library(deldir)
## deldir 0.1-16
set.seed(2018)
x <- runif(10, 0, 12)
y <- runif(10, 0, 6)
CEA <- runif(100, 3, 5)
cos <- rnorm(10, 2.5, 0.35)
plot(x, y, cex = cos, col='gray',pch=19)
x2 <- runif(100, 1, 12)
y2 <- runif(100, 1, 6)
points(x2, y2, pch = 19, col = 'red',
cex = CEA*0.3)
D <- deldir(x, y, sort = T, plotit = T,
digits = 4, z = cos)
##
## PLEASE NOTE: The components "delsgs" and "summary" of the
## object returned by deldir() are now DATA FRAMES rather than
## matrices (as they were prior to release 0.0-18).
## See help("deldir").
##
## PLEASE NOTE: The process that deldir() uses for determining
## duplicated points has changed from that used in version
## 0.0-9 of this package (and previously). See help("deldir").
L <- tile.list(D)
G <- tile.centroids(L)
plot(L, close = T,
fillcol = gray.colors(cos),
border = 0.2, showpoints = T,
number = T)
## Warning in seq.int(from = start^gamma, to = end^gamma, length.out = n):
## first element used of 'length.out' argument
points(G, pch = 20, col = 'lightblue')
points(x2, y2, pch = 19, col = 'red',
cex = CEA*0.2)
### Creating polygons
library(sp)
df_xy <- data.frame(x = x, y = y)
coordinates(df_xy) <- ~x+y
z <- deldir(x, y, plot = T, wl = 'triang',
wp = 'none')
w <- tile.list(z)
Polys <- vector(mode = 'list',
length = length(w))
for (i in seq(along = Polys)) {
pcrds <- cbind(w[[i]]$x, w[[i]]$y)
pcrds <- rbind(pcrds, pcrds[1,])
Polys[[i]] <- Polygons(list(Polygon(pcrds)),
ID = as.character(i))
}
SP <- SpatialPolygons(Polys)
plot(SP)
plot(df_xy, add = T, col = 'RED')
class(SP)
## [1] "SpatialPolygons"
## attr(,"package")
## [1] "sp"
### weight matrix
library(spdep)
## Loading required package: spData
## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`
##
## Attaching package: 'spData'
## The following objects are masked _by_ '.GlobalEnv':
##
## x, y
## Loading required package: sf
## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3
Contnb <- poly2nb(SP, queen = F)
W = nb2listw(Contnb, glist = NULL,
style = 'W')
W$weights
## [[1]]
## [1] 0.25 0.25 0.25 0.25
##
## [[2]]
## [1] 0.2 0.2 0.2 0.2 0.2
##
## [[3]]
## [1] 0.5 0.5
##
## [[4]]
## [1] 0.3333333 0.3333333 0.3333333
##
## [[5]]
## [1] 0.3333333 0.3333333 0.3333333
##
## [[6]]
## [1] 0.2 0.2 0.2 0.2 0.2
##
## [[7]]
## [1] 0.25 0.25 0.25 0.25
##
## [[8]]
## [1] 0.25 0.25 0.25 0.25
##
## [[9]]
## [1] 0.5 0.5
##
## [[10]]
## [1] 0.25 0.25 0.25 0.25
##
## attr(,"mode")
## [1] "binary"
## attr(,"W")
## [1] TRUE
## attr(,"comp")
## attr(,"comp")$d
## [1] 4 5 2 3 3 5 4 4 2 4
summary(W)
## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 10
## Number of nonzero links: 36
## Percentage nonzero weights: 36
## Average number of links: 3.6
## Link number distribution:
##
## 2 3 4 5
## 2 2 4 2
## 2 least connected regions:
## 3 9 with 2 links
## 2 most connected regions:
## 2 6 with 5 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 10 100 10 5.838333 40.97389
class(W)
## [1] "listw" "nb"
plot(W, coordinates(SP))
dists <- as.matrix(dist(cbind(x, y)))
inv.d <- 1/dists
diag(inv.d) <- 0
View(inv.d)
sumd <- apply(inv.d, 1, sum)
w.d <- round((inv.d/sumd),2)
View(w.d)
L2 <- w.d%*%cos
cor(cos, L2)
## [,1]
## [1,] -0.4117648
summary(w.d)
## 1 2 3 4
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.085 1st Qu.:0.090 1st Qu.:0.050 1st Qu.:0.0725
## Median :0.105 Median :0.130 Median :0.065 Median :0.0850
## Mean :0.103 Mean :0.129 Mean :0.065 Mean :0.1230
## 3rd Qu.:0.120 3rd Qu.:0.155 3rd Qu.:0.070 3rd Qu.:0.1825
## Max. :0.180 Max. :0.270 Max. :0.120 Max. :0.3000
## 5 6 7 8
## Min. :0.000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.085 1st Qu.:0.100 1st Qu.:0.0525 1st Qu.:0.0525
## Median :0.110 Median :0.115 Median :0.0650 Median :0.0800
## Mean :0.133 Mean :0.125 Mean :0.0690 Mean :0.0990
## 3rd Qu.:0.130 3rd Qu.:0.155 3rd Qu.:0.0800 3rd Qu.:0.1200
## Max. :0.320 Max. :0.220 Max. :0.1500 Max. :0.2500
## 9 10
## Min. :0.00 Min. :0.000
## 1st Qu.:0.03 1st Qu.:0.065
## Median :0.04 Median :0.085
## Mean :0.04 Mean :0.111
## 3rd Qu.:0.04 3rd Qu.:0.155
## Max. :0.09 Max. :0.270