counterclockwise.
nb.ex2<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30, rot.angle = 45)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of pi/8 radians
### and a maximum distance of 1.5 m. The coordinates are rotated at
### an angle of pi/4 radians clockwise.
nb.ex3<-tolerance.nb(ex.data, unit.angle = "radians", max.dist=1.5,
tolerance = pi/8, rot.angle = -pi*2/3)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
par(mfrow=c(1,3))
plot(nb.ex,ex.data,asp=1)
## user system elapsed
-## 0.0440 0.0000 0.0445dwithin=FALSE and s2::s2_closest_edges();
although running in similar time, s2::s2_closest_edges()
@@ -563,7 +563,7 @@
system.time(for (i in 1:(reps/5)) pts_ll5_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, dwithin=FALSE))/(reps/5)
## user system elapsed
-## 0.027 0.000 0.027
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3_nb, pts_ll5_nb, check.attributes=FALSE)## [1] TRUEDistance neighbours=0.75*max_1nn_ll))/(reps/5)
}
## user system elapsed
-## 0.0725 0.0000 0.0725
+## 0.0730 0.0000 0.0735
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3a_nb, pts_ll5a_nb, check.attributes=FALSE)
## [1] TRUE
@@ -595,7 +595,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0097 0.0000 0.0098
Minor differences may occur between the legacy ellipsoid and
s2 spherical approaches:
@@ -625,7 +625,7 @@ Distance neighbours
system.time(for (i in 1:reps) pts_ll6a_nb <- dnearneigh(pts_ll, d1=5, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
+## 0.0098 0.0000 0.0097
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll5a_nb, pts_ll6a_nb, check.attributes=FALSE)
## [1] "Component 20: Numeric: lengths (6, 5) differ"
@@ -676,7 +676,7 @@ Contiguity neighbours for spherical polygon support
system.time(for (i in 1:reps) NY8_sf_1_nb_ll <- poly2nb(NY8_sf_ll, queen=TRUE, snap=eps))/reps
## user system elapsed
-## 0.1553 0.0013 0.1572
+## 0.1524 0.0019 0.1548
all.equal(NY8_sf_1_nb, NY8_sf_1_nb_ll, check.attributes=FALSE)
## [1] TRUE
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 5f78f8ad..a8b299ea 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,5 +6,5 @@ articles:
nb: nb.html
nb_sf: nb_sf.html
sids: sids.html
-last_built: 2023-11-04T17:52Z
+last_built: 2023-11-06T13:06Z
diff --git a/docs/reference/EBImoran.mc.html b/docs/reference/EBImoran.mc.html
index db555f47..4886f83f 100644
--- a/docs/reference/EBImoran.mc.html
+++ b/docs/reference/EBImoran.mc.html
@@ -184,7 +184,7 @@ Examples
#> weights: nb2listw(ncCC89_nb, style = "B", zero.policy = TRUE)
#> number of simulations + 1: 1000
#>
-#> statistic = 0.25789, observed rank = 998, p-value = 0.004
+#> statistic = 0.25789, observed rank = 1000, p-value < 2.2e-16
#> alternative hypothesis: two.sided
#>
sids.p <- nc.sids$SID74 / nc.sids$BIR74
diff --git a/docs/reference/LOSH.mc-1.png b/docs/reference/LOSH.mc-1.png
index 5a905795..3b4cabfc 100644
Binary files a/docs/reference/LOSH.mc-1.png and b/docs/reference/LOSH.mc-1.png differ
diff --git a/docs/reference/LOSH.mc.html b/docs/reference/LOSH.mc.html
index c0a6763a..30a4b3ed 100644
--- a/docs/reference/LOSH.mc.html
+++ b/docs/reference/LOSH.mc.html
@@ -154,10 +154,10 @@ Examples
summary(resLOSH_mc)
#> Hi x_bar_i ei Pr()
#> Min. :0.03438 Min. :13.85 Min. : 0.0298 Min. :0.009901
-#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.118812
-#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.702970
-#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.542332
-#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.891089
+#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.079208
+#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.712871
+#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.554860
+#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.910891
#> Max. :4.68765 Max. :54.91 Max. :2455.2201 Max. :0.990099
resLOSH_cs <- LOSH.cs(columbus$CRIME, nb2listw(col.gal.nb))
summary(resLOSH_cs)
diff --git a/docs/reference/bhicv.html b/docs/reference/bhicv.html
index 2e1f9102..072425ba 100644
--- a/docs/reference/bhicv.html
+++ b/docs/reference/bhicv.html
@@ -105,7 +105,7 @@ Examples
bh <- st_read(system.file("etc/shapes/bhicv.shp",
package="spdep")[1])
#> Reading layer `bhicv' from data source
-#> `/tmp/Rtmpu0jdcX/temp_libpath70612704761e5/spdep/etc/shapes/bhicv.shp'
+#> `/tmp/RtmpU193Yl/temp_libpath9f2c94443f9d4/spdep/etc/shapes/bhicv.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 98 features and 8 fields
#> Geometry type: POLYGON
diff --git a/docs/reference/cell2nb.html b/docs/reference/cell2nb.html
index 9b67215a..e3badaf1 100644
--- a/docs/reference/cell2nb.html
+++ b/docs/reference/cell2nb.html
@@ -137,9 +137,9 @@ Examples
#> 2 3 4
#> 4 20 25
#> 4 least connected regions:
-#> with 2 links
+#> 1:1 7:1 1:7 7:7 with 2 links
#> 25 most connected regions:
-#> with 4 links
+#> 2:2 3:2 4:2 5:2 6:2 2:3 3:3 4:3 5:3 6:3 2:4 3:4 4:4 5:4 6:4 2:5 3:5 4:5 5:5 6:5 2:6 3:6 4:6 5:6 6:6 with 4 links
xyc <- attr(nb7rt, "region.id")
xy <- matrix(as.integer(unlist(strsplit(xyc, ":"))), ncol=2, byrow=TRUE)
plot(nb7rt, xy)
@@ -156,9 +156,9 @@ Examples
#> 4
#> 49
#> 49 least connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
#> 49 most connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
run <- FALSE
if (require("sp", quietly=TRUE)) run <- TRUE
if (run) {
diff --git a/docs/reference/dnearneigh.html b/docs/reference/dnearneigh.html
index afbf4662..937dd9d6 100644
--- a/docs/reference/dnearneigh.html
+++ b/docs/reference/dnearneigh.html
@@ -149,9 +149,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11
#> 4 8 6 2 5 8 6 2 6 1 1
#> 4 least connected regions:
-#> with 1 link
+#> 6 10 21 47 with 1 link
#> 1 most connected region:
-#> with 11 links
+#> 28 with 11 links
opar <- par(no.readonly=TRUE)
plot(st_geometry(columbus), border="grey", reset=FALSE,
main=paste("Distance based neighbours 0-", format(all.linked), sep=""))
@@ -198,9 +198,9 @@ Examples
#> 1 2 3 4 5 7 8 9 10
#> 11 9 4 8 4 4 4 3 1
#> 11 least connected regions:
-#> with 1 link
+#> 2 4 8 10 24 26 29 32 35 41 45 with 1 link
#> 1 most connected region:
-#> with 10 links
+#> 28 with 10 links
gck1 <- knn2nb(knearneigh(xy, k=1, longlat=TRUE))
(all.linked <- max(unlist(nbdists(gck1, xy, longlat=TRUE))))
#> [1] 523.5819
@@ -217,9 +217,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
plot(ll.nb, xy)
plot(diffnb(ll.nb, gc.nb), xy, add=TRUE, col="red", lty=2)
title(main="Differences Euclidean/Great Circle")
@@ -239,13 +239,13 @@ Examples
gck1b <- knn2nb(knearneigh(xy1, k=1))
system.time(o <- nbdists(gck1b, xy1))
#> user system elapsed
-#> 0.007 0.000 0.006
+#> 0.006 0.000 0.006
(all.linked <- max(unlist(o)))
#> [1] 522.4464
# use s2 brute-force dwithin_matrix approach for s2 <= 1.0.7
system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE))
#> user system elapsed
-#> 0.01 0.00 0.01
+#> 0.009 0.000 0.010
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -258,9 +258,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use s2 closest_edges approach s2 > 1.0.7
if (packageVersion("s2") > "1.0.7") {
(system.time(gc.nb.closest <- dnearneigh(xy1, 0, all.linked, dwithin=FALSE)))
@@ -288,9 +288,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") {
summary(gc.nb.closest, xy1, scale=0.5)
}
@@ -307,13 +307,13 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use legacy symmetric brute-force approach
system.time(gc.nb.legacy <- dnearneigh(xy1, 0, all.linked, use_s2=FALSE))
#> user system elapsed
-#> 0.003 0.000 0.003
+#> 0.003 0.000 0.004
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -326,9 +326,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") all.equal(gc.nb.closest, gc.nb.dwithin, check.attributes=FALSE)
#> [1] TRUE
# legacy is ellipsoidal, s2 spherical, so minor differences expected
diff --git a/docs/reference/edit.nb.html b/docs/reference/edit.nb.html
index 44773c64..d59c98e1 100644
--- a/docs/reference/edit.nb.html
+++ b/docs/reference/edit.nb.html
@@ -121,12 +121,11 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
class(columbus)
-#> [1] "sf" "data.frame"
if (FALSE) nnb1 <- edit.nb(col.gal.nb, polys=as(columbus, "Spatial"))
-# }
+}
diff --git a/docs/reference/geary.mc.html b/docs/reference/geary.mc.html
index 236e80b0..3164b159 100644
--- a/docs/reference/geary.mc.html
+++ b/docs/reference/geary.mc.html
@@ -160,12 +160,12 @@ Examples
#> alternative hypothesis: less
#>
mean(sim1$res)
-#> [1] 1.012612
+#> [1] 0.9890232
var(sim1$res)
-#> [1] 0.0119779
+#> [1] 0.01235098
summary(sim1$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.5299 0.9401 1.0218 1.0126 1.0906 1.2388
+#> 0.5299 0.9288 1.0029 0.9890 1.0515 1.3024
colold.lags <- nblag(COL.nb, 3)
sim2 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[2]],
style="W"), nsim=99)
@@ -177,12 +177,12 @@ Examples
#> weights: nb2listw(colold.lags[[2]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 0.81129, observed rank = 1, p-value = 0.01
+#> statistic = 0.81129, observed rank = 5, p-value = 0.05
#> alternative hypothesis: greater
#>
summary(sim2$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8113 0.9541 1.0113 1.0014 1.0569 1.1619
+#> 0.7211 0.9490 0.9896 0.9948 1.0550 1.1807
sim3 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[3]],
style="W"), nsim=99)
sim3
@@ -193,12 +193,12 @@ Examples
#> weights: nb2listw(colold.lags[[3]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 1.1303, observed rank = 92, p-value = 0.92
+#> statistic = 1.1303, observed rank = 95, p-value = 0.95
#> alternative hypothesis: greater
#>
summary(sim3$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8264 0.9586 1.0079 1.0099 1.0562 1.2047
+#> 0.8034 0.9550 1.0098 1.0076 1.0474 1.2499
diff --git a/docs/reference/graphneigh.html b/docs/reference/graphneigh.html
index f41bced1..008fcf4f 100644
--- a/docs/reference/graphneigh.html
+++ b/docs/reference/graphneigh.html
@@ -285,9 +285,9 @@ Examples
#> 0 1 2 3 4 5
#> 1 8 10 18 55 8
#> 8 least connected regions:
-#> with 1 link
+#> 46 47 48 49 96 97 98 99 with 1 link
#> 8 most connected regions:
-#> with 5 links
+#> 10 15 20 25 30 35 40 45 with 5 links
grd <- as.matrix(expand.grid(x=1:5, y=1:5)) #gridded data
r2 <- gabrielneigh(grd)
set.seed(1)
diff --git a/docs/reference/grid2nb.html b/docs/reference/grid2nb.html
index 27f554ac..eb1e9527 100644
--- a/docs/reference/grid2nb.html
+++ b/docs/reference/grid2nb.html
@@ -150,9 +150,9 @@ Examples
#> 7 11 17 26
#> 8 36 54 27
#> 8 least connected regions:
-#> with 7 links
+#> 1 5 21 25 101 105 121 125 with 7 links
#> 27 most connected regions:
-#> with 26 links
+#> 32 33 34 37 38 39 42 43 44 57 58 59 62 63 64 67 68 69 82 83 84 87 88 89 92 93 94 with 26 links
if (require("sp", quietly=TRUE)) {
gt <- GridTopology(c(.125,.1), c(.25,.2), c(4L, 5L))
nb1 <- grid2nb(gt, queen = FALSE)
diff --git a/docs/reference/include.self.html b/docs/reference/include.self.html
index 1b8fad9a..287bd036 100644
--- a/docs/reference/include.self.html
+++ b/docs/reference/include.self.html
@@ -114,9 +114,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
summary(include.self(col.gal.nb), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -128,9 +128,9 @@ Examples
#> 3 4 5 6 7 8 9 10 11
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 3 links
+#> 1 6 31 39 42 46 47 with 3 links
#> 1 most connected region:
-#> with 11 links
+#> 20 with 11 links
summary(remove.self(include.self(col.gal.nb)), coords)
#> Neighbour list object:
#> Number of regions: 49
@@ -142,9 +142,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
diff --git a/docs/reference/joincount.multi.html b/docs/reference/joincount.multi.html
index 528fbbcd..8415a202 100644
--- a/docs/reference/joincount.multi.html
+++ b/docs/reference/joincount.multi.html
@@ -161,95 +161,30 @@ Examples
#> high:high 52.000 28.138 18.342 5.5716
#> high:low 28.000 61.170 25.882 -6.5200
#> Jtot 28.000 61.170 33.190 -5.7577
-# \dontrun{
+if (FALSE) {
data(oldcol)
HICRIME <- cut(COL.OLD$CRIME, breaks=c(0,35,80), labels=c("low","high"))
names(HICRIME) <- rownames(COL.OLD)
joincount.multi(HICRIME, nb2listw(COL.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> low:low 34.000 29.592 18.895 1.0141
-#> high:high 54.000 27.224 17.888 6.3307
-#> high:low 28.000 59.184 26.233 -6.0884
-#> Jtot 28.000 59.184 26.233 -6.0884
data(hopkins, package="spData")
image(1:32, 1:32, hopkins[5:36,36:5], breaks=c(-0.5, 3.5, 20),
col=c("white", "black"))
box()
-
hopkins.rook.nb <- cell2nb(32, 32, type="rook")
unlist(spweights.constants(nb2listw(hopkins.rook.nb, style="B")))
-#> n n1 n2 n3 nn S0 S1 S2
-#> 1024 1023 1022 1021 1048576 3968 7936 61984
hopkins.queen.nb <- cell2nb(32, 32, type="queen")
hopkins.bishop.nb <- diffnb(hopkins.rook.nb, hopkins.queen.nb, verbose=FALSE)
hopkins4 <- hopkins[5:36,36:5]
hopkins4[which(hopkins4 > 3, arr.ind=TRUE)] <- 4
hopkins4.f <- factor(hopkins4)
table(hopkins4.f)
-#> hopkins4.f
-#> 0 1 2 3 4
-#> 657 215 98 30 24
joincount.multi(hopkins4.f, nb2listw(hopkins.rook.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 864.00000 816.27273 116.05233 4.4304
-#> 1:1 94.00000 87.14015 55.25216 0.9229
-#> 2:2 18.00000 18.00379 14.81562 -0.0010
-#> 3:3 2.00000 1.64773 1.55539 0.2825
-#> 4:4 5.00000 1.04545 0.99845 3.9576
-#> 1:0 503.00000 535.05682 227.76750 -2.1241
-#> 2:0 213.00000 243.88636 97.21769 -3.1325
-#> 2:1 99.00000 79.81061 59.01930 2.4978
-#> 3:0 61.00000 74.65909 28.58592 -2.5547
-#> 3:1 28.00000 24.43182 18.99976 0.8186
-#> 3:2 15.00000 11.13636 9.82411 1.2327
-#> 4:0 40.00000 59.72727 22.78583 -4.1327
-#> 4:1 23.00000 19.54545 15.26564 0.8842
-#> 4:2 14.00000 8.90909 7.90051 1.8112
-#> 4:3 5.00000 2.72727 2.58616 1.4133
-#> Jtot 1001.00000 1059.89015 273.78610 -3.5591
cat("replicates Upton & Fingleton table 3.4 (p. 166)\n")
-#> replicates Upton & Fingleton table 3.4 (p. 166)
joincount.multi(hopkins4.f, nb2listw(hopkins.bishop.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 823.00000 790.76420 144.44877 2.6821
-#> 1:1 101.00000 84.41702 55.98143 2.2164
-#> 2:2 19.00000 17.44117 14.61542 0.4077
-#> 3:3 3.00000 1.59624 1.51444 1.1407
-#> 4:4 3.00000 1.01278 0.97111 2.0166
-#> 1:0 497.00000 518.33629 234.93545 -1.3920
-#> 2:0 216.00000 236.26491 104.42142 -1.9831
-#> 2:1 81.00000 77.31652 58.70829 0.4807
-#> 3:0 58.00000 72.32599 31.49151 -2.5529
-#> 3:1 21.00000 23.66832 18.85316 -0.6145
-#> 3:2 17.00000 10.78835 9.62487 2.0022
-#> 4:0 48.00000 57.86080 25.15973 -1.9659
-#> 4:1 21.00000 18.93466 15.14473 0.5307
-#> 4:2 10.00000 8.63068 7.73708 0.4923
-#> 4:3 4.00000 2.64205 2.51686 0.8560
-#> Jtot 973.00000 1026.76858 284.51030 -3.1877
cat("replicates Upton & Fingleton table 3.6 (p. 168)\n")
-#> replicates Upton & Fingleton table 3.6 (p. 168)
joincount.multi(hopkins4.f, nb2listw(hopkins.queen.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 1687.0000 1607.0369 303.8034 4.5877
-#> 1:1 195.0000 171.5572 114.2057 2.1936
-#> 2:2 37.0000 35.4450 29.6821 0.2854
-#> 3:3 5.0000 3.2440 3.0687 1.0024
-#> 4:4 8.0000 2.0582 1.9674 4.2361
-#> 1:0 1000.0000 1053.3931 480.6959 -2.4353
-#> 2:0 429.0000 480.1513 215.0360 -3.4882
-#> 2:1 180.0000 157.1271 119.3987 2.0932
-#> 3:0 119.0000 146.9851 65.1029 -3.4684
-#> 3:1 49.0000 48.1001 38.3268 0.1454
-#> 3:2 32.0000 21.9247 19.5237 2.2802
-#> 4:0 88.0000 117.5881 52.0312 -4.1019
-#> 4:1 44.0000 38.4801 30.7868 0.9948
-#> 4:2 24.0000 17.5398 15.6933 1.6308
-#> 4:3 9.0000 5.3693 5.0994 1.6078
-#> Jtot 1974.0000 2086.6587 582.8326 -4.6665
cat("replicates Upton & Fingleton table 3.7 (p. 169)\n")
-#> replicates Upton & Fingleton table 3.7 (p. 169)
-# }
+}
diff --git a/docs/reference/knearneigh.html b/docs/reference/knearneigh.html
index f5a9e742..5f4466fa 100644
--- a/docs/reference/knearneigh.html
+++ b/docs/reference/knearneigh.html
@@ -161,9 +161,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
summary(gck4.nb, xy, longlat=TRUE, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -176,9 +176,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#xy1 <- SpatialPoints((as.data.frame(state.center))[m50.48,],
# proj4string=CRS("+proj=longlat +ellps=GRS80"))
@@ -191,7 +191,7 @@ Examples
sf_use_s2(TRUE)
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.008 0.000 0.009
+#> 0.009 0.000 0.010
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -204,14 +204,14 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(FALSE)
#> Spherical geometry (s2) switched off
system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4)))
#> user system elapsed
-#> 0.005 0.000 0.006
+#> 0.005 0.000 0.005
summary(gck4a.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -224,9 +224,9 @@ Examples
#> 4
#> 48
#> 48 least connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
#> 48 most connected regions:
-#> with 4 links
+#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links
sf_use_s2(old_use_s2)
#> Spherical geometry (s2) switched on
diff --git a/docs/reference/localC.html b/docs/reference/localC.html
index 1efd5e5b..03cfd45b 100644
--- a/docs/reference/localC.html
+++ b/docs/reference/localC.html
@@ -245,11 +245,9 @@ Examples
}
#> [1] 0.985611
# pseudo-p values probably wrongly folded https://github.com/GeoDaCenter/rgeoda/issues/28
-# \dontrun{
+if (FALSE) {
tmap_ok <- FALSE
if (require(tmap, quietly=TRUE)) tmap_ok <- TRUE
-#> Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
-#> remotes::install_github('r-tmap/tmap')
if (run) {
# doi: 10.1111/gean.12164
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
@@ -263,25 +261,10 @@ Examples
moran(g$Infants, lw, n=nrow(g), S0=Szero(lw))$I
moran(g$Suicids, lw, n=nrow(g), S0=Szero(lw))$I
}
-#> Reading layer `Guerry' from data source
-#> `/home/rsb/lib/r_libs/rgeoda/extdata/Guerry.shp' using driver `ESRI Shapefile'
-#> Simple feature collection with 85 features and 29 fields
-#> Geometry type: MULTIPOLYGON
-#> Dimension: XY
-#> Bounding box: xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441
-#> Projected CRS: NTF (Paris) / Lambert zone II
-#> [1] 0.4016812
if (run) {
o <- prcomp(st_drop_geometry(g), scale.=TRUE)
cor(st_drop_geometry(g), o$x[,1:2])^2 #(Tab. 2)
}
-#> PC1 PC2
-#> Crm_prs 0.009286863 0.418851254
-#> Crm_prp 0.561825664 0.009377232
-#> Litercy 0.560570048 0.020095011
-#> Donatns 0.024138865 0.586720363
-#> Infants 0.436025658 0.012817054
-#> Suicids 0.548623327 0.152958961
if (run) {
g$PC1 <- o$x[, "PC1"]
brks <- c(min(g$PC1), natural_breaks(k=6, g["PC1"]), max(g$PC1))
@@ -289,7 +272,6 @@ Examples
tm_borders() # Fig. 1
else pplot(g["PC1"], breaks=brks)
}
-
if (run) {
g$PC2 <- -1*o$x[, "PC2"] # eigenvalue sign arbitrary
brks <- c(min(g$PC2), natural_breaks(k=6, g["PC2"]), max(g$PC2))
@@ -297,7 +279,6 @@ Examples
tm_borders() # Fig. 2
else plot(g["PC2"], breaks=brks)
}
-
if (run) {
w <- queen_weights(g)
lm_PC1 <- local_moran(w, g["PC1"], significance_cutoff=0.01,
@@ -310,7 +291,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 3
else plot(g["lm_PC1"])
}
-
if (run) {
set.seed(1)
lm_PC1_spdep <- localmoran_perm(g$PC1, lw, nsim=9999)
@@ -322,7 +302,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 3
else plot(g["lm_PC1_spdep"])
}
-
if (run) {
lg_PC1 <- local_g(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -340,7 +319,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 4 (guess)
else plot(g["lg_PC1"])
}
-
if (run) {
lc_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -352,14 +330,11 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 5
else plot(g["lc_PC1"])
}
-
if (run) {
set.seed(1)
system.time(lc_PC1_spdep <- localC_perm(g$PC1, lw, nsim=9999,
alternative="two.sided"))
}
-#> user system elapsed
-#> 0.376 0.001 0.377
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -372,7 +347,6 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
g$lc_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.01
@@ -381,7 +355,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 5
else plot(g["lc_PC1_spdep"])
}
-
if (run) {
g$both_PC1 <- interaction(g$lc_PC1, g$lm_PC1)
g$both_PC1 <- droplevels(g$both_PC1)
@@ -389,7 +362,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 6
else plot(g["both_PC1"])
}
-
if (run) {
lc005_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.005,
permutations=99999)
@@ -401,7 +373,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 7
else plot(g["lc005_PC1"])
}
-
if (run) {
g$lc005_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc005_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.005
@@ -410,7 +381,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 7
else plot(g["lc005_PC1_spdep"])
}
-
if (run) {
lc001_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.001,
permutations=99999)
@@ -430,7 +400,6 @@ Examples
else plot(g["lc001_PC1_spdep"])
}
}
-
if (run) {
lc_PC2 <- local_geary(w, g["PC2"], significance_cutoff=0.01,
permutations=99999)
@@ -442,7 +411,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 9
else plot(g["lc_PC2"])
}
-
if (run) {
lmc_PC <- local_multigeary(w, g[c("PC1","PC2")], significance_cutoff=0.00247,
permutations=99999)
@@ -452,25 +420,19 @@ Examples
g$lmc_PC <- droplevels(g$lmc_PC)
table(interaction((p.adjust(lisa_pvalues(lmc_PC), "fdr") < 0.01), g$lmc_PC))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_PC", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 10
else plot(g["lmc_PC"])
}
-
if (run) {
set.seed(1)
lmc_PC_spdep <- localC_perm(g[c("PC1","PC2")], lw, nsim=9999, alternative="two.sided")
all.equal(lisa_values(lmc_PC), c(lmc_PC_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_PC_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_PC))
}
-#> [1] 0.99052
if (run) {
g$lmc_PC_spdep <- attr(lmc_PC_spdep, "cluster")
is.na(g$lmc_PC_spdep) <- p.adjust(attr(lmc_PC_spdep, "pseudo-p")[,6], "fdr") > 0.01
@@ -479,7 +441,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 10
else plot(g["lmc_PC_spdep"])
}
-
if (run) {
lmc_vars <- local_multigeary(w, st_drop_geometry(g)[, 1:6],
significance_cutoff=0.00247, permutations=99999)
@@ -490,30 +451,22 @@ Examples
table(interaction((p.adjust(lisa_pvalues(lmc_vars), "fdr") < 0.01),
g$lmc_vars))
}
-#>
-#> FALSE.Positive TRUE.Positive
-#> 0 21
if (run) {
if (tmap_ok) tm_shape(g) + tm_fill("lmc_vars", textNA="Insignificant",
colorNA="gray95") + tm_borders() # Fig. 11
else plot(g["lmc_vars"])
}
-
if (run) {
set.seed(1)
system.time(lmc_vars_spdep <- localC_perm(st_drop_geometry(g)[, 1:6], lw,
nsim=9999, alternative="two.sided"))
}
-#> user system elapsed
-#> 0.824 0.002 0.828
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -526,15 +479,12 @@ Examples
set.coresOption(cores)
}
}
-#> [1] 1
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep1))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep1, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
g$lmc_vars_spdep <- attr(lmc_vars_spdep1, "cluster")
is.na(g$lmc_vars_spdep) <- p.adjust(attr(lmc_vars_spdep1, "pseudo-p")[,6], "fdr") > 0.01
@@ -543,10 +493,9 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 11
else plot(g["lmc_vars_spdep"])
}
-
-# }
-# \dontrun{
+}
+if (FALSE) {
library(reticulate)
use_python("/usr/bin/python", required = TRUE)
gp <- import("geopandas")
@@ -557,7 +506,6 @@ Examples
esda <- import("esda")
lM <- esda$Moran_Local(x, w)
all.equal(unname(localmoran(x, listw, mlvar=FALSE)[,1]), c(lM$Is))
-#> [1] TRUE
# confirm x and w the same
lC <- esda$Geary_Local(connectivity=w)$fit(scale(x))
# np$std missing ddof=1
@@ -566,19 +514,10 @@ Examples
# lC components probably wrongly ordered https://github.com/pysal/esda/issues/192
o <- match(round(D0, 6), round(lC$localG, 6))
all.equal(c(lC$localG)[o], D0)
-#> [1] TRUE
# simulation order not retained
lC$p_sim[o]
-#> [1] 0.278 0.460 0.112 0.019 0.121 0.038 0.330 0.412 0.147 0.001 0.113 0.141
-#> [13] 0.188 0.246 0.450 0.172 0.102 0.298 0.001 0.027 0.038 0.091 0.055 0.297
-#> [25] 0.153 0.031 0.107 0.280 0.450 0.001 0.066 0.104 0.080 0.231 0.306 0.001
-#> [37] 0.326 0.482 0.321 0.216 0.235 0.288
attr(C, "pseudo-p")[,6]
-#> [1] 0.408 0.374 0.144 0.056 0.010 0.284 0.032 0.264 0.186 0.004 0.150 0.224
-#> [13] 0.022 0.150 0.438 0.084 0.126 0.184 0.002 0.238 0.126 0.100 0.068 0.372
-#> [25] 0.124 0.234 0.230 0.016 0.318 0.176 0.272 0.164 0.164 0.206 0.402 0.250
-#> [37] 0.410 0.402 0.328 0.294 0.438 0.248
-# }
+}
diff --git a/docs/reference/mat2listw.html b/docs/reference/mat2listw.html
index 096c6e5d..656b612c 100644
--- a/docs/reference/mat2listw.html
+++ b/docs/reference/mat2listw.html
@@ -140,13 +140,14 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
-col005.w.mat <- nb2mat(col005, zero.policy=TRUE)
-col005.w.b <- mat2listw(col005.w.mat)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> 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"))
+#> Error in mat2listw(col005.w.mat, style = "W") :
+#> no-neighbour observations found, set zero.policy to TRUE
+col005.w.b <- mat2listw(col005.w.mat, style="W", zero.policy=TRUE)
summary(col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -161,9 +162,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
diffnb(col005, col005.w.b$neighbours)
#> Neighbour list object:
#> Number of regions: 49
@@ -177,9 +178,7 @@ Examples
#> 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")
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
+col005.w.b.3T <- mat2listw(col005.w.mat.3T, style="W", zero.policy=TRUE)
summary(col005.w.b.3T$neighbours)
#> Neighbour list object:
#> Number of regions: 147
@@ -194,21 +193,22 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 2 5 9 10 31 34 36 39 42 46 47 51 54 58 59 80 83 85 88 91 95 96 100 103 107 108 129 132 134 137 140 144 145 with 1 link
#> 6 most connected regions:
-#> with 9 links
+#> 11 16 60 65 109 114 with 9 links
run <- FALSE
if (require("spatialreg", quiet=TRUE)) run <- TRUE
if (run) {
W <- as(nb2listw(col005, style="W", zero.policy=TRUE), "CsparseMatrix")
-col005.spM <- mat2listw(W)
+try(col005.spM <- mat2listw(W))
+col005.spM <- mat2listw(W, style="W", zero.policy=TRUE)
summary(col005.spM$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, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
+#> Error in sn2listw(df, style = style, zero.policy = zero.policy) :
+#> no-neighbour observations found, set zero.policy to TRUE
#> Neighbour list object:
#> Number of regions: 49
#> Number of nonzero links: 170
@@ -222,9 +222,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
if (run) {
diffnb(col005, col005.spM$neighbours)
}
@@ -241,14 +241,9 @@ Examples
#> 49 disjoint connected subgraphs
if (run && require("Matrix", quiet=TRUE)) {
IW <- kronecker(Diagonal(3), W)
-col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W")
+col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W", zero.policy=TRUE)
summary(col005.spM.3T$neighbours)
}
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: 1, 3, 6, 21, 50, 52, 55, 70, 99, 101, 104, 119 are not origins
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
-#> Warning: zero sum general weights
#> Neighbour list object:
#> Number of regions: 147
#> Number of nonzero links: 510
@@ -262,9 +257,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 12 33 15 24 9 27 6 6 9 6
#> 33 least connected regions:
-#> with 1 link
+#> 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:
-#> with 9 links
+#> 11 16 60 65 109 114 with 9 links
diff --git a/docs/reference/nb2blocknb.html b/docs/reference/nb2blocknb.html
index c2276d23..460ced1b 100644
--- a/docs/reference/nb2blocknb.html
+++ b/docs/reference/nb2blocknb.html
@@ -127,96 +127,31 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
data(boston, package="spData")
summary(as.vector(table(boston.c$TOWN)))
-#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 1.0 2.0 4.0 5.5 7.0 30.0
townaggr <- aggregate(boston.utm, list(town=boston.c$TOWN), mean)
block.rel <- graph2nb(relativeneigh(as.matrix(townaggr[,2:3])),
as.character(townaggr[,1]), sym=TRUE)
block.rel
-#> Neighbour list object:
-#> Number of regions: 92
-#> Number of nonzero links: 240
-#> Percentage nonzero weights: 2.835539
-#> Average number of links: 2.608696
print(is.symmetric.nb(block.rel))
-#> [1] TRUE
plot(block.rel, as.matrix(townaggr[,2:3]))
points(boston.utm, pch=18, col="lightgreen")
-
block.nb <- nb2blocknb(block.rel, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 15234
-#> Percentage nonzero weights: 5.949945
-#> Average number of links: 30.10672
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
points(boston.utm, pch=18, col="lightgreen")
-
n.comp.nb(block.nb)$nc
-#> [1] 1
moran.test(boston.c$CMEDV, nb2listw(boston.soi))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(boston.soi)
-#>
-#> Moran I statistic standard deviate = 21.786, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.690285059 -0.001980198 0.001009685
-#>
moran.test(boston.c$CMEDV, nb2listw(block.nb))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb)
-#>
-#> Moran I statistic standard deviate = 22.455, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.3122905961 -0.0019801980 0.0001958827
-#>
block.nb <- nb2blocknb(NULL, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 4868
-#> Percentage nonzero weights: 1.901295
-#> Average number of links: 9.620553
-#> 17 regions with no links:
-#> 1 55 56 57 58 65 196 257 284 285 286 287 342 343 348 349 354
-#> 92 disjoint connected subgraphs
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
-
n.comp.nb(block.nb)$nc
-#> [1] 92
moran.test(boston.c$CMEDV, nb2listw(block.nb, zero.policy=TRUE), zero.policy=TRUE)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb, zero.policy = TRUE) n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 21.145, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.6188830566 -0.0020491803 0.0008623116
-#>
-# }
+}
diff --git a/docs/reference/nb2lines.html b/docs/reference/nb2lines.html
index 38b287ec..1b49a033 100644
--- a/docs/reference/nb2lines.html
+++ b/docs/reference/nb2lines.html
@@ -158,12 +158,12 @@ Examples
tf <- paste0(tempfile(), ".gpkg")
st_write(res, dsn=tf, driver="GPKG")
#> writing: substituting ENGCRS["Undefined Cartesian SRS with unknown unit"] for missing CRS
-#> Writing layer `file706d874b42afe' to data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Writing layer `file9f36c3d2d676e' to data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Writing 230 features with 5 fields and geometry type Line String.
inMap <- st_read(tf)
-#> Reading layer `file706d874b42afe' from data source
-#> `/tmp/RtmpMRGWgk/file706d874b42afe.gpkg' using driver `GPKG'
+#> Reading layer `file9f36c3d2d676e' from data source
+#> `/tmp/Rtmp84DoOB/file9f36c3d2d676e.gpkg' using driver `GPKG'
#> Simple feature collection with 230 features and 5 fields
#> Geometry type: LINESTRING
#> Dimension: XY
diff --git a/docs/reference/nb2listw.html b/docs/reference/nb2listw.html
index fd9ebeee..ef19145f 100644
--- a/docs/reference/nb2listw.html
+++ b/docs/reference/nb2listw.html
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: minmax
#> Weights constants summary:
diff --git a/docs/reference/nb2listwdist.html b/docs/reference/nb2listwdist.html
index a6ffbf2d..98dcafb5 100644
--- a/docs/reference/nb2listwdist.html
+++ b/docs/reference/nb2listwdist.html
@@ -171,43 +171,15 @@ Examples
#> Moran I statistic Expectation Variance
#> 0.473883371 -0.007042254 0.029994057
#>
-# \dontrun{
+if (FALSE) {
# Moran's I (life expectancy) with IDW with alpha = 2, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "idw",
alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 2.7769, p-value = 0.002744
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.473883371 -0.007042254 0.029994057
-#>
# Moran's I (life expectancy), DPD, alpha = 2, dmax = 1000 km, no coding scheme
world_weights <- nb2listwdist(nb_world, pts, type = "dpd",
dmax = 1000000, alpha = 2, zero.policy = TRUE)
moran.test(world$lifeExp, world_weights, zero.policy = TRUE, na.action = na.pass)
-#> Warning: NAs in lagged values
-#>
-#> Moran I test under randomisation
-#>
-#> data: world$lifeExp
-#> weights: world_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 8.8557, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.601498970 -0.007042254 0.004722063
-#>
# Boston examples
data(boston, package="spData")
boston_coords <- data.frame(x = boston.utm[,1], y = boston.utm[,2])
@@ -217,37 +189,11 @@ Examples
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "exp", alpha = 2,
style="raw", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 51.34, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.8845114518 -0.0019960080 0.0002981586
-#>
# Moran's I (crime) with idw weights with alpha = 2, coding scheme = W
boston_weights <- nb2listwdist(nb_boston, boston.geoms, type = "idw", alpha = 2,
style="W", zero.policy = TRUE)
moran.test(boston.c$CRIM, boston_weights, zero.policy = TRUE, na.action = na.pass)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CRIM
-#> weights: boston_weights n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 18.976, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.4392852330 -0.0019960080 0.0005408065
-#>
-# }
+}
diff --git a/docs/reference/nb2mat.html b/docs/reference/nb2mat.html
index 7f17e8bb..39ec4504 100644
--- a/docs/reference/nb2mat.html
+++ b/docs/reference/nb2mat.html
@@ -137,9 +137,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9
#> 4 11 5 8 3 9 2 2 3 2
#> 11 least connected regions:
-#> with 1 link
+#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link
#> 2 most connected regions:
-#> with 9 links
+#> 1038 1036 with 9 links
col005.w.mat <- nb2mat(col005, style="B", zero.policy=TRUE)
table(round(rowSums(col005.w.mat)))
#>
diff --git a/docs/reference/nblag.html b/docs/reference/nblag.html
index 08180e37..bd822ea5 100644
--- a/docs/reference/nblag.html
+++ b/docs/reference/nblag.html
@@ -122,9 +122,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.lags <- nblag(col.gal.nb, 2)
print(col.lags)
#> [[1]]
@@ -154,9 +154,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#> 1 2 5 4 2 3 5 4 2 3 7 3 4 1 1 1 1
#> 1 least connected region:
-#> with 1 link
+#> 36 with 1 link
#> 1 most connected region:
-#> with 17 links
+#> 25 with 17 links
plot(st_geometry(columbus), border="grey")
plot(col.gal.nb, coords, add=TRUE)
title(main="GAL order 1 (black) and 2 (red) links")
diff --git a/docs/reference/poly2nb.html b/docs/reference/poly2nb.html
index 1cad8008..79b13c61 100644
--- a/docs/reference/poly2nb.html
+++ b/docs/reference/poly2nb.html
@@ -174,10 +174,10 @@ Examples
nc.sids <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE)
system.time(xxnb <- poly2nb(nc.sids))
#> user system elapsed
-#> 0.018 0.000 0.018
+#> 0.019 0.000 0.019
system.time(xxnb <- poly2nb(as(nc.sids, "Spatial")))
#> user system elapsed
-#> 0.029 0.000 0.029
+#> 0.034 0.000 0.035
plot(st_geometry(nc.sids))
plot(xxnb, st_coordinates(st_centroid(nc.sids)), add=TRUE, col="blue")
#> Warning: st_centroid assumes attributes are constant over geometries
diff --git a/docs/reference/skater.html b/docs/reference/skater.html
index aefe0190..d2b9c2e6 100644
--- a/docs/reference/skater.html
+++ b/docs/reference/skater.html
@@ -292,22 +292,18 @@ Examples
table(res5d$groups)
#> Error in eval(expr, envir, enclos): object 'res5d' not found
-# \dontrun{
+if (FALSE) {
data(boston, package="spData")
bh.nb <- boston.soi
dpad <- data.frame(scale(boston.c[,c(7:10)]))
### calculating costs
system.time(lcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.042 0.000 0.043
### making listw
nb.w <- nb2listw(bh.nb, lcosts, style="B")
### find a minimum spanning tree
mst.bh <- mstree(nb.w,5)
### three groups with no restriction
system.time(res1 <- skater(mst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
library(parallel)
nc <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
# set nc to 1L here
@@ -321,26 +317,20 @@ Examples
}
### calculating costs
system.time(plcosts <- nbcosts(bh.nb, dpad))
-#> user system elapsed
-#> 0.04 0.00 0.04
all.equal(lcosts, plcosts, check.attributes=FALSE)
-#> [1] TRUE
### making listw
pnb.w <- nb2listw(bh.nb, plcosts, style="B")
### find a minimum spanning tree
pmst.bh <- mstree(pnb.w,5)
### three groups with no restriction
system.time(pres1 <- skater(pmst.bh[,1:2], dpad, 2))
-#> Error in if (w$num_obs < 1) { stop("The weights is not valid.")}: argument is of length zero
-#> Timing stopped at: 0 0 0
if(!get.mcOption()) {
set.ClusterOption(NULL)
stopCluster(cl)
}
all.equal(res1, pres1, check.attributes=FALSE)
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'target' in selecting a method for function 'all.equal': object 'res1' not found
invisible(set.coresOption(coresOpt))
-# }
+}
diff --git a/docs/reference/summary.nb.html b/docs/reference/summary.nb.html
index 79407597..674042bf 100644
--- a/docs/reference/summary.nb.html
+++ b/docs/reference/summary.nb.html
@@ -145,9 +145,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
col.listw <- nb2listw(col.gal.nb, style="W")
col.listw
#> Characteristics of weights list object:
@@ -173,9 +173,9 @@ Examples
#> 2 3 4 5 6 7 8 9 10
#> 7 7 13 4 9 6 1 1 1
#> 7 least connected regions:
-#> with 2 links
+#> 1 6 31 39 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -199,9 +199,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
try(nb2listw(nb, style="W"))
#> Error in nb2listw(nb, style = "W") : Empty neighbour sets found
summary(nb2listw(nb, style="W", zero.policy=TRUE))
@@ -219,9 +219,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
@@ -242,9 +242,9 @@ Examples
#> 0 2 3 4 5 6 7 8 9 10
#> 1 5 9 12 4 10 2 4 1 1
#> 5 least connected regions:
-#> with 2 links
+#> 1 6 42 46 47 with 2 links
#> 1 most connected region:
-#> with 10 links
+#> 20 with 10 links
#>
#> Weights style: W
#> Weights constants summary:
diff --git a/docs/reference/tolerance.nb.html b/docs/reference/tolerance.nb.html
index 14a72c2e..e793d2ac 100644
--- a/docs/reference/tolerance.nb.html
+++ b/docs/reference/tolerance.nb.html
@@ -130,24 +130,18 @@ Examples
### and a maximum distance of 2 m.
nb.ex<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of 30 degrees
### and a maximum distance of 2 m. The coordinates are rotated at an angle
### of 45 degrees counterclockwise.
nb.ex2<-tolerance.nb(ex.data, unit.angle = "degrees", max.dist=1,
tolerance = 30, rot.angle = 45)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
### Construct object of class nb with a tolerance angle of pi/8 radians
### and a maximum distance of 1.5 m. The coordinates are rotated at
### an angle of pi/4 radians clockwise.
nb.ex3<-tolerance.nb(ex.data, unit.angle = "radians", max.dist=1.5,
tolerance = pi/8, rot.angle = -pi*2/3)
-#> Warning: style is M (missing); style should be set to a valid value
-#> Warning: no-neighbour observations found, zero.policy set to TRUE
par(mfrow=c(1,3))
plot(nb.ex,ex.data,asp=1)
## user system elapsed
-## 0.0725 0.0000 0.0725
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll3a_nb, pts_ll5a_nb, check.attributes=FALSE)## [1] TRUE
system.time(for (i in 1:reps) pts_ll6_nb <- dnearneigh(pts_ll, d1=0, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097Minor differences may occur between the legacy ellipsoid and s2 spherical approaches:
@@ -625,7 +625,7 @@Distance neighbours
system.time(for (i in 1:reps) pts_ll6a_nb <- dnearneigh(pts_ll, d1=5, d2=0.75*max_1nn_ll, use_s2=FALSE))/reps
## user system elapsed
-## 0.0097 0.0000 0.0097
if (packageVersion("s2") > "1.0.7") all.equal(pts_ll5a_nb, pts_ll6a_nb, check.attributes=FALSE)## [1] "Component 20: Numeric: lengths (6, 5) differ"
@@ -676,7 +676,7 @@ Contiguity neighbours for spherical polygon support
system.time(for (i in 1:reps) NY8_sf_1_nb_ll <- poly2nb(NY8_sf_ll, queen=TRUE, snap=eps))/reps
## user system elapsed
-## 0.1553 0.0013 0.1572
+## 0.1524 0.0019 0.1548
all.equal(NY8_sf_1_nb, NY8_sf_1_nb_ll, check.attributes=FALSE)## [1] TRUEExamples
#> weights: nb2listw(ncCC89_nb, style = "B", zero.policy = TRUE) #> number of simulations + 1: 1000 #> -#> statistic = 0.25789, observed rank = 998, p-value = 0.004 +#> statistic = 0.25789, observed rank = 1000, p-value < 2.2e-16 #> alternative hypothesis: two.sided #> sids.p <- nc.sids$SID74 / nc.sids$BIR74 diff --git a/docs/reference/LOSH.mc-1.png b/docs/reference/LOSH.mc-1.png index 5a905795..3b4cabfc 100644 Binary files a/docs/reference/LOSH.mc-1.png and b/docs/reference/LOSH.mc-1.png differ diff --git a/docs/reference/LOSH.mc.html b/docs/reference/LOSH.mc.html index c0a6763a..30a4b3ed 100644 --- a/docs/reference/LOSH.mc.html +++ b/docs/reference/LOSH.mc.html @@ -154,10 +154,10 @@Examples
summary(resLOSH_mc) #> Hi x_bar_i ei Pr() #> Min. :0.03438 Min. :13.85 Min. : 0.0298 Min. :0.009901 -#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.118812 -#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.702970 -#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.542332 -#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.891089 +#> 1st Qu.:0.23838 1st Qu.:24.71 1st Qu.: 7.0114 1st Qu.:0.079208 +#> Median :0.66689 Median :35.90 Median : 52.1094 Median :0.712871 +#> Mean :1.06592 Mean :34.88 Mean : 151.9232 Mean :0.554860 +#> 3rd Qu.:1.59680 3rd Qu.:45.39 3rd Qu.: 105.0551 3rd Qu.:0.910891 #> Max. :4.68765 Max. :54.91 Max. :2455.2201 Max. :0.990099 resLOSH_cs <- LOSH.cs(columbus$CRIME, nb2listw(col.gal.nb)) summary(resLOSH_cs) diff --git a/docs/reference/bhicv.html b/docs/reference/bhicv.html index 2e1f9102..072425ba 100644 --- a/docs/reference/bhicv.html +++ b/docs/reference/bhicv.html @@ -105,7 +105,7 @@Examples
bh <- st_read(system.file("etc/shapes/bhicv.shp",
package="spdep")[1])
#> Reading layer `bhicv' from data source
-#> `/tmp/Rtmpu0jdcX/temp_libpath70612704761e5/spdep/etc/shapes/bhicv.shp'
+#> `/tmp/RtmpU193Yl/temp_libpath9f2c94443f9d4/spdep/etc/shapes/bhicv.shp'
#> using driver `ESRI Shapefile'
#> Simple feature collection with 98 features and 8 fields
#> Geometry type: POLYGON
diff --git a/docs/reference/cell2nb.html b/docs/reference/cell2nb.html
index 9b67215a..e3badaf1 100644
--- a/docs/reference/cell2nb.html
+++ b/docs/reference/cell2nb.html
@@ -137,9 +137,9 @@ Examples
#> 2 3 4
#> 4 20 25
#> 4 least connected regions:
-#> with 2 links
+#> 1:1 7:1 1:7 7:7 with 2 links
#> 25 most connected regions:
-#> with 4 links
+#> 2:2 3:2 4:2 5:2 6:2 2:3 3:3 4:3 5:3 6:3 2:4 3:4 4:4 5:4 6:4 2:5 3:5 4:5 5:5 6:5 2:6 3:6 4:6 5:6 6:6 with 4 links
xyc <- attr(nb7rt, "region.id")
xy <- matrix(as.integer(unlist(strsplit(xyc, ":"))), ncol=2, byrow=TRUE)
plot(nb7rt, xy)
@@ -156,9 +156,9 @@ Examples
#> 4
#> 49
#> 49 least connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
#> 49 most connected regions:
-#> with 4 links
+#> 1:1 2:1 3:1 4:1 5:1 6:1 7:1 1:2 2:2 3:2 4:2 5:2 6:2 7:2 1:3 2:3 3:3 4:3 5:3 6:3 7:3 1:4 2:4 3:4 4:4 5:4 6:4 7:4 1:5 2:5 3:5 4:5 5:5 6:5 7:5 1:6 2:6 3:6 4:6 5:6 6:6 7:6 1:7 2:7 3:7 4:7 5:7 6:7 7:7 with 4 links
run <- FALSE
if (require("sp", quietly=TRUE)) run <- TRUE
if (run) {
diff --git a/docs/reference/dnearneigh.html b/docs/reference/dnearneigh.html
index afbf4662..937dd9d6 100644
--- a/docs/reference/dnearneigh.html
+++ b/docs/reference/dnearneigh.html
@@ -149,9 +149,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10 11
#> 4 8 6 2 5 8 6 2 6 1 1
#> 4 least connected regions:
-#> with 1 link
+#> 6 10 21 47 with 1 link
#> 1 most connected region:
-#> with 11 links
+#> 28 with 11 links
opar <- par(no.readonly=TRUE)
plot(st_geometry(columbus), border="grey", reset=FALSE,
main=paste("Distance based neighbours 0-", format(all.linked), sep=""))
@@ -198,9 +198,9 @@ Examples
#> 1 2 3 4 5 7 8 9 10
#> 11 9 4 8 4 4 4 3 1
#> 11 least connected regions:
-#> with 1 link
+#> 2 4 8 10 24 26 29 32 35 41 45 with 1 link
#> 1 most connected region:
-#> with 10 links
+#> 28 with 10 links
gck1 <- knn2nb(knearneigh(xy, k=1, longlat=TRUE))
(all.linked <- max(unlist(nbdists(gck1, xy, longlat=TRUE))))
#> [1] 523.5819
@@ -217,9 +217,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
plot(ll.nb, xy)
plot(diffnb(ll.nb, gc.nb), xy, add=TRUE, col="red", lty=2)
title(main="Differences Euclidean/Great Circle")
@@ -239,13 +239,13 @@ Examples
gck1b <- knn2nb(knearneigh(xy1, k=1))
system.time(o <- nbdists(gck1b, xy1))
#> user system elapsed
-#> 0.007 0.000 0.006
+#> 0.006 0.000 0.006
(all.linked <- max(unlist(o)))
#> [1] 522.4464
# use s2 brute-force dwithin_matrix approach for s2 <= 1.0.7
system.time(gc.nb.dwithin <- dnearneigh(xy1, 0, all.linked, use_s2=TRUE, dwithin=TRUE))
#> user system elapsed
-#> 0.01 0.00 0.01
+#> 0.009 0.000 0.010
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -258,9 +258,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use s2 closest_edges approach s2 > 1.0.7
if (packageVersion("s2") > "1.0.7") {
(system.time(gc.nb.closest <- dnearneigh(xy1, 0, all.linked, dwithin=FALSE)))
@@ -288,9 +288,9 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") {
summary(gc.nb.closest, xy1, scale=0.5)
}
@@ -307,13 +307,13 @@ Examples
#> 0 1 2 3 4 5 6 7 8 9 10
#> 1 5 8 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 4 8 29 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
# use legacy symmetric brute-force approach
system.time(gc.nb.legacy <- dnearneigh(xy1, 0, all.linked, use_s2=FALSE))
#> user system elapsed
-#> 0.003 0.000 0.003
+#> 0.003 0.000 0.004
summary(gc.nb, xy1, scale=0.5)
#> Neighbour list object:
#> Number of regions: 48
@@ -326,9 +326,9 @@ Examples
#> 1 2 3 4 5 6 7 8 9 10
#> 5 9 6 8 5 2 3 3 5 2
#> 5 least connected regions:
-#> with 1 link
+#> 2 4 8 41 45 with 1 link
#> 2 most connected regions:
-#> with 10 links
+#> 7 28 with 10 links
if (packageVersion("s2") > "1.0.7") all.equal(gc.nb.closest, gc.nb.dwithin, check.attributes=FALSE)
#> [1] TRUE
# legacy is ellipsoidal, s2 spherical, so minor differences expected
diff --git a/docs/reference/edit.nb.html b/docs/reference/edit.nb.html
index 44773c64..d59c98e1 100644
--- a/docs/reference/edit.nb.html
+++ b/docs/reference/edit.nb.html
@@ -121,12 +121,11 @@ See also
Examples
- # \dontrun{
+ if (FALSE) {
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
class(columbus)
-#> [1] "sf" "data.frame"
if (FALSE) nnb1 <- edit.nb(col.gal.nb, polys=as(columbus, "Spatial"))
-# }
+}
diff --git a/docs/reference/geary.mc.html b/docs/reference/geary.mc.html
index 236e80b0..3164b159 100644
--- a/docs/reference/geary.mc.html
+++ b/docs/reference/geary.mc.html
@@ -160,12 +160,12 @@ Examples
#> alternative hypothesis: less
#>
mean(sim1$res)
-#> [1] 1.012612
+#> [1] 0.9890232
var(sim1$res)
-#> [1] 0.0119779
+#> [1] 0.01235098
summary(sim1$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.5299 0.9401 1.0218 1.0126 1.0906 1.2388
+#> 0.5299 0.9288 1.0029 0.9890 1.0515 1.3024
colold.lags <- nblag(COL.nb, 3)
sim2 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[2]],
style="W"), nsim=99)
@@ -177,12 +177,12 @@ Examples
#> weights: nb2listw(colold.lags[[2]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 0.81129, observed rank = 1, p-value = 0.01
+#> statistic = 0.81129, observed rank = 5, p-value = 0.05
#> alternative hypothesis: greater
#>
summary(sim2$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8113 0.9541 1.0113 1.0014 1.0569 1.1619
+#> 0.7211 0.9490 0.9896 0.9948 1.0550 1.1807
sim3 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[3]],
style="W"), nsim=99)
sim3
@@ -193,12 +193,12 @@ Examples
#> weights: nb2listw(colold.lags[[3]], style = "W")
#> number of simulations + 1: 100
#>
-#> statistic = 1.1303, observed rank = 92, p-value = 0.92
+#> statistic = 1.1303, observed rank = 95, p-value = 0.95
#> alternative hypothesis: greater
#>
summary(sim3$res)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 0.8264 0.9586 1.0079 1.0099 1.0562 1.2047
+#> 0.8034 0.9550 1.0098 1.0076 1.0474 1.2499
Examples
#> 0 1 2 3 4 5 #> 1 8 10 18 55 8 #> 8 least connected regions: -#> with 1 link +#> 46 47 48 49 96 97 98 99 with 1 link #> 8 most connected regions: -#> with 5 links +#> 10 15 20 25 30 35 40 45 with 5 links grd <- as.matrix(expand.grid(x=1:5, y=1:5)) #gridded data r2 <- gabrielneigh(grd) set.seed(1) diff --git a/docs/reference/grid2nb.html b/docs/reference/grid2nb.html index 27f554ac..eb1e9527 100644 --- a/docs/reference/grid2nb.html +++ b/docs/reference/grid2nb.html @@ -150,9 +150,9 @@Examples
#> 7 11 17 26 #> 8 36 54 27 #> 8 least connected regions: -#> with 7 links +#> 1 5 21 25 101 105 121 125 with 7 links #> 27 most connected regions: -#> with 26 links +#> 32 33 34 37 38 39 42 43 44 57 58 59 62 63 64 67 68 69 82 83 84 87 88 89 92 93 94 with 26 links if (require("sp", quietly=TRUE)) { gt <- GridTopology(c(.125,.1), c(.25,.2), c(4L, 5L)) nb1 <- grid2nb(gt, queen = FALSE) diff --git a/docs/reference/include.self.html b/docs/reference/include.self.html index 1b8fad9a..287bd036 100644 --- a/docs/reference/include.self.html +++ b/docs/reference/include.self.html @@ -114,9 +114,9 @@Examples
#> 2 3 4 5 6 7 8 9 10 #> 7 7 13 4 9 6 1 1 1 #> 7 least connected regions: -#> with 2 links +#> 1 6 31 39 42 46 47 with 2 links #> 1 most connected region: -#> with 10 links +#> 20 with 10 links summary(include.self(col.gal.nb), coords) #> Neighbour list object: #> Number of regions: 49 @@ -128,9 +128,9 @@Examples
#> 3 4 5 6 7 8 9 10 11 #> 7 7 13 4 9 6 1 1 1 #> 7 least connected regions: -#> with 3 links +#> 1 6 31 39 42 46 47 with 3 links #> 1 most connected region: -#> with 11 links +#> 20 with 11 links summary(remove.self(include.self(col.gal.nb)), coords) #> Neighbour list object: #> Number of regions: 49 @@ -142,9 +142,9 @@Examples
#> 2 3 4 5 6 7 8 9 10 #> 7 7 13 4 9 6 1 1 1 #> 7 least connected regions: -#> with 2 links +#> 1 6 31 39 42 46 47 with 2 links #> 1 most connected region: -#> with 10 links +#> 20 with 10 links diff --git a/docs/reference/joincount.multi.html b/docs/reference/joincount.multi.html index 528fbbcd..8415a202 100644 --- a/docs/reference/joincount.multi.html +++ b/docs/reference/joincount.multi.html @@ -161,95 +161,30 @@Examples
#> high:high 52.000 28.138 18.342 5.5716 #> high:low 28.000 61.170 25.882 -6.5200 #> Jtot 28.000 61.170 33.190 -5.7577 -# \dontrun{ +if (FALSE) { data(oldcol) HICRIME <- cut(COL.OLD$CRIME, breaks=c(0,35,80), labels=c("low","high")) names(HICRIME) <- rownames(COL.OLD) joincount.multi(HICRIME, nb2listw(COL.nb, style="B")) -#> Joincount Expected Variance z-value -#> low:low 34.000 29.592 18.895 1.0141 -#> high:high 54.000 27.224 17.888 6.3307 -#> high:low 28.000 59.184 26.233 -6.0884 -#> Jtot 28.000 59.184 26.233 -6.0884 data(hopkins, package="spData") image(1:32, 1:32, hopkins[5:36,36:5], breaks=c(-0.5, 3.5, 20), col=c("white", "black")) box() -
hopkins.rook.nb <- cell2nb(32, 32, type="rook")
unlist(spweights.constants(nb2listw(hopkins.rook.nb, style="B")))
-#> n n1 n2 n3 nn S0 S1 S2
-#> 1024 1023 1022 1021 1048576 3968 7936 61984
hopkins.queen.nb <- cell2nb(32, 32, type="queen")
hopkins.bishop.nb <- diffnb(hopkins.rook.nb, hopkins.queen.nb, verbose=FALSE)
hopkins4 <- hopkins[5:36,36:5]
hopkins4[which(hopkins4 > 3, arr.ind=TRUE)] <- 4
hopkins4.f <- factor(hopkins4)
table(hopkins4.f)
-#> hopkins4.f
-#> 0 1 2 3 4
-#> 657 215 98 30 24
joincount.multi(hopkins4.f, nb2listw(hopkins.rook.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 864.00000 816.27273 116.05233 4.4304
-#> 1:1 94.00000 87.14015 55.25216 0.9229
-#> 2:2 18.00000 18.00379 14.81562 -0.0010
-#> 3:3 2.00000 1.64773 1.55539 0.2825
-#> 4:4 5.00000 1.04545 0.99845 3.9576
-#> 1:0 503.00000 535.05682 227.76750 -2.1241
-#> 2:0 213.00000 243.88636 97.21769 -3.1325
-#> 2:1 99.00000 79.81061 59.01930 2.4978
-#> 3:0 61.00000 74.65909 28.58592 -2.5547
-#> 3:1 28.00000 24.43182 18.99976 0.8186
-#> 3:2 15.00000 11.13636 9.82411 1.2327
-#> 4:0 40.00000 59.72727 22.78583 -4.1327
-#> 4:1 23.00000 19.54545 15.26564 0.8842
-#> 4:2 14.00000 8.90909 7.90051 1.8112
-#> 4:3 5.00000 2.72727 2.58616 1.4133
-#> Jtot 1001.00000 1059.89015 273.78610 -3.5591
cat("replicates Upton & Fingleton table 3.4 (p. 166)\n")
-#> replicates Upton & Fingleton table 3.4 (p. 166)
joincount.multi(hopkins4.f, nb2listw(hopkins.bishop.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 823.00000 790.76420 144.44877 2.6821
-#> 1:1 101.00000 84.41702 55.98143 2.2164
-#> 2:2 19.00000 17.44117 14.61542 0.4077
-#> 3:3 3.00000 1.59624 1.51444 1.1407
-#> 4:4 3.00000 1.01278 0.97111 2.0166
-#> 1:0 497.00000 518.33629 234.93545 -1.3920
-#> 2:0 216.00000 236.26491 104.42142 -1.9831
-#> 2:1 81.00000 77.31652 58.70829 0.4807
-#> 3:0 58.00000 72.32599 31.49151 -2.5529
-#> 3:1 21.00000 23.66832 18.85316 -0.6145
-#> 3:2 17.00000 10.78835 9.62487 2.0022
-#> 4:0 48.00000 57.86080 25.15973 -1.9659
-#> 4:1 21.00000 18.93466 15.14473 0.5307
-#> 4:2 10.00000 8.63068 7.73708 0.4923
-#> 4:3 4.00000 2.64205 2.51686 0.8560
-#> Jtot 973.00000 1026.76858 284.51030 -3.1877
cat("replicates Upton & Fingleton table 3.6 (p. 168)\n")
-#> replicates Upton & Fingleton table 3.6 (p. 168)
joincount.multi(hopkins4.f, nb2listw(hopkins.queen.nb, style="B"))
-#> Joincount Expected Variance z-value
-#> 0:0 1687.0000 1607.0369 303.8034 4.5877
-#> 1:1 195.0000 171.5572 114.2057 2.1936
-#> 2:2 37.0000 35.4450 29.6821 0.2854
-#> 3:3 5.0000 3.2440 3.0687 1.0024
-#> 4:4 8.0000 2.0582 1.9674 4.2361
-#> 1:0 1000.0000 1053.3931 480.6959 -2.4353
-#> 2:0 429.0000 480.1513 215.0360 -3.4882
-#> 2:1 180.0000 157.1271 119.3987 2.0932
-#> 3:0 119.0000 146.9851 65.1029 -3.4684
-#> 3:1 49.0000 48.1001 38.3268 0.1454
-#> 3:2 32.0000 21.9247 19.5237 2.2802
-#> 4:0 88.0000 117.5881 52.0312 -4.1019
-#> 4:1 44.0000 38.4801 30.7868 0.9948
-#> 4:2 24.0000 17.5398 15.6933 1.6308
-#> 4:3 9.0000 5.3693 5.0994 1.6078
-#> Jtot 1974.0000 2086.6587 582.8326 -4.6665
cat("replicates Upton & Fingleton table 3.7 (p. 169)\n")
-#> replicates Upton & Fingleton table 3.7 (p. 169)
-# }
+}
diff --git a/docs/reference/knearneigh.html b/docs/reference/knearneigh.html
index f5a9e742..5f4466fa 100644
--- a/docs/reference/knearneigh.html
+++ b/docs/reference/knearneigh.html
@@ -161,9 +161,9 @@ Examples
#> 4 #> 48 #> 48 least connected regions: -#> with 4 links +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links #> 48 most connected regions: -#> with 4 links +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links summary(gck4.nb, xy, longlat=TRUE, scale=0.5) #> Neighbour list object: #> Number of regions: 48 @@ -176,9 +176,9 @@Examples
#> 4 #> 48 #> 48 least connected regions: -#> with 4 links +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links #> 48 most connected regions: -#> with 4 links +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links #xy1 <- SpatialPoints((as.data.frame(state.center))[m50.48,], # proj4string=CRS("+proj=longlat +ellps=GRS80")) @@ -191,7 +191,7 @@Examples
sf_use_s2(TRUE) system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4))) #> user system elapsed -#> 0.008 0.000 0.009 +#> 0.009 0.000 0.010 summary(gck4a.nb, xy1, scale=0.5) #> Neighbour list object: #> Number of regions: 48 @@ -204,14 +204,14 @@Examples
#> 4 #> 48 #> 48 least connected regions: -#> with 4 links +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links #> 48 most connected regions: -#> with 4 links +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links sf_use_s2(FALSE) #> Spherical geometry (s2) switched off system.time(gck4a.nb <- knn2nb(knearneigh(xy1, k=4))) #> user system elapsed -#> 0.005 0.000 0.006 +#> 0.005 0.000 0.005 summary(gck4a.nb, xy1, scale=0.5) #> Neighbour list object: #> Number of regions: 48 @@ -224,9 +224,9 @@Examples
#> 4 #> 48 #> 48 least connected regions: -#> with 4 links +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links #> 48 most connected regions: -#> with 4 links +#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 with 4 links sf_use_s2(old_use_s2) #> Spherical geometry (s2) switched on diff --git a/docs/reference/localC.html b/docs/reference/localC.html index 1efd5e5b..03cfd45b 100644 --- a/docs/reference/localC.html +++ b/docs/reference/localC.html @@ -245,11 +245,9 @@Examples
} #> [1] 0.985611 # pseudo-p values probably wrongly folded https://github.com/GeoDaCenter/rgeoda/issues/28 -# \dontrun{ +if (FALSE) { tmap_ok <- FALSE if (require(tmap, quietly=TRUE)) tmap_ok <- TRUE -#> Breaking News: tmap 3.x is retiring. Please test v4, e.g. with -#> remotes::install_github('r-tmap/tmap') if (run) { # doi: 10.1111/gean.12164 guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda") @@ -263,25 +261,10 @@Examples
moran(g$Infants, lw, n=nrow(g), S0=Szero(lw))$I moran(g$Suicids, lw, n=nrow(g), S0=Szero(lw))$I } -#> Reading layer `Guerry' from data source -#> `/home/rsb/lib/r_libs/rgeoda/extdata/Guerry.shp' using driver `ESRI Shapefile' -#> Simple feature collection with 85 features and 29 fields -#> Geometry type: MULTIPOLYGON -#> Dimension: XY -#> Bounding box: xmin: 47680 ymin: 1703258 xmax: 1031401 ymax: 2677441 -#> Projected CRS: NTF (Paris) / Lambert zone II -#> [1] 0.4016812 if (run) { o <- prcomp(st_drop_geometry(g), scale.=TRUE) cor(st_drop_geometry(g), o$x[,1:2])^2 #(Tab. 2) } -#> PC1 PC2 -#> Crm_prs 0.009286863 0.418851254 -#> Crm_prp 0.561825664 0.009377232 -#> Litercy 0.560570048 0.020095011 -#> Donatns 0.024138865 0.586720363 -#> Infants 0.436025658 0.012817054 -#> Suicids 0.548623327 0.152958961 if (run) { g$PC1 <- o$x[, "PC1"] brks <- c(min(g$PC1), natural_breaks(k=6, g["PC1"]), max(g$PC1)) @@ -289,7 +272,6 @@Examples
tm_borders() # Fig. 1 else pplot(g["PC1"], breaks=brks) } -
if (run) {
g$PC2 <- -1*o$x[, "PC2"] # eigenvalue sign arbitrary
brks <- c(min(g$PC2), natural_breaks(k=6, g["PC2"]), max(g$PC2))
@@ -297,7 +279,6 @@ Examples
tm_borders() # Fig. 2 else plot(g["PC2"], breaks=brks) } -
if (run) {
w <- queen_weights(g)
lm_PC1 <- local_moran(w, g["PC1"], significance_cutoff=0.01,
@@ -310,7 +291,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 3 else plot(g["lm_PC1"]) } -
if (run) {
set.seed(1)
lm_PC1_spdep <- localmoran_perm(g$PC1, lw, nsim=9999)
@@ -322,7 +302,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 3 else plot(g["lm_PC1_spdep"]) } -
if (run) {
lg_PC1 <- local_g(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -340,7 +319,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 4 (guess) else plot(g["lg_PC1"]) } -
if (run) {
lc_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.01,
permutations=99999)
@@ -352,14 +330,11 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 5 else plot(g["lc_PC1"]) } -
if (run) {
set.seed(1)
system.time(lc_PC1_spdep <- localC_perm(g$PC1, lw, nsim=9999,
alternative="two.sided"))
}
-#> user system elapsed
-#> 0.376 0.001 0.377
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -372,7 +347,6 @@ Examples
set.coresOption(cores) } } -#> [1] 1 if (run) { g$lc_PC1_spdep <- attr(lc_PC1_spdep, "cluster") is.na(g$lc_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.01 @@ -381,7 +355,6 @@Examples
colorNA="gray95") + tm_borders() # rep. Fig. 5 else plot(g["lc_PC1_spdep"]) } -
if (run) {
g$both_PC1 <- interaction(g$lc_PC1, g$lm_PC1)
g$both_PC1 <- droplevels(g$both_PC1)
@@ -389,7 +362,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 6 else plot(g["both_PC1"]) } -
if (run) {
lc005_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.005,
permutations=99999)
@@ -401,7 +373,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 7 else plot(g["lc005_PC1"]) } -
if (run) {
g$lc005_PC1_spdep <- attr(lc_PC1_spdep, "cluster")
is.na(g$lc005_PC1_spdep) <- attr(lc_PC1_spdep, "pseudo-p")[,6] > 0.005
@@ -410,7 +381,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 7 else plot(g["lc005_PC1_spdep"]) } -
if (run) {
lc001_PC1 <- local_geary(w, g["PC1"], significance_cutoff=0.001,
permutations=99999)
@@ -430,7 +400,6 @@ Examples
else plot(g["lc001_PC1_spdep"]) } } -
if (run) {
lc_PC2 <- local_geary(w, g["PC2"], significance_cutoff=0.01,
permutations=99999)
@@ -442,7 +411,6 @@ Examples
colorNA="gray95") + tm_borders() # Fig. 9 else plot(g["lc_PC2"]) } -
if (run) {
lmc_PC <- local_multigeary(w, g[c("PC1","PC2")], significance_cutoff=0.00247,
permutations=99999)
@@ -452,25 +420,19 @@ Examples
g$lmc_PC <- droplevels(g$lmc_PC) table(interaction((p.adjust(lisa_pvalues(lmc_PC), "fdr") < 0.01), g$lmc_PC)) } -#> -#> FALSE.Positive TRUE.Positive -#> 0 21 if (run) { if (tmap_ok) tm_shape(g) + tm_fill("lmc_PC", textNA="Insignificant", colorNA="gray95") + tm_borders() # Fig. 10 else plot(g["lmc_PC"]) } -
if (run) {
set.seed(1)
lmc_PC_spdep <- localC_perm(g[c("PC1","PC2")], lw, nsim=9999, alternative="two.sided")
all.equal(lisa_values(lmc_PC), c(lmc_PC_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_PC_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_PC))
}
-#> [1] 0.99052
if (run) {
g$lmc_PC_spdep <- attr(lmc_PC_spdep, "cluster")
is.na(g$lmc_PC_spdep) <- p.adjust(attr(lmc_PC_spdep, "pseudo-p")[,6], "fdr") > 0.01
@@ -479,7 +441,6 @@ Examples
colorNA="gray95") + tm_borders() # rep. Fig. 10 else plot(g["lmc_PC_spdep"]) } -
if (run) {
lmc_vars <- local_multigeary(w, st_drop_geometry(g)[, 1:6],
significance_cutoff=0.00247, permutations=99999)
@@ -490,30 +451,22 @@ Examples
table(interaction((p.adjust(lisa_pvalues(lmc_vars), "fdr") < 0.01), g$lmc_vars)) } -#> -#> FALSE.Positive TRUE.Positive -#> 0 21 if (run) { if (tmap_ok) tm_shape(g) + tm_fill("lmc_vars", textNA="Insignificant", colorNA="gray95") + tm_borders() # Fig. 11 else plot(g["lmc_vars"]) } -
if (run) {
set.seed(1)
system.time(lmc_vars_spdep <- localC_perm(st_drop_geometry(g)[, 1:6], lw,
nsim=9999, alternative="two.sided"))
}
-#> user system elapsed
-#> 0.824 0.002 0.828
if (run) {
all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep))
}
-#> [1] TRUE
if (run) {
cor(attr(lmc_vars_spdep, "pseudo-p")[,6], lisa_pvalues(lmc_vars))
}
-#> [1] 0.9938141
if (run) {
if (require(parallel, quietly=TRUE)) {
ncpus <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
@@ -526,15 +479,12 @@ Examples
set.coresOption(cores) } } -#> [1] 1 if (run) { all.equal(lisa_values(lmc_vars), c(lmc_vars_spdep1)) } -#> [1] TRUE if (run) { cor(attr(lmc_vars_spdep1, "pseudo-p")[,6], lisa_pvalues(lmc_vars)) } -#> [1] 0.9938141 if (run) { g$lmc_vars_spdep <- attr(lmc_vars_spdep1, "cluster") is.na(g$lmc_vars_spdep) <- p.adjust(attr(lmc_vars_spdep1, "pseudo-p")[,6], "fdr") > 0.01 @@ -543,10 +493,9 @@Examples
colorNA="gray95") + tm_borders() # rep. Fig. 11 else plot(g["lmc_vars_spdep"]) } -
-# }
-# \dontrun{
+}
+if (FALSE) {
library(reticulate)
use_python("/usr/bin/python", required = TRUE)
gp <- import("geopandas")
@@ -557,7 +506,6 @@ Examples
esda <- import("esda") lM <- esda$Moran_Local(x, w) all.equal(unname(localmoran(x, listw, mlvar=FALSE)[,1]), c(lM$Is)) -#> [1] TRUE # confirm x and w the same lC <- esda$Geary_Local(connectivity=w)$fit(scale(x)) # np$std missing ddof=1 @@ -566,19 +514,10 @@Examples
# lC components probably wrongly ordered https://github.com/pysal/esda/issues/192 o <- match(round(D0, 6), round(lC$localG, 6)) all.equal(c(lC$localG)[o], D0) -#> [1] TRUE # simulation order not retained lC$p_sim[o] -#> [1] 0.278 0.460 0.112 0.019 0.121 0.038 0.330 0.412 0.147 0.001 0.113 0.141 -#> [13] 0.188 0.246 0.450 0.172 0.102 0.298 0.001 0.027 0.038 0.091 0.055 0.297 -#> [25] 0.153 0.031 0.107 0.280 0.450 0.001 0.066 0.104 0.080 0.231 0.306 0.001 -#> [37] 0.326 0.482 0.321 0.216 0.235 0.288 attr(C, "pseudo-p")[,6] -#> [1] 0.408 0.374 0.144 0.056 0.010 0.284 0.032 0.264 0.186 0.004 0.150 0.224 -#> [13] 0.022 0.150 0.438 0.084 0.126 0.184 0.002 0.238 0.126 0.100 0.068 0.372 -#> [25] 0.124 0.234 0.230 0.016 0.318 0.176 0.272 0.164 0.164 0.206 0.402 0.250 -#> [37] 0.410 0.402 0.328 0.294 0.438 0.248 -# } +} diff --git a/docs/reference/mat2listw.html b/docs/reference/mat2listw.html index 096c6e5d..656b612c 100644 --- a/docs/reference/mat2listw.html +++ b/docs/reference/mat2listw.html @@ -140,13 +140,14 @@Examples
#> 0 1 2 3 4 5 6 7 8 9 #> 4 11 5 8 3 9 2 2 3 2 #> 11 least connected regions: -#> with 1 link +#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link #> 2 most connected regions: -#> with 9 links -col005.w.mat <- nb2mat(col005, zero.policy=TRUE) -col005.w.b <- mat2listw(col005.w.mat) -#> Warning: style is M (missing); style should be set to a valid value -#> Warning: no-neighbour observations found, zero.policy set to TRUE +#> 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")) +#> Error in mat2listw(col005.w.mat, style = "W") : +#> no-neighbour observations found, set zero.policy to TRUE +col005.w.b <- mat2listw(col005.w.mat, style="W", zero.policy=TRUE) summary(col005.w.b$neighbours) #> Neighbour list object: #> Number of regions: 49 @@ -161,9 +162,9 @@Examples
#> 0 1 2 3 4 5 6 7 8 9 #> 4 11 5 8 3 9 2 2 3 2 #> 11 least connected regions: -#> with 1 link +#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link #> 2 most connected regions: -#> with 9 links +#> 1038 1036 with 9 links diffnb(col005, col005.w.b$neighbours) #> Neighbour list object: #> Number of regions: 49 @@ -177,9 +178,7 @@Examples
#> 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") -#> Warning: no-neighbour observations found, zero.policy set to TRUE -#> Warning: zero sum general weights +col005.w.b.3T <- mat2listw(col005.w.mat.3T, style="W", zero.policy=TRUE) summary(col005.w.b.3T$neighbours) #> Neighbour list object: #> Number of regions: 147 @@ -194,21 +193,22 @@Examples
#> 0 1 2 3 4 5 6 7 8 9 #> 12 33 15 24 9 27 6 6 9 6 #> 33 least connected regions: -#> with 1 link +#> 2 5 9 10 31 34 36 39 42 46 47 51 54 58 59 80 83 85 88 91 95 96 100 103 107 108 129 132 134 137 140 144 145 with 1 link #> 6 most connected regions: -#> with 9 links +#> 11 16 60 65 109 114 with 9 links run <- FALSE if (require("spatialreg", quiet=TRUE)) run <- TRUE if (run) { W <- as(nb2listw(col005, style="W", zero.policy=TRUE), "CsparseMatrix") -col005.spM <- mat2listw(W) +try(col005.spM <- mat2listw(W)) +col005.spM <- mat2listw(W, style="W", zero.policy=TRUE) summary(col005.spM$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, zero.policy set to TRUE -#> Warning: no-neighbour observations found, zero.policy set to TRUE +#> Error in sn2listw(df, style = style, zero.policy = zero.policy) : +#> no-neighbour observations found, set zero.policy to TRUE #> Neighbour list object: #> Number of regions: 49 #> Number of nonzero links: 170 @@ -222,9 +222,9 @@Examples
#> 0 1 2 3 4 5 6 7 8 9 #> 4 11 5 8 3 9 2 2 3 2 #> 11 least connected regions: -#> with 1 link +#> 1001 1007 1018 1010 1045 1044 1046 1047 1049 1048 1015 with 1 link #> 2 most connected regions: -#> with 9 links +#> 1038 1036 with 9 links if (run) { diffnb(col005, col005.spM$neighbours) } @@ -241,14 +241,9 @@Examples
#> 49 disjoint connected subgraphs if (run && require("Matrix", quiet=TRUE)) { IW <- kronecker(Diagonal(3), W) -col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W") +col005.spM.3T <- mat2listw(as(IW, "CsparseMatrix"), style="W", zero.policy=TRUE) summary(col005.spM.3T$neighbours) } -#> Warning: style is M (missing); style should be set to a valid value -#> Warning: 1, 3, 6, 21, 50, 52, 55, 70, 99, 101, 104, 119 are not origins -#> Warning: no-neighbour observations found, zero.policy set to TRUE -#> Warning: no-neighbour observations found, zero.policy set to TRUE -#> Warning: zero sum general weights #> Neighbour list object: #> Number of regions: 147 #> Number of nonzero links: 510 @@ -262,9 +257,9 @@Examples
#> 0 1 2 3 4 5 6 7 8 9 #> 12 33 15 24 9 27 6 6 9 6 #> 33 least connected regions: -#> with 1 link +#> 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: -#> with 9 links +#> 11 16 60 65 109 114 with 9 links diff --git a/docs/reference/nb2blocknb.html b/docs/reference/nb2blocknb.html index c2276d23..460ced1b 100644 --- a/docs/reference/nb2blocknb.html +++ b/docs/reference/nb2blocknb.html @@ -127,96 +127,31 @@See also
Examples
-# \dontrun{
+ if (FALSE) {
data(boston, package="spData")
summary(as.vector(table(boston.c$TOWN)))
-#> Min. 1st Qu. Median Mean 3rd Qu. Max.
-#> 1.0 2.0 4.0 5.5 7.0 30.0
townaggr <- aggregate(boston.utm, list(town=boston.c$TOWN), mean)
block.rel <- graph2nb(relativeneigh(as.matrix(townaggr[,2:3])),
as.character(townaggr[,1]), sym=TRUE)
block.rel
-#> Neighbour list object:
-#> Number of regions: 92
-#> Number of nonzero links: 240
-#> Percentage nonzero weights: 2.835539
-#> Average number of links: 2.608696
print(is.symmetric.nb(block.rel))
-#> [1] TRUE
plot(block.rel, as.matrix(townaggr[,2:3]))
points(boston.utm, pch=18, col="lightgreen")
-
block.nb <- nb2blocknb(block.rel, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 15234
-#> Percentage nonzero weights: 5.949945
-#> Average number of links: 30.10672
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
points(boston.utm, pch=18, col="lightgreen")
-
n.comp.nb(block.nb)$nc
-#> [1] 1
moran.test(boston.c$CMEDV, nb2listw(boston.soi))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(boston.soi)
-#>
-#> Moran I statistic standard deviate = 21.786, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.690285059 -0.001980198 0.001009685
-#>
moran.test(boston.c$CMEDV, nb2listw(block.nb))
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb)
-#>
-#> Moran I statistic standard deviate = 22.455, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.3122905961 -0.0019801980 0.0001958827
-#>
block.nb <- nb2blocknb(NULL, as.character(boston.c$TOWN))
block.nb
-#> Neighbour list object:
-#> Number of regions: 506
-#> Number of nonzero links: 4868
-#> Percentage nonzero weights: 1.901295
-#> Average number of links: 9.620553
-#> 17 regions with no links:
-#> 1 55 56 57 58 65 196 257 284 285 286 287 342 343 348 349 354
-#> 92 disjoint connected subgraphs
print(is.symmetric.nb(block.nb))
-#> [1] TRUE
plot(block.nb, boston.utm)
-
n.comp.nb(block.nb)$nc
-#> [1] 92
moran.test(boston.c$CMEDV, nb2listw(block.nb, zero.policy=TRUE), zero.policy=TRUE)
-#>
-#> Moran I test under randomisation
-#>
-#> data: boston.c$CMEDV
-#> weights: nb2listw(block.nb, zero.policy = TRUE) n reduced by no-neighbour observations
-#>
-#>
-#> Moran I statistic standard deviate = 21.145, p-value < 2.2e-16
-#> alternative hypothesis: greater
-#> sample estimates:
-#> Moran I statistic Expectation Variance
-#> 0.6188830566 -0.0020491803 0.0008623116
-#>
-# }
+}