Laporan Praktikum Spasial Sesi UAS
Tugas STA553 - Analisis Statistika Spasial
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Spatial Weights (Responsi Pertemuan 8)
Bobot Spasial
Bobot spasial digunakan agar pola spasial pada data yang diamati dapat dimodelkan dengan baik.
Steps in determining spatial weights:
choose the neighbour criterion to be used (pilih kriteria ketetanggaan yang akan digunakan)
assign weights to the identified neighbour links (tentukan bobot untuk mengidentifikasi hubungan ketetanggaan)
Beberapa istilah yang digunakan:
Scale & Resolution (skala dan resolusi)
Aggregation
Scale & Resolution (Skala dan Resolusi)
Ilustrasi:
Zonation (Zonasi) and Aggregation (Agregasi)
Data geografis sering dikumpulkan berdasarkan zona. Meskipun kita ingin memiliki data pada tingkat yang paling detail (terperinci) yang memungkinkan atau bermakna (individu, rumah tangga, plot, situs), kenyataannya sering kali kita hanya mendapatkan data agregat.
Efek Zonasi dan Aggregasi (Ilustrasi)
Membangkitkan Data
Membangkitkan data sebagai ilustrasi data income
Packages
library(raster)
library(deldir)
library(spdep) # pembobot data spasial
library(rgdal)
library(spatialreg)
library(corrplot)membangkitkan data
set.seed(0)
xy <- cbind(x=runif(1000, 0, 100), y=runif(1000, 0, 100))
income <- (runif(1000) * abs((xy[,1] - 50) * (xy[,2] - 50))) / 500Eksplorasi Data
par(mfrow=c(1,3), las=1)
plot(sort(income),
col=rev(terrain.colors(1000)),
pch=20, cex=.75,
ylab='income')
hist(income, main='',
col=rev(terrain.colors(10)),
xlim=c(0,5),
breaks=seq(0,5,0.5))
plot(xy, xlim=c(0,100), ylim=c(0,100),
cex=income,
col=rev(terrain.colors(50))[10*(income+1)])
Agregasi
r1 <- raster(ncol=1, nrow=4, xmn=0, xmx=100, ymn=0, ymx=100, crs=NA)
r1 <- rasterize(xy, r1, income, mean)
r2 <- raster(ncol=4, nrow=1, xmn=0, xmx=100, ymn=0, ymx=100, crs=NA)
r2 <- rasterize(xy, r2, income, mean)
r3 <- raster(ncol=2, nrow=2, xmn=0, xmx=100, ymn=0, ymx=100, crs=NA)
r3 <- rasterize(xy, r3, income, mean)
r4 <- raster(ncol=3, nrow=3, xmn=0, xmx=100, ymn=0, ymx=100, crs=NA)
r4 <- rasterize(xy, r4, income, mean)
r5 <- raster(ncol=5, nrow=5, xmn=0, xmx=100, ymn=0, ymx=100, crs=NA)
r5 <- rasterize(xy, r5, income, mean)
r6 <- raster(ncol=10, nrow=10, xmn=0, xmx=100, ymn=0, ymx=100, crs=NA)
r6 <- rasterize(xy, r6, income, mean)Visualisasi Agregasi
par(mfrow=c(2,3), las=1)
plot(r1); plot(r2); plot(r3);
plot(r4); plot(r5); plot(r6)
par(mfrow=c(1,3), las=1)
hist(r4, col=rev(terrain.colors(10)),
xlim=c(0,5),
breaks=seq(0, 5, 0.5))
hist(r5, col=rev(terrain.colors(10)),
xlim=c(0,5),
breaks=seq(0, 5, 0.5))
hist(r6, col=rev(terrain.colors(10)),
xlim=c(0,5),
breaks=seq(0, 5, 0.5))
Distance (Jarak)
Jarak (distance) digunakan untuk mengukur seberapa jauh jaraknya.
Mungkin kita juga perlu mempertimbangkan perbatasan negara, pegunungan, atau hambatan lainnya.
Jarak antara A dan B bahkan mungkin asimetris, artinya jarak dari A ke B tidak sama dengan jarak dari B ke A; karena kita berjalan lebih cepat saat berjalan menuruni bukit daripada saat berjalan menanjak.
Matriks Jarak
A <- c(40, 43)
B <- c(101, 1)
C <- c(111, 54)
D <- c(104, 65)
E <- c(60, 22)
G <- c(20, 2)
pts <- rbind(A, B, C, D, E, G)
pts## [,1] [,2]
## A 40 43
## B 101 1
## C 111 54
## D 104 65
## E 60 22
## G 20 2## [,1] [,2]
## A 40 43
## B 101 1
## C 111 54
## D 104 65
## E 60 22
## F 20 2
plot(pts, xlim=c(0,120), ylim=c(0,120),
pch=20, cex=2, col='red',
xlab='X', ylab='Y', las=1)
text(pts+5, LETTERS[1:6])
dis <- dist(pts)
dis## A B C D E
## B 74.06079
## C 71.84706 53.93515
## D 67.67570 64.07027 13.03840
## E 29.00000 46.06517 60.20797 61.52235
## G 45.61798 81.00617 104.80935 105.00000 44.72136Periksa jarak titik pertama menggunakan teorema Pythagoras.
A## [1] 40 43B## [1] 101 1sqrt((A[1]-B[1])^2 + (A[2]-B[2])^2) ## [1] 74.06079Jarak untuk Koordinat Longitude (Garis Bujur)/ Latitude (Garis Lintang)
gdis <- pointDistance(pts, lonlat = TRUE)
gdis## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 NA NA NA NA NA
## [2,] 7614198 0 NA NA NA NA
## [3,] 5155577 5946748 0 NA NA NA
## [4,] 4581656 7104895 1286094 0 NA NA
## [5,] 2976166 5011592 5536367 5737063 0 NA
## [6,] 4957298 9013726 9894640 9521864 4859627 0Matriks Ketetanggaan (Adjacency Matrix)
Misalkan: dianggap bertentangga jika jarak < 50
D <- as.matrix(dis)
round(D)## A B C D E G
## A 0 74 72 68 29 46
## B 74 0 54 64 46 81
## C 72 54 0 13 60 105
## D 68 64 13 0 62 105
## E 29 46 60 62 0 45
## G 46 81 105 105 45 0a <- D<50
a## A B C D E G
## A TRUE FALSE FALSE FALSE TRUE TRUE
## B FALSE TRUE FALSE FALSE TRUE FALSE
## C FALSE FALSE TRUE TRUE FALSE FALSE
## D FALSE FALSE TRUE TRUE FALSE FALSE
## E TRUE TRUE FALSE FALSE TRUE TRUE
## G TRUE FALSE FALSE FALSE TRUE TRUEdiag(a) <- NA
Adj50 <- a*1
Adj50## A B C D E G
## A NA 0 0 0 1 1
## B 0 NA 0 0 1 0
## C 0 0 NA 1 0 0
## D 0 0 1 NA 0 0
## E 1 1 0 0 NA 1
## G 1 0 0 0 1 NATwo Nearest Neighbour (Dua Tetangga Terdekat)
Pertama-tama, dapatkan nomor kolom yang sesuai dengan urutan nilai di baris tersebut (angka yang menunjukkan bagaimana nilai diurutkan)
First, get the column numbers in order of the values in that row (that is, the numbers indicate how the values are ordered):
cols <- apply(D, 1, order)
cols <- t(cols)
cols## [,1] [,2] [,3] [,4] [,5] [,6]
## A 1 5 6 4 3 2
## B 2 5 3 4 1 6
## C 3 4 2 5 1 6
## D 4 3 5 2 1 6
## E 5 1 6 2 3 4
## G 6 5 1 2 3 4Lalu ambil kolom 2 sampai 3.
And then get columns 2 to 3:
cols <- cols[,2:3]
cols## [,1] [,2]
## A 5 6
## B 5 3
## C 4 2
## D 3 5
## E 1 6
## G 5 1Buat pasangan baris-kolom.
Make the row-column pairs:
rowcols <- cbind(rep(1:6, each = 2), as.vector(t(cols)))
head(rowcols)## [,1] [,2]
## [1,] 1 5
## [2,] 1 6
## [3,] 2 5
## [4,] 2 3
## [5,] 3 4
## [6,] 3 2Gunakan pasangan ini sebagai indeks untuk mengubah nilai dalam matriks.
Use these pairs as indices to change the values in matrix:
Ak3 <- Adj50*0
Ak3[rowcols] <- 1
Ak3## A B C D E G
## A NA 0 0 0 1 1
## B 0 NA 1 0 1 0
## C 0 1 NA 1 0 0
## D 0 0 1 NA 1 0
## E 1 0 0 0 NA 1
## G 1 0 0 0 1 NAJenis-jenis Matriks Bobot (Type of Weight Matrix)
Berdasarkan Jarak (Based on Distance)
Bobot Kedekatan Spasial (Spatial Continuity Weights)
Pengaruh Spasial untuk Poligon (Spatial influence for polygons)
p <- shapefile(system.file("external/lux.shp", package="raster"))Create a “rook’s case” neighbors-list (Membuat Daftar Tetangga “rook’s case”)
wr <- poly2nb(p, row.names=p$ID_2, queen=FALSE)
wr## Neighbour list object:
## Number of regions: 12
## Number of nonzero links: 46
## Percentage nonzero weights: 31.94444
## Average number of links: 3.833333Inspect the content (Memeriksa Isinya)
wm <- nb2mat(wr, style='B',
zero.policy = TRUE)
dim(wm)## [1] 12 12wr[1:6]## [[1]]
## [1] 2 4 5
##
## [[2]]
## [1] 1 3 4 5 6 12
##
## [[3]]
## [1] 2 5 9 12
##
## [[4]]
## [1] 1 2
##
## [[5]]
## [1] 1 2 3
##
## [[6]]
## [1] 2 8 12wm[1:6,1:11]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
## 1 0 1 0 1 1 0 0 0 0 0 0
## 2 1 0 1 1 1 1 0 0 0 0 0
## 3 0 1 0 0 1 0 0 0 1 0 0
## 4 1 1 0 0 0 0 0 0 0 0 0
## 5 1 1 1 0 0 0 0 0 0 0 0
## 6 0 1 0 0 0 0 0 1 0 0 0Compute the number of neighbors for each area (Menghitung Jumlah Tetangga untuk Setiap Daerah)
i <- rowSums(wm)
i## 1 2 3 4 5 6 7 12 8 9 10 11
## 3 6 4 2 3 3 3 4 4 3 5 6Plot the links between the polygons (Plot Hubungan Antar Poligon)
par(mai=c(0,0,0,0))
plot(p, col='gray', border='blue')
xy <- coordinates(p)
plot(wr, xy, col='red', lwd=2, add=TRUE)
Compute Spatial Influence (Menghitung Pengaruh Spasial)
Berdasarkan Jarak (Distance Based)
wd10 <- dnearneigh(xy, 0, 10)
wd25 <- dnearneigh(xy, 0, 25, longlat=TRUE)Plot hubungannya (plot the links)
par(mfrow=c(1,2))
plot(p, col='gray', border='blue', main ="wd10")
plot(wd10, xy, col='red', lwd=2, add=TRUE)
plot(p, col='gray', border='blue', main ="wd25")
plot(wd25, xy, col='red', lwd=2, add=TRUE)
Tetangga Terdekat (Nearest Neighbours)
k3 <- knn2nb(knearneigh(xy, k=3))
k6 <- knn2nb(knearneigh(xy, k=6))## Warning in knearneigh(xy, k = 6): k greater than one-third of the number of data
## pointspar(mfrow=c(1,2))
plot(p, col='gray', border='blue', main ="k3")
plot(k3, xy, col='red', lwd=2, add=TRUE)
plot(p, col='gray', border='blue', main ="k6")
plot(k6, xy, col='red', lwd=2, add=TRUE)
Lag-two Rook
wr2 <- wr
for (i in 1:length(wr)) {
lag1 <- wr[[i]]
lag2 <- wr[lag1]
lag2 <- sort(unique(unlist(lag2)))
lag2 <- lag2[!(lag2 %in% c(wr[[i]], i))]
wr2[[i]] <- lag2
}
plot(p, col='gray', border='blue', main ="wr")
plot(wr, xy, col='red', lwd=2, add=TRUE)
plotit <- function(nb, lab='') {
plot(p, col='gray', border='white')
plot(nb, xy, add=TRUE, pch=20)
text(6.3, 50.1, paste0('(', lab, ')'),
cex=1.25)
}
par(mfrow=c(2, 3), mai=c(0,0,0,0))
plotit(wr, 'adjacency')
plotit(wr2, 'lag-2 adj.')
plotit(wd10, '10 km')
plotit(wd25, '25 km')
plotit(k3, 'k=3')
plotit(k6, 'k=6')
Exercise: Using Columbus data set (Latihan: Menggunakan Columbus Dataset)
data(columbus)
summary(columbus)## AREA PERIMETER COLUMBUS. COLUMBUS.I POLYID
## Min. :0.03438 Min. :0.9021 Min. : 2 Min. : 1 Min. : 1
## 1st Qu.:0.09315 1st Qu.:1.4023 1st Qu.:14 1st Qu.:13 1st Qu.:13
## Median :0.17477 Median :1.8410 Median :26 Median :25 Median :25
## Mean :0.18649 Mean :1.8887 Mean :26 Mean :25 Mean :25
## 3rd Qu.:0.24669 3rd Qu.:2.1992 3rd Qu.:38 3rd Qu.:37 3rd Qu.:37
## Max. :0.69926 Max. :5.0775 Max. :50 Max. :49 Max. :49
## NEIG HOVAL INC CRIME
## Min. : 1 Min. :17.90 Min. : 4.477 Min. : 0.1783
## 1st Qu.:13 1st Qu.:25.70 1st Qu.: 9.963 1st Qu.:20.0485
## Median :25 Median :33.50 Median :13.380 Median :34.0008
## Mean :25 Mean :38.44 Mean :14.375 Mean :35.1288
## 3rd Qu.:37 3rd Qu.:43.30 3rd Qu.:18.324 3rd Qu.:48.5855
## Max. :49 Max. :96.40 Max. :31.070 Max. :68.8920
## OPEN PLUMB DISCBD X
## Min. : 0.0000 Min. : 0.1327 Min. :0.370 Min. :24.25
## 1st Qu.: 0.2598 1st Qu.: 0.3323 1st Qu.:1.700 1st Qu.:36.15
## Median : 1.0061 Median : 1.0239 Median :2.670 Median :39.61
## Mean : 2.7709 Mean : 2.3639 Mean :2.852 Mean :39.46
## 3rd Qu.: 3.9364 3rd Qu.: 2.5343 3rd Qu.:3.890 3rd Qu.:43.44
## Max. :24.9981 Max. :18.8111 Max. :5.570 Max. :51.24
## Y AREA NSA NSB
## Min. :24.96 Min. : 1.093 Min. :0.0000 Min. :0.0000
## 1st Qu.:28.26 1st Qu.: 3.193 1st Qu.:0.0000 1st Qu.:0.0000
## Median :31.91 Median : 6.029 Median :0.0000 Median :1.0000
## Mean :32.37 Mean : 6.372 Mean :0.4898 Mean :0.5102
## 3rd Qu.:35.92 3rd Qu.: 7.989 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :44.07 Max. :21.282 Max. :1.0000 Max. :1.0000
## EW CP THOUS NEIGNO
## Min. :0.0000 Min. :0.0000 Min. :1000 Min. :1001
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1000 1st Qu.:1013
## Median :1.0000 Median :0.0000 Median :1000 Median :1025
## Mean :0.5918 Mean :0.4898 Mean :1000 Mean :1025
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1000 3rd Qu.:1037
## Max. :1.0000 Max. :1.0000 Max. :1000 Max. :1049
## PERIM
## Min. :0.9021
## 1st Qu.:1.4023
## Median :1.8410
## Mean :1.8887
## 3rd Qu.:2.1992
## Max. :5.0775Columbus Data Set
The columbus data frame has 49 rows and 22 columns. Unit of analysis: 49 neighbourhoods in Columbus, OH, 1980 data.
This data frame contains the following columns:
X : x coordinate
Y : y coordinate
HOVAL : housing value (in “$1,000”)
INC : household income (in “$1,000”)
CRIME : residential burglaries and vehicle thefts per thousand households in the neighborhood
OPEN : open space in neighborhood
PLUMB : percentage housing units without plumbing
DISCBD : distance to CBD
columbus: the data frame, contains 22 variables
col.gal.nb: is an object of class “nb”, a list of vectors, one for each spatial unit, and containing the sequence numbers of the neighbors (this contiguity file uses the queen definition for Columbus)
coords: centroid coordinates that can be used to construct distancebased weights
col.listw <- nb2listw(col.gal.nb)
print(col.gal.nb)## Neighbour list object:
## Number of regions: 49
## Number of nonzero links: 230
## Percentage nonzero weights: 9.579342
## Average number of links: 4.693878Exercise! (Latihan!)
matnb <- nb2mat(col.gal.nb)
View(matnb)Apakah matriks bobot sudah dinormalisasi?
rowSums(matnb,na.rm=TRUE) ## 1005 1001 1006 1002 1007 1008 1004 1003 1018 1010 1038 1037 1039 1040 1009 1036
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1011 1042 1041 1017 1043 1019 1012 1035 1032 1020 1021 1031 1033 1034 1045 1013
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1022 1044 1023 1046 1030 1024 1047 1016 1014 1049 1029 1025 1028 1048 1015 1027
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1026
## 1Karena semua baris berjumlah 1 sehingga matriks bobot sudah dinormalisasi.
Buatlah matriks bobot dengan pendekatan berikut:
Power distance weights
#menggunakan centroid coordinates
coords ## [,1] [,2]
## [1,] 8.827218 14.36908
## [2,] 8.332658 14.03162
## [3,] 9.012265 13.81972
## [4,] 8.460801 13.71696
## [5,] 9.007982 13.29637
## [6,] 9.739926 13.47463
## [7,] 8.118750 13.29570
## [8,] 8.496488 13.40261
## [9,] 9.630793 12.94272
## [10,] 10.366383 13.00189
## [11,] 8.669735 12.98012
## [12,] 8.544996 12.95313
## [13,] 8.349223 12.99609
## [14,] 8.292702 12.86371
## [15,] 8.973462 12.74159
## [16,] 8.655866 12.62732
## [17,] 10.528621 12.64748
## [18,] 8.487918 12.54534
## [19,] 8.312368 12.66672
## [20,] 10.190582 12.25690
## [21,] 7.847424 12.08500
## [22,] 9.655244 12.46277
## [23,] 10.671381 12.29157
## [24,] 8.420744 12.31801
## [25,] 8.938752 12.38051
## [26,] 9.250921 12.41378
## [27,] 9.737004 12.14969
## [28,] 9.297976 11.97779
## [29,] 8.977862 11.99401
## [30,] 8.688719 11.93872
## [31,] 6.892482 11.91415
## [32,] 10.763784 11.84443
## [33,] 9.783876 11.92271
## [34,] 7.339431 11.62855
## [35,] 9.668249 11.69254
## [36,] 6.728838 11.63436
## [37,] 8.912363 11.63031
## [38,] 9.210527 11.65984
## [39,] 6.221943 11.40251
## [40,] 10.492493 11.50720
## [41,] 10.953587 11.47925
## [42,] 7.110051 11.29544
## [43,] 9.214330 11.43277
## [44,] 9.641904 11.39104
## [45,] 8.910340 11.14864
## [46,] 6.423385 11.21924
## [47,] 10.935302 11.01003
## [48,] 9.251957 11.18125
## [49,] 9.492144 11.01496(head)## standardGeneric for "head" defined from package "utils"
##
## function (x, ...)
## standardGeneric("head")
## <environment: 0x0000000018715488>
## Methods may be defined for arguments: x
## Use showMethods(head) for currently available ones.koordinat<-coords
jarak<-dist(koordinat)
jarak## 1 2 3 4 5 6 7
## 2 0.5987183
## 3 0.5796856 0.7118774
## 4 0.7480069 0.3397537 0.5609564
## 5 1.0878333 0.9983315 0.5233702 0.6901508
## 6 1.2779137 1.5134864 0.8053411 1.3018772 0.7533395
## 7 1.2861008 0.7663780 1.0358386 0.5426392 0.8892323 1.6310201
## 8 1.0214864 0.6499968 0.6633281 0.3163691 0.5224120 1.2455216 0.3925755
## 9 1.6371347 1.6943600 1.0731708 1.4029704 0.7162096 0.5429878 1.5526974
## 10 2.0586952 2.2795588 1.5819226 2.0353307 1.3899528 0.7848150 2.2667553
## 11 1.3978539 1.1042094 0.9067805 0.7658901 0.4630569 1.1789185 0.6349622
## 12 1.4438023 1.0992023 0.9845431 0.7684627 0.5763426 1.3037745 0.5468501
## 13 1.4538083 1.0356624 1.0573470 0.7294520 0.7239657 1.4707319 0.3780000
## 14 1.5974486 1.1685996 1.1965492 0.8696555 0.8359545 1.5708875 0.4657036
## 15 1.6340467 1.4404255 1.0788304 1.1018978 0.5558526 1.0605767 1.0186160
## 16 1.7501686 1.4410222 1.2445264 1.1069687 0.7560518 1.3759110 0.8574593
## 17 2.4204709 2.5957888 1.9166350 2.3280211 1.6533005 1.1429039 2.4955318
## 18 1.8550280 1.4943689 1.3780333 1.1719335 0.9135117 1.5591997 0.8362579
## 19 1.7785063 1.3650540 1.3488001 1.0606787 0.9382606 1.6403182 0.6581090
## 20 2.5139710 2.5693436 1.9572519 2.2636089 1.5744947 1.2984474 2.3176714
## 21 2.4853598 2.0061925 2.0895253 1.7434279 1.6775909 2.3479036 1.2407370
## 22 2.0783756 2.0519624 1.5015805 1.7319652 1.0553854 1.0154041 1.7477415
## 23 2.7779420 2.9150313 2.2556375 2.6302859 1.9433243 1.5057368 2.7430289
## 24 2.0909517 1.7158730 1.6140066 1.3995221 1.1410623 1.7544255 1.0232688
## 25 1.9916915 1.7588426 1.4410855 1.4193456 0.9184695 1.3560917 1.2288137
## 26 2.0006731 1.8602727 1.4260475 1.5239963 0.9154081 1.1681296 1.4351289
## 27 2.3986187 2.3481593 1.8205037 2.0211452 1.3587985 1.3249431 1.9829492
## 28 2.4371851 2.2693788 1.8639584 1.9301784 1.3500910 1.5607256 1.7684671
## 29 2.3798366 2.1373230 1.8260311 1.7988632 1.3027026 1.6652266 1.5596391
## 30 2.4343008 2.1229775 1.9086240 1.7927905 1.3946817 1.8612006 1.4718270
## 31 3.1256777 2.5608200 2.8503807 2.3895086 2.5270257 3.2470045 1.8472739
## 32 3.1818417 3.2701961 2.6399959 2.9681816 2.2783667 1.9250564 3.0170175
## 33 2.6267679 2.5599919 2.0479339 2.2293217 1.5776401 1.5525468 2.1581848
## 34 3.1183364 2.6002479 2.7567407 2.3704339 2.3591718 3.0282695 1.8403144
## 35 2.8055607 2.6935312 2.2260279 2.3571617 1.7344185 1.7835324 2.2295913
## 36 3.4470055 2.8842836 3.1606669 2.7086728 2.8207710 3.5289146 2.1660803
## 37 2.7400887 2.4702963 2.1916866 2.1349527 1.6687976 2.0214809 1.8448187
## 38 2.7362185 2.5290351 2.1689607 2.1894853 1.6490140 1.8904337 1.9667313
## 39 3.9481582 3.3715488 3.6917180 3.2201176 3.3687821 4.0828766 2.6799341
## 40 3.3111158 3.3222905 2.7456929 3.0018048 2.3248435 2.1064565 2.9721052
## 41 3.5878355 3.6584017 3.0408134 3.3498297 2.6621997 2.3354964 3.3668713
## 42 3.5207825 2.9969114 3.1607611 2.7727785 2.7578713 3.4154250 2.2402085
## 43 2.9617120 2.7443357 2.3954848 2.4052718 1.8749839 2.1084228 2.1612061
## 44 3.0874641 2.9473427 2.5089731 2.6086274 2.0080191 2.0859009 2.4388026
## 45 3.2215095 2.9402923 2.6730240 2.6073683 2.1499457 2.4695057 2.2883406
## 46 3.9623092 3.3992410 3.6694418 3.2233040 3.3158113 4.0107665 2.6806665
## 47 3.9657538 3.9879546 3.4047643 3.6675108 2.9903000 2.7391948 3.6272949
## 48 3.2159933 2.9949492 2.6493303 2.6562653 2.1291372 2.3447174 2.3989701
## 49 3.4193877 3.2318201 2.8455141 2.8921406 2.3322141 2.4721207 2.6623293
## 8 9 10 11 12 13 14
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9 1.2239873
## 10 1.9123505 0.7379648
## 11 0.4566323 0.9617853 1.6967869
## 12 0.4520965 1.0858477 1.8220398 0.1276276
## 13 0.4323702 1.2826807 2.0171676 0.3209097 0.2004320
## 14 0.5761486 1.3404230 2.0782800 0.3945969 0.2676712 0.1439476
## 15 0.8151436 0.6874161 1.4170339 0.3861979 0.4778414 0.6741276 0.6916275
## 16 0.7915088 1.0246784 1.7510487 0.3530782 0.3441575 0.4796116 0.4333241
## 17 2.1679013 0.9451279 0.3897826 1.8884146 2.0070358 2.2071043 2.2463511
## 18 0.8573127 1.2099904 1.9331488 0.4712647 0.4117584 0.4716071 0.3734514
## 19 0.7585755 1.3470058 2.0811813 0.4753227 0.3689763 0.3314289 0.1979661
## 20 2.0451433 0.8852794 0.7654518 1.6840497 1.7868085 1.9841897 1.9925273
## 21 1.4688071 1.9789150 2.6806428 1.2155016 1.1136667 1.0401445 0.8970297
## 22 1.4919877 0.4805815 0.8923967 1.1130520 1.2137154 1.4107195 1.4203089
## 23 2.4422472 1.2275275 0.7730302 2.1167633 2.2269193 2.4266785 2.4465193
## 24 1.0872405 1.3617944 2.0623278 0.7073782 0.6471522 0.6818421 0.5605150
## 25 1.1136834 0.8916319 1.5569979 0.6571941 0.6949337 0.8523425 0.8067597
## 26 1.2437651 0.6512158 1.2610008 0.8114898 0.8883810 1.0733799 1.0585918
## 27 1.7631470 0.8001129 1.0594132 1.3522849 1.4374939 1.6255246 1.6111571
## 28 1.6347804 1.0207208 1.4799585 1.1829449 1.2321781 1.3917897 1.3399366
## 29 1.4885816 1.1516841 1.7157528 1.0331281 1.0522698 1.1829430 1.1071652
## 30 1.4764615 1.3767834 1.9861738 1.0415762 1.0245383 1.1105409 1.0061985
## 31 2.1882304 2.9251175 3.6402141 2.0724193 1.9519909 1.8145787 1.6918250
## 32 2.7511011 1.5779468 1.2237799 2.3821903 2.4803677 2.6751501 2.6730453
## 33 1.9615004 1.0314400 1.2263548 1.5360444 1.6113923 1.7917548 1.7632588
## 34 2.1180407 2.6414791 3.3239303 1.8964360 1.7910612 1.6999623 1.5602409
## 35 2.0730091 1.2507447 1.4838407 1.6293841 1.6884227 1.8544753 1.8065884
## 36 2.5002575 3.1832615 3.8861109 2.3618098 2.2444509 2.1165909 1.9892094
## 37 1.8204410 1.4961865 1.9988502 1.3714437 1.3728797 1.4773256 1.3803075
## 38 1.8833777 1.3499705 1.7711864 1.4267454 1.4544839 1.5897869 1.5138376
## 39 3.0288531 3.7406567 4.4423399 2.9121358 2.7930219 2.6579732 2.5343893
## 40 2.7525682 1.6742954 1.5000027 2.3434909 2.4255831 2.6096774 2.5844151
## 41 3.1203646 1.9727024 1.6319475 2.7328754 2.8237628 3.0138912 2.9995080
## 42 2.5223775 3.0112612 3.6763664 2.2958161 2.1924853 2.1042284 1.9642122
## 43 2.0965613 1.5663333 1.9466265 1.6403886 1.6611697 1.7867251 1.7020509
## 44 2.3148255 1.5517285 1.7662720 1.8628754 1.9087516 2.0608821 1.9972756
## 45 2.2916521 1.9333382 2.3568190 1.8472192 1.8410999 1.9307880 1.8228928
## 46 3.0107939 3.6411366 4.3272481 2.8542601 2.7399996 2.6203189 2.4897037
## 47 3.4164691 2.3317484 2.0715141 3.0023410 3.0804520 3.2607134 3.2279184
## 48 2.3463093 1.8017483 2.1346328 1.8907426 1.9077014 2.0269617 1.9367041
## 49 2.5869302 1.9327434 2.1707552 2.1303077 2.1572137 2.2871717 2.2037525
## 15 16 17 18 19 20 21
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 16 0.3375278
## 17 1.5580040 1.8728634
## 18 0.5237030 0.1868856 2.0432572
## 19 0.6653201 0.3457514 2.2163370 0.2134260
## 20 1.3100769 1.5787841 0.5165470 1.7269228 1.9224050
## 21 1.3034848 0.9734932 2.7395624 0.7887654 0.7446981 2.3494551
## 22 0.7365922 1.0128342 0.8926952 1.1702433 1.3582766 0.5735564 1.8468690
## 23 1.7565428 2.0432876 0.3834687 2.1981607 2.3886568 0.4820481 2.8315029
## 24 0.6963560 0.3885234 2.1334693 0.2370461 0.3651606 1.7708923 0.6188642
## 25 0.3627414 0.3754163 1.6121276 0.4800217 0.6886754 1.2579178 1.1306303
## 26 0.4294629 0.6322074 1.2988955 0.7742618 0.9720390 0.9526670 1.4414942
## 27 0.9660925 1.1819402 0.9351178 1.3102498 1.5155550 0.4660747 1.8906877
## 28 0.8298781 0.9133406 1.4010602 0.9890963 1.2025189 0.9352262 1.4545087
## 29 0.7475876 0.7104608 1.6828162 0.7375701 0.9462658 1.2408866 1.1340935
## 30 0.8518662 0.6893807 1.9716935 0.6389945 0.8195293 1.5351966 0.8539182
## 31 2.2394480 1.9021376 3.7093491 1.7157558 1.6069959 3.3158611 0.9701038
## 32 2.0025331 2.2486048 0.8367687 2.3813526 2.5856527 0.7061802 2.9262651
## 33 1.1520993 1.3299917 1.0391983 1.4377692 1.6489062 0.5263962 1.9432410
## 34 1.9770990 1.6524363 3.3480076 1.4695376 1.4228194 2.9195701 0.6829388
## 35 1.2582628 1.3779413 1.2853560 1.4561768 1.6695628 0.7689806 1.8626397
## 36 2.5028543 2.1678079 3.9325245 1.9809706 1.8903244 3.5172745 1.2059454
## 37 1.1129546 1.0294707 1.9096900 1.0086807 1.1975560 1.4235365 1.1579446
## 38 1.1074197 1.1151953 1.6470573 1.1429264 1.3492598 1.1476006 1.4278696
## 39 3.0600613 2.7247245 4.4830133 2.5378537 2.4429686 4.0595649 1.7629440
## 40 1.9573376 2.1512465 1.1408505 2.2574464 2.4692993 0.8082099 2.7074421
## 41 2.3482760 2.5685768 1.2431239 2.6862776 2.8958830 1.0894598 3.1646783
## 42 2.3587381 2.0404513 3.6762244 1.8603164 1.8237269 3.2270846 1.0803338
## 43 1.3307945 1.3186423 1.7896553 1.3287167 1.5284524 1.2775963 1.5145402
## 44 1.5069180 1.5813470 1.5378253 1.6322093 1.8425632 1.0250684 1.9239915
## 45 1.5941978 1.5004139 2.2057523 1.4591850 1.6316077 1.6932978 1.4165306
## 46 2.9699224 2.6394423 4.3465879 2.4537420 2.3798029 3.9074941 1.6665608
## 47 2.6166969 2.7948952 1.6871916 2.8890953 3.1023230 1.4523395 3.2696403
## 48 1.5849916 1.5641035 1.9441390 1.5634877 1.7576805 1.4275952 1.6701699
## 49 1.8028495 1.8163280 1.9337501 1.8304469 2.0298229 1.4248599 1.9621624
## 22 23 24 25 26 27 28
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 16
## 17
## 18
## 19
## 20
## 21
## 22
## 23 1.0304569
## 24 1.2429579 2.2507924
## 25 0.7211987 1.7349105 0.5217642
## 26 0.4072794 1.4257077 0.8356829 0.3139376
## 27 0.3235728 0.9450872 1.3269787 0.8309532 0.5531913
## 28 0.6023657 1.4087946 0.9408979 0.5396543 0.4385274 0.4714840
## 29 0.8237583 1.7194622 0.6444817 0.3884715 0.5007690 0.7749413 0.3205252
## 30 1.0994526 2.0138154 0.4644089 0.5076375 0.7360418 1.0693040 0.6105077
## 31 2.8167060 3.7976996 1.5807242 2.0987400 2.4107813 2.8542572 2.4063351
## 32 1.2693294 0.4565880 2.3904213 1.9021359 1.6164513 1.0711959 1.4718615
## 33 0.5551652 0.9611063 1.4192938 0.9611541 0.7247040 0.2317736 0.4890121
## 34 2.4614868 3.3972779 1.2824216 1.7672801 2.0664934 2.4535593 1.9894394
## 35 0.7703344 1.1683797 1.3955228 1.0027297 0.8332785 0.4622931 0.4674055
## 36 3.0413985 3.9969451 1.8248076 2.3324779 2.6397725 3.0519877 2.5919897
## 37 1.1157306 1.8792049 0.8453545 0.7506633 0.8534938 0.9745723 0.5190740
## 38 0.9178589 1.5915966 1.0280814 0.7702133 0.7550260 0.7191208 0.3297557
## 39 3.5932845 4.5373917 2.3817780 2.8874777 3.1933330 3.5935960 3.1293639
## 40 1.2704707 0.8045146 2.2247617 1.7823538 1.5373348 0.9917476 1.2838715
## 41 1.6288052 0.8599494 2.6681129 2.2072243 1.9422753 1.3890911 1.7290443
## 42 2.8001182 3.6980201 1.6624005 2.1263880 2.4153716 2.7623601 2.2918585
## 43 1.1203986 1.6913113 1.1888784 0.9869908 0.9816939 0.8872225 0.5513975
## 44 1.0718127 1.3677667 1.5331404 1.2138705 1.0949338 0.7645939 0.6801209
## 45 1.5105665 2.0994185 1.2677307 1.2321983 1.3101853 1.2982609 0.9152863
## 46 3.4628422 4.3812520 2.2796382 2.7704913 3.0695112 3.4417754 2.9729900
## 47 1.9362303 1.3084345 2.8343997 2.4216579 2.1926381 1.6537074 1.9019443
## 48 1.3434706 1.8021011 1.4082393 1.2394808 1.2325301 1.0831179 0.7978623
## 49 1.4569628 1.7379111 1.6869624 1.4734197 1.4194690 1.1608500 0.9822101
## 29 30 31 32 33 34 35
## 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 0.2943815
## 31 2.0869078 1.7964052
## 32 1.7921752 2.0772050 3.8719290
## 33 0.8091623 1.0952736 2.8914064 0.9830290
## 34 1.6786964 1.3844810 0.5304089 3.4311512 2.4620814
## 35 0.7533389 1.0099907 2.7845989 1.1060138 0.2575780 2.3296974
## 36 2.2775985 1.9833729 0.3241293 4.0404105 3.0686156 0.6106209 2.9399871
## 37 0.3695523 0.3809616 2.0397261 1.8637613 0.9192560 1.5729331 0.7584434
## 38 0.4071918 0.5916563 2.3319535 1.5641864 0.6307369 1.8713583 0.4588884
## 39 2.8186801 2.5243815 0.8434429 4.5632891 3.5997180 1.1401181 3.4584883
## 40 1.5909418 1.8546719 3.6229387 0.4328110 0.8214540 3.1553961 0.8448254
## 41 2.0416849 2.3110045 4.0843258 0.4115650 1.2509531 3.6172391 1.3029159
## 42 1.9941717 1.7047003 0.6558511 3.6947469 2.7464172 0.4044439 2.5888352
## 43 0.6090223 0.7295537 2.3712241 1.6032058 0.7512789 1.8850930 0.5229936
## 44 0.8969576 1.0993258 2.7987445 1.2100329 0.5503008 2.3146913 0.3026538
## 45 0.8480652 0.8205737 2.1581844 1.9797418 1.1671535 1.6425795 0.9328745
## 46 2.6693866 2.3768450 0.8384248 4.3851939 3.4333322 1.0033304 3.2792008
## 47 2.1908432 2.4309659 4.1426835 0.8518466 1.4692728 3.6486782 1.4391820
## 48 0.8577322 0.9439223 2.4706802 1.6508853 0.9125195 1.9641354 0.6593280
## 49 1.1059054 1.2242621 2.7507782 1.5182518 0.9534740 2.2384499 0.7000914
## 36 37 38 39 40 41 42
## 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 2.1835291
## 38 2.4818205 0.2996231
## 39 0.5574022 2.7000464 2.9996421
## 40 3.7658028 1.5849185 1.2910206 4.2718325
## 41 4.2275967 2.0468069 1.7523905 4.7322666 0.4619413
## 42 0.5100927 1.8331578 2.1318510 0.8945394 3.3890638 3.8479291
## 43 2.4936547 0.3608405 0.2270989 2.9925403 1.2803273 1.7398780 2.1087560
## 44 2.9232119 0.7677780 0.5082726 3.4199808 0.8584833 1.3146457 2.5336577
## 45 2.2349239 0.4816757 0.5928215 2.7003580 1.6222729 2.0698210 1.8062649
## 46 0.5153946 2.5226952 2.8217532 0.2723395 4.0792838 4.5376578 0.6908810
## 47 4.2525450 2.1158996 1.8431222 4.7296722 0.6657740 0.4695714 3.8358840
## 48 2.5634827 0.5630065 0.4803749 3.0380822 1.2826405 1.7275254 2.1449479
## 49 2.8318759 0.8454588 0.7036867 3.2930850 1.1148967 1.5334206 2.3985481
## 43 44 45 46 47 48
## 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 0.4296062
## 45 0.4161024 0.7706765
## 46 2.7991020 3.2231013 2.4879573
## 47 1.7721328 1.3483483 2.0297001 4.5167649
## 48 0.2543169 0.4427949 0.3431703 2.8288274 1.6920305
## 49 0.5017429 0.4047973 0.5969628 3.0755500 1.4431672 0.2921347koordinat<-cbind(columbus$X,columbus$Y)
View(koordinat)
plot(koordinat)
jarak<-dist(koordinat)
jarak## 1 2 3 4 5 6 7
## 2 3.6011799
## 3 3.0647189 4.3680672
## 4 4.2299522 2.0576699 3.3849668
## 5 6.1894263 6.2013305 3.1856709 4.3209358
## 6 6.8881507 8.7009729 4.3651923 7.3552772 3.9529751
## 7 7.8504254 4.5682052 7.0288326 3.7830799 6.6626241 10.4263597
## 8 5.7530610 3.8284481 3.9715250 1.8221425 3.3755136 7.0630386 3.3634039
## 9 9.3782798 10.1431770 6.3852967 8.3260798 4.0113982 3.3742714 10.3829894
## 10 11.6678475 13.3896148 9.1291694 11.8423874 7.7625029 4.8040830 14.3882802
## 11 7.8278616 6.4760574 5.2626341 4.4306775 2.7487458 6.6098732 4.9610661
## 12 8.1235097 6.4395443 5.7305938 4.4448746 3.4393003 7.3355291 4.4110095
## 13 8.1823809 5.9890975 6.1502255 4.1565469 4.3638938 8.3147079 3.2784435
## 14 9.0126201 6.7807405 6.9243349 4.9814474 4.9156977 8.8330794 3.6695655
## 15 9.1957438 8.4688270 6.2735157 6.4143750 3.1157820 6.0196747 7.1632744
## 16 10.0618121 8.5328998 7.4389771 6.5332664 4.6015741 8.0537254 6.0602357
## 17 13.7113303 15.1884202 11.0406544 13.5148089 9.2723555 6.8235855 15.7242150
## 18 10.4902347 8.4765886 8.1348949 6.6092757 5.6165813 9.2999774 5.2862296
## 19 9.8888678 7.7212703 7.6807562 5.9065390 5.4053770 9.2295462 4.4522450
## 20 14.5172489 15.2660749 11.5620500 13.3710508 9.0689871 7.9496720 14.8662999
## 21 14.4430216 12.0587402 12.1952932 10.3858746 9.6239936 13.1151990 8.0223750
## 22 11.5523600 11.8164688 8.4971118 9.8509137 5.6481161 5.8323924 11.1670305
## 23 15.7249006 17.0409224 12.9847982 15.2908141 10.9901108 8.8485294 17.2406519
## 24 12.1803286 10.3365622 9.6434700 8.4305931 6.8232104 10.1399514 7.1021110
## 25 11.2040028 10.2096824 8.3127362 8.1577687 5.1610935 7.7609320 8.1597113
## 26 11.1755909 10.7581485 8.1460247 8.7121366 4.9873857 6.6756269 9.3886000
## 27 13.4823624 13.6154997 10.4231372 11.6142411 7.5070441 7.6726051 12.5852329
## 28 13.4070508 12.8127493 10.3874204 10.7554874 7.2180335 8.7279997 10.9362974
## 29 13.4614758 12.4353393 10.5404754 10.3914785 7.3657120 9.5336505 10.0410416
## 30 13.7294755 12.3227365 10.9375407 10.3263258 7.8409750 10.4669227 9.4482009
## 31 17.8926789 14.6963701 16.4165549 13.6792720 14.5639050 18.4087194 10.1316336
## 32 18.1224100 19.1170365 15.2609639 17.2570052 12.9405626 11.3245969 18.7833038
## 33 14.6614764 14.6775613 11.5985681 12.6560013 8.6306489 8.8868938 13.3980460
## 34 17.9406378 15.0819235 16.0367204 13.7295302 13.7391288 17.3725438 10.5954757
## 35 15.6755261 15.4766424 12.6116774 13.4326324 9.5607804 10.1001228 13.8603211
## 36 19.6081745 16.4472320 18.0478094 15.3860009 16.0670989 19.8528411 11.8797362
## 37 15.2575522 13.8261937 12.4506867 11.8407094 9.3342645 11.7854350 10.7967444
## 38 15.4532093 14.6402330 12.4598707 12.5872628 9.2773535 10.8440791 12.3174381
## 39 22.6664253 19.3766090 21.2693435 18.4751555 19.3982890 23.2111622 14.8442074
## 40 18.7997128 19.3172185 15.8071782 17.3514171 13.1327764 12.2808211 18.3640066
## 41 20.4808839 21.3672870 17.5909315 19.4696195 15.1707946 13.7073167 20.7910211
## 42 20.0386746 17.1230844 18.1603965 15.8200506 15.8491784 19.4489813 12.6009725
## 43 16.7373984 15.8629455 13.7487818 13.8148109 10.5653445 12.0863426 13.3790614
## 44 17.3355860 16.8824487 14.2830329 14.8255532 11.1644317 11.9812896 14.8535148
## 45 18.2274189 16.9390360 15.3381518 14.9385843 12.1695767 14.1417740 13.9023447
## 46 22.6887315 19.5169533 21.1007929 18.4661055 19.0382692 22.7662763 14.9513263
## 47 22.3771535 22.9554467 19.4069183 20.9892942 16.7687812 15.7512272 21.9260058
## 48 18.3291298 17.4022565 15.3430928 15.3599639 12.1593586 13.6189566 14.7689732
## 49 19.4979237 18.7925239 16.4684827 16.7386531 13.3085396 14.3606687 16.3578290
## 8 9 10 11 12 13 14
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9 7.2853967
## 10 11.1379598 4.1998710
## 11 2.6660266 5.6029021 9.7607407
## 12 2.6238334 6.3131668 10.4842930 0.7421561
## 13 2.4318066 7.5053630 11.6600171 1.9026269 1.2127661
## 14 3.2637712 7.7266273 11.9182423 2.2420724 1.5028310 0.8324100
## 15 4.7801678 3.9609318 8.1412587 2.2427010 2.7594379 3.9516713 3.9507211
## 16 4.7147609 6.1230055 10.2798707 2.2340292 2.0934385 2.8469089 2.4175973
## 17 12.6079130 5.3433349 2.1868923 10.8866242 11.5697071 12.7781273 12.9104308
## 18 4.8226244 7.5593688 11.7295173 2.9247891 2.4084025 2.4880555 1.7341571
## 19 4.1599271 7.7826737 11.9799902 2.6566326 2.0021252 1.7503451 0.9408486
## 20 12.1095172 5.1869250 4.5950499 9.9058834 10.4825604 11.6776923 11.6118975
## 21 8.6971816 10.8563125 14.8007043 6.9847876 6.4647106 6.2655662 5.4334129
## 22 8.4804540 2.4609947 5.1709971 6.2077380 6.7833012 7.9812272 7.9436808
## 23 14.2524951 6.9829649 4.2676539 12.3134134 12.9534273 14.1653839 14.1998090
## 24 6.6209129 7.8392920 11.8308516 4.3916502 4.0647011 4.3587399 3.6186297
## 25 6.4040134 5.0880218 8.9774205 3.7385151 3.9332540 4.9008680 4.5440052
## 26 7.1106482 3.6144827 7.2628365 4.5545603 4.9880257 6.1204591 5.9430375
## 27 10.1403945 4.3400664 6.0441435 7.6945512 8.1763367 9.3243825 9.1462001
## 28 9.0640004 5.4531891 8.3761792 6.4133930 6.6873604 7.6798191 7.3032925
## 29 8.6130722 6.4588524 9.7806180 5.9609153 6.0612292 6.8840783 6.3719377
## 30 8.5079956 7.5859974 11.1223773 5.9662460 5.8854472 6.4867653 5.8452869
## 31 12.4664529 16.6461651 20.7382201 11.8241502 11.1251511 10.3471071 9.6742378
## 32 16.0312207 8.9782516 6.9735267 13.8253304 14.3890405 15.5764478 15.4741131
## 33 11.1206191 5.5593144 6.9685362 8.5949106 9.0166095 10.1203990 9.8622679
## 34 12.2488598 15.1924305 19.1188311 11.0155551 10.3997932 9.8972998 9.1123862
## 35 11.8186720 6.7450111 8.2283456 9.2150378 9.5565939 10.5920219 10.2419126
## 36 14.1196169 17.9140434 21.9344415 13.3185757 12.6407167 11.9339250 11.2256226
## 37 10.0201888 8.7535342 12.0026700 7.4947506 7.3963561 7.9418717 7.2614301
## 38 10.8329949 7.5325658 10.0764306 8.1686283 8.3164698 9.1636519 8.6495525
## 39 17.3127677 21.2943398 25.3053846 16.6526877 15.9630601 15.2044120 14.5240824
## 40 15.9311418 9.4255009 8.5304726 13.4983334 13.9639585 15.0910896 14.8535255
## 41 18.1700052 11.2594152 9.3531433 15.8600822 16.3803092 17.5426390 17.3698133
## 42 14.3622060 17.1805685 21.0336325 13.1319170 12.5213297 12.0190413 11.2361193
## 43 12.0446165 8.7514205 11.0569108 9.3868895 9.4951817 10.2862882 9.7267136
## 44 13.1327991 8.6125596 10.1082387 10.4768565 10.7156935 11.6458808 11.1858915
## 45 13.1221827 10.8802230 13.4331297 10.5528851 10.5009035 11.0751720 10.3933851
## 46 17.1853891 20.6679699 24.5943849 16.2914847 15.6308588 14.9701906 14.2434062
## 47 19.5579465 13.0217072 11.6512120 17.0986901 17.5417195 18.6480343 18.3700408
## 48 13.5774104 10.2651478 12.3138290 10.9292994 11.0043066 11.7438333 11.1458530
## 49 14.9861299 10.9870141 12.4793913 12.3218505 12.4594208 13.2677062 12.7087386
## 15 16 17 18 19 20 21
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 16 2.1746284
## 17 8.9812816 10.9865775
## 18 3.5988466 1.4587973 12.4417242
## 19 3.8581113 1.9292480 12.8215640 0.8364844
## 20 7.7262142 9.3843404 3.1503972 10.7692363 11.3094405
## 21 7.1232709 5.0779904 15.0522952 4.0663111 4.5607002 12.7374467
## 22 4.0298992 5.8032159 5.3051043 7.2346358 7.7040326 3.7002843 9.7747449
## 23 10.2521993 12.0948366 2.0825434 13.5197804 13.9950947 2.9735515 15.6892886
## 24 4.1246234 2.2271300 12.2121821 1.8845130 2.7037936 10.1316011 3.0231242
## 25 2.0453352 2.1301674 9.3544007 3.3742684 4.0160202 7.4335582 5.8249459
## 26 2.3330888 3.6530161 7.5660369 5.0407131 5.5809798 5.7334881 7.5565125
## 27 5.4521182 6.8134751 5.4368555 8.1348676 8.7394876 2.7961391 9.9414779
## 28 4.3554678 4.8863621 8.0948445 5.9787940 6.7037402 5.5133011 7.2459151
## 29 4.2714176 4.0407961 9.6484205 4.8480188 5.6428744 7.1241132 5.6442881
## 30 4.7472097 3.7932861 11.0804428 4.1622688 4.9977010 8.5829589 4.2105809
## 31 12.7197681 10.5455421 21.1485634 9.1634601 9.1808743 18.9025835 6.1664019
## 32 11.6297033 13.1867013 4.8003529 14.5245191 15.1157231 3.9273919 16.0381099
## 33 6.3639746 7.4694320 6.0459927 8.6944133 9.3649987 3.0326573 10.0200160
## 34 11.4248247 9.3204503 19.2838705 8.1255462 8.3996177 16.8110261 4.3362674
## 35 7.0399574 7.8243246 7.1897705 8.9145985 9.6474892 4.0784548 9.6995866
## 36 14.0415682 11.8813212 22.2076267 10.5573919 10.6622033 19.8086246 7.1596251
## 37 6.2251267 5.3090511 11.7079985 5.5424607 6.3764575 8.9596987 4.5318410
## 38 6.2945840 6.3185150 9.4400208 7.0944809 7.9027097 6.4940729 7.1506072
## 39 17.4211944 15.2589174 25.5405925 13.9241841 14.0000335 23.0781019 10.5138876
## 40 11.2556326 12.4630713 6.5314997 13.6720190 14.3553239 4.3318006 14.5742792
## 41 13.6311789 15.0162627 7.1715549 16.2838106 16.9319266 6.1018216 17.3588596
## 42 13.4735675 11.3958938 21.0876282 10.2329125 10.5220708 18.4946749 6.3521036
## 43 7.5661613 7.4570933 10.2313741 8.1085421 8.9340820 7.1656123 7.5976952
## 44 8.4181000 8.8051044 8.9300068 9.6859929 10.4796682 5.7801392 9.6640866
## 45 9.0646401 8.4078142 12.6443553 8.6702800 9.5020900 9.5724448 6.9528756
## 46 16.8694895 14.7374973 24.7110246 13.4709520 13.6376903 22.1456013 9.8140848
## 47 14.8575656 15.9615088 9.5049707 17.1058434 17.8228317 7.8609473 17.5900085
## 48 9.1509613 8.9411711 11.2955977 9.4828233 10.3169817 8.1603009 8.4419937
## 49 10.4099040 10.4345270 11.1883040 11.0811520 11.9095810 8.0489832 10.1783909
## 22 23 24 25 26 27 28
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 16
## 17
## 18
## 19
## 20
## 21
## 22
## 23 6.2923541
## 24 6.9072289 13.0220498
## 25 4.0517258 10.2525801 2.8708393
## 26 2.2627397 8.4941449 4.6500995 1.7889938
## 27 1.9387870 5.7547547 7.3656597 4.7624573 3.2083164
## 28 3.2859073 8.4824824 4.8580066 2.7790113 2.2472195 2.7338071
## 29 4.6124706 10.0921857 3.4404828 2.2687461 2.8702091 4.3397931 1.6111178
## 30 5.9563809 11.5512947 2.4256360 2.7469629 3.9811675 5.7985518 3.0696742
## 31 15.8466694 21.8501467 8.9450069 11.8151304 13.5941235 16.1064491 13.4111670
## 32 7.6177503 2.7852488 13.6847573 11.1527309 9.5432329 6.3902741 8.8555124
## 33 3.1458691 5.9516924 7.6880492 5.3495772 4.0408510 1.2192627 2.8344458
## 34 14.0478633 19.7832317 7.3557647 10.1521440 11.8604600 14.0356487 11.3026423
## 35 4.2927023 6.8533943 7.6732094 5.7031226 4.7208571 2.4428272 2.9445713
## 36 16.9343576 22.7769460 10.1046163 12.9605958 14.7106943 17.0229976 14.2953745
## 37 6.8579939 11.9219561 3.6947675 4.1910870 5.1534937 6.2833921 3.6423215
## 38 5.2279319 9.4032871 5.5380249 4.4308464 4.3609268 4.0591871 2.1371232
## 39 20.2831761 26.0509359 13.4789629 16.3287547 18.0703323 20.3050841 17.5717878
## 40 7.5023855 4.8628514 12.5495507 10.3416088 8.9766453 5.8053845 7.7238443
## 41 9.6939438 5.1165841 15.2556162 12.9205590 11.4336224 8.2255839 10.4026403
## 42 15.9140459 21.4660911 9.3710442 12.1080813 13.7726436 15.7545498 13.0271457
## 43 6.3905621 9.9607248 6.4280726 5.6608310 5.6501406 5.0153777 3.4170895
## 44 6.1515933 8.3198585 8.1734531 6.7824638 6.1966843 4.3598645 4.0711796
## 45 8.6205903 12.3338618 6.8070560 7.0301148 7.5113374 7.3901638 5.4279466
## 46 19.5057043 25.1177893 12.8289240 15.6314134 17.3332214 19.3989819 16.6685864
## 47 11.1408610 7.5197423 15.8469754 13.8315028 12.5565738 9.4202168 11.1270824
## 48 7.8625177 10.7760178 7.7180444 7.2210045 7.2404410 6.3333816 5.0009999
## 49 8.5287749 10.3585611 9.3638568 8.5838518 8.3237005 6.7720702 6.0937020
## 29 30 31 32 33 34 35
## 2
## 3
## 4
## 5
## 6
## 7
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## 10
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## 14
## 15
## 16
## 17
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## 21
## 22
## 23
## 24
## 25
## 26
## 27
## 28
## 29
## 30 1.4591091
## 31 11.8064600 10.3620703
## 32 10.4156132 11.8281867 22.1700812
## 33 4.3865674 5.8102121 16.1693538 6.0299449
## 34 9.6966187 8.2394791 2.8622535 19.8586666 13.9451764
## 35 4.2393392 5.5350700 15.7742353 6.4514339 1.2838994 13.4115930
## 36 12.6849290 11.2258642 1.7715825 22.9031799 16.9718867 3.0504098 16.4586097
## 37 2.2961705 1.5302941 10.4309769 11.8402797 5.9920595 8.0195760 5.3920322
## 38 2.2807877 3.1666065 13.1436258 9.1401202 3.4623660 10.7326056 2.6899069
## 39 15.9663213 14.5092116 4.8581582 26.0597016 20.1937246 6.2697525 19.6087387
## 40 9.1313997 10.4345033 20.5897139 2.5902329 4.9936468 18.1230037 4.9020487
## 41 11.8649072 13.1986754 23.3900096 2.3935343 7.5986619 20.9250588 7.6652474
## 42 11.4443704 10.0072883 3.4437331 21.3512773 15.5563087 2.1242410 14.9084684
## 43 3.4350242 4.0024486 13.3600926 9.3389945 4.1866679 10.7974904 3.1093738
## 44 4.8382841 5.8004479 15.4995379 7.3548908 3.2101406 12.9449064 1.9281610
## 45 4.8066715 4.6163728 12.0625195 11.5285611 6.5993091 9.3170865 5.5021186
## 46 15.0787087 13.6338571 4.8208421 24.9953937 19.2112720 5.4795086 18.5573134
## 47 12.4068064 13.5967661 23.4084281 4.7346908 8.5281718 20.7905089 8.1987603
## 48 4.9686703 5.3224889 13.8454089 9.7616666 5.3480926 11.1427859 4.1180711
## 49 6.4003814 6.9452491 15.5204678 8.9362271 5.6248121 12.7832882 4.3438824
## 36 37 38 39 40 41 42
## 2
## 3
## 4
## 5
## 6
## 7
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## 10
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## 26
## 27
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## 29
## 30
## 31
## 32
## 33
## 34
## 35
## 36
## 37 11.0668244
## 38 13.7817934 2.7202951
## 39 3.3809457 14.2303126 16.9195814
## 40 21.1718506 10.1590590 7.4486557 24.2217598
## 41 23.9735082 12.9602027 10.2469715 27.0128174 2.8030193
## 42 2.3915260 9.5659605 12.2196738 4.7712682 19.4668657 22.2534691
## 43 13.8387427 3.0727512 1.2909696 16.8589916 7.3631573 10.1547349 12.1042555
## 44 15.9853558 5.1369449 2.6376128 18.9901237 5.2467600 8.0249755 14.2287385
## 45 12.2789289 3.1338315 3.3975446 15.0933926 9.3541853 12.0784496 10.3258365
## 46 3.0805849 13.2209921 15.8697787 1.5648647 23.0770298 25.8546157 3.6550525
## 47 23.8059918 13.0698613 10.4668079 26.6794312 3.6384887 2.5838144 21.9110122
## 48 14.1300979 4.1083445 2.8850139 16.9783883 7.5095805 10.2083768 12.2089506
## 49 15.7392007 5.8156763 4.1532283 18.5010603 6.4895160 9.0283204 13.7457939
## 43 44 45 46 47 48
## 2
## 3
## 4
## 5
## 6
## 7
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## 17
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## 24
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## 26
## 27
## 28
## 29
## 30
## 31
## 32
## 33
## 34
## 35
## 36
## 37
## 38
## 39
## 40
## 41
## 42
## 43
## 44 2.1475568
## 45 2.4129857 4.1736911
## 46 15.7210952 17.8319274 13.8507086
## 47 10.0549729 7.9347641 11.5860682 25.4243446
## 48 1.5945218 2.4634328 1.8906603 15.7411467 9.7024739
## 49 2.9828340 2.4158026 3.4662066 17.2163430 8.2247685 1.7364328D<-as.matrix(jarak)Power distance weigth dengan alpha=1
###power distance weigth dengan alpha=1
alpha1=1
W1<-1/(D^alpha1)
round(W1,4) #menjadikan 4 angka di belakang koma## 1 2 3 4 5 6 7 8 9 10 11
## 1 Inf 0.2777 0.3263 0.2364 0.1616 0.1452 0.1274 0.1738 0.1066 0.0857 0.1277
## 2 0.2777 Inf 0.2289 0.4860 0.1613 0.1149 0.2189 0.2612 0.0986 0.0747 0.1544
## 3 0.3263 0.2289 Inf 0.2954 0.3139 0.2291 0.1423 0.2518 0.1566 0.1095 0.1900
## 4 0.2364 0.4860 0.2954 Inf 0.2314 0.1360 0.2643 0.5488 0.1201 0.0844 0.2257
## 5 0.1616 0.1613 0.3139 0.2314 Inf 0.2530 0.1501 0.2963 0.2493 0.1288 0.3638
## 6 0.1452 0.1149 0.2291 0.1360 0.2530 Inf 0.0959 0.1416 0.2964 0.2082 0.1513
## 7 0.1274 0.2189 0.1423 0.2643 0.1501 0.0959 Inf 0.2973 0.0963 0.0695 0.2016
## 8 0.1738 0.2612 0.2518 0.5488 0.2963 0.1416 0.2973 Inf 0.1373 0.0898 0.3751
## 9 0.1066 0.0986 0.1566 0.1201 0.2493 0.2964 0.0963 0.1373 Inf 0.2381 0.1785
## 10 0.0857 0.0747 0.1095 0.0844 0.1288 0.2082 0.0695 0.0898 0.2381 Inf 0.1025
## 11 0.1277 0.1544 0.1900 0.2257 0.3638 0.1513 0.2016 0.3751 0.1785 0.1025 Inf
## 12 0.1231 0.1553 0.1745 0.2250 0.2908 0.1363 0.2267 0.3811 0.1584 0.0954 1.3474
## 13 0.1222 0.1670 0.1626 0.2406 0.2292 0.1203 0.3050 0.4112 0.1332 0.0858 0.5256
## 14 0.1110 0.1475 0.1444 0.2007 0.2034 0.1132 0.2725 0.3064 0.1294 0.0839 0.4460
## 15 0.1087 0.1181 0.1594 0.1559 0.3209 0.1661 0.1396 0.2092 0.2525 0.1228 0.4459
## 16 0.0994 0.1172 0.1344 0.1531 0.2173 0.1242 0.1650 0.2121 0.1633 0.0973 0.4476
## 17 0.0729 0.0658 0.0906 0.0740 0.1078 0.1466 0.0636 0.0793 0.1871 0.4573 0.0919
## 18 0.0953 0.1180 0.1229 0.1513 0.1780 0.1075 0.1892 0.2074 0.1323 0.0853 0.3419
## 19 0.1011 0.1295 0.1302 0.1693 0.1850 0.1083 0.2246 0.2404 0.1285 0.0835 0.3764
## 20 0.0689 0.0655 0.0865 0.0748 0.1103 0.1258 0.0673 0.0826 0.1928 0.2176 0.1010
## 21 0.0692 0.0829 0.0820 0.0963 0.1039 0.0762 0.1247 0.1150 0.0921 0.0676 0.1432
## 22 0.0866 0.0846 0.1177 0.1015 0.1771 0.1715 0.0895 0.1179 0.4063 0.1934 0.1611
## 23 0.0636 0.0587 0.0770 0.0654 0.0910 0.1130 0.0580 0.0702 0.1432 0.2343 0.0812
## 24 0.0821 0.0967 0.1037 0.1186 0.1466 0.0986 0.1408 0.1510 0.1276 0.0845 0.2277
## 25 0.0893 0.0979 0.1203 0.1226 0.1938 0.1289 0.1226 0.1562 0.1965 0.1114 0.2675
## 26 0.0895 0.0930 0.1228 0.1148 0.2005 0.1498 0.1065 0.1406 0.2767 0.1377 0.2196
## 27 0.0742 0.0734 0.0959 0.0861 0.1332 0.1303 0.0795 0.0986 0.2304 0.1654 0.1300
## 28 0.0746 0.0780 0.0963 0.0930 0.1385 0.1146 0.0914 0.1103 0.1834 0.1194 0.1559
## 29 0.0743 0.0804 0.0949 0.0962 0.1358 0.1049 0.0996 0.1161 0.1548 0.1022 0.1678
## 30 0.0728 0.0812 0.0914 0.0968 0.1275 0.0955 0.1058 0.1175 0.1318 0.0899 0.1676
## 31 0.0559 0.0680 0.0609 0.0731 0.0687 0.0543 0.0987 0.0802 0.0601 0.0482 0.0846
## 32 0.0552 0.0523 0.0655 0.0579 0.0773 0.0883 0.0532 0.0624 0.1114 0.1434 0.0723
## 33 0.0682 0.0681 0.0862 0.0790 0.1159 0.1125 0.0746 0.0899 0.1799 0.1435 0.1163
## 34 0.0557 0.0663 0.0624 0.0728 0.0728 0.0576 0.0944 0.0816 0.0658 0.0523 0.0908
## 35 0.0638 0.0646 0.0793 0.0744 0.1046 0.0990 0.0721 0.0846 0.1483 0.1215 0.1085
## 36 0.0510 0.0608 0.0554 0.0650 0.0622 0.0504 0.0842 0.0708 0.0558 0.0456 0.0751
## 37 0.0655 0.0723 0.0803 0.0845 0.1071 0.0849 0.0926 0.0998 0.1142 0.0833 0.1334
## 38 0.0647 0.0683 0.0803 0.0794 0.1078 0.0922 0.0812 0.0923 0.1328 0.0992 0.1224
## 39 0.0441 0.0516 0.0470 0.0541 0.0516 0.0431 0.0674 0.0578 0.0470 0.0395 0.0601
## 40 0.0532 0.0518 0.0633 0.0576 0.0761 0.0814 0.0545 0.0628 0.1061 0.1172 0.0741
## 41 0.0488 0.0468 0.0568 0.0514 0.0659 0.0730 0.0481 0.0550 0.0888 0.1069 0.0631
## 42 0.0499 0.0584 0.0551 0.0632 0.0631 0.0514 0.0794 0.0696 0.0582 0.0475 0.0762
## 43 0.0597 0.0630 0.0727 0.0724 0.0946 0.0827 0.0747 0.0830 0.1143 0.0904 0.1065
## 44 0.0577 0.0592 0.0700 0.0675 0.0896 0.0835 0.0673 0.0761 0.1161 0.0989 0.0954
## 45 0.0549 0.0590 0.0652 0.0669 0.0822 0.0707 0.0719 0.0762 0.0919 0.0744 0.0948
## 46 0.0441 0.0512 0.0474 0.0542 0.0525 0.0439 0.0669 0.0582 0.0484 0.0407 0.0614
## 47 0.0447 0.0436 0.0515 0.0476 0.0596 0.0635 0.0456 0.0511 0.0768 0.0858 0.0585
## 48 0.0546 0.0575 0.0652 0.0651 0.0822 0.0734 0.0677 0.0737 0.0974 0.0812 0.0915
## 49 0.0513 0.0532 0.0607 0.0597 0.0751 0.0696 0.0611 0.0667 0.0910 0.0801 0.0812
## 12 13 14 15 16 17 18 19 20 21 22
## 1 0.1231 0.1222 0.1110 0.1087 0.0994 0.0729 0.0953 0.1011 0.0689 0.0692 0.0866
## 2 0.1553 0.1670 0.1475 0.1181 0.1172 0.0658 0.1180 0.1295 0.0655 0.0829 0.0846
## 3 0.1745 0.1626 0.1444 0.1594 0.1344 0.0906 0.1229 0.1302 0.0865 0.0820 0.1177
## 4 0.2250 0.2406 0.2007 0.1559 0.1531 0.0740 0.1513 0.1693 0.0748 0.0963 0.1015
## 5 0.2908 0.2292 0.2034 0.3209 0.2173 0.1078 0.1780 0.1850 0.1103 0.1039 0.1771
## 6 0.1363 0.1203 0.1132 0.1661 0.1242 0.1466 0.1075 0.1083 0.1258 0.0762 0.1715
## 7 0.2267 0.3050 0.2725 0.1396 0.1650 0.0636 0.1892 0.2246 0.0673 0.1247 0.0895
## 8 0.3811 0.4112 0.3064 0.2092 0.2121 0.0793 0.2074 0.2404 0.0826 0.1150 0.1179
## 9 0.1584 0.1332 0.1294 0.2525 0.1633 0.1871 0.1323 0.1285 0.1928 0.0921 0.4063
## 10 0.0954 0.0858 0.0839 0.1228 0.0973 0.4573 0.0853 0.0835 0.2176 0.0676 0.1934
## 11 1.3474 0.5256 0.4460 0.4459 0.4476 0.0919 0.3419 0.3764 0.1010 0.1432 0.1611
## 12 Inf 0.8246 0.6654 0.3624 0.4777 0.0864 0.4152 0.4995 0.0954 0.1547 0.1474
## 13 0.8246 Inf 1.2013 0.2531 0.3513 0.0783 0.4019 0.5713 0.0856 0.1596 0.1253
## 14 0.6654 1.2013 Inf 0.2531 0.4136 0.0775 0.5766 1.0629 0.0861 0.1840 0.1259
## 15 0.3624 0.2531 0.2531 Inf 0.4598 0.1113 0.2779 0.2592 0.1294 0.1404 0.2481
## 16 0.4777 0.3513 0.4136 0.4598 Inf 0.0910 0.6855 0.5183 0.1066 0.1969 0.1723
## 17 0.0864 0.0783 0.0775 0.1113 0.0910 Inf 0.0804 0.0780 0.3174 0.0664 0.1885
## 18 0.4152 0.4019 0.5766 0.2779 0.6855 0.0804 Inf 1.1955 0.0929 0.2459 0.1382
## 19 0.4995 0.5713 1.0629 0.2592 0.5183 0.0780 1.1955 Inf 0.0884 0.2193 0.1298
## 20 0.0954 0.0856 0.0861 0.1294 0.1066 0.3174 0.0929 0.0884 Inf 0.0785 0.2702
## 21 0.1547 0.1596 0.1840 0.1404 0.1969 0.0664 0.2459 0.2193 0.0785 Inf 0.1023
## 22 0.1474 0.1253 0.1259 0.2481 0.1723 0.1885 0.1382 0.1298 0.2702 0.1023 Inf
## 23 0.0772 0.0706 0.0704 0.0975 0.0827 0.4802 0.0740 0.0715 0.3363 0.0637 0.1589
## 24 0.2460 0.2294 0.2763 0.2424 0.4490 0.0819 0.5306 0.3699 0.0987 0.3308 0.1448
## 25 0.2542 0.2040 0.2201 0.4889 0.4694 0.1069 0.2964 0.2490 0.1345 0.1717 0.2468
## 26 0.2005 0.1634 0.1683 0.4286 0.2737 0.1322 0.1984 0.1792 0.1744 0.1323 0.4419
## 27 0.1223 0.1072 0.1093 0.1834 0.1468 0.1839 0.1229 0.1144 0.3576 0.1006 0.5158
## 28 0.1495 0.1302 0.1369 0.2296 0.2047 0.1235 0.1673 0.1492 0.1814 0.1380 0.3043
## 29 0.1650 0.1453 0.1569 0.2341 0.2475 0.1036 0.2063 0.1772 0.1404 0.1772 0.2168
## 30 0.1699 0.1542 0.1711 0.2107 0.2636 0.0902 0.2403 0.2001 0.1165 0.2375 0.1679
## 31 0.0899 0.0966 0.1034 0.0786 0.0948 0.0473 0.1091 0.1089 0.0529 0.1622 0.0631
## 32 0.0695 0.0642 0.0646 0.0860 0.0758 0.2083 0.0688 0.0662 0.2546 0.0624 0.1313
## 33 0.1109 0.0988 0.1014 0.1571 0.1339 0.1654 0.1150 0.1068 0.3297 0.0998 0.3179
## 34 0.0962 0.1010 0.1097 0.0875 0.1073 0.0519 0.1231 0.1191 0.0595 0.2306 0.0712
## 35 0.1046 0.0944 0.0976 0.1420 0.1278 0.1391 0.1122 0.1037 0.2452 0.1031 0.2330
## 36 0.0791 0.0838 0.0891 0.0712 0.0842 0.0450 0.0947 0.0938 0.0505 0.1397 0.0591
## 37 0.1352 0.1259 0.1377 0.1606 0.1884 0.0854 0.1804 0.1568 0.1116 0.2207 0.1458
## 38 0.1202 0.1091 0.1156 0.1589 0.1583 0.1059 0.1410 0.1265 0.1540 0.1398 0.1913
## 39 0.0626 0.0658 0.0689 0.0574 0.0655 0.0392 0.0718 0.0714 0.0433 0.0951 0.0493
## 40 0.0716 0.0663 0.0673 0.0888 0.0802 0.1531 0.0731 0.0697 0.2309 0.0686 0.1333
## 41 0.0610 0.0570 0.0576 0.0734 0.0666 0.1394 0.0614 0.0591 0.1639 0.0576 0.1032
## 42 0.0799 0.0832 0.0890 0.0742 0.0878 0.0474 0.0977 0.0950 0.0541 0.1574 0.0628
## 43 0.1053 0.0972 0.1028 0.1322 0.1341 0.0977 0.1233 0.1119 0.1396 0.1316 0.1565
## 44 0.0933 0.0859 0.0894 0.1188 0.1136 0.1120 0.1032 0.0954 0.1730 0.1035 0.1626
## 45 0.0952 0.0903 0.0962 0.1103 0.1189 0.0791 0.1153 0.1052 0.1045 0.1438 0.1160
## 46 0.0640 0.0668 0.0702 0.0593 0.0679 0.0405 0.0742 0.0733 0.0452 0.1019 0.0513
## 47 0.0570 0.0536 0.0544 0.0673 0.0627 0.1052 0.0585 0.0561 0.1272 0.0569 0.0898
## 48 0.0909 0.0852 0.0897 0.1093 0.1118 0.0885 0.1055 0.0969 0.1225 0.1185 0.1272
## 49 0.0803 0.0754 0.0787 0.0961 0.0958 0.0894 0.0902 0.0840 0.1242 0.0982 0.1173
## 23 24 25 26 27 28 29 30 31 32 33
## 1 0.0636 0.0821 0.0893 0.0895 0.0742 0.0746 0.0743 0.0728 0.0559 0.0552 0.0682
## 2 0.0587 0.0967 0.0979 0.0930 0.0734 0.0780 0.0804 0.0812 0.0680 0.0523 0.0681
## 3 0.0770 0.1037 0.1203 0.1228 0.0959 0.0963 0.0949 0.0914 0.0609 0.0655 0.0862
## 4 0.0654 0.1186 0.1226 0.1148 0.0861 0.0930 0.0962 0.0968 0.0731 0.0579 0.0790
## 5 0.0910 0.1466 0.1938 0.2005 0.1332 0.1385 0.1358 0.1275 0.0687 0.0773 0.1159
## 6 0.1130 0.0986 0.1289 0.1498 0.1303 0.1146 0.1049 0.0955 0.0543 0.0883 0.1125
## 7 0.0580 0.1408 0.1226 0.1065 0.0795 0.0914 0.0996 0.1058 0.0987 0.0532 0.0746
## 8 0.0702 0.1510 0.1562 0.1406 0.0986 0.1103 0.1161 0.1175 0.0802 0.0624 0.0899
## 9 0.1432 0.1276 0.1965 0.2767 0.2304 0.1834 0.1548 0.1318 0.0601 0.1114 0.1799
## 10 0.2343 0.0845 0.1114 0.1377 0.1654 0.1194 0.1022 0.0899 0.0482 0.1434 0.1435
## 11 0.0812 0.2277 0.2675 0.2196 0.1300 0.1559 0.1678 0.1676 0.0846 0.0723 0.1163
## 12 0.0772 0.2460 0.2542 0.2005 0.1223 0.1495 0.1650 0.1699 0.0899 0.0695 0.1109
## 13 0.0706 0.2294 0.2040 0.1634 0.1072 0.1302 0.1453 0.1542 0.0966 0.0642 0.0988
## 14 0.0704 0.2763 0.2201 0.1683 0.1093 0.1369 0.1569 0.1711 0.1034 0.0646 0.1014
## 15 0.0975 0.2424 0.4889 0.4286 0.1834 0.2296 0.2341 0.2107 0.0786 0.0860 0.1571
## 16 0.0827 0.4490 0.4694 0.2737 0.1468 0.2047 0.2475 0.2636 0.0948 0.0758 0.1339
## 17 0.4802 0.0819 0.1069 0.1322 0.1839 0.1235 0.1036 0.0902 0.0473 0.2083 0.1654
## 18 0.0740 0.5306 0.2964 0.1984 0.1229 0.1673 0.2063 0.2403 0.1091 0.0688 0.1150
## 19 0.0715 0.3699 0.2490 0.1792 0.1144 0.1492 0.1772 0.2001 0.1089 0.0662 0.1068
## 20 0.3363 0.0987 0.1345 0.1744 0.3576 0.1814 0.1404 0.1165 0.0529 0.2546 0.3297
## 21 0.0637 0.3308 0.1717 0.1323 0.1006 0.1380 0.1772 0.2375 0.1622 0.0624 0.0998
## 22 0.1589 0.1448 0.2468 0.4419 0.5158 0.3043 0.2168 0.1679 0.0631 0.1313 0.3179
## 23 Inf 0.0768 0.0975 0.1177 0.1738 0.1179 0.0991 0.0866 0.0458 0.3590 0.1680
## 24 0.0768 Inf 0.3483 0.2150 0.1358 0.2058 0.2907 0.4123 0.1118 0.0731 0.1301
## 25 0.0975 0.3483 Inf 0.5590 0.2100 0.3598 0.4408 0.3640 0.0846 0.0897 0.1869
## 26 0.1177 0.2150 0.5590 Inf 0.3117 0.4450 0.3484 0.2512 0.0736 0.1048 0.2475
## 27 0.1738 0.1358 0.2100 0.3117 Inf 0.3658 0.2304 0.1725 0.0621 0.1565 0.8202
## 28 0.1179 0.2058 0.3598 0.4450 0.3658 Inf 0.6207 0.3258 0.0746 0.1129 0.3528
## 29 0.0991 0.2907 0.4408 0.3484 0.2304 0.6207 Inf 0.6853 0.0847 0.0960 0.2280
## 30 0.0866 0.4123 0.3640 0.2512 0.1725 0.3258 0.6853 Inf 0.0965 0.0845 0.1721
## 31 0.0458 0.1118 0.0846 0.0736 0.0621 0.0746 0.0847 0.0965 Inf 0.0451 0.0618
## 32 0.3590 0.0731 0.0897 0.1048 0.1565 0.1129 0.0960 0.0845 0.0451 Inf 0.1658
## 33 0.1680 0.1301 0.1869 0.2475 0.8202 0.3528 0.2280 0.1721 0.0618 0.1658 Inf
## 34 0.0505 0.1359 0.0985 0.0843 0.0712 0.0885 0.1031 0.1214 0.3494 0.0504 0.0717
## 35 0.1459 0.1303 0.1753 0.2118 0.4094 0.3396 0.2359 0.1807 0.0634 0.1550 0.7789
## 36 0.0439 0.0990 0.0772 0.0680 0.0587 0.0700 0.0788 0.0891 0.5645 0.0437 0.0589
## 37 0.0839 0.2707 0.2386 0.1940 0.1591 0.2746 0.4355 0.6535 0.0959 0.0845 0.1669
## 38 0.1063 0.1806 0.2257 0.2293 0.2464 0.4679 0.4384 0.3158 0.0761 0.1094 0.2888
## 39 0.0384 0.0742 0.0612 0.0553 0.0492 0.0569 0.0626 0.0689 0.2058 0.0384 0.0495
## 40 0.2056 0.0797 0.0967 0.1114 0.1723 0.1295 0.1095 0.0958 0.0486 0.3861 0.2003
## 41 0.1954 0.0655 0.0774 0.0875 0.1216 0.0961 0.0843 0.0758 0.0428 0.4178 0.1316
## 42 0.0466 0.1067 0.0826 0.0726 0.0635 0.0768 0.0874 0.0999 0.2904 0.0468 0.0643
## 43 0.1004 0.1556 0.1767 0.1770 0.1994 0.2926 0.2911 0.2498 0.0748 0.1071 0.2389
## 44 0.1202 0.1223 0.1474 0.1614 0.2294 0.2456 0.2067 0.1724 0.0645 0.1360 0.3115
## 45 0.0811 0.1469 0.1422 0.1331 0.1353 0.1842 0.2080 0.2166 0.0829 0.0867 0.1515
## 46 0.0398 0.0779 0.0640 0.0577 0.0515 0.0600 0.0663 0.0733 0.2074 0.0400 0.0521
## 47 0.1330 0.0631 0.0723 0.0796 0.1062 0.0899 0.0806 0.0735 0.0427 0.2112 0.1173
## 48 0.0928 0.1296 0.1385 0.1381 0.1579 0.2000 0.2013 0.1879 0.0722 0.1024 0.1870
## 49 0.0965 0.1068 0.1165 0.1201 0.1477 0.1641 0.1562 0.1440 0.0644 0.1119 0.1778
## 34 35 36 37 38 39 40 41 42 43 44
## 1 0.0557 0.0638 0.0510 0.0655 0.0647 0.0441 0.0532 0.0488 0.0499 0.0597 0.0577
## 2 0.0663 0.0646 0.0608 0.0723 0.0683 0.0516 0.0518 0.0468 0.0584 0.0630 0.0592
## 3 0.0624 0.0793 0.0554 0.0803 0.0803 0.0470 0.0633 0.0568 0.0551 0.0727 0.0700
## 4 0.0728 0.0744 0.0650 0.0845 0.0794 0.0541 0.0576 0.0514 0.0632 0.0724 0.0675
## 5 0.0728 0.1046 0.0622 0.1071 0.1078 0.0516 0.0761 0.0659 0.0631 0.0946 0.0896
## 6 0.0576 0.0990 0.0504 0.0849 0.0922 0.0431 0.0814 0.0730 0.0514 0.0827 0.0835
## 7 0.0944 0.0721 0.0842 0.0926 0.0812 0.0674 0.0545 0.0481 0.0794 0.0747 0.0673
## 8 0.0816 0.0846 0.0708 0.0998 0.0923 0.0578 0.0628 0.0550 0.0696 0.0830 0.0761
## 9 0.0658 0.1483 0.0558 0.1142 0.1328 0.0470 0.1061 0.0888 0.0582 0.1143 0.1161
## 10 0.0523 0.1215 0.0456 0.0833 0.0992 0.0395 0.1172 0.1069 0.0475 0.0904 0.0989
## 11 0.0908 0.1085 0.0751 0.1334 0.1224 0.0601 0.0741 0.0631 0.0762 0.1065 0.0954
## 12 0.0962 0.1046 0.0791 0.1352 0.1202 0.0626 0.0716 0.0610 0.0799 0.1053 0.0933
## 13 0.1010 0.0944 0.0838 0.1259 0.1091 0.0658 0.0663 0.0570 0.0832 0.0972 0.0859
## 14 0.1097 0.0976 0.0891 0.1377 0.1156 0.0689 0.0673 0.0576 0.0890 0.1028 0.0894
## 15 0.0875 0.1420 0.0712 0.1606 0.1589 0.0574 0.0888 0.0734 0.0742 0.1322 0.1188
## 16 0.1073 0.1278 0.0842 0.1884 0.1583 0.0655 0.0802 0.0666 0.0878 0.1341 0.1136
## 17 0.0519 0.1391 0.0450 0.0854 0.1059 0.0392 0.1531 0.1394 0.0474 0.0977 0.1120
## 18 0.1231 0.1122 0.0947 0.1804 0.1410 0.0718 0.0731 0.0614 0.0977 0.1233 0.1032
## 19 0.1191 0.1037 0.0938 0.1568 0.1265 0.0714 0.0697 0.0591 0.0950 0.1119 0.0954
## 20 0.0595 0.2452 0.0505 0.1116 0.1540 0.0433 0.2309 0.1639 0.0541 0.1396 0.1730
## 21 0.2306 0.1031 0.1397 0.2207 0.1398 0.0951 0.0686 0.0576 0.1574 0.1316 0.1035
## 22 0.0712 0.2330 0.0591 0.1458 0.1913 0.0493 0.1333 0.1032 0.0628 0.1565 0.1626
## 23 0.0505 0.1459 0.0439 0.0839 0.1063 0.0384 0.2056 0.1954 0.0466 0.1004 0.1202
## 24 0.1359 0.1303 0.0990 0.2707 0.1806 0.0742 0.0797 0.0655 0.1067 0.1556 0.1223
## 25 0.0985 0.1753 0.0772 0.2386 0.2257 0.0612 0.0967 0.0774 0.0826 0.1767 0.1474
## 26 0.0843 0.2118 0.0680 0.1940 0.2293 0.0553 0.1114 0.0875 0.0726 0.1770 0.1614
## 27 0.0712 0.4094 0.0587 0.1591 0.2464 0.0492 0.1723 0.1216 0.0635 0.1994 0.2294
## 28 0.0885 0.3396 0.0700 0.2746 0.4679 0.0569 0.1295 0.0961 0.0768 0.2926 0.2456
## 29 0.1031 0.2359 0.0788 0.4355 0.4384 0.0626 0.1095 0.0843 0.0874 0.2911 0.2067
## 30 0.1214 0.1807 0.0891 0.6535 0.3158 0.0689 0.0958 0.0758 0.0999 0.2498 0.1724
## 31 0.3494 0.0634 0.5645 0.0959 0.0761 0.2058 0.0486 0.0428 0.2904 0.0748 0.0645
## 32 0.0504 0.1550 0.0437 0.0845 0.1094 0.0384 0.3861 0.4178 0.0468 0.1071 0.1360
## 33 0.0717 0.7789 0.0589 0.1669 0.2888 0.0495 0.2003 0.1316 0.0643 0.2389 0.3115
## 34 Inf 0.0746 0.3278 0.1247 0.0932 0.1595 0.0552 0.0478 0.4708 0.0926 0.0773
## 35 0.0746 Inf 0.0608 0.1855 0.3718 0.0510 0.2040 0.1305 0.0671 0.3216 0.5186
## 36 0.3278 0.0608 Inf 0.0904 0.0726 0.2958 0.0472 0.0417 0.4181 0.0723 0.0626
## 37 0.1247 0.1855 0.0904 Inf 0.3676 0.0703 0.0984 0.0772 0.1045 0.3254 0.1947
## 38 0.0932 0.3718 0.0726 0.3676 Inf 0.0591 0.1343 0.0976 0.0818 0.7746 0.3791
## 39 0.1595 0.0510 0.2958 0.0703 0.0591 Inf 0.0413 0.0370 0.2096 0.0593 0.0527
## 40 0.0552 0.2040 0.0472 0.0984 0.1343 0.0413 Inf 0.3568 0.0514 0.1358 0.1906
## 41 0.0478 0.1305 0.0417 0.0772 0.0976 0.0370 0.3568 Inf 0.0449 0.0985 0.1246
## 42 0.4708 0.0671 0.4181 0.1045 0.0818 0.2096 0.0514 0.0449 Inf 0.0826 0.0703
## 43 0.0926 0.3216 0.0723 0.3254 0.7746 0.0593 0.1358 0.0985 0.0826 Inf 0.4656
## 44 0.0773 0.5186 0.0626 0.1947 0.3791 0.0527 0.1906 0.1246 0.0703 0.4656 Inf
## 45 0.1073 0.1817 0.0814 0.3191 0.2943 0.0663 0.1069 0.0828 0.0968 0.4144 0.2396
## 46 0.1825 0.0539 0.3246 0.0756 0.0630 0.6390 0.0433 0.0387 0.2736 0.0636 0.0561
## 47 0.0481 0.1220 0.0420 0.0765 0.0955 0.0375 0.2748 0.3870 0.0456 0.0995 0.1260
## 48 0.0897 0.2428 0.0708 0.2434 0.3466 0.0589 0.1332 0.0980 0.0819 0.6271 0.4059
## 49 0.0782 0.2302 0.0635 0.1719 0.2408 0.0541 0.1541 0.1108 0.0727 0.3353 0.4139
## 45 46 47 48 49
## 1 0.0549 0.0441 0.0447 0.0546 0.0513
## 2 0.0590 0.0512 0.0436 0.0575 0.0532
## 3 0.0652 0.0474 0.0515 0.0652 0.0607
## 4 0.0669 0.0542 0.0476 0.0651 0.0597
## 5 0.0822 0.0525 0.0596 0.0822 0.0751
## 6 0.0707 0.0439 0.0635 0.0734 0.0696
## 7 0.0719 0.0669 0.0456 0.0677 0.0611
## 8 0.0762 0.0582 0.0511 0.0737 0.0667
## 9 0.0919 0.0484 0.0768 0.0974 0.0910
## 10 0.0744 0.0407 0.0858 0.0812 0.0801
## 11 0.0948 0.0614 0.0585 0.0915 0.0812
## 12 0.0952 0.0640 0.0570 0.0909 0.0803
## 13 0.0903 0.0668 0.0536 0.0852 0.0754
## 14 0.0962 0.0702 0.0544 0.0897 0.0787
## 15 0.1103 0.0593 0.0673 0.1093 0.0961
## 16 0.1189 0.0679 0.0627 0.1118 0.0958
## 17 0.0791 0.0405 0.1052 0.0885 0.0894
## 18 0.1153 0.0742 0.0585 0.1055 0.0902
## 19 0.1052 0.0733 0.0561 0.0969 0.0840
## 20 0.1045 0.0452 0.1272 0.1225 0.1242
## 21 0.1438 0.1019 0.0569 0.1185 0.0982
## 22 0.1160 0.0513 0.0898 0.1272 0.1173
## 23 0.0811 0.0398 0.1330 0.0928 0.0965
## 24 0.1469 0.0779 0.0631 0.1296 0.1068
## 25 0.1422 0.0640 0.0723 0.1385 0.1165
## 26 0.1331 0.0577 0.0796 0.1381 0.1201
## 27 0.1353 0.0515 0.1062 0.1579 0.1477
## 28 0.1842 0.0600 0.0899 0.2000 0.1641
## 29 0.2080 0.0663 0.0806 0.2013 0.1562
## 30 0.2166 0.0733 0.0735 0.1879 0.1440
## 31 0.0829 0.2074 0.0427 0.0722 0.0644
## 32 0.0867 0.0400 0.2112 0.1024 0.1119
## 33 0.1515 0.0521 0.1173 0.1870 0.1778
## 34 0.1073 0.1825 0.0481 0.0897 0.0782
## 35 0.1817 0.0539 0.1220 0.2428 0.2302
## 36 0.0814 0.3246 0.0420 0.0708 0.0635
## 37 0.3191 0.0756 0.0765 0.2434 0.1719
## 38 0.2943 0.0630 0.0955 0.3466 0.2408
## 39 0.0663 0.6390 0.0375 0.0589 0.0541
## 40 0.1069 0.0433 0.2748 0.1332 0.1541
## 41 0.0828 0.0387 0.3870 0.0980 0.1108
## 42 0.0968 0.2736 0.0456 0.0819 0.0727
## 43 0.4144 0.0636 0.0995 0.6271 0.3353
## 44 0.2396 0.0561 0.1260 0.4059 0.4139
## 45 Inf 0.0722 0.0863 0.5289 0.2885
## 46 0.0722 Inf 0.0393 0.0635 0.0581
## 47 0.0863 0.0393 Inf 0.1031 0.1216
## 48 0.5289 0.0635 0.1031 Inf 0.5759
## 49 0.2885 0.0581 0.1216 0.5759 Inf#dinormalisasi
diag(W1)<-0
rtot<-rowSums(W1,na.rm=TRUE)
rtot## 1 2 3 4 5 6 7 8
## 4.490196 5.103564 5.676610 6.310571 7.108957 5.478479 5.691182 7.288011
## 9 10 11 12 13 14 15 16
## 6.902367 5.425782 9.581784 10.094120 9.359682 9.797781 8.738324 9.516698
## 17 18 19 20 21 22 23 24
## 5.754481 9.593866 9.809223 6.669706 6.244303 7.934210 5.561231 8.322323
## 25 26 27 28 29 30 31 32
## 9.180313 8.799460 8.378807 8.987369 9.167168 8.669201 4.845950 5.405748
## 33 34 35 36 37 38 39 40
## 8.451071 5.183734 8.165668 4.889638 7.848904 8.697936 3.905338 5.563630
## 41 42 43 44 45 46 47 48
## 4.774236 4.809938 8.278231 7.427048 6.518552 4.120513 4.191281 7.232815
## 49
## 6.155136W1<-W1/rtot #row-normalized
rowSums(W1,na.rm=TRUE)## 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
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1W1## 1 2 3 4 5 6
## 1 0.000000000 0.061842900 0.072668135 0.052650101 0.03598191 0.03233196
## 2 0.054410363 0.000000000 0.044857714 0.095224946 0.03159669 0.02251949
## 3 0.057480466 0.040329387 0.000000000 0.052042305 0.05529808 0.04035595
## 4 0.037462427 0.077011513 0.046814130 0.000000000 0.03667360 0.02154430
## 5 0.022727083 0.022683456 0.044156352 0.032554894 0.00000000 0.03558525
## 6 0.026499481 0.020978392 0.041815436 0.024816525 0.04617596 0.00000000
## 7 0.022382281 0.038463776 0.024998522 0.046446395 0.02637256 0.01685252
## 8 0.023850198 0.035840017 0.034548855 0.075302368 0.04064912 0.01942672
## 9 0.015448231 0.014283280 0.022689287 0.017400486 0.03611654 0.04293603
## 10 0.015795993 0.013764790 0.020188610 0.015563183 0.02374302 0.03836429
## 11 0.013332465 0.016115468 0.019831266 0.023555020 0.03796811 0.01578921
## 12 0.012195170 0.015384253 0.017287489 0.022288048 0.02880457 0.01350517
## 13 0.013057476 0.017839289 0.017371923 0.025704327 0.02448301 0.01284967
## 14 0.011324556 0.015052032 0.014739889 0.020488809 0.02076286 0.01155474
## 15 0.012444716 0.013512901 0.018241513 0.017840931 0.03672864 0.01901073
## 16 0.010443294 0.012314508 0.014125392 0.016083603 0.02283533 0.01304719
## 17 0.012674015 0.011441454 0.015739792 0.012858310 0.01874147 0.02546720
## 18 0.009936219 0.012296605 0.012813105 0.015770755 0.01855813 0.01120791
## 19 0.010309054 0.013203122 0.013272765 0.017259663 0.01885990 0.01104549
## 20 0.010327827 0.009821231 0.012967566 0.011213153 0.01653235 0.01886010
## 21 0.011088122 0.013280491 0.013131786 0.015419596 0.01664028 0.01221072
## 22 0.010910021 0.010666172 0.014832862 0.012794396 0.02231478 0.02160974
## 23 0.011435130 0.010552028 0.013848216 0.011759759 0.01636164 0.02032160
## 24 0.009864986 0.011624635 0.012460117 0.014252706 0.01761030 0.01185003
## 25 0.009722306 0.010669161 0.013103838 0.013352763 0.02110575 0.01403552
## 26 0.010168888 0.010563466 0.013950772 0.013044255 0.02278615 0.01702362
## 27 0.008852212 0.008765652 0.011450366 0.010276068 0.01589823 0.01555518
## 28 0.008299160 0.008684105 0.010711732 0.010345163 0.01541518 0.01274831
## 29 0.008103490 0.008772172 0.010349148 0.010497538 0.01480983 0.01144210
## 30 0.008401697 0.009360818 0.010546328 0.011170565 0.01471129 0.01102052
## 31 0.011533090 0.014041418 0.012570108 0.015085443 0.01416913 0.01120979
## 32 0.010207708 0.009676618 0.012121663 0.010719604 0.01429523 0.01633509
## 33 0.008070688 0.008061843 0.010201966 0.009349572 0.01371023 0.01331491
## 34 0.010752747 0.012790884 0.012029338 0.014050818 0.01404100 0.01110437
## 35 0.007812430 0.007912825 0.009710362 0.009116899 0.01280899 0.01212500
## 36 0.010430044 0.012434561 0.011331797 0.013292220 0.01272875 0.01030150
## 37 0.008350377 0.009214851 0.010232875 0.010760024 0.01364932 0.01081049
## 38 0.007439866 0.007853004 0.009227207 0.009133821 0.01239252 0.01060208
## 39 0.011296875 0.013214891 0.012038913 0.013859681 0.01320012 0.01103175
## 40 0.009560719 0.009304589 0.011370706 0.010358737 0.01368627 0.01463573
## 41 0.010226981 0.009802724 0.011907136 0.010758177 0.01380663 0.01528072
## 42 0.010375081 0.012141672 0.011448146 0.013141732 0.01311758 0.01068965
## 43 0.007217296 0.007615153 0.008786143 0.008744149 0.01143349 0.00999465
## 44 0.007766856 0.007975324 0.009426780 0.009081820 0.01206000 0.01123777
## 45 0.008416348 0.009056495 0.010001746 0.010269266 0.01260589 0.01084788
## 46 0.010696422 0.012434741 0.011501381 0.013142363 0.01274739 0.01065999
## 47 0.010662239 0.010393636 0.012294098 0.011367250 0.01422826 0.01514743
## 48 0.007543116 0.007944875 0.009011139 0.009001242 0.01137056 0.01015193
## 49 0.008332475 0.008645244 0.009865266 0.009706035 0.01220765 0.01131326
## 7 8 9 10 11 12
## 1 0.028368833 0.03871112 0.02374715 0.019087275 0.02845061 0.02741517
## 2 0.042892448 0.05118040 0.01931757 0.014633842 0.03025630 0.03042785
## 3 0.025062693 0.04435613 0.02758861 0.019296550 0.03347401 0.03074053
## 4 0.041887635 0.08696591 0.01903228 0.013381109 0.03576525 0.03565101
## 5 0.021112944 0.04167295 0.03506698 0.018121424 0.05117520 0.04090007
## 6 0.017506821 0.02584333 0.05409536 0.037995267 0.02761512 0.02488333
## 7 0.000000000 0.05224184 0.01692291 0.012212052 0.03541788 0.03983451
## 8 0.040795470 0.00000000 0.01883379 0.012319280 0.05146672 0.05229434
## 9 0.013953383 0.01988606 0.00000000 0.034495781 0.02585764 0.02294852
## 10 0.012809400 0.01654749 0.04388355 0.000000000 0.01888230 0.01757918
## 11 0.021036747 0.03914616 0.01862690 0.010692293 0.00000000 0.14062364
## 12 0.022459163 0.03775681 0.01569222 0.009449143 0.13348617 0.00000000
## 13 0.032589014 0.04393493 0.01423532 0.009163043 0.05615459 0.08809715
## 14 0.027813627 0.03127178 0.01320938 0.008563673 0.04552214 0.06791444
## 15 0.015975714 0.02394025 0.02889179 0.014056600 0.05102705 0.04147164
## 16 0.017339006 0.02228712 0.01716126 0.010221769 0.04703540 0.05019420
## 17 0.011051592 0.01378322 0.03252231 0.079463266 0.01596249 0.01502005
## 18 0.019717885 0.02161339 0.01378862 0.008886407 0.03563787 0.04327901
## 19 0.022897408 0.02450641 0.01309895 0.008509596 0.03837372 0.05091833
## 20 0.010085337 0.01238131 0.02890569 0.032628947 0.01513562 0.01430296
## 21 0.019962416 0.01841355 0.01475142 0.010820160 0.02292782 0.02477234
## 22 0.011286482 0.01486200 0.05121363 0.024373730 0.02030313 0.01858041
## 23 0.010429785 0.01261648 0.02575071 0.042134693 0.01460329 0.01388175
## 24 0.016918739 0.01814837 0.01532776 0.010156392 0.02736073 0.02956153
## 25 0.013349584 0.01700945 0.02140886 0.012133636 0.02913690 0.02769431
## 26 0.012104396 0.01598213 0.03144111 0.015647238 0.02495155 0.02278323
## 27 0.009483236 0.01176963 0.02749929 0.019746178 0.01551081 0.01459685
## 28 0.010174126 0.01227573 0.02040407 0.013283773 0.01734920 0.01663844
## 29 0.010863907 0.01266504 0.01688921 0.011153174 0.01830003 0.01799716
## 30 0.012208768 0.01355794 0.01520576 0.010371064 0.01933391 0.01959934
## 31 0.020367680 0.01655305 0.01239672 0.009950607 0.01745224 0.01854877
## 32 0.009848548 0.01153925 0.02060404 0.026527219 0.01338039 0.01285619
## 33 0.008831750 0.01064043 0.02128467 0.016980352 0.01376724 0.01312336
## 34 0.018206934 0.01574931 0.01269785 0.010090111 0.01751261 0.01854952
## 35 0.008835579 0.01036190 0.01815623 0.014883181 0.01328958 0.01281460
## 36 0.017215375 0.01448440 0.01141641 0.009323881 0.01535555 0.01617900
## 37 0.011800439 0.01271496 0.01455484 0.010614831 0.01699941 0.01722555
## 38 0.009333906 0.01061293 0.01526303 0.011409775 0.01407455 0.01382435
## 39 0.017249811 0.01479023 0.01202478 0.010118786 0.01537648 0.01604077
## 40 0.009787558 0.01128223 0.01906941 0.021070201 0.01331563 0.01287162
## 41 0.010074426 0.01152766 0.01860289 0.022394355 0.01320659 0.01278716
## 42 0.016498954 0.01447569 0.01210105 0.009884306 0.01583188 0.01660390
## 43 0.009028941 0.01002927 0.01380333 0.010925182 0.01286888 0.01272211
## 44 0.009064723 0.01025242 0.01563333 0.013320125 0.01285147 0.01256503
## 45 0.011034707 0.01169076 0.01409974 0.011420146 0.01453710 0.01460906
## 46 0.016231888 0.01412178 0.01174224 0.009867629 0.01489663 0.01552623
## 47 0.010881625 0.01219916 0.01832253 0.020477746 0.01395373 0.01360132
## 48 0.009361433 0.01018300 0.01346875 0.011227925 0.01265028 0.01256406
## 49 0.009932000 0.01084109 0.01478709 0.013018740 0.01318519 0.01303961
## 13 14 15 16 17 18
## 1 0.02721792 0.02471062 0.02421853 0.02213393 0.016242582 0.02122997
## 2 0.03271637 0.02889677 0.02313679 0.02296306 0.012900717 0.02311561
## 3 0.02864309 0.02544092 0.02808018 0.02368087 0.015955709 0.02165504
## 4 0.03812402 0.03181089 0.02470455 0.02425498 0.011725232 0.02397604
## 5 0.03223442 0.02861600 0.04514681 0.03056945 0.015170644 0.02504506
## 6 0.02195296 0.02066464 0.03032264 0.02266435 0.026750221 0.01962719
## 7 0.05359568 0.04788317 0.02452934 0.02899399 0.011174512 0.03323927
## 8 0.05642375 0.04204083 0.02870436 0.02910257 0.010882978 0.02845165
## 9 0.01930324 0.01875046 0.03657670 0.02366123 0.027113747 0.01916533
## 10 0.01580660 0.01546413 0.02263842 0.01792875 0.084277236 0.01571294
## 11 0.05485295 0.04654832 0.04653527 0.04671590 0.009586507 0.03568281
## 12 0.08168729 0.06592064 0.03590136 0.04732290 0.008562670 0.04113415
## 13 0.00000000 0.12835170 0.02703698 0.03752886 0.008361260 0.04294166
## 14 0.12261257 0.00000000 0.02583425 0.04221709 0.007905540 0.05885506
## 15 0.02895950 0.02896646 0.00000000 0.05262436 0.012741881 0.03179864
## 16 0.03690967 0.04346401 0.04832019 0.00000000 0.009564258 0.07203089
## 17 0.01359961 0.01346025 0.01934887 0.01581726 0.000000000 0.01396733
## 18 0.04189346 0.06010601 0.02896296 0.07145150 0.008377719 0.00000000
## 19 0.05824273 0.10835418 0.02642352 0.05284177 0.007951048 0.12187302
## 20 0.01283915 0.01291190 0.01940558 0.01597679 0.047591346 0.01392222
## 21 0.02555970 0.02947429 0.02248209 0.03153728 0.010639307 0.03938360
## 22 0.01579162 0.01586626 0.03127534 0.02171839 0.023757589 0.01742126
## 23 0.01269406 0.01266329 0.01753929 0.01486719 0.086344557 0.01330024
## 24 0.02756732 0.03320560 0.02913206 0.05395229 0.009839254 0.06376118
## 25 0.02222642 0.02397197 0.05325716 0.05113624 0.011644653 0.03228218
## 26 0.01856778 0.01912210 0.04870939 0.03110945 0.015020193 0.02254509
## 27 0.01279964 0.01304900 0.02189034 0.01751657 0.021951795 0.01467126
## 28 0.01448827 0.01523522 0.02554657 0.02277098 0.013745448 0.01861032
## 29 0.01584598 0.01711959 0.02553835 0.02699590 0.011305989 0.02250093
## 30 0.01778250 0.01973400 0.02429867 0.03040923 0.010410314 0.02771346
## 31 0.01994353 0.02133066 0.01622340 0.01956826 0.009757536 0.02251965
## 32 0.01187615 0.01195469 0.01590653 0.01402840 0.038536389 0.01273627
## 33 0.01169205 0.01199807 0.01859344 0.01584166 0.019571343 0.01360968
## 34 0.01949129 0.02117021 0.01688526 0.02069762 0.010003756 0.02374131
## 35 0.01156191 0.01195714 0.01739555 0.01565170 0.017033083 0.01373746
## 36 0.01713720 0.01821851 0.01456491 0.01721308 0.009209184 0.01937165
## 37 0.01604235 0.01754562 0.02046646 0.02399795 0.010881990 0.02298732
## 38 0.01254629 0.01329199 0.01826488 0.01819570 0.012178978 0.01620553
## 39 0.01684115 0.01763001 0.01469818 0.01678099 0.010025600 0.01838957
## 40 0.01191026 0.01210075 0.01596878 0.01442171 0.027518761 0.01314647
## 41 0.01193991 0.01205871 0.01536607 0.01394872 0.029206722 0.01286294
## 42 0.01729779 0.01850308 0.01543042 0.01824367 0.009858997 0.02031708
## 43 0.01174367 0.01241928 0.01596566 0.01619917 0.011806699 0.01489772
## 44 0.01156143 0.01203686 0.01599446 0.01529147 0.015077592 0.01390080
## 45 0.01385155 0.01476019 0.01692382 0.01824592 0.012132552 0.01769358
## 46 0.01621143 0.01703864 0.01438622 0.01646740 0.009821052 0.01801567
## 47 0.01279441 0.01298803 0.01605852 0.01494787 0.025101662 0.01394790
## 48 0.01177288 0.01240450 0.01510866 0.01546316 0.012240056 0.01457991
## 49 0.01224522 0.01278380 0.01560686 0.01557004 0.014521053 0.01466147
## 19 20 21 22 23 24
## 1 0.02252102 0.015340882 0.01541972 0.01927809 0.014162723 0.01828419
## 2 0.02537685 0.012835094 0.01624892 0.01658207 0.011498292 0.01895616
## 3 0.02293543 0.015236180 0.01444504 0.02073192 0.013566747 0.01826744
## 4 0.02682862 0.011851295 0.01525767 0.01608625 0.010363364 0.01879634
## 5 0.02602364 0.015510840 0.01461634 0.02490523 0.012799471 0.02061604
## 6 0.01977697 0.022961000 0.01391762 0.03129632 0.020628560 0.01800131
## 7 0.03946558 0.011819378 0.02190254 0.01573475 0.010191635 0.02474059
## 8 0.03298415 0.011330893 0.01577656 0.01617975 0.009627202 0.02072398
## 9 0.01861543 0.027931353 0.01334503 0.05886962 0.020747324 0.01848098
## 10 0.01538442 0.040109519 0.01245246 0.03564211 0.043186548 0.01557836
## 11 0.03928458 0.010535627 0.01494171 0.01681203 0.008475692 0.02376435
## 12 0.04948121 0.009450704 0.01532436 0.01460463 0.007647982 0.02437266
## 13 0.06104010 0.009149174 0.01705213 0.01338657 0.007542418 0.02451196
## 14 0.10848072 0.008789599 0.01878450 0.01284844 0.007187697 0.02820513
## 15 0.02966177 0.014811707 0.01606543 0.02839734 0.011162329 0.02774518
## 16 0.05446602 0.011197214 0.02069292 0.01810694 0.008687878 0.04718111
## 17 0.01355354 0.055160539 0.01154492 0.03275668 0.083444890 0.01422986
## 18 0.12460874 0.009678798 0.02563337 0.01440753 0.007709686 0.05531045
## 19 0.00000000 0.009014139 0.02235290 0.01323266 0.007284329 0.03770438
## 20 0.01325721 0.000000000 0.01177093 0.04051895 0.050421740 0.01479842
## 21 0.03511434 0.012572848 0.00000000 0.01638365 0.010207345 0.05297367
## 22 0.01635981 0.034061298 0.01289409 0.00000000 0.020030101 0.01824704
## 23 0.01284852 0.060471892 0.01146109 0.02857695 0.000000000 0.01380860
## 24 0.04444080 0.011859800 0.03974655 0.01739609 0.009227331 0.00000000
## 25 0.02712356 0.014653649 0.01870039 0.02688453 0.010624521 0.03794317
## 26 0.02036261 0.019820976 0.01503912 0.05022378 0.013379020 0.02443890
## 27 0.01365626 0.042683404 0.01200513 0.06155846 0.020739152 0.01620340
## 28 0.01659779 0.020181605 0.01535586 0.03386196 0.013117300 0.02290389
## 29 0.01933145 0.015312073 0.01932661 0.02365000 0.010808852 0.03170629
## 30 0.02308079 0.013439525 0.02739548 0.01936594 0.009985971 0.04755490
## 31 0.02247693 0.010916914 0.03346488 0.01302216 0.009444233 0.02306962
## 32 0.01223814 0.047102065 0.01153429 0.02428384 0.066417142 0.01351783
## 33 0.01263515 0.039017990 0.01180918 0.03761383 0.019881437 0.01539119
## 34 0.02296666 0.011475274 0.04448783 0.01373242 0.009751245 0.02622584
## 35 0.01269387 0.030027047 0.01262569 0.02852841 0.017869095 0.01595994
## 36 0.01918122 0.010324499 0.02856492 0.01207687 0.008978996 0.02023967
## 37 0.01998074 0.014219934 0.02811359 0.01857778 0.010686696 0.03448291
## 38 0.01454815 0.017703806 0.01607833 0.02199145 0.012226555 0.02076007
## 39 0.01828994 0.011095357 0.02435443 0.01262424 0.009829197 0.01899699
## 40 0.01252070 0.041492855 0.01233260 0.02395755 0.036961601 0.01432233
## 41 0.01237057 0.034327062 0.01206632 0.02160706 0.040937001 0.01372987
## 42 0.01975874 0.011241229 0.03272977 0.01306411 0.009685176 0.02218567
## 43 0.01352112 0.016858121 0.01589940 0.01890268 0.012127507 0.01879238
## 44 0.01284802 0.023294076 0.01393231 0.02188750 0.016183329 0.01647321
## 45 0.01614469 0.016026031 0.02206401 0.01779557 0.012437978 0.02253666
## 46 0.01779541 0.010958757 0.02472857 0.01244191 0.009662007 0.01891727
## 47 0.01338679 0.030351376 0.01356398 0.02141581 0.031728556 0.01505591
## 48 0.01340108 0.016942849 0.01637750 0.01758454 0.012830226 0.01791370
## 49 0.01364162 0.020184656 0.01596185 0.01904916 0.015684220 0.01735032
## 25 26 27 28 29 30
## 1 0.01987749 0.01992802 0.01651843 0.01661122 0.01654406 0.01622112
## 2 0.01919173 0.01821331 0.01439106 0.01529270 0.01575683 0.01590081
## 3 0.02119176 0.02162545 0.01690100 0.01695912 0.01671286 0.01610613
## 4 0.01942495 0.01818891 0.01364396 0.01473334 0.01524944 0.01534566
## 5 0.02725539 0.02820468 0.01873808 0.01948836 0.01909763 0.01794007
## 6 0.02351939 0.02734311 0.02379015 0.02091343 0.01914612 0.01743898
## 7 0.02153390 0.01871530 0.01396163 0.01606672 0.01749922 0.01859724
## 8 0.02142588 0.01929664 0.01353119 0.01513809 0.01593063 0.01612738
## 9 0.02847429 0.04008259 0.03338148 0.02656754 0.02243089 0.01909806
## 10 0.02052987 0.02537648 0.03049319 0.02200350 0.01884393 0.01657067
## 11 0.02791608 0.02291433 0.01356345 0.01627293 0.01750817 0.01749252
## 12 0.02518718 0.01986108 0.01211638 0.01481415 0.01634447 0.01683263
## 13 0.02180047 0.01745641 0.01145826 0.01391195 0.01552005 0.01647065
## 14 0.02246123 0.01717370 0.01115916 0.01397506 0.01601772 0.01746089
## 15 0.05595094 0.04905018 0.02098972 0.02627466 0.02679167 0.02410646
## 16 0.04932874 0.02876485 0.01542215 0.02150444 0.02600440 0.02770117
## 17 0.01857710 0.02296812 0.03196289 0.02146769 0.01801099 0.01568327
## 18 0.03089063 0.02067828 0.01281315 0.01743383 0.02150018 0.02504242
## 19 0.02538455 0.01826648 0.01166486 0.01520716 0.01806612 0.02039835
## 20 0.02016957 0.02615016 0.05362095 0.02719453 0.02104566 0.01746853
## 21 0.02749313 0.02119311 0.01610887 0.02210155 0.02837311 0.03803418
## 22 0.03110686 0.05570083 0.06500791 0.03835668 0.02732516 0.02115991
## 23 0.01753864 0.02116944 0.03124656 0.02119855 0.01781738 0.01556676
## 24 0.04185493 0.02584004 0.01631337 0.02473417 0.03492497 0.04953701
## 25 0.00000000 0.06088828 0.02287238 0.03919694 0.04801275 0.03965425
## 26 0.06352360 0.00000000 0.03542149 0.05057064 0.03959410 0.02854523
## 27 0.02506033 0.03719980 0.00000000 0.04365660 0.02750102 0.02058251
## 28 0.04003844 0.04951331 0.04070048 0.00000000 0.06906215 0.03624726
## 29 0.04808160 0.03800592 0.02513598 0.06770761 0.00000000 0.07476133
## 30 0.04199215 0.02897414 0.01989305 0.03757757 0.07905570 0.00000000
## 31 0.01746556 0.01517993 0.01281213 0.01538702 0.01747839 0.01991473
## 32 0.01658681 0.01938423 0.02894841 0.02088962 0.01776067 0.01563961
## 33 0.02211917 0.02928299 0.09704897 0.04174650 0.02697512 0.02036555
## 34 0.01900201 0.01626506 0.01374437 0.01706779 0.01989468 0.02341302
## 35 0.02147314 0.02594104 0.05013206 0.04158974 0.02888751 0.02212510
## 36 0.01577968 0.01390241 0.01201399 0.01430631 0.01612261 0.01821812
## 37 0.03039935 0.02472232 0.02027668 0.03497943 0.05548644 0.08325610
## 38 0.02594759 0.02636362 0.02832336 0.05379653 0.05040794 0.03630694
## 39 0.01568152 0.01417018 0.01261062 0.01457221 0.01603749 0.01764808
## 40 0.01738016 0.02002293 0.03096070 0.02327064 0.01968360 0.01722543
## 41 0.01621119 0.01831944 0.02546416 0.02013504 0.01765354 0.01586959
## 42 0.01717059 0.01509535 0.01319637 0.01595920 0.01816639 0.02077515
## 43 0.02133940 0.02137978 0.02408568 0.03535136 0.03516679 0.03018121
## 44 0.01985164 0.02172823 0.03088238 0.03307223 0.02782867 0.02321252
## 45 0.02182159 0.02042357 0.02075844 0.02826268 0.03191570 0.03323135
## 46 0.01552568 0.01400134 0.01251036 0.01455962 0.01609476 0.01780041
## 47 0.01724979 0.01900125 0.02532750 0.02144233 0.01923062 0.01754760
## 48 0.01914675 0.01909535 0.02183016 0.02764622 0.02782611 0.02597633
## 49 0.01892693 0.01951848 0.02399059 0.02666129 0.02538379 0.02339239
## 31 32 33 34 35 36
## 1 0.012446845 0.012289061 0.01518997 0.012413573 0.014207332 0.011357886
## 2 0.013332646 0.010249575 0.01334973 0.012991811 0.012660466 0.011913342
## 3 0.010730721 0.011543273 0.01518821 0.010984882 0.013968124 0.009760823
## 4 0.011584262 0.009182606 0.01252088 0.011541857 0.011796963 0.010299250
## 5 0.009658646 0.010870285 0.01629861 0.010238466 0.014712984 0.008755010
## 6 0.009915541 0.016118227 0.02053951 0.010506948 0.018072297 0.009194272
## 7 0.017342753 0.009354607 0.01311463 0.016583533 0.012677226 0.014790768
## 8 0.011006470 0.008559027 0.01233849 0.011201993 0.011609734 0.009717802
## 9 0.008703376 0.016136531 0.02606038 0.009536185 0.021479258 0.008087389
## 10 0.008887226 0.026429273 0.02644820 0.009639985 0.022398821 0.008402550
## 11 0.008826401 0.007548803 0.01214262 0.009474302 0.011325477 0.007836025
## 12 0.008904830 0.006884933 0.01098723 0.009525918 0.010366411 0.007837180
## 13 0.010325711 0.006859153 0.01055702 0.010794989 0.010086954 0.008952733
## 14 0.010550074 0.006595785 0.01034893 0.011200571 0.009965319 0.009092050
## 15 0.008996895 0.009840184 0.01798222 0.010016646 0.016255556 0.008149974
## 16 0.009964254 0.007968518 0.01406780 0.011273969 0.013429717 0.008844005
## 17 0.008216993 0.036201007 0.02874261 0.009011552 0.024170119 0.007825132
## 18 0.011374881 0.007176366 0.01198853 0.012827847 0.011692424 0.009873013
## 19 0.011104048 0.006744293 0.01088573 0.012136846 0.010566985 0.009561333
## 20 0.007931807 0.038175880 0.04943903 0.008918649 0.036761874 0.007569008
## 21 0.025970734 0.009985340 0.01598261 0.036931760 0.016510599 0.022367929
## 22 0.007953500 0.016545106 0.04006412 0.008971933 0.029360640 0.007442649
## 23 0.008229523 0.064560224 0.03021263 0.009089328 0.026237552 0.007894662
## 24 0.013433055 0.008780482 0.01562929 0.016335319 0.015659519 0.011891472
## 25 0.009219428 0.009767002 0.02036212 0.010729630 0.019099844 0.008404610
## 26 0.008359740 0.011908264 0.02812361 0.009581697 0.024072606 0.007725219
## 27 0.007409997 0.018676622 0.09788599 0.008503258 0.048856806 0.007011029
## 28 0.008296613 0.012564746 0.03925539 0.009844359 0.037787255 0.007783445
## 29 0.009239428 0.010473213 0.02486795 0.011249791 0.025731590 0.008599570
## 30 0.011132031 0.009752204 0.01985313 0.013999779 0.020840005 0.010275457
## 31 0.000000000 0.009307944 0.01276228 0.072096294 0.013081957 0.116482224
## 32 0.008344050 0.000000000 0.03067827 0.009315241 0.028673977 0.008076969
## 33 0.007318053 0.019623429 0.00000000 0.008485242 0.092163138 0.006972012
## 34 0.067398338 0.009714204 0.01383354 0.000000000 0.014383909 0.063241053
## 35 0.007763543 0.018982440 0.09538439 0.009131201 0.000000000 0.007440723
## 36 0.115441485 0.008929507 0.01205017 0.067044800 0.012425965 0.000000000
## 37 0.012214227 0.010760415 0.02126253 0.015886915 0.023628628 0.011512455
## 38 0.008747191 0.012578588 0.03320556 0.010712199 0.042741183 0.008342151
## 39 0.052707170 0.009825890 0.01268017 0.040840491 0.013058452 0.075736138
## 40 0.008729542 0.069390969 0.03599349 0.009917714 0.036666053 0.008489516
## 41 0.008955003 0.087509758 0.02756507 0.010009893 0.027325616 0.008737045
## 42 0.060371365 0.009737257 0.01336454 0.097871601 0.013945287 0.086933142
## 43 0.009041761 0.012934878 0.02885320 0.011187670 0.038849867 0.008729027
## 44 0.008686904 0.018306594 0.04194302 0.010401234 0.069829753 0.008422897
## 45 0.012717766 0.013306804 0.02324612 0.016465265 0.027881679 0.012493622
## 46 0.050341466 0.009709319 0.01263260 0.044290149 0.013077769 0.078779927
## 47 0.010192507 0.050392005 0.02797675 0.011475937 0.029100809 0.010022290
## 48 0.009985891 0.014163437 0.02585197 0.012407916 0.033573666 0.009784698
## 49 0.010467852 0.018180598 0.02888380 0.012709246 0.037401094 0.010322377
## 37 38 39 40 41 42
## 1 0.01459654 0.01441173 0.009825431 0.011846320 0.010873916 0.011113879
## 2 0.01417176 0.01338377 0.010112270 0.010143360 0.009170163 0.011443120
## 3 0.01414874 0.01413831 0.008282412 0.011144397 0.010014335 0.009700310
## 4 0.01338300 0.01258926 0.008577155 0.009132642 0.008139053 0.010016673
## 5 0.01507003 0.01516247 0.007251547 0.010711186 0.009272264 0.008875388
## 6 0.01548797 0.01683245 0.007863993 0.014863210 0.013316422 0.009385192
## 7 0.01627439 0.01426518 0.011836969 0.009568196 0.008451265 0.013944195
## 8 0.01369352 0.01266609 0.007925460 0.008612794 0.007551547 0.009553661
## 9 0.01655078 0.01923353 0.006803584 0.015370836 0.012867261 0.008432656
## 10 0.01535535 0.01829073 0.007283242 0.021605513 0.019705166 0.008762407
## 11 0.01392504 0.01277628 0.006267138 0.007731673 0.006580338 0.007947407
## 12 0.01339411 0.01191222 0.006206052 0.007094520 0.006047968 0.007911906
## 13 0.01345290 0.01165924 0.007026989 0.007079757 0.006090374 0.008889331
## 14 0.01405562 0.01179991 0.007027220 0.006871360 0.005875937 0.009083557
## 15 0.01838331 0.01818046 0.006568919 0.010167214 0.008395343 0.008493550
## 16 0.01979232 0.01663025 0.006886364 0.008431185 0.006997644 0.009220730
## 17 0.01484264 0.01840860 0.006803977 0.026606081 0.024231510 0.008240737
## 18 0.01880632 0.01469216 0.007485772 0.007623839 0.006401036 0.010186080
## 19 0.01598770 0.01289999 0.007281759 0.007101537 0.006020867 0.009688670
## 20 0.01673400 0.02308746 0.006496706 0.034611852 0.024571620 0.008106747
## 21 0.03533795 0.02239614 0.015231853 0.010988261 0.009225605 0.025211488
## 22 0.01837804 0.02410829 0.006213844 0.016799521 0.013001570 0.007919827
## 23 0.01508278 0.01912271 0.006902489 0.036977541 0.035143816 0.008376760
## 24 0.03252133 0.02169704 0.008914541 0.009574746 0.007876363 0.012822345
## 25 0.02599057 0.02458419 0.006670977 0.010533056 0.008430653 0.008996368
## 26 0.02205171 0.02605945 0.006288945 0.012659889 0.009939399 0.008251381
## 27 0.01899432 0.02940213 0.005877776 0.020558283 0.014509455 0.007575509
## 28 0.03054845 0.05206404 0.006332154 0.014405685 0.010696060 0.008541185
## 29 0.04750733 0.04782775 0.006832190 0.011946136 0.009193914 0.009531755
## 30 0.07537825 0.03642729 0.007950183 0.011054756 0.008739581 0.011526688
## 31 0.01978318 0.01570022 0.042476565 0.010022377 0.008822479 0.059922725
## 32 0.01562364 0.02023915 0.007098633 0.071417618 0.077286657 0.008664037
## 33 0.01974750 0.03417553 0.005859652 0.023695748 0.015572241 0.007606444
## 34 0.02405503 0.01797431 0.030768540 0.010644545 0.009219144 0.090814143
## 35 0.02271202 0.04552721 0.006245377 0.024982199 0.015976517 0.008214389
## 36 0.01847993 0.01483944 0.060490210 0.009659718 0.008530838 0.085516157
## 37 0.00000000 0.04683548 0.008953164 0.012541154 0.009830581 0.013318717
## 38 0.04226373 0.00000000 0.006795074 0.015434974 0.011219881 0.009408582
## 39 0.01799397 0.01513393 0.000000000 0.010571477 0.009479195 0.053667025
## 40 0.01769246 0.02413036 0.007420550 0.000000000 0.064123275 0.009233062
## 41 0.01616160 0.02044093 0.007754008 0.074725711 0.000000000 0.009412358
## 42 0.02173361 0.01701378 0.043573922 0.010679833 0.009342493 0.000000000
## 43 0.03931290 0.09357212 0.007165242 0.016405837 0.011895806 0.009979859
## 44 0.02621072 0.05104730 0.007090159 0.025662123 0.016777995 0.009462751
## 45 0.04895231 0.04515270 0.010163938 0.016399964 0.012700993 0.014856743
## 46 0.01835628 0.01529248 0.155085777 0.010516442 0.009386651 0.066398022
## 47 0.01825502 0.02279497 0.008942865 0.065574084 0.092340440 0.010889071
## 48 0.03365315 0.04792308 0.008143220 0.018410981 0.013543656 0.011324376
## 49 0.02793587 0.03911799 0.008781440 0.025035142 0.017995147 0.011819321
## 43 44 45 46 47 48
## 1 0.01330598 0.012846835 0.012218264 0.009815772 0.009952446 0.012150463
## 2 0.01235215 0.011606225 0.011567453 0.010039554 0.008535730 0.011259546
## 3 0.01281288 0.012333618 0.011485183 0.008348571 0.009077251 0.011481484
## 4 0.01147061 0.010688591 0.010607717 0.008581359 0.007549767 0.010316709
## 5 0.01331406 0.012599621 0.011558956 0.007388676 0.008388660 0.011568670
## 6 0.01510237 0.015234789 0.012907321 0.008017667 0.011588457 0.013402820
## 7 0.01313324 0.011829552 0.012638906 0.011752163 0.008013791 0.011897267
## 8 0.01139195 0.010448012 0.010456465 0.007984203 0.007015647 0.010105877
## 9 0.01655478 0.016821693 0.013315704 0.007009776 0.011125871 0.014113565
## 10 0.01666878 0.018233171 0.013720201 0.007493793 0.015818547 0.014967338
## 11 0.01111813 0.009961451 0.009889684 0.006406089 0.006103666 0.009549075
## 12 0.01043346 0.009245093 0.009434196 0.006337949 0.005647541 0.009002619
## 13 0.01038676 0.009174166 0.009646915 0.007136933 0.005729357 0.009097646
## 14 0.01049316 0.009124344 0.009820085 0.007165696 0.005555999 0.009157121
## 15 0.01512503 0.013594329 0.012624706 0.006783751 0.007702367 0.012505617
## 16 0.01409108 0.011933813 0.012497715 0.007130007 0.006583241 0.011752204
## 17 0.01698478 0.019459964 0.013743493 0.007032392 0.018282814 0.015384543
## 18 0.01285475 0.010761237 0.012021903 0.007737632 0.006093430 0.010991797
## 19 0.01141078 0.009727872 0.010728679 0.007475230 0.005719903 0.009881269
## 20 0.02092377 0.025939106 0.015662837 0.006770267 0.019072974 0.018373298
## 21 0.02107823 0.016571249 0.023033058 0.016317975 0.009104372 0.018970162
## 22 0.01972228 0.020488430 0.014620401 0.006461519 0.011312993 0.016030042
## 23 0.01805253 0.021612902 0.014579074 0.007158922 0.023912560 0.016686710
## 24 0.01869281 0.014701102 0.017652090 0.009366239 0.007582442 0.015568551
## 25 0.01924254 0.016060351 0.015494590 0.006968580 0.007875410 0.015084986
## 26 0.02011336 0.018339378 0.015129574 0.006556388 0.009050505 0.015695637
## 27 0.02379656 0.027374414 0.016149674 0.006152320 0.012669426 0.018844394
## 28 0.03256200 0.027330473 0.020498961 0.006675267 0.009999680 0.022249004
## 29 0.03175667 0.022546204 0.022694486 0.007234369 0.008792346 0.021954554
## 30 0.02882008 0.019886548 0.024987343 0.008460620 0.008483700 0.021672359
## 31 0.01544584 0.013313808 0.017107361 0.042805358 0.008815537 0.014904426
## 32 0.01980816 0.025151735 0.016046084 0.007400894 0.039070823 0.018950480
## 33 0.02826310 0.036860751 0.017930391 0.006159311 0.013874978 0.022125308
## 34 0.01786629 0.014902474 0.020705092 0.035205918 0.009278808 0.017312648
## 35 0.03938541 0.063513347 0.022257600 0.006599229 0.014936887 0.029738184
## 36 0.01477837 0.012793842 0.016655697 0.066388079 0.008590867 0.014473652
## 37 0.04146327 0.024801963 0.040655127 0.009636669 0.009748100 0.031011596
## 38 0.08905694 0.043588582 0.033839086 0.007244575 0.010984228 0.039850694
## 39 0.01518832 0.013483839 0.016965024 0.163630615 0.009597647 0.015081512
## 40 0.02441056 0.034257098 0.019214797 0.007788644 0.049399295 0.023934596
## 41 0.02062660 0.026100716 0.017341432 0.008101362 0.081065268 0.020518209
## 42 0.01717601 0.014611476 0.020134240 0.056880953 0.009488511 0.017028726
## 43 0.00000000 0.056249389 0.050061946 0.007683864 0.012013832 0.075758612
## 44 0.06269590 0.000000000 0.032259935 0.007550670 0.016968747 0.054656657
## 45 0.06357613 0.036756027 0.000000000 0.011075845 0.013240756 0.081140068
## 46 0.01543711 0.013609760 0.017521721 0.000000000 0.009545507 0.015417445
## 47 0.02372861 0.030069018 0.020592884 0.009384335 0.000000000 0.024590693
## 48 0.08670860 0.056124424 0.073127229 0.008783270 0.014249845 0.000000000
## 49 0.05446698 0.067251337 0.046871400 0.009436729 0.019753256 0.093563053
## 49
## 1 0.011422109
## 2 0.010426567
## 3 0.010696885
## 4 0.009466967
## 5 0.010569725
## 6 0.012710579
## 7 0.010741671
## 8 0.009155909
## 9 0.013186279
## 10 0.014768768
## 11 0.008469888
## 12 0.007951219
## 13 0.008052729
## 14 0.008031004
## 15 0.010993225
## 16 0.010070266
## 17 0.015532078
## 18 0.009406356
## 19 0.008559904
## 20 0.018627401
## 21 0.015733920
## 22 0.014777795
## 23 0.017359196
## 24 0.012832187
## 25 0.012689962
## 26 0.013652982
## 27 0.017623671
## 28 0.018259388
## 29 0.017043506
## 30 0.016608604
## 31 0.013295854
## 32 0.020700936
## 33 0.021036826
## 34 0.015090885
## 35 0.028192282
## 36 0.012993933
## 37 0.021907395
## 38 0.027682033
## 39 0.013840275
## 40 0.027696792
## 41 0.023200064
## 42 0.015124835
## 43 0.040497982
## 44 0.055734273
## 45 0.044258268
## 46 0.014096388
## 47 0.029008788
## 48 0.079622283
## 49 0.000000000W1 = mat2listw(W1,style='W')
summary(W1)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 49
## Number of nonzero links: 2352
## Percentage nonzero weights: 97.95918
## Average number of links: 48
## Link number distribution:
##
## 48
## 49
## 49 least connected regions:
## 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 49 with 48 links
## 49 most connected regions:
## 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 49 with 48 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 49 2401 49 3.256803 197.9015Power distance weigth dengan alpha=2
###power distance weigth dengan alpha=2
alpha2=2
W2<-1/(D^alpha2)
round(W2,4)## 1 2 3 4 5 6 7 8 9 10 11
## 1 Inf 0.0771 0.1065 0.0559 0.0261 0.0211 0.0162 0.0302 0.0114 0.0073 0.0163
## 2 0.0771 Inf 0.0524 0.2362 0.0260 0.0132 0.0479 0.0682 0.0097 0.0056 0.0238
## 3 0.1065 0.0524 Inf 0.0873 0.0985 0.0525 0.0202 0.0634 0.0245 0.0120 0.0361
## 4 0.0559 0.2362 0.0873 Inf 0.0536 0.0185 0.0699 0.3012 0.0144 0.0071 0.0509
## 5 0.0261 0.0260 0.0985 0.0536 Inf 0.0640 0.0225 0.0878 0.0621 0.0166 0.1324
## 6 0.0211 0.0132 0.0525 0.0185 0.0640 Inf 0.0092 0.0200 0.0878 0.0433 0.0229
## 7 0.0162 0.0479 0.0202 0.0699 0.0225 0.0092 Inf 0.0884 0.0093 0.0048 0.0406
## 8 0.0302 0.0682 0.0634 0.3012 0.0878 0.0200 0.0884 Inf 0.0188 0.0081 0.1407
## 9 0.0114 0.0097 0.0245 0.0144 0.0621 0.0878 0.0093 0.0188 Inf 0.0567 0.0319
## 10 0.0073 0.0056 0.0120 0.0071 0.0166 0.0433 0.0048 0.0081 0.0567 Inf 0.0105
## 11 0.0163 0.0238 0.0361 0.0509 0.1324 0.0229 0.0406 0.1407 0.0319 0.0105 Inf
## 12 0.0152 0.0241 0.0305 0.0506 0.0845 0.0186 0.0514 0.1453 0.0251 0.0091 1.8156
## 13 0.0149 0.0279 0.0264 0.0579 0.0525 0.0145 0.0930 0.1691 0.0178 0.0074 0.2762
## 14 0.0123 0.0217 0.0209 0.0403 0.0414 0.0128 0.0743 0.0939 0.0168 0.0070 0.1989
## 15 0.0118 0.0139 0.0254 0.0243 0.1030 0.0276 0.0195 0.0438 0.0637 0.0151 0.1988
## 16 0.0099 0.0137 0.0181 0.0234 0.0472 0.0154 0.0272 0.0450 0.0267 0.0095 0.2004
## 17 0.0053 0.0043 0.0082 0.0055 0.0116 0.0215 0.0040 0.0063 0.0350 0.2091 0.0084
## 18 0.0091 0.0139 0.0151 0.0229 0.0317 0.0116 0.0358 0.0430 0.0175 0.0073 0.1169
## 19 0.0102 0.0168 0.0170 0.0287 0.0342 0.0117 0.0504 0.0578 0.0165 0.0070 0.1417
## 20 0.0047 0.0043 0.0075 0.0056 0.0122 0.0158 0.0045 0.0068 0.0372 0.0474 0.0102
## 21 0.0048 0.0069 0.0067 0.0093 0.0108 0.0058 0.0155 0.0132 0.0085 0.0046 0.0205
## 22 0.0075 0.0072 0.0139 0.0103 0.0313 0.0294 0.0080 0.0139 0.1651 0.0374 0.0259
## 23 0.0040 0.0034 0.0059 0.0043 0.0083 0.0128 0.0034 0.0049 0.0205 0.0549 0.0066
## 24 0.0067 0.0094 0.0108 0.0141 0.0215 0.0097 0.0198 0.0228 0.0163 0.0071 0.0518
## 25 0.0080 0.0096 0.0145 0.0150 0.0375 0.0166 0.0150 0.0244 0.0386 0.0124 0.0715
## 26 0.0080 0.0086 0.0151 0.0132 0.0402 0.0224 0.0113 0.0198 0.0765 0.0190 0.0482
## 27 0.0055 0.0054 0.0092 0.0074 0.0177 0.0170 0.0063 0.0097 0.0531 0.0274 0.0169
## 28 0.0056 0.0061 0.0093 0.0086 0.0192 0.0131 0.0084 0.0122 0.0336 0.0143 0.0243
## 29 0.0055 0.0065 0.0090 0.0093 0.0184 0.0110 0.0099 0.0135 0.0240 0.0105 0.0281
## 30 0.0053 0.0066 0.0084 0.0094 0.0163 0.0091 0.0112 0.0138 0.0174 0.0081 0.0281
## 31 0.0031 0.0046 0.0037 0.0053 0.0047 0.0030 0.0097 0.0064 0.0036 0.0023 0.0072
## 32 0.0030 0.0027 0.0043 0.0034 0.0060 0.0078 0.0028 0.0039 0.0124 0.0206 0.0052
## 33 0.0047 0.0046 0.0074 0.0062 0.0134 0.0127 0.0056 0.0081 0.0324 0.0206 0.0135
## 34 0.0031 0.0044 0.0039 0.0053 0.0053 0.0033 0.0089 0.0067 0.0043 0.0027 0.0082
## 35 0.0041 0.0042 0.0063 0.0055 0.0109 0.0098 0.0052 0.0072 0.0220 0.0148 0.0118
## 36 0.0026 0.0037 0.0031 0.0042 0.0039 0.0025 0.0071 0.0050 0.0031 0.0021 0.0056
## 37 0.0043 0.0052 0.0065 0.0071 0.0115 0.0072 0.0086 0.0100 0.0131 0.0069 0.0178
## 38 0.0042 0.0047 0.0064 0.0063 0.0116 0.0085 0.0066 0.0085 0.0176 0.0098 0.0150
## 39 0.0019 0.0027 0.0022 0.0029 0.0027 0.0019 0.0045 0.0033 0.0022 0.0016 0.0036
## 40 0.0028 0.0027 0.0040 0.0033 0.0058 0.0066 0.0030 0.0039 0.0113 0.0137 0.0055
## 41 0.0024 0.0022 0.0032 0.0026 0.0043 0.0053 0.0023 0.0030 0.0079 0.0114 0.0040
## 42 0.0025 0.0034 0.0030 0.0040 0.0040 0.0026 0.0063 0.0048 0.0034 0.0023 0.0058
## 43 0.0036 0.0040 0.0053 0.0052 0.0090 0.0068 0.0056 0.0069 0.0131 0.0082 0.0113
## 44 0.0033 0.0035 0.0049 0.0045 0.0080 0.0070 0.0045 0.0058 0.0135 0.0098 0.0091
## 45 0.0030 0.0035 0.0043 0.0045 0.0068 0.0050 0.0052 0.0058 0.0084 0.0055 0.0090
## 46 0.0019 0.0026 0.0022 0.0029 0.0028 0.0019 0.0045 0.0034 0.0023 0.0017 0.0038
## 47 0.0020 0.0019 0.0027 0.0023 0.0036 0.0040 0.0021 0.0026 0.0059 0.0074 0.0034
## 48 0.0030 0.0033 0.0042 0.0042 0.0068 0.0054 0.0046 0.0054 0.0095 0.0066 0.0084
## 49 0.0026 0.0028 0.0037 0.0036 0.0056 0.0048 0.0037 0.0045 0.0083 0.0064 0.0066
## 12 13 14 15 16 17 18 19 20 21 22
## 1 0.0152 0.0149 0.0123 0.0118 0.0099 0.0053 0.0091 0.0102 0.0047 0.0048 0.0075
## 2 0.0241 0.0279 0.0217 0.0139 0.0137 0.0043 0.0139 0.0168 0.0043 0.0069 0.0072
## 3 0.0305 0.0264 0.0209 0.0254 0.0181 0.0082 0.0151 0.0170 0.0075 0.0067 0.0139
## 4 0.0506 0.0579 0.0403 0.0243 0.0234 0.0055 0.0229 0.0287 0.0056 0.0093 0.0103
## 5 0.0845 0.0525 0.0414 0.1030 0.0472 0.0116 0.0317 0.0342 0.0122 0.0108 0.0313
## 6 0.0186 0.0145 0.0128 0.0276 0.0154 0.0215 0.0116 0.0117 0.0158 0.0058 0.0294
## 7 0.0514 0.0930 0.0743 0.0195 0.0272 0.0040 0.0358 0.0504 0.0045 0.0155 0.0080
## 8 0.1453 0.1691 0.0939 0.0438 0.0450 0.0063 0.0430 0.0578 0.0068 0.0132 0.0139
## 9 0.0251 0.0178 0.0168 0.0637 0.0267 0.0350 0.0175 0.0165 0.0372 0.0085 0.1651
## 10 0.0091 0.0074 0.0070 0.0151 0.0095 0.2091 0.0073 0.0070 0.0474 0.0046 0.0374
## 11 1.8156 0.2762 0.1989 0.1988 0.2004 0.0084 0.1169 0.1417 0.0102 0.0205 0.0259
## 12 Inf 0.6799 0.4428 0.1313 0.2282 0.0075 0.1724 0.2495 0.0091 0.0239 0.0217
## 13 0.6799 Inf 1.4432 0.0640 0.1234 0.0061 0.1615 0.3264 0.0073 0.0255 0.0157
## 14 0.4428 1.4432 Inf 0.0641 0.1711 0.0060 0.3325 1.1297 0.0074 0.0339 0.0158
## 15 0.1313 0.0640 0.0641 Inf 0.2115 0.0124 0.0772 0.0672 0.0168 0.0197 0.0616
## 16 0.2282 0.1234 0.1711 0.2115 Inf 0.0083 0.4699 0.2687 0.0114 0.0388 0.0297
## 17 0.0075 0.0061 0.0060 0.0124 0.0083 Inf 0.0065 0.0061 0.1008 0.0044 0.0355
## 18 0.1724 0.1615 0.3325 0.0772 0.4699 0.0065 Inf 1.4292 0.0086 0.0605 0.0191
## 19 0.2495 0.3264 1.1297 0.0672 0.2687 0.0061 1.4292 Inf 0.0078 0.0481 0.0168
## 20 0.0091 0.0073 0.0074 0.0168 0.0114 0.1008 0.0086 0.0078 Inf 0.0062 0.0730
## 21 0.0239 0.0255 0.0339 0.0197 0.0388 0.0044 0.0605 0.0481 0.0062 Inf 0.0105
## 22 0.0217 0.0157 0.0158 0.0616 0.0297 0.0355 0.0191 0.0168 0.0730 0.0105 Inf
## 23 0.0060 0.0050 0.0050 0.0095 0.0068 0.2306 0.0055 0.0051 0.1131 0.0041 0.0253
## 24 0.0605 0.0526 0.0764 0.0588 0.2016 0.0067 0.2816 0.1368 0.0097 0.1094 0.0210
## 25 0.0646 0.0416 0.0484 0.2390 0.2204 0.0114 0.0878 0.0620 0.0181 0.0295 0.0609
## 26 0.0402 0.0267 0.0283 0.1837 0.0749 0.0175 0.0394 0.0321 0.0304 0.0175 0.1953
## 27 0.0150 0.0115 0.0120 0.0336 0.0215 0.0338 0.0151 0.0131 0.1279 0.0101 0.2660
## 28 0.0224 0.0170 0.0187 0.0527 0.0419 0.0153 0.0280 0.0223 0.0329 0.0190 0.0926
## 29 0.0272 0.0211 0.0246 0.0548 0.0612 0.0107 0.0425 0.0314 0.0197 0.0314 0.0470
## 30 0.0289 0.0238 0.0293 0.0444 0.0695 0.0081 0.0577 0.0400 0.0136 0.0564 0.0282
## 31 0.0081 0.0093 0.0107 0.0062 0.0090 0.0022 0.0119 0.0119 0.0028 0.0263 0.0040
## 32 0.0048 0.0041 0.0042 0.0074 0.0058 0.0434 0.0047 0.0044 0.0648 0.0039 0.0172
## 33 0.0123 0.0098 0.0103 0.0247 0.0179 0.0274 0.0132 0.0114 0.1087 0.0100 0.1010
## 34 0.0092 0.0102 0.0120 0.0077 0.0115 0.0027 0.0151 0.0142 0.0035 0.0532 0.0051
## 35 0.0109 0.0089 0.0095 0.0202 0.0163 0.0193 0.0126 0.0107 0.0601 0.0106 0.0543
## 36 0.0063 0.0070 0.0079 0.0051 0.0071 0.0020 0.0090 0.0088 0.0025 0.0195 0.0035
## 37 0.0183 0.0159 0.0190 0.0258 0.0355 0.0073 0.0326 0.0246 0.0125 0.0487 0.0213
## 38 0.0145 0.0119 0.0134 0.0252 0.0250 0.0112 0.0199 0.0160 0.0237 0.0196 0.0366
## 39 0.0039 0.0043 0.0047 0.0033 0.0043 0.0015 0.0052 0.0051 0.0019 0.0090 0.0024
## 40 0.0051 0.0044 0.0045 0.0079 0.0064 0.0234 0.0053 0.0049 0.0533 0.0047 0.0178
## 41 0.0037 0.0032 0.0033 0.0054 0.0044 0.0194 0.0038 0.0035 0.0269 0.0033 0.0106
## 42 0.0064 0.0069 0.0079 0.0055 0.0077 0.0022 0.0095 0.0090 0.0029 0.0248 0.0039
## 43 0.0111 0.0095 0.0106 0.0175 0.0180 0.0096 0.0152 0.0125 0.0195 0.0173 0.0245
## 44 0.0087 0.0074 0.0080 0.0141 0.0129 0.0125 0.0107 0.0091 0.0299 0.0107 0.0264
## 45 0.0091 0.0082 0.0093 0.0122 0.0141 0.0063 0.0133 0.0111 0.0109 0.0207 0.0135
## 46 0.0041 0.0045 0.0049 0.0035 0.0046 0.0016 0.0055 0.0054 0.0020 0.0104 0.0026
## 47 0.0032 0.0029 0.0030 0.0045 0.0039 0.0111 0.0034 0.0031 0.0162 0.0032 0.0081
## 48 0.0083 0.0073 0.0080 0.0119 0.0125 0.0078 0.0111 0.0094 0.0150 0.0140 0.0162
## 49 0.0064 0.0057 0.0062 0.0092 0.0092 0.0080 0.0081 0.0071 0.0154 0.0097 0.0137
## 23 24 25 26 27 28 29 30 31 32 33
## 1 0.0040 0.0067 0.0080 0.0080 0.0055 0.0056 0.0055 0.0053 0.0031 0.0030 0.0047
## 2 0.0034 0.0094 0.0096 0.0086 0.0054 0.0061 0.0065 0.0066 0.0046 0.0027 0.0046
## 3 0.0059 0.0108 0.0145 0.0151 0.0092 0.0093 0.0090 0.0084 0.0037 0.0043 0.0074
## 4 0.0043 0.0141 0.0150 0.0132 0.0074 0.0086 0.0093 0.0094 0.0053 0.0034 0.0062
## 5 0.0083 0.0215 0.0375 0.0402 0.0177 0.0192 0.0184 0.0163 0.0047 0.0060 0.0134
## 6 0.0128 0.0097 0.0166 0.0224 0.0170 0.0131 0.0110 0.0091 0.0030 0.0078 0.0127
## 7 0.0034 0.0198 0.0150 0.0113 0.0063 0.0084 0.0099 0.0112 0.0097 0.0028 0.0056
## 8 0.0049 0.0228 0.0244 0.0198 0.0097 0.0122 0.0135 0.0138 0.0064 0.0039 0.0081
## 9 0.0205 0.0163 0.0386 0.0765 0.0531 0.0336 0.0240 0.0174 0.0036 0.0124 0.0324
## 10 0.0549 0.0071 0.0124 0.0190 0.0274 0.0143 0.0105 0.0081 0.0023 0.0206 0.0206
## 11 0.0066 0.0518 0.0715 0.0482 0.0169 0.0243 0.0281 0.0281 0.0072 0.0052 0.0135
## 12 0.0060 0.0605 0.0646 0.0402 0.0150 0.0224 0.0272 0.0289 0.0081 0.0048 0.0123
## 13 0.0050 0.0526 0.0416 0.0267 0.0115 0.0170 0.0211 0.0238 0.0093 0.0041 0.0098
## 14 0.0050 0.0764 0.0484 0.0283 0.0120 0.0187 0.0246 0.0293 0.0107 0.0042 0.0103
## 15 0.0095 0.0588 0.2390 0.1837 0.0336 0.0527 0.0548 0.0444 0.0062 0.0074 0.0247
## 16 0.0068 0.2016 0.2204 0.0749 0.0215 0.0419 0.0612 0.0695 0.0090 0.0058 0.0179
## 17 0.2306 0.0067 0.0114 0.0175 0.0338 0.0153 0.0107 0.0081 0.0022 0.0434 0.0274
## 18 0.0055 0.2816 0.0878 0.0394 0.0151 0.0280 0.0425 0.0577 0.0119 0.0047 0.0132
## 19 0.0051 0.1368 0.0620 0.0321 0.0131 0.0223 0.0314 0.0400 0.0119 0.0044 0.0114
## 20 0.1131 0.0097 0.0181 0.0304 0.1279 0.0329 0.0197 0.0136 0.0028 0.0648 0.1087
## 21 0.0041 0.1094 0.0295 0.0175 0.0101 0.0190 0.0314 0.0564 0.0263 0.0039 0.0100
## 22 0.0253 0.0210 0.0609 0.1953 0.2660 0.0926 0.0470 0.0282 0.0040 0.0172 0.1010
## 23 Inf 0.0059 0.0095 0.0139 0.0302 0.0139 0.0098 0.0075 0.0021 0.1289 0.0282
## 24 0.0059 Inf 0.1213 0.0462 0.0184 0.0424 0.0845 0.1700 0.0125 0.0053 0.0169
## 25 0.0095 0.1213 Inf 0.3125 0.0441 0.1295 0.1943 0.1325 0.0072 0.0080 0.0349
## 26 0.0139 0.0462 0.3125 Inf 0.0972 0.1980 0.1214 0.0631 0.0054 0.0110 0.0612
## 27 0.0302 0.0184 0.0441 0.0972 Inf 0.1338 0.0531 0.0297 0.0039 0.0245 0.6727
## 28 0.0139 0.0424 0.1295 0.1980 0.1338 Inf 0.3853 0.1061 0.0056 0.0128 0.1245
## 29 0.0098 0.0845 0.1943 0.1214 0.0531 0.3853 Inf 0.4697 0.0072 0.0092 0.0520
## 30 0.0075 0.1700 0.1325 0.0631 0.0297 0.1061 0.4697 Inf 0.0093 0.0071 0.0296
## 31 0.0021 0.0125 0.0072 0.0054 0.0039 0.0056 0.0072 0.0093 Inf 0.0020 0.0038
## 32 0.1289 0.0053 0.0080 0.0110 0.0245 0.0128 0.0092 0.0071 0.0020 Inf 0.0275
## 33 0.0282 0.0169 0.0349 0.0612 0.6727 0.1245 0.0520 0.0296 0.0038 0.0275 Inf
## 34 0.0026 0.0185 0.0097 0.0071 0.0051 0.0078 0.0106 0.0147 0.1221 0.0025 0.0051
## 35 0.0213 0.0170 0.0307 0.0449 0.1676 0.1153 0.0556 0.0326 0.0040 0.0240 0.6066
## 36 0.0019 0.0098 0.0060 0.0046 0.0035 0.0049 0.0062 0.0079 0.3186 0.0019 0.0035
## 37 0.0070 0.0733 0.0569 0.0377 0.0253 0.0754 0.1897 0.4270 0.0092 0.0071 0.0279
## 38 0.0113 0.0326 0.0509 0.0526 0.0607 0.2189 0.1922 0.0997 0.0058 0.0120 0.0834
## 39 0.0015 0.0055 0.0038 0.0031 0.0024 0.0032 0.0039 0.0048 0.0424 0.0015 0.0025
## 40 0.0423 0.0063 0.0094 0.0124 0.0297 0.0168 0.0120 0.0092 0.0024 0.1490 0.0401
## 41 0.0382 0.0043 0.0060 0.0076 0.0148 0.0092 0.0071 0.0057 0.0018 0.1746 0.0173
## 42 0.0022 0.0114 0.0068 0.0053 0.0040 0.0059 0.0076 0.0100 0.0843 0.0022 0.0041
## 43 0.0101 0.0242 0.0312 0.0313 0.0398 0.0856 0.0848 0.0624 0.0056 0.0115 0.0571
## 44 0.0144 0.0150 0.0217 0.0260 0.0526 0.0603 0.0427 0.0297 0.0042 0.0185 0.0970
## 45 0.0066 0.0216 0.0202 0.0177 0.0183 0.0339 0.0433 0.0469 0.0069 0.0075 0.0230
## 46 0.0016 0.0061 0.0041 0.0033 0.0027 0.0036 0.0044 0.0054 0.0430 0.0016 0.0027
## 47 0.0177 0.0040 0.0052 0.0063 0.0113 0.0081 0.0065 0.0054 0.0018 0.0446 0.0137
## 48 0.0086 0.0168 0.0192 0.0191 0.0249 0.0400 0.0405 0.0353 0.0052 0.0105 0.0350
## 49 0.0093 0.0114 0.0136 0.0144 0.0218 0.0269 0.0244 0.0207 0.0042 0.0125 0.0316
## 34 35 36 37 38 39 40 41 42 43 44
## 1 0.0031 0.0041 0.0026 0.0043 0.0042 0.0019 0.0028 0.0024 0.0025 0.0036 0.0033
## 2 0.0044 0.0042 0.0037 0.0052 0.0047 0.0027 0.0027 0.0022 0.0034 0.0040 0.0035
## 3 0.0039 0.0063 0.0031 0.0065 0.0064 0.0022 0.0040 0.0032 0.0030 0.0053 0.0049
## 4 0.0053 0.0055 0.0042 0.0071 0.0063 0.0029 0.0033 0.0026 0.0040 0.0052 0.0045
## 5 0.0053 0.0109 0.0039 0.0115 0.0116 0.0027 0.0058 0.0043 0.0040 0.0090 0.0080
## 6 0.0033 0.0098 0.0025 0.0072 0.0085 0.0019 0.0066 0.0053 0.0026 0.0068 0.0070
## 7 0.0089 0.0052 0.0071 0.0086 0.0066 0.0045 0.0030 0.0023 0.0063 0.0056 0.0045
## 8 0.0067 0.0072 0.0050 0.0100 0.0085 0.0033 0.0039 0.0030 0.0048 0.0069 0.0058
## 9 0.0043 0.0220 0.0031 0.0131 0.0176 0.0022 0.0113 0.0079 0.0034 0.0131 0.0135
## 10 0.0027 0.0148 0.0021 0.0069 0.0098 0.0016 0.0137 0.0114 0.0023 0.0082 0.0098
## 11 0.0082 0.0118 0.0056 0.0178 0.0150 0.0036 0.0055 0.0040 0.0058 0.0113 0.0091
## 12 0.0092 0.0109 0.0063 0.0183 0.0145 0.0039 0.0051 0.0037 0.0064 0.0111 0.0087
## 13 0.0102 0.0089 0.0070 0.0159 0.0119 0.0043 0.0044 0.0032 0.0069 0.0095 0.0074
## 14 0.0120 0.0095 0.0079 0.0190 0.0134 0.0047 0.0045 0.0033 0.0079 0.0106 0.0080
## 15 0.0077 0.0202 0.0051 0.0258 0.0252 0.0033 0.0079 0.0054 0.0055 0.0175 0.0141
## 16 0.0115 0.0163 0.0071 0.0355 0.0250 0.0043 0.0064 0.0044 0.0077 0.0180 0.0129
## 17 0.0027 0.0193 0.0020 0.0073 0.0112 0.0015 0.0234 0.0194 0.0022 0.0096 0.0125
## 18 0.0151 0.0126 0.0090 0.0326 0.0199 0.0052 0.0053 0.0038 0.0095 0.0152 0.0107
## 19 0.0142 0.0107 0.0088 0.0246 0.0160 0.0051 0.0049 0.0035 0.0090 0.0125 0.0091
## 20 0.0035 0.0601 0.0025 0.0125 0.0237 0.0019 0.0533 0.0269 0.0029 0.0195 0.0299
## 21 0.0532 0.0106 0.0195 0.0487 0.0196 0.0090 0.0047 0.0033 0.0248 0.0173 0.0107
## 22 0.0051 0.0543 0.0035 0.0213 0.0366 0.0024 0.0178 0.0106 0.0039 0.0245 0.0264
## 23 0.0026 0.0213 0.0019 0.0070 0.0113 0.0015 0.0423 0.0382 0.0022 0.0101 0.0144
## 24 0.0185 0.0170 0.0098 0.0733 0.0326 0.0055 0.0063 0.0043 0.0114 0.0242 0.0150
## 25 0.0097 0.0307 0.0060 0.0569 0.0509 0.0038 0.0094 0.0060 0.0068 0.0312 0.0217
## 26 0.0071 0.0449 0.0046 0.0377 0.0526 0.0031 0.0124 0.0076 0.0053 0.0313 0.0260
## 27 0.0051 0.1676 0.0035 0.0253 0.0607 0.0024 0.0297 0.0148 0.0040 0.0398 0.0526
## 28 0.0078 0.1153 0.0049 0.0754 0.2189 0.0032 0.0168 0.0092 0.0059 0.0856 0.0603
## 29 0.0106 0.0556 0.0062 0.1897 0.1922 0.0039 0.0120 0.0071 0.0076 0.0848 0.0427
## 30 0.0147 0.0326 0.0079 0.4270 0.0997 0.0048 0.0092 0.0057 0.0100 0.0624 0.0297
## 31 0.1221 0.0040 0.3186 0.0092 0.0058 0.0424 0.0024 0.0018 0.0843 0.0056 0.0042
## 32 0.0025 0.0240 0.0019 0.0071 0.0120 0.0015 0.1490 0.1746 0.0022 0.0115 0.0185
## 33 0.0051 0.6066 0.0035 0.0279 0.0834 0.0025 0.0401 0.0173 0.0041 0.0571 0.0970
## 34 Inf 0.0056 0.1075 0.0155 0.0087 0.0254 0.0030 0.0023 0.2216 0.0086 0.0060
## 35 0.0056 Inf 0.0037 0.0344 0.1382 0.0026 0.0416 0.0170 0.0045 0.1034 0.2690
## 36 0.1075 0.0037 Inf 0.0082 0.0053 0.0875 0.0022 0.0017 0.1748 0.0052 0.0039
## 37 0.0155 0.0344 0.0082 Inf 0.1351 0.0049 0.0097 0.0060 0.0109 0.1059 0.0379
## 38 0.0087 0.1382 0.0053 0.1351 Inf 0.0035 0.0180 0.0095 0.0067 0.6000 0.1437
## 39 0.0254 0.0026 0.0875 0.0049 0.0035 Inf 0.0017 0.0014 0.0439 0.0035 0.0028
## 40 0.0030 0.0416 0.0022 0.0097 0.0180 0.0017 Inf 0.1273 0.0026 0.0184 0.0363
## 41 0.0023 0.0170 0.0017 0.0060 0.0095 0.0014 0.1273 Inf 0.0020 0.0097 0.0155
## 42 0.2216 0.0045 0.1748 0.0109 0.0067 0.0439 0.0026 0.0020 Inf 0.0068 0.0049
## 43 0.0086 0.1034 0.0052 0.1059 0.6000 0.0035 0.0184 0.0097 0.0068 Inf 0.2168
## 44 0.0060 0.2690 0.0039 0.0379 0.1437 0.0028 0.0363 0.0155 0.0049 0.2168 Inf
## 45 0.0115 0.0330 0.0066 0.1018 0.0866 0.0044 0.0114 0.0069 0.0094 0.1717 0.0574
## 46 0.0333 0.0029 0.1054 0.0057 0.0040 0.4084 0.0019 0.0015 0.0749 0.0040 0.0031
## 47 0.0023 0.0149 0.0018 0.0059 0.0091 0.0014 0.0755 0.1498 0.0021 0.0099 0.0159
## 48 0.0081 0.0590 0.0050 0.0592 0.1201 0.0035 0.0177 0.0096 0.0067 0.3933 0.1648
## 49 0.0061 0.0530 0.0040 0.0296 0.0580 0.0029 0.0237 0.0123 0.0053 0.1124 0.1713
## 45 46 47 48 49
## 1 0.0030 0.0019 0.0020 0.0030 0.0026
## 2 0.0035 0.0026 0.0019 0.0033 0.0028
## 3 0.0043 0.0022 0.0027 0.0042 0.0037
## 4 0.0045 0.0029 0.0023 0.0042 0.0036
## 5 0.0068 0.0028 0.0036 0.0068 0.0056
## 6 0.0050 0.0019 0.0040 0.0054 0.0048
## 7 0.0052 0.0045 0.0021 0.0046 0.0037
## 8 0.0058 0.0034 0.0026 0.0054 0.0045
## 9 0.0084 0.0023 0.0059 0.0095 0.0083
## 10 0.0055 0.0017 0.0074 0.0066 0.0064
## 11 0.0090 0.0038 0.0034 0.0084 0.0066
## 12 0.0091 0.0041 0.0032 0.0083 0.0064
## 13 0.0082 0.0045 0.0029 0.0073 0.0057
## 14 0.0093 0.0049 0.0030 0.0080 0.0062
## 15 0.0122 0.0035 0.0045 0.0119 0.0092
## 16 0.0141 0.0046 0.0039 0.0125 0.0092
## 17 0.0063 0.0016 0.0111 0.0078 0.0080
## 18 0.0133 0.0055 0.0034 0.0111 0.0081
## 19 0.0111 0.0054 0.0031 0.0094 0.0071
## 20 0.0109 0.0020 0.0162 0.0150 0.0154
## 21 0.0207 0.0104 0.0032 0.0140 0.0097
## 22 0.0135 0.0026 0.0081 0.0162 0.0137
## 23 0.0066 0.0016 0.0177 0.0086 0.0093
## 24 0.0216 0.0061 0.0040 0.0168 0.0114
## 25 0.0202 0.0041 0.0052 0.0192 0.0136
## 26 0.0177 0.0033 0.0063 0.0191 0.0144
## 27 0.0183 0.0027 0.0113 0.0249 0.0218
## 28 0.0339 0.0036 0.0081 0.0400 0.0269
## 29 0.0433 0.0044 0.0065 0.0405 0.0244
## 30 0.0469 0.0054 0.0054 0.0353 0.0207
## 31 0.0069 0.0430 0.0018 0.0052 0.0042
## 32 0.0075 0.0016 0.0446 0.0105 0.0125
## 33 0.0230 0.0027 0.0137 0.0350 0.0316
## 34 0.0115 0.0333 0.0023 0.0081 0.0061
## 35 0.0330 0.0029 0.0149 0.0590 0.0530
## 36 0.0066 0.1054 0.0018 0.0050 0.0040
## 37 0.1018 0.0057 0.0059 0.0592 0.0296
## 38 0.0866 0.0040 0.0091 0.1201 0.0580
## 39 0.0044 0.4084 0.0014 0.0035 0.0029
## 40 0.0114 0.0019 0.0755 0.0177 0.0237
## 41 0.0069 0.0015 0.1498 0.0096 0.0123
## 42 0.0094 0.0749 0.0021 0.0067 0.0053
## 43 0.1717 0.0040 0.0099 0.3933 0.1124
## 44 0.0574 0.0031 0.0159 0.1648 0.1713
## 45 Inf 0.0052 0.0074 0.2798 0.0832
## 46 0.0052 Inf 0.0015 0.0040 0.0034
## 47 0.0074 0.0015 Inf 0.0106 0.0148
## 48 0.2798 0.0040 0.0106 Inf 0.3317
## 49 0.0832 0.0034 0.0148 0.3317 Inf#dinormalisasi
diag(W2)<-0
rtot<-rowSums(W2,na.rm=TRUE)
rtot## 1 2 3 4 5 6 7 8
## 0.5807206 0.8372952 0.9063766 1.3385460 1.3558081 0.7552440 0.8990409 1.6911778
## 9 10 11 12 13 14 15 16
## 1.2307118 0.8407320 3.9501602 4.6553325 3.9512268 4.5868775 2.1665503 2.9214462
## 17 18 19 20 21 22 23 24
## 1.0661230 3.8299233 4.4663054 1.2426265 0.9749528 1.7684559 0.9913192 2.0062833
## 25 26 27 28 29 30 31 32
## 2.4363089 2.1917322 2.3272144 2.3850832 2.6354337 2.3757629 0.8764532 0.9548105
## 33 34 35 36 37 38 39 40
## 2.5779029 0.8720104 2.2378504 1.0132783 1.8665719 2.4831230 0.7474567 0.9212769
## 41 42 43 44 45 46 47 48
## 0.7954880 0.8501494 2.4574531 1.7561572 1.3118318 0.8169138 0.5480305 1.9111089
## 49
## 1.2540298W2<-W2/rtot #row-normalized
rowSums(W2,na.rm=TRUE)## 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
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1W2 #matriks bobot power distance dengan alpha=2## 1 2 3 4 5 6
## 1 0.000000000 0.132783205 0.183337575 0.096241385 0.044950238 0.036293387
## 2 0.092094087 0.000000000 0.062595450 0.282078486 0.031056437 0.015775595
## 3 0.117465419 0.057824610 0.000000000 0.096290294 0.108715009 0.057900801
## 4 0.041753779 0.176447408 0.065201544 0.000000000 0.040013918 0.013809203
## 5 0.019253114 0.019179267 0.072677494 0.039504462 0.000000000 0.047201261
## 6 0.027906634 0.017489488 0.069487386 0.024474546 0.084735339 0.000000000
## 7 0.018048211 0.053300259 0.022514076 0.077719411 0.025057048 0.010231873
## 8 0.017865401 0.040342722 0.037488331 0.178092675 0.051895655 0.011852980
## 9 0.009238410 0.007897609 0.019928814 0.011720941 0.050495431 0.071364727
## 10 0.008736972 0.006634470 0.014271837 0.008481329 0.019739624 0.051537271
## 11 0.004131417 0.006036204 0.009140689 0.012895703 0.033505503 0.005794274
## 12 0.003255082 0.005180110 0.006541089 0.010872521 0.018159718 0.003991962
## 13 0.003780147 0.007055784 0.006690922 0.014648821 0.013289838 0.003660783
## 14 0.002683989 0.004741640 0.004547019 0.008785607 0.009022201 0.002794207
## 15 0.005458301 0.006435536 0.011727603 0.011218183 0.047544150 0.012737529
## 16 0.003381035 0.004701201 0.006185512 0.008019391 0.016165505 0.005277260
## 17 0.004989230 0.004066001 0.007694901 0.005135383 0.010909693 0.020145006
## 18 0.002372682 0.003633856 0.003945536 0.005977261 0.008276864 0.003018882
## 19 0.002289594 0.003755555 0.003795278 0.006417789 0.007663014 0.002628397
## 20 0.003818482 0.003453064 0.006019906 0.004501200 0.009784572 0.012733887
## 21 0.004917001 0.007053628 0.006896550 0.009508900 0.011074032 0.005963025
## 22 0.004237057 0.004049770 0.007831827 0.005827104 0.017725501 0.016623111
## 23 0.004079535 0.003473764 0.005982959 0.004314448 0.008351843 0.012883809
## 24 0.003359617 0.004665041 0.005359707 0.007012800 0.010706073 0.004847703
## 25 0.003269800 0.003937705 0.005939907 0.006167723 0.015409346 0.006814593
## 26 0.003653184 0.003942189 0.006875766 0.006011227 0.018342841 0.010238313
## 27 0.002363914 0.002317909 0.003955184 0.003185532 0.007624751 0.007299245
## 28 0.002332545 0.002553947 0.003885805 0.003624401 0.008047457 0.005503856
## 29 0.002093932 0.002453763 0.003415289 0.003513931 0.006993884 0.004174740
## 30 0.002233001 0.002771932 0.003518500 0.003947347 0.006846313 0.003842013
## 31 0.003563859 0.005282641 0.004233575 0.006097409 0.005379174 0.003366853
## 32 0.003188973 0.002865772 0.004496958 0.003516835 0.006254268 0.008166525
## 33 0.001804588 0.001800635 0.002883531 0.002421812 0.005207707 0.004911719
## 34 0.003562891 0.005041559 0.004459100 0.006083689 0.006075191 0.003799719
## 35 0.001818548 0.001865587 0.002809462 0.002476548 0.004888575 0.004380419
## 36 0.002566829 0.003648259 0.003029858 0.004168883 0.003822930 0.002503952
## 37 0.002301365 0.002802528 0.003455960 0.003821201 0.006148867 0.003857128
## 38 0.001686415 0.001878909 0.002594028 0.002541787 0.004679006 0.003424652
## 39 0.002604041 0.003563349 0.002957371 0.003919564 0.003555389 0.002483248
## 40 0.003071195 0.002908845 0.004344113 0.003605288 0.006293565 0.007197058
## 41 0.002996877 0.002753389 0.004062456 0.003316282 0.005461975 0.006690544
## 42 0.002929320 0.004011814 0.003566598 0.004699904 0.004682645 0.003109647
## 43 0.001452576 0.001617138 0.002152714 0.002132185 0.003645419 0.002785640
## 44 0.001894783 0.001997863 0.002791231 0.002590686 0.004568392 0.003966701
## 45 0.002294412 0.002656712 0.003240230 0.003415883 0.005147198 0.003811659
## 46 0.002377953 0.003213658 0.002749326 0.003589824 0.003377294 0.002361782
## 47 0.003644061 0.003462771 0.004844870 0.004141900 0.006489217 0.007354726
## 48 0.001557510 0.001727840 0.002222740 0.002217860 0.003539103 0.002821155
## 49 0.002097565 0.002257990 0.002940255 0.002846106 0.004502268 0.003866721
## 7 8 9 10 11 12
## 1 0.027941287 0.052027724 0.019578815 0.012648857 0.028102600 0.026094284
## 2 0.057230846 0.081484660 0.011608427 0.006661702 0.028477378 0.028801235
## 3 0.022331862 0.069948228 0.027060086 0.013238196 0.039836849 0.033596349
## 4 0.052200621 0.225010112 0.010776694 0.005327067 0.038056288 0.037813570
## 5 0.016615413 0.064732450 0.045836371 0.012240473 0.097618609 0.062353606
## 6 0.012180001 0.026541751 0.116292771 0.057370909 0.030305851 0.024606500
## 7 0.000000000 0.098324665 0.010317529 0.005372826 0.045192930 0.057166888
## 8 0.052270020 0.000000000 0.011140490 0.004766498 0.083192039 0.085889131
## 9 0.007537005 0.015308661 0.000000000 0.046065068 0.025883175 0.020386801
## 10 0.005745458 0.009588069 0.067432695 0.000000000 0.012484664 0.010820918
## 11 0.010285733 0.035616918 0.008064162 0.002657172 0.000000000 0.459615606
## 12 0.011040108 0.031201594 0.005389578 0.001954209 0.389994759 0.000000000
## 13 0.023546839 0.042796671 0.004492878 0.001861528 0.069913455 0.172073471
## 14 0.016190250 0.020466494 0.003651766 0.001534823 0.043369418 0.096530057
## 15 0.008995143 0.020199701 0.029419582 0.006963829 0.091767409 0.060616376
## 16 0.009320154 0.015398654 0.009130043 0.003239118 0.068584252 0.078105511
## 17 0.003793628 0.005900737 0.032852459 0.196127221 0.007914185 0.007007255
## 18 0.009343678 0.011226465 0.004569186 0.001897796 0.030522550 0.045014434
## 19 0.011295188 0.012938387 0.003696527 0.001560051 0.031724048 0.055855909
## 20 0.003641276 0.005487894 0.029911588 0.038113509 0.008201116 0.007323601
## 21 0.015937144 0.013559989 0.008702654 0.004682218 0.021023741 0.024542469
## 22 0.004534510 0.007862625 0.093365063 0.021147409 0.014673679 0.012289199
## 23 0.003393744 0.004965977 0.020687440 0.055387030 0.006653184 0.006011974
## 24 0.009881726 0.011370302 0.008110620 0.003561031 0.025843555 0.030168277
## 25 0.006164787 0.010008366 0.015855127 0.005092893 0.029367662 0.026531614
## 26 0.005176197 0.009023897 0.034923697 0.008649679 0.021994779 0.018338134
## 27 0.002712946 0.004178822 0.022812394 0.011762349 0.007257667 0.006427559
## 28 0.003505547 0.005103365 0.014099229 0.005975917 0.010193440 0.009375340
## 29 0.003763487 0.005114836 0.009095722 0.003966572 0.010678789 0.010328244
## 30 0.004715184 0.005814902 0.007314279 0.003402528 0.011824822 0.012151726
## 31 0.011115073 0.007341511 0.004117588 0.002652945 0.008160775 0.009218482
## 32 0.002968512 0.004075206 0.012992695 0.021536636 0.005479384 0.005058469
## 33 0.002160979 0.003136718 0.012551366 0.007988226 0.005251105 0.004771412
## 34 0.010214977 0.007643407 0.004968483 0.003137297 0.009450734 0.010603004
## 35 0.002326068 0.003199128 0.009822083 0.006599999 0.005262289 0.004892859
## 36 0.006992905 0.004950231 0.003075275 0.002051248 0.005563597 0.006176292
## 37 0.004595890 0.005335848 0.006991791 0.003718772 0.009537640 0.009793088
## 38 0.002654367 0.003431663 0.007097672 0.003966325 0.006035364 0.005822691
## 39 0.006071555 0.004463556 0.002950432 0.002089239 0.004824422 0.005250270
## 40 0.003218659 0.004276771 0.012218023 0.014916387 0.005957301 0.005566635
## 41 0.002908136 0.003807643 0.009915958 0.014369815 0.004997531 0.004685136
## 42 0.007407926 0.005702463 0.003985013 0.002658742 0.006821007 0.007502463
## 43 0.002273335 0.002804974 0.005313218 0.003328489 0.004618185 0.004513446
## 44 0.002580941 0.003301581 0.007676657 0.005572956 0.005187697 0.004959022
## 45 0.003944080 0.004427002 0.006439413 0.004224423 0.006845094 0.006913031
## 46 0.005476011 0.004144814 0.002865683 0.002023727 0.004612139 0.005010235
## 47 0.003795563 0.004770334 0.010761169 0.013441665 0.006241222 0.005929949
## 48 0.002398910 0.002838446 0.004965742 0.003450868 0.004380563 0.004321049
## 49 0.002980168 0.003550693 0.006605912 0.005120417 0.005252188 0.005136845
## 13 14 15 16 17 18
## 1 0.025720144 0.021199746 0.020363811 0.017009062 0.009159539 0.015648127
## 2 0.033296502 0.025975693 0.016652324 0.016403181 0.005177215 0.016621843
## 3 0.029168175 0.023010984 0.028032985 0.019937233 0.009051107 0.016671990
## 4 0.043241557 0.030106176 0.018157582 0.017502737 0.004090222 0.017102475
## 5 0.038730528 0.030523296 0.075974462 0.034832843 0.008578703 0.023380709
## 6 0.019152203 0.016970259 0.036539844 0.020413576 0.028437241 0.015309075
## 7 0.103486838 0.082602128 0.021676909 0.030285970 0.004498654 0.039804162
## 8 0.099989103 0.055510011 0.025877626 0.026600596 0.003719840 0.025423998
## 9 0.014424482 0.013610175 0.051790356 0.021672765 0.028458947 0.014219115
## 10 0.008748710 0.008373712 0.017945655 0.011255560 0.248706780 0.008645341
## 11 0.069932333 0.050360036 0.050331809 0.050723310 0.002135988 0.029593490
## 12 0.146047854 0.095110618 0.028210322 0.049014984 0.001604740 0.037033194
## 13 0.000000000 0.365252724 0.016207141 0.031226327 0.001550007 0.040883485
## 14 0.314635907 0.000000000 0.013967868 0.037300525 0.001307981 0.072494653
## 15 0.029557630 0.029571849 0.000000000 0.097602537 0.005722089 0.035637280
## 16 0.042233297 0.058564468 0.072382236 0.000000000 0.002835811 0.160846712
## 17 0.005744580 0.005627444 0.011628295 0.007770837 0.000000000 0.006059427
## 18 0.042178369 0.086822651 0.020159662 0.122693059 0.001686742 0.000000000
## 19 0.073080953 0.252936872 0.015041878 0.060155520 0.001361976 0.319989655
## 20 0.005901268 0.005968333 0.013481118 0.009138016 0.081082808 0.006938883
## 21 0.026127374 0.034743294 0.020214243 0.039777058 0.004527005 0.062031918
## 22 0.008877004 0.008961118 0.034819088 0.016790681 0.020091757 0.010803697
## 23 0.005027242 0.005002896 0.009597373 0.006895828 0.232593906 0.005518832
## 24 0.026235288 0.038064441 0.029298078 0.100488557 0.003342114 0.140349052
## 25 0.017089196 0.019878793 0.098115739 0.090456588 0.004690679 0.036050260
## 26 0.012179922 0.012918008 0.083820444 0.034190805 0.007970317 0.017956784
## 27 0.004942236 0.005136677 0.014455499 0.009256062 0.014536774 0.006493258
## 28 0.007108770 0.007860661 0.022101760 0.017560027 0.006398519 0.011729224
## 29 0.008006751 0.009345550 0.020797151 0.023238818 0.004076012 0.016144304
## 30 0.010003236 0.012319278 0.018677557 0.029252684 0.003428330 0.024296105
## 31 0.010656961 0.012190956 0.007052009 0.010259671 0.002550995 0.013587898
## 32 0.004316641 0.004373924 0.007743647 0.006022967 0.045450270 0.004964543
## 33 0.003787374 0.003988227 0.009578038 0.006952777 0.010612024 0.005131601
## 34 0.011706980 0.013810649 0.008785758 0.013200930 0.003083826 0.017368936
## 35 0.003983008 0.004259973 0.009016311 0.007299199 0.008644480 0.005622965
## 36 0.006929544 0.007831598 0.005005414 0.006991040 0.002001092 0.008854376
## 37 0.008493947 0.010160404 0.013824801 0.019007346 0.003908319 0.017440143
## 38 0.004795839 0.005382877 0.010164071 0.010087226 0.004519141 0.008001299
## 39 0.005787283 0.006342150 0.004408168 0.005746012 0.002050936 0.006900403
## 40 0.004766160 0.004919837 0.008567814 0.006988109 0.025443920 0.005806902
## 41 0.004084854 0.004166545 0.006765494 0.005574971 0.024442166 0.004740829
## 42 0.008142638 0.009316914 0.006479471 0.009057492 0.002645144 0.011233271
## 43 0.003845895 0.004301129 0.007108264 0.007317717 0.003887289 0.006189124
## 44 0.004198478 0.004550880 0.008035417 0.007344591 0.007140572 0.006069436
## 45 0.006214710 0.007056800 0.009277281 0.010783398 0.004767915 0.010140412
## 46 0.005462219 0.006033871 0.004301500 0.005636069 0.002004667 0.006745703
## 47 0.005247219 0.005407232 0.008266087 0.007162216 0.020197319 0.006236003
## 48 0.003793978 0.004211996 0.006248577 0.006545243 0.004101060 0.005818878
## 49 0.004530023 0.004937274 0.007358659 0.007323971 0.006370355 0.006494150
## 19 20 21 22 23 24
## 1 0.017609201 0.008170792 0.008254992 0.012903019 0.006963971 0.011606861
## 2 0.020032902 0.005124678 0.008213297 0.008553541 0.004112777 0.011178129
## 3 0.018701798 0.008253186 0.007418342 0.015280891 0.006543663 0.011863823
## 4 0.021414136 0.004178646 0.006925969 0.007698634 0.003195255 0.010511155
## 5 0.025243513 0.008967765 0.007963264 0.023120357 0.006106574 0.015842519
## 6 0.015543615 0.020951461 0.007697735 0.038924162 0.016911049 0.012877780
## 7 0.056112862 0.005032859 0.017282820 0.008919595 0.003742081 0.022051880
## 8 0.034169551 0.004032339 0.007817244 0.008221906 0.002910911 0.013488852
## 9 0.013414855 0.030201165 0.006894122 0.134159754 0.016663411 0.013221781
## 10 0.008287614 0.056332880 0.005429723 0.044482976 0.065307647 0.008497876
## 11 0.035869250 0.002579876 0.005188943 0.006569292 0.001669661 0.013125922
## 12 0.053587912 0.001954855 0.005139858 0.004668390 0.001280206 0.013001458
## 13 0.082607723 0.001855897 0.006446847 0.003973093 0.001261280 0.013321285
## 14 0.246288092 0.001616875 0.007384778 0.003454930 0.001081229 0.016649246
## 15 0.031008568 0.007732105 0.009096458 0.028421229 0.004391341 0.027130802
## 16 0.091965728 0.003886822 0.013274506 0.010164000 0.002339926 0.069009834
## 17 0.005705723 0.094506582 0.004139875 0.033327660 0.216274116 0.006289355
## 18 0.373159301 0.002251335 0.015790967 0.004988576 0.001428468 0.073521044
## 19 0.000000000 0.001750529 0.010764368 0.003772379 0.001143141 0.030627003
## 20 0.006291833 0.000000000 0.004960148 0.058774538 0.091014054 0.007839768
## 21 0.049312084 0.006321959 0.000000000 0.010735087 0.004166870 0.112228831
## 22 0.009527293 0.041298624 0.005918272 0.000000000 0.014281685 0.011852175
## 23 0.005150327 0.114086832 0.004098076 0.025477696 0.000000000 0.005948778
## 24 0.068180574 0.004855697 0.054537567 0.010447203 0.002939335 0.000000000
## 25 0.025449302 0.007428039 0.012097172 0.025002738 0.003904823 0.049802357
## 26 0.014648448 0.013879527 0.007990422 0.089113460 0.006323728 0.021100271
## 27 0.005625896 0.054959917 0.004347721 0.114315054 0.012975078 0.007920276
## 28 0.009329581 0.013793455 0.007985645 0.038831652 0.005827074 0.017765616
## 29 0.011916457 0.007476293 0.011910488 0.017835311 0.003725439 0.032055986
## 30 0.016852191 0.005713771 0.023741758 0.011864023 0.003154537 0.071539457
## 31 0.013536400 0.003193221 0.030005967 0.004543550 0.002389806 0.014259684
## 32 0.004583792 0.067900712 0.004071706 0.018048010 0.135006538 0.005592534
## 33 0.004423011 0.042178076 0.003863640 0.039196939 0.010950968 0.006562981
## 34 0.016253964 0.004057793 0.060988257 0.005811092 0.002930110 0.021194475
## 35 0.004801095 0.026864430 0.004749660 0.024249758 0.009513876 0.007589529
## 36 0.008681151 0.002515143 0.019252657 0.003441389 0.001902305 0.009665662
## 37 0.013176387 0.006673728 0.026085908 0.011390980 0.003769296 0.039244704
## 38 0.006448367 0.009549210 0.007876185 0.014734721 0.004554518 0.013130823
## 39 0.006825835 0.002511966 0.012102840 0.003251936 0.001971366 0.007363783
## 40 0.005267249 0.057845945 0.005110174 0.019284620 0.045901602 0.006892130
## 41 0.004384838 0.033763491 0.004171805 0.013377204 0.048018228 0.005401406
## 42 0.010624349 0.003438835 0.029152084 0.004644549 0.002552694 0.013394570
## 43 0.005098177 0.007925162 0.007049382 0.009964067 0.004101407 0.009848117
## 44 0.005184914 0.017043548 0.006096983 0.015047397 0.008226304 0.008523647
## 45 0.008442743 0.008319095 0.015768593 0.010257649 0.005010992 0.016451413
## 46 0.006581761 0.002496024 0.012709374 0.003217366 0.001940265 0.007437780
## 47 0.005744363 0.029528781 0.005897435 0.014701369 0.032269287 0.007266119
## 48 0.004915970 0.007857822 0.007342171 0.008464305 0.004506070 0.008784155
## 49 0.005622108 0.012308641 0.007697220 0.010962733 0.007431787 0.009094579
## 25 26 27 28 29 30
## 1 0.013717860 0.013787699 0.009473289 0.009580017 0.009502709 0.009135344
## 2 0.011457686 0.010319207 0.006442497 0.007275062 0.007723357 0.007865151
## 3 0.015966265 0.016626463 0.010155339 0.010225297 0.009930496 0.009222570
## 4 0.011225971 0.009842770 0.005538409 0.006458126 0.006918501 0.007006079
## 5 0.027689704 0.029652127 0.013087715 0.014156764 0.013594784 0.011996695
## 6 0.021982900 0.029711776 0.022491946 0.017381341 0.014567810 0.012085780
## 7 0.016705941 0.012618823 0.007022601 0.009299935 0.011032222 0.012460122
## 8 0.014418041 0.011694788 0.005750439 0.007197321 0.007970665 0.008168761
## 9 0.031386703 0.062194406 0.043137093 0.027323889 0.019477486 0.014119465
## 10 0.014758400 0.022549135 0.032559137 0.016953154 0.012433970 0.009614953
## 11 0.018112859 0.012203724 0.004275813 0.006154738 0.007124582 0.007111857
## 12 0.013884982 0.008633600 0.003213156 0.004803301 0.005846930 0.006201409
## 13 0.010537122 0.006756162 0.002910904 0.004291074 0.005340432 0.006014668
## 14 0.010558573 0.006172568 0.002606162 0.004087384 0.005369574 0.006380742
## 15 0.110332194 0.084794693 0.015527470 0.024331092 0.025298056 0.020481152
## 16 0.075435307 0.025650683 0.007373349 0.014336093 0.020963714 0.023788712
## 17 0.010719160 0.016385351 0.031731974 0.014314483 0.010075816 0.007639738
## 18 0.022932461 0.010276044 0.003945563 0.007304370 0.011109163 0.015071264
## 19 0.013882248 0.007188374 0.002931431 0.004982155 0.007031546 0.008964190
## 20 0.014563505 0.024480572 0.102929972 0.026475000 0.015856151 0.010924092
## 21 0.030229616 0.017962782 0.010378020 0.019535743 0.032195714 0.057853865
## 22 0.034444959 0.110442585 0.150433853 0.052371517 0.026578994 0.015938257
## 23 0.009596660 0.013981286 0.030460205 0.014019758 0.009904122 0.007560058
## 24 0.060476964 0.023050654 0.009187227 0.021119884 0.042108422 0.084714250
## 25 0.000000000 0.128247811 0.018096953 0.053148011 0.079743603 0.054395387
## 26 0.142559059 0.000000000 0.044325959 0.090348619 0.055384144 0.028786682
## 27 0.018945297 0.041745459 0.000000000 0.057494707 0.022815268 0.012779814
## 28 0.054289500 0.083024350 0.056099725 0.000000000 0.161525796 0.044495070
## 29 0.073718435 0.046059673 0.020146976 0.146181806 0.000000000 0.178226535
## 30 0.055781646 0.026556816 0.012518660 0.044669627 0.197706689 0.000000000
## 31 0.008173240 0.006174032 0.004398166 0.006343634 0.008185249 0.010626205
## 32 0.008420160 0.011499839 0.025647428 0.013355354 0.009654129 0.007485942
## 33 0.013554844 0.023756791 0.260938930 0.048283320 0.020159690 0.011490779
## 34 0.011126608 0.008152208 0.005821211 0.008976734 0.012196570 0.016891912
## 35 0.013738633 0.020050593 0.074883035 0.051537672 0.024864092 0.014585546
## 36 0.005875182 0.004560421 0.003405643 0.004829255 0.006133313 0.007831261
## 37 0.030500147 0.020172132 0.013569597 0.040383014 0.101612469 0.228773383
## 38 0.020512962 0.021176012 0.024441262 0.088174424 0.077416234 0.040161837
## 39 0.005017736 0.004097148 0.003244923 0.004332934 0.005248126 0.006355158
## 40 0.010149242 0.013470438 0.032206821 0.018194613 0.013017726 0.009969337
## 41 0.007530152 0.009616093 0.018579451 0.011616604 0.008929716 0.007216152
## 42 0.008023320 0.006201120 0.004739066 0.006931165 0.008980923 0.011745512
## 43 0.012698557 0.012746656 0.016177353 0.034849942 0.034486980 0.025401727
## 44 0.012378318 0.014829205 0.029956469 0.034355485 0.024325058 0.016924410
## 45 0.015424000 0.013510994 0.013957696 0.025873234 0.032993847 0.035770095
## 46 0.005009879 0.004074414 0.003252864 0.004405815 0.005383881 0.006585463
## 47 0.009537983 0.011573187 0.020562385 0.014737801 0.011854283 0.009870156
## 48 0.010035045 0.009981241 0.013044975 0.020921887 0.021195038 0.018470771
## 49 0.010822503 0.011509574 0.017387985 0.021474829 0.019466166 0.016531661
## 31 32 33 34 35 36
## 1 0.005378759 0.0052432537 0.008010827 0.005350040 0.007007910 0.004478767
## 2 0.005529696 0.0032679864 0.005543877 0.005250588 0.004986179 0.004415051
## 3 0.004093806 0.0047372619 0.008201298 0.004290029 0.006936584 0.003387212
## 4 0.003992461 0.0025086255 0.004664163 0.003963285 0.004140420 0.003155842
## 5 0.003477332 0.0044044882 0.009901816 0.003907360 0.008068914 0.002857109
## 6 0.003907199 0.0103244573 0.016765355 0.004387185 0.012979544 0.003359444
## 7 0.010835815 0.0031526559 0.006196374 0.009907854 0.005789939 0.007881465
## 8 0.003804739 0.0023007929 0.004781375 0.003941118 0.004233245 0.002965957
## 9 0.002932346 0.0100799894 0.026290640 0.003520376 0.017859870 0.002531957
## 10 0.002765664 0.0244589322 0.024493977 0.003254017 0.017567800 0.002472232
## 11 0.001810695 0.0013244460 0.003426909 0.002086280 0.002981200 0.001427150
## 12 0.001735551 0.0010374941 0.002642182 0.001986094 0.002352031 0.001344330
## 13 0.002363906 0.0010431126 0.002471000 0.002583655 0.002255850 0.001777057
## 14 0.002329428 0.0009104819 0.002241451 0.002625540 0.002078360 0.001730063
## 15 0.002852810 0.0034126673 0.011396575 0.003536162 0.009313033 0.002340992
## 16 0.003077969 0.0019684744 0.006135175 0.003940291 0.005591243 0.002424782
## 17 0.002097157 0.0407048674 0.025660046 0.002522344 0.018145236 0.001901903
## 18 0.003109503 0.0012376744 0.003454056 0.003954620 0.003285537 0.002342592
## 19 0.002656339 0.0009799270 0.002552914 0.003173456 0.002405598 0.001969508
## 20 0.002252252 0.0521736150 0.087500939 0.002847547 0.048380247 0.002050930
## 21 0.026974460 0.0039875858 0.010215970 0.054548687 0.010902095 0.020009482
## 22 0.002251800 0.0097443370 0.057137925 0.002865400 0.030686279 0.001971825
## 23 0.002112894 0.1300344619 0.028477739 0.002577460 0.021477069 0.001944444
## 24 0.006229402 0.0026615433 0.008432871 0.009211961 0.008465520 0.004881666
## 25 0.002940293 0.0032999336 0.014342628 0.003982466 0.012619503 0.002443530
## 26 0.002468937 0.0050098124 0.027942602 0.003243467 0.020472496 0.002108367
## 27 0.001656395 0.0105226381 0.289047382 0.002181216 0.072007562 0.001482831
## 28 0.002331113 0.0053464940 0.052186737 0.003281984 0.048356219 0.002051660
## 29 0.002722128 0.0034976651 0.019719609 0.004035592 0.021113079 0.002358152
## 30 0.003920160 0.0030085731 0.012468463 0.006200081 0.013738859 0.003340084
## 31 0.000000000 0.0023213291 0.004364012 0.139269198 0.004585373 0.363536901
## 32 0.002130827 0.0000000000 0.028804219 0.002655722 0.025163456 0.001996601
## 33 0.001483707 0.0106685834 0.000000000 0.001994736 0.235326847 0.001346707
## 34 0.139978757 0.0029078913 0.005896989 0.000000000 0.006375549 0.123242911
## 35 0.001795859 0.0107363443 0.271085924 0.002484324 0.000000000 0.001649615
## 36 0.314447739 0.0018813937 0.003426187 0.106060792 0.003643216 0.000000000
## 37 0.004923856 0.0038214780 0.014921176 0.008330143 0.018426818 0.004374307
## 38 0.002331156 0.0048205651 0.033593544 0.003496161 0.055657958 0.002120266
## 39 0.056685325 0.0019700401 0.003280810 0.034034012 0.003479482 0.117040873
## 40 0.002560408 0.1617827425 0.043528549 0.003304833 0.045170469 0.002421543
## 41 0.002297765 0.2194254701 0.021771688 0.002870999 0.021395084 0.002187274
## 42 0.099184951 0.0025802218 0.004860620 0.260673671 0.005292230 0.205662228
## 43 0.002279795 0.0046656754 0.023215484 0.003490351 0.042089028 0.002124817
## 44 0.002370277 0.0105265037 0.055257152 0.003398121 0.153161651 0.002228394
## 45 0.005238969 0.0057355046 0.017503500 0.008781356 0.025180361 0.005055928
## 46 0.052671775 0.0019593129 0.003316741 0.040769983 0.003554623 0.128990399
## 47 0.003330050 0.0813976825 0.025089003 0.004221483 0.027145566 0.003219754
## 48 0.002729625 0.0054911918 0.018294339 0.004214315 0.030855093 0.002620742
## 49 0.003310416 0.0099858204 0.025204385 0.004879856 0.042260635 0.003219043
## 37 38 39 40 41 42
## 1 0.007397127 0.007210999 0.0033517121 0.004872259 0.0041052101 0.004288395
## 2 0.006247642 0.005572183 0.0031810158 0.003200606 0.0026159089 0.004073403
## 3 0.007117127 0.007106639 0.0024388392 0.004415528 0.0035654443 0.003345343
## 4 0.005328577 0.004715243 0.0021887214 0.002481400 0.0019708418 0.002985046
## 5 0.008465285 0.008569462 0.0019600851 0.004276502 0.0032046832 0.002936218
## 6 0.009532822 0.011259714 0.0024576439 0.008779260 0.0070470579 0.003500411
## 7 0.009541901 0.007331279 0.0050478507 0.003298266 0.0025731725 0.007005069
## 8 0.005889235 0.005038644 0.0019727757 0.002329791 0.0017910208 0.002866609
## 9 0.010604172 0.014320486 0.0017919062 0.009146075 0.0064093200 0.002752761
## 10 0.008256323 0.011714641 0.0018574475 0.016345426 0.0135965037 0.002688524
## 11 0.004506827 0.003793910 0.0009128861 0.001389393 0.0010064088 0.001468010
## 12 0.003926573 0.003105784 0.0008429795 0.001101621 0.0008005807 0.001370088
## 13 0.004012567 0.003013914 0.0010947849 0.001111289 0.0008223907 0.001751977
## 14 0.004134648 0.002914040 0.0010334880 0.000988152 0.0007225910 0.001726832
## 15 0.011910633 0.011649228 0.0015208114 0.003643270 0.0024840732 0.002542530
## 16 0.012144183 0.008573775 0.0014701264 0.002203697 0.0015180232 0.002635757
## 17 0.006842698 0.010525598 0.0014379068 0.021987045 0.0182375299 0.002109294
## 18 0.008499721 0.005187626 0.0013466986 0.001396833 0.0009846862 0.002493512
## 19 0.005506716 0.003585086 0.0011423349 0.001086490 0.0007809781 0.002022317
## 20 0.010024728 0.019082052 0.0015109818 0.042886685 0.0216142611 0.002352697
## 21 0.049942132 0.020059984 0.0092787561 0.004828834 0.0034038784 0.025420335
## 22 0.012022965 0.020689306 0.0013744655 0.010046320 0.0060173429 0.002232773
## 23 0.007097272 0.011408461 0.0014864140 0.042658391 0.0385324156 0.002189175
## 24 0.036511822 0.016251667 0.0027434357 0.003164837 0.0021416487 0.005675861
## 25 0.023367610 0.020907122 0.0015394356 0.003837880 0.0024586972 0.002799735
## 26 0.017179441 0.023991363 0.0013972697 0.005662189 0.0034901559 0.002405348
## 27 0.010883668 0.026078672 0.0010422071 0.012749749 0.0063508250 0.001731217
## 28 0.031603845 0.091798871 0.0013578900 0.007027963 0.0038744433 0.002470574
## 29 0.071968032 0.072942086 0.0014884635 0.004550648 0.0026953751 0.002897104
## 30 0.179740984 0.041976739 0.0019994442 0.003865924 0.0024162185 0.004203045
## 31 0.010486277 0.006604515 0.0483423734 0.002691353 0.0020855013 0.096208246
## 32 0.007470658 0.012536577 0.0015422114 0.156100808 0.1828114872 0.002297392
## 33 0.010803916 0.032358434 0.0009512630 0.015555995 0.0067182972 0.001602951
## 34 0.017830992 0.009955613 0.0291727609 0.003491548 0.0026190570 0.254138663
## 35 0.015369651 0.061758173 0.0011621699 0.018595750 0.0076053041 0.002010494
## 36 0.008057962 0.005195888 0.0863365823 0.002201677 0.0017171493 0.172552408
## 37 0.000000000 0.072397447 0.0026456136 0.005190968 0.0031895668 0.005854612
## 38 0.054421403 0.000000000 0.0014067682 0.007258485 0.0038354004 0.002697010
## 39 0.006606707 0.004673419 0.0000000000 0.002280356 0.0018334716 0.058768728
## 40 0.010517266 0.019563837 0.0018501138 0.000000000 0.1381521565 0.002864295
## 41 0.007484155 0.011972237 0.0017227672 0.159997863 0.0000000000 0.002538463
## 42 0.012854275 0.007877448 0.0516698381 0.003103936 0.0023752499 0.000000000
## 43 0.043098283 0.244164622 0.0014316985 0.007505621 0.0039461863 0.002777401
## 44 0.021578775 0.081849214 0.0015789960 0.020684937 0.0088419728 0.002812573
## 45 0.077619450 0.066037636 0.0033461708 0.008711840 0.0052251585 0.007149429
## 46 0.007003186 0.004860512 0.4998850918 0.002298603 0.0018312498 0.091629755
## 47 0.010682018 0.016655849 0.0025635521 0.137833005 0.2733207770 0.003800760
## 48 0.031001368 0.062866422 0.0018151881 0.009278616 0.0050211266 0.003510410
## 49 0.023577171 0.046229642 0.0023296941 0.018935097 0.0097831389 0.004220389
## 43 44 45 46 47 48
## 1 0.006146910 0.005730015 0.005183015 0.0033451250 0.0034389281 0.005125652
## 2 0.004746284 0.004190352 0.004162402 0.0031354316 0.0022664695 0.003943758
## 3 0.005836640 0.005408172 0.004689702 0.0024779571 0.0029293966 0.004686682
## 4 0.003914505 0.003398951 0.003347710 0.0021908673 0.0016957859 0.003166550
## 5 0.006607458 0.005917367 0.004980246 0.0020349177 0.0026230029 0.004988620
## 6 0.009064063 0.009223709 0.006620716 0.0025546342 0.0053368371 0.007138798
## 7 0.006213971 0.005041527 0.005754988 0.0049757791 0.0023136704 0.005099410
## 8 0.004075912 0.003428437 0.003433986 0.0020021286 0.0015458388 0.003207575
## 9 0.010609295 0.010954162 0.006863854 0.0019021641 0.0047919003 0.007711044
## 10 0.009729148 0.011641031 0.006591557 0.0019663938 0.0087619387 0.007844336
## 11 0.002873041 0.002306340 0.002273227 0.0009538146 0.0008658838 0.002119340
## 12 0.002382554 0.001870720 0.001948031 0.0008791918 0.0006980796 0.001773879
## 13 0.002391942 0.001866050 0.002063322 0.0011293105 0.0007277830 0.001835052
## 14 0.002304361 0.001742375 0.002018221 0.0010746205 0.0006460448 0.001754916
## 15 0.008062691 0.006513330 0.005617332 0.0016219122 0.0020909128 0.005511855
## 16 0.006155495 0.004415024 0.004842124 0.0015759943 0.0013435512 0.004281671
## 17 0.008960345 0.011762214 0.005866773 0.0015360706 0.0103822411 0.007351471
## 18 0.003971224 0.002783054 0.003473311 0.0014388428 0.0008923206 0.002903585
## 19 0.002805122 0.002038715 0.002479781 0.0012038432 0.0007048524 0.002103518
## 20 0.015673023 0.024087005 0.008782409 0.0016409088 0.0130229578 0.012085011
## 21 0.017768578 0.010982338 0.021217172 0.0106491955 0.0033150060 0.014392173
## 22 0.013846106 0.014942750 0.007609073 0.0014862179 0.0045558378 0.009147080
## 23 0.010167275 0.014573189 0.006631142 0.0015989086 0.0178394123 0.008687001
## 24 0.012062746 0.007460992 0.010756949 0.0030284979 0.0019847919 0.008367451
## 25 0.012808766 0.008922626 0.008305061 0.0016798525 0.0021455020 0.007871770
## 26 0.014292033 0.011882116 0.008086824 0.0015186369 0.0028938113 0.008703270
## 27 0.017082692 0.022605683 0.007867839 0.0011418413 0.0048421899 0.010712536
## 28 0.035907385 0.025296239 0.014230670 0.0015090337 0.0033863658 0.016764198
## 29 0.032157947 0.016209334 0.016423247 0.0016688588 0.0024650623 0.015369776
## 30 0.026275161 0.012510476 0.019751276 0.0022644328 0.0022768040 0.014858240
## 31 0.006392229 0.004749346 0.007841429 0.0490936664 0.0020822207 0.005951956
## 32 0.012008328 0.019361114 0.007880115 0.0016763428 0.0467196469 0.010990940
## 33 0.022130765 0.037643096 0.008907104 0.0010510448 0.0053336139 0.013562371
## 34 0.009836321 0.006843535 0.013210464 0.0381939963 0.0026530662 0.009236145
## 35 0.046219270 0.120193884 0.014760771 0.0012975937 0.0066477175 0.026350038
## 36 0.005153212 0.003862128 0.006545612 0.1039931829 0.0017414006 0.004942890
## 37 0.056741458 0.020302311 0.054551161 0.0030649768 0.0031362692 0.031741072
## 38 0.241640511 0.057886817 0.034887627 0.0015990426 0.0036759810 0.048384466
## 39 0.004707071 0.003709867 0.005872732 0.5463366922 0.0018795800 0.004641101
## 40 0.020020812 0.039430060 0.012405032 0.0020382151 0.0819913008 0.019247684
## 41 0.012190715 0.019519960 0.008616759 0.0018805729 0.1882971358 0.012062934
## 42 0.008028393 0.005809944 0.011032000 0.0880475992 0.0024500778 0.007891291
## 43 0.000000000 0.088231859 0.069888427 0.0016464523 0.0040248801 0.160049323
## 44 0.123465971 0.000000000 0.032688577 0.0017907709 0.0090441670 0.093832906
## 45 0.130921915 0.043760397 0.000000000 0.0039735425 0.0056787099 0.213252856
## 46 0.004952884 0.003849703 0.006380868 0.0000000000 0.0018937569 0.004940274
## 47 0.018048182 0.028981926 0.013593244 0.0028229017 0.0000000000 0.019383416
## 48 0.205803916 0.086224982 0.146381962 0.0021117467 0.0055583973 0.000000000
## 49 0.089625991 0.136637293 0.066371747 0.0026903596 0.0117881262 0.264469849
## 49
## 1 0.004529560
## 2 0.003381825
## 3 0.004068030
## 4 0.002666402
## 5 0.004164290
## 6 0.006420420
## 7 0.004156896
## 8 0.002632884
## 9 0.006731072
## 10 0.007637577
## 11 0.001667376
## 12 0.001383737
## 13 0.001437727
## 14 0.001349827
## 15 0.004259296
## 16 0.003143812
## 17 0.007493145
## 18 0.002126376
## 19 0.001578551
## 20 0.012421595
## 21 0.009900523
## 22 0.007773784
## 23 0.009401293
## 24 0.005684578
## 25 0.005570616
## 26 0.006585361
## 27 0.009369593
## 28 0.011291043
## 29 0.009262670
## 30 0.008726122
## 31 0.004736545
## 32 0.013115184
## 33 0.012260761
## 34 0.007017674
## 35 0.023681697
## 36 0.003983877
## 37 0.015839988
## 38 0.023346950
## 39 0.003908595
## 40 0.025774202
## 41 0.015422417
## 42 0.006225369
## 43 0.045735832
## 44 0.097569419
## 45 0.063447272
## 46 0.004129923
## 47 0.026974159
## 48 0.173539595
## 49 0.000000000W2 = mat2listw(W2,style='W')
summary(W2)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 49
## Number of nonzero links: 2352
## Percentage nonzero weights: 97.95918
## Average number of links: 48
## Link number distribution:
##
## 48
## 49
## 49 least connected regions:
## 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 49 with 48 links
## 49 most connected regions:
## 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 49 with 48 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 49 2401 49 10.31491 200.14Exponential distance weights (Bobot Jarak Eksponensial)
matriks bobot Exponential distance dengan alpha=1
alpha=1
W3<-exp((-alpha)*D)
round(W3,4)## 1 2 3 4 5 6 7 8 9 10 11
## 1 1.0000 0.0273 0.0467 0.0146 0.0021 0.0010 0.0004 0.0032 0.0001 0.0000 0.0004
## 2 0.0273 1.0000 0.0127 0.1278 0.0020 0.0002 0.0104 0.0217 0.0000 0.0000 0.0015
## 3 0.0467 0.0127 1.0000 0.0339 0.0414 0.0127 0.0009 0.0188 0.0017 0.0001 0.0052
## 4 0.0146 0.1278 0.0339 1.0000 0.0133 0.0006 0.0228 0.1617 0.0002 0.0000 0.0119
## 5 0.0021 0.0020 0.0414 0.0133 1.0000 0.0192 0.0013 0.0342 0.0181 0.0004 0.0640
## 6 0.0010 0.0002 0.0127 0.0006 0.0192 1.0000 0.0000 0.0009 0.0342 0.0082 0.0013
## 7 0.0004 0.0104 0.0009 0.0228 0.0013 0.0000 1.0000 0.0346 0.0000 0.0000 0.0070
## 8 0.0032 0.0217 0.0188 0.1617 0.0342 0.0009 0.0346 1.0000 0.0007 0.0000 0.0695
## 9 0.0001 0.0000 0.0017 0.0002 0.0181 0.0342 0.0000 0.0007 1.0000 0.0150 0.0037
## 10 0.0000 0.0000 0.0001 0.0000 0.0004 0.0082 0.0000 0.0000 0.0150 1.0000 0.0001
## 11 0.0004 0.0015 0.0052 0.0119 0.0640 0.0013 0.0070 0.0695 0.0037 0.0001 1.0000
## 12 0.0003 0.0016 0.0032 0.0117 0.0321 0.0007 0.0121 0.0725 0.0018 0.0000 0.4761
## 13 0.0003 0.0025 0.0021 0.0157 0.0127 0.0002 0.0377 0.0879 0.0006 0.0000 0.1492
## 14 0.0001 0.0011 0.0010 0.0069 0.0073 0.0001 0.0255 0.0382 0.0004 0.0000 0.1062
## 15 0.0001 0.0002 0.0019 0.0016 0.0443 0.0024 0.0008 0.0084 0.0190 0.0003 0.1062
## 16 0.0000 0.0002 0.0006 0.0015 0.0100 0.0003 0.0023 0.0090 0.0022 0.0000 0.1071
## 17 0.0000 0.0000 0.0000 0.0000 0.0001 0.0011 0.0000 0.0000 0.0048 0.1123 0.0000
## 18 0.0000 0.0002 0.0003 0.0013 0.0036 0.0001 0.0051 0.0080 0.0005 0.0000 0.0537
## 19 0.0001 0.0004 0.0005 0.0027 0.0045 0.0001 0.0117 0.0156 0.0004 0.0000 0.0702
## 20 0.0000 0.0000 0.0000 0.0000 0.0001 0.0004 0.0000 0.0000 0.0056 0.0101 0.0000
## 21 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0003 0.0002 0.0000 0.0000 0.0009
## 22 0.0000 0.0000 0.0002 0.0001 0.0035 0.0029 0.0000 0.0002 0.0854 0.0057 0.0020
## 23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0009 0.0140 0.0000
## 24 0.0000 0.0000 0.0001 0.0002 0.0011 0.0000 0.0008 0.0013 0.0004 0.0000 0.0124
## 25 0.0000 0.0000 0.0002 0.0003 0.0057 0.0004 0.0003 0.0017 0.0062 0.0001 0.0238
## 26 0.0000 0.0000 0.0003 0.0002 0.0068 0.0013 0.0001 0.0008 0.0269 0.0007 0.0105
## 27 0.0000 0.0000 0.0000 0.0000 0.0005 0.0005 0.0000 0.0000 0.0130 0.0024 0.0005
## 28 0.0000 0.0000 0.0000 0.0000 0.0007 0.0002 0.0000 0.0001 0.0043 0.0002 0.0016
## 29 0.0000 0.0000 0.0000 0.0000 0.0006 0.0001 0.0000 0.0002 0.0016 0.0001 0.0026
## 30 0.0000 0.0000 0.0000 0.0000 0.0004 0.0000 0.0001 0.0002 0.0005 0.0000 0.0026
## 31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0009 0.0000
## 33 0.0000 0.0000 0.0000 0.0000 0.0002 0.0001 0.0000 0.0000 0.0039 0.0009 0.0002
## 34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 35 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0012 0.0003 0.0001
## 36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 37 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0002 0.0000 0.0006
## 38 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0005 0.0000 0.0003
## 39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0002 0.0000
## 41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0001
## 44 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0000
## 45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 48 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 12 13 14 15 16 17 18 19 20 21 22
## 1 0.0003 0.0003 0.0001 0.0001 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000
## 2 0.0016 0.0025 0.0011 0.0002 0.0002 0.0000 0.0002 0.0004 0.0000 0.0000 0.0000
## 3 0.0032 0.0021 0.0010 0.0019 0.0006 0.0000 0.0003 0.0005 0.0000 0.0000 0.0002
## 4 0.0117 0.0157 0.0069 0.0016 0.0015 0.0000 0.0013 0.0027 0.0000 0.0000 0.0001
## 5 0.0321 0.0127 0.0073 0.0443 0.0100 0.0001 0.0036 0.0045 0.0001 0.0001 0.0035
## 6 0.0007 0.0002 0.0001 0.0024 0.0003 0.0011 0.0001 0.0001 0.0004 0.0000 0.0029
## 7 0.0121 0.0377 0.0255 0.0008 0.0023 0.0000 0.0051 0.0117 0.0000 0.0003 0.0000
## 8 0.0725 0.0879 0.0382 0.0084 0.0090 0.0000 0.0080 0.0156 0.0000 0.0002 0.0002
## 9 0.0018 0.0006 0.0004 0.0190 0.0022 0.0048 0.0005 0.0004 0.0056 0.0000 0.0854
## 10 0.0000 0.0000 0.0000 0.0003 0.0000 0.1123 0.0000 0.0000 0.0101 0.0000 0.0057
## 11 0.4761 0.1492 0.1062 0.1062 0.1071 0.0000 0.0537 0.0702 0.0000 0.0009 0.0020
## 12 1.0000 0.2974 0.2225 0.0633 0.1233 0.0000 0.0900 0.1350 0.0000 0.0016 0.0011
## 13 0.2974 1.0000 0.4350 0.0192 0.0580 0.0000 0.0831 0.1737 0.0000 0.0019 0.0003
## 14 0.2225 0.4350 1.0000 0.0192 0.0891 0.0000 0.1765 0.3903 0.0000 0.0044 0.0004
## 15 0.0633 0.0192 0.0192 1.0000 0.1137 0.0001 0.0274 0.0211 0.0004 0.0008 0.0178
## 16 0.1233 0.0580 0.0891 0.1137 1.0000 0.0000 0.2325 0.1453 0.0001 0.0062 0.0030
## 17 0.0000 0.0000 0.0000 0.0001 0.0000 1.0000 0.0000 0.0000 0.0428 0.0000 0.0050
## 18 0.0900 0.0831 0.1765 0.0274 0.2325 0.0000 1.0000 0.4332 0.0000 0.0171 0.0007
## 19 0.1350 0.1737 0.3903 0.0211 0.1453 0.0000 0.4332 1.0000 0.0000 0.0105 0.0005
## 20 0.0000 0.0000 0.0000 0.0004 0.0001 0.0428 0.0000 0.0000 1.0000 0.0000 0.0247
## 21 0.0016 0.0019 0.0044 0.0008 0.0062 0.0000 0.0171 0.0105 0.0000 1.0000 0.0001
## 22 0.0011 0.0003 0.0004 0.0178 0.0030 0.0050 0.0007 0.0005 0.0247 0.0001 1.0000
## 23 0.0000 0.0000 0.0000 0.0000 0.0000 0.1246 0.0000 0.0000 0.0511 0.0000 0.0019
## 24 0.0172 0.0128 0.0268 0.0162 0.1078 0.0000 0.1519 0.0670 0.0000 0.0486 0.0010
## 25 0.0196 0.0074 0.0106 0.1293 0.1188 0.0001 0.0342 0.0180 0.0006 0.0030 0.0174
## 26 0.0068 0.0022 0.0026 0.0970 0.0259 0.0005 0.0065 0.0038 0.0032 0.0005 0.1041
## 27 0.0003 0.0001 0.0001 0.0043 0.0011 0.0044 0.0003 0.0002 0.0610 0.0000 0.1439
## 28 0.0012 0.0005 0.0007 0.0128 0.0075 0.0003 0.0025 0.0012 0.0040 0.0007 0.0374
## 29 0.0023 0.0010 0.0017 0.0140 0.0176 0.0001 0.0078 0.0035 0.0008 0.0035 0.0099
## 30 0.0028 0.0015 0.0029 0.0087 0.0225 0.0000 0.0156 0.0068 0.0002 0.0148 0.0026
## 31 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0021 0.0000
## 32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0082 0.0000 0.0000 0.0197 0.0000 0.0005
## 33 0.0001 0.0000 0.0001 0.0017 0.0006 0.0024 0.0002 0.0001 0.0482 0.0000 0.0430
## 34 0.0000 0.0001 0.0001 0.0000 0.0001 0.0000 0.0003 0.0002 0.0000 0.0131 0.0000
## 35 0.0001 0.0000 0.0000 0.0009 0.0004 0.0008 0.0001 0.0001 0.0169 0.0001 0.0137
## 36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0008 0.0000
## 37 0.0006 0.0004 0.0007 0.0020 0.0049 0.0000 0.0039 0.0017 0.0001 0.0108 0.0011
## 38 0.0002 0.0001 0.0002 0.0018 0.0018 0.0001 0.0008 0.0004 0.0015 0.0008 0.0054
## 39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0015 0.0000 0.0000 0.0131 0.0000 0.0006
## 41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0008 0.0000 0.0000 0.0022 0.0000 0.0001
## 42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0017 0.0000
## 43 0.0001 0.0000 0.0001 0.0005 0.0006 0.0000 0.0003 0.0001 0.0008 0.0005 0.0017
## 44 0.0000 0.0000 0.0000 0.0002 0.0001 0.0001 0.0001 0.0000 0.0031 0.0001 0.0021
## 45 0.0000 0.0000 0.0000 0.0001 0.0002 0.0000 0.0002 0.0001 0.0001 0.0010 0.0002
## 46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0004 0.0000 0.0000
## 48 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0001 0.0000 0.0003 0.0002 0.0004
## 49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0003 0.0000 0.0002
## 23 24 25 26 27 28 29 30 31 32 33
## 1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 3 0.0000 0.0001 0.0002 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 4 0.0000 0.0002 0.0003 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 5 0.0000 0.0011 0.0057 0.0068 0.0005 0.0007 0.0006 0.0004 0.0000 0.0000 0.0002
## 6 0.0001 0.0000 0.0004 0.0013 0.0005 0.0002 0.0001 0.0000 0.0000 0.0000 0.0001
## 7 0.0000 0.0008 0.0003 0.0001 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000
## 8 0.0000 0.0013 0.0017 0.0008 0.0000 0.0001 0.0002 0.0002 0.0000 0.0000 0.0000
## 9 0.0009 0.0004 0.0062 0.0269 0.0130 0.0043 0.0016 0.0005 0.0000 0.0001 0.0039
## 10 0.0140 0.0000 0.0001 0.0007 0.0024 0.0002 0.0001 0.0000 0.0000 0.0009 0.0009
## 11 0.0000 0.0124 0.0238 0.0105 0.0005 0.0016 0.0026 0.0026 0.0000 0.0000 0.0002
## 12 0.0000 0.0172 0.0196 0.0068 0.0003 0.0012 0.0023 0.0028 0.0000 0.0000 0.0001
## 13 0.0000 0.0128 0.0074 0.0022 0.0001 0.0005 0.0010 0.0015 0.0000 0.0000 0.0000
## 14 0.0000 0.0268 0.0106 0.0026 0.0001 0.0007 0.0017 0.0029 0.0001 0.0000 0.0001
## 15 0.0000 0.0162 0.1293 0.0970 0.0043 0.0128 0.0140 0.0087 0.0000 0.0000 0.0017
## 16 0.0000 0.1078 0.1188 0.0259 0.0011 0.0075 0.0176 0.0225 0.0000 0.0000 0.0006
## 17 0.1246 0.0000 0.0001 0.0005 0.0044 0.0003 0.0001 0.0000 0.0000 0.0082 0.0024
## 18 0.0000 0.1519 0.0342 0.0065 0.0003 0.0025 0.0078 0.0156 0.0001 0.0000 0.0002
## 19 0.0000 0.0670 0.0180 0.0038 0.0002 0.0012 0.0035 0.0068 0.0001 0.0000 0.0001
## 20 0.0511 0.0000 0.0006 0.0032 0.0610 0.0040 0.0008 0.0002 0.0000 0.0197 0.0482
## 21 0.0000 0.0486 0.0030 0.0005 0.0000 0.0007 0.0035 0.0148 0.0021 0.0000 0.0000
## 22 0.0019 0.0010 0.0174 0.1041 0.1439 0.0374 0.0099 0.0026 0.0000 0.0005 0.0430
## 23 1.0000 0.0000 0.0000 0.0002 0.0032 0.0002 0.0000 0.0000 0.0000 0.0617 0.0026
## 24 0.0000 1.0000 0.0567 0.0096 0.0006 0.0078 0.0320 0.0884 0.0001 0.0000 0.0005
## 25 0.0000 0.0567 1.0000 0.1671 0.0085 0.0621 0.1034 0.0641 0.0000 0.0000 0.0048
## 26 0.0002 0.0096 0.1671 1.0000 0.0404 0.1057 0.0567 0.0187 0.0000 0.0001 0.0176
## 27 0.0032 0.0006 0.0085 0.0404 1.0000 0.0650 0.0130 0.0030 0.0000 0.0017 0.2954
## 28 0.0002 0.0078 0.0621 0.1057 0.0650 1.0000 0.1997 0.0464 0.0000 0.0001 0.0588
## 29 0.0000 0.0320 0.1034 0.0567 0.0130 0.1997 1.0000 0.2324 0.0000 0.0000 0.0124
## 30 0.0000 0.0884 0.0641 0.0187 0.0030 0.0464 0.2324 1.0000 0.0000 0.0000 0.0030
## 31 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000
## 32 0.0617 0.0000 0.0000 0.0001 0.0017 0.0001 0.0000 0.0000 0.0000 1.0000 0.0024
## 33 0.0026 0.0005 0.0048 0.0176 0.2954 0.0588 0.0124 0.0030 0.0000 0.0024 1.0000
## 34 0.0000 0.0006 0.0000 0.0000 0.0000 0.0000 0.0001 0.0003 0.0571 0.0000 0.0000
## 35 0.0011 0.0005 0.0033 0.0089 0.0869 0.0526 0.0144 0.0039 0.0000 0.0016 0.2770
## 36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1701 0.0000 0.0000
## 37 0.0000 0.0249 0.0151 0.0058 0.0019 0.0262 0.1006 0.2165 0.0000 0.0000 0.0025
## 38 0.0001 0.0039 0.0119 0.0128 0.0173 0.1180 0.1022 0.0421 0.0000 0.0001 0.0314
## 39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0078 0.0000 0.0000
## 40 0.0077 0.0000 0.0000 0.0001 0.0030 0.0004 0.0001 0.0000 0.0000 0.0750 0.0068
## 41 0.0060 0.0000 0.0000 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0913 0.0005
## 42 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0319 0.0000 0.0000
## 43 0.0000 0.0016 0.0035 0.0035 0.0066 0.0328 0.0322 0.0183 0.0000 0.0001 0.0152
## 44 0.0002 0.0003 0.0011 0.0020 0.0128 0.0171 0.0079 0.0030 0.0000 0.0006 0.0404
## 45 0.0000 0.0011 0.0009 0.0005 0.0006 0.0044 0.0082 0.0099 0.0000 0.0000 0.0014
## 46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0081 0.0000 0.0000
## 47 0.0005 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0088 0.0002
## 48 0.0000 0.0004 0.0007 0.0007 0.0018 0.0067 0.0070 0.0049 0.0000 0.0001 0.0048
## 49 0.0000 0.0001 0.0002 0.0002 0.0011 0.0023 0.0017 0.0010 0.0000 0.0001 0.0036
## 34 35 36 37 38 39 40 41 42 43 44
## 1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 5 0.0000 0.0001 0.0000 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 9 0.0000 0.0012 0.0000 0.0002 0.0005 0.0000 0.0001 0.0000 0.0000 0.0002 0.0002
## 10 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0002 0.0001 0.0000 0.0000 0.0000
## 11 0.0000 0.0001 0.0000 0.0006 0.0003 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 12 0.0000 0.0001 0.0000 0.0006 0.0002 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 13 0.0001 0.0000 0.0000 0.0004 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 14 0.0001 0.0000 0.0000 0.0007 0.0002 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 15 0.0000 0.0009 0.0000 0.0020 0.0018 0.0000 0.0000 0.0000 0.0000 0.0005 0.0002
## 16 0.0001 0.0004 0.0000 0.0049 0.0018 0.0000 0.0000 0.0000 0.0000 0.0006 0.0001
## 17 0.0000 0.0008 0.0000 0.0000 0.0001 0.0000 0.0015 0.0008 0.0000 0.0000 0.0001
## 18 0.0003 0.0001 0.0000 0.0039 0.0008 0.0000 0.0000 0.0000 0.0000 0.0003 0.0001
## 19 0.0002 0.0001 0.0000 0.0017 0.0004 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 20 0.0000 0.0169 0.0000 0.0001 0.0015 0.0000 0.0131 0.0022 0.0000 0.0008 0.0031
## 21 0.0131 0.0001 0.0008 0.0108 0.0008 0.0000 0.0000 0.0000 0.0017 0.0005 0.0001
## 22 0.0000 0.0137 0.0000 0.0011 0.0054 0.0000 0.0006 0.0001 0.0000 0.0017 0.0021
## 23 0.0000 0.0011 0.0000 0.0000 0.0001 0.0000 0.0077 0.0060 0.0000 0.0000 0.0002
## 24 0.0006 0.0005 0.0000 0.0249 0.0039 0.0000 0.0000 0.0000 0.0001 0.0016 0.0003
## 25 0.0000 0.0033 0.0000 0.0151 0.0119 0.0000 0.0000 0.0000 0.0000 0.0035 0.0011
## 26 0.0000 0.0089 0.0000 0.0058 0.0128 0.0000 0.0001 0.0000 0.0000 0.0035 0.0020
## 27 0.0000 0.0869 0.0000 0.0019 0.0173 0.0000 0.0030 0.0003 0.0000 0.0066 0.0128
## 28 0.0000 0.0526 0.0000 0.0262 0.1180 0.0000 0.0004 0.0000 0.0000 0.0328 0.0171
## 29 0.0001 0.0144 0.0000 0.1006 0.1022 0.0000 0.0001 0.0000 0.0000 0.0322 0.0079
## 30 0.0003 0.0039 0.0000 0.2165 0.0421 0.0000 0.0000 0.0000 0.0000 0.0183 0.0030
## 31 0.0571 0.0000 0.1701 0.0000 0.0000 0.0078 0.0000 0.0000 0.0319 0.0000 0.0000
## 32 0.0000 0.0016 0.0000 0.0000 0.0001 0.0000 0.0750 0.0913 0.0000 0.0001 0.0006
## 33 0.0000 0.2770 0.0000 0.0025 0.0314 0.0000 0.0068 0.0005 0.0000 0.0152 0.0404
## 34 1.0000 0.0000 0.0473 0.0003 0.0000 0.0019 0.0000 0.0000 0.1195 0.0000 0.0000
## 35 0.0000 1.0000 0.0000 0.0046 0.0679 0.0000 0.0074 0.0005 0.0000 0.0446 0.1454
## 36 0.0473 0.0000 1.0000 0.0000 0.0000 0.0340 0.0000 0.0000 0.0915 0.0000 0.0000
## 37 0.0003 0.0046 0.0000 1.0000 0.0659 0.0000 0.0000 0.0000 0.0001 0.0463 0.0059
## 38 0.0000 0.0679 0.0000 0.0659 1.0000 0.0000 0.0006 0.0000 0.0000 0.2750 0.0715
## 39 0.0019 0.0000 0.0340 0.0000 0.0000 1.0000 0.0000 0.0000 0.0085 0.0000 0.0000
## 40 0.0000 0.0074 0.0000 0.0000 0.0006 0.0000 1.0000 0.0606 0.0000 0.0006 0.0053
## 41 0.0000 0.0005 0.0000 0.0000 0.0000 0.0000 0.0606 1.0000 0.0000 0.0000 0.0003
## 42 0.1195 0.0000 0.0915 0.0001 0.0000 0.0085 0.0000 0.0000 1.0000 0.0000 0.0000
## 43 0.0000 0.0446 0.0000 0.0463 0.2750 0.0000 0.0006 0.0000 0.0000 1.0000 0.1168
## 44 0.0000 0.1454 0.0000 0.0059 0.0715 0.0000 0.0053 0.0003 0.0000 0.1168 1.0000
## 45 0.0001 0.0041 0.0000 0.0436 0.0335 0.0000 0.0001 0.0000 0.0000 0.0895 0.0154
## 46 0.0042 0.0000 0.0459 0.0000 0.0000 0.2091 0.0000 0.0000 0.0259 0.0000 0.0000
## 47 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0263 0.0755 0.0000 0.0000 0.0004
## 48 0.0000 0.0163 0.0000 0.0164 0.0559 0.0000 0.0005 0.0000 0.0000 0.2030 0.0851
## 49 0.0000 0.0130 0.0000 0.0030 0.0157 0.0000 0.0015 0.0001 0.0000 0.0506 0.0893
## 45 46 47 48 49
## 1 0.0000 0.0000 0.0000 0.0000 0.0000
## 2 0.0000 0.0000 0.0000 0.0000 0.0000
## 3 0.0000 0.0000 0.0000 0.0000 0.0000
## 4 0.0000 0.0000 0.0000 0.0000 0.0000
## 5 0.0000 0.0000 0.0000 0.0000 0.0000
## 6 0.0000 0.0000 0.0000 0.0000 0.0000
## 7 0.0000 0.0000 0.0000 0.0000 0.0000
## 8 0.0000 0.0000 0.0000 0.0000 0.0000
## 9 0.0000 0.0000 0.0000 0.0000 0.0000
## 10 0.0000 0.0000 0.0000 0.0000 0.0000
## 11 0.0000 0.0000 0.0000 0.0000 0.0000
## 12 0.0000 0.0000 0.0000 0.0000 0.0000
## 13 0.0000 0.0000 0.0000 0.0000 0.0000
## 14 0.0000 0.0000 0.0000 0.0000 0.0000
## 15 0.0001 0.0000 0.0000 0.0001 0.0000
## 16 0.0002 0.0000 0.0000 0.0001 0.0000
## 17 0.0000 0.0000 0.0001 0.0000 0.0000
## 18 0.0002 0.0000 0.0000 0.0001 0.0000
## 19 0.0001 0.0000 0.0000 0.0000 0.0000
## 20 0.0001 0.0000 0.0004 0.0003 0.0003
## 21 0.0010 0.0001 0.0000 0.0002 0.0000
## 22 0.0002 0.0000 0.0000 0.0004 0.0002
## 23 0.0000 0.0000 0.0005 0.0000 0.0000
## 24 0.0011 0.0000 0.0000 0.0004 0.0001
## 25 0.0009 0.0000 0.0000 0.0007 0.0002
## 26 0.0005 0.0000 0.0000 0.0007 0.0002
## 27 0.0006 0.0000 0.0001 0.0018 0.0011
## 28 0.0044 0.0000 0.0000 0.0067 0.0023
## 29 0.0082 0.0000 0.0000 0.0070 0.0017
## 30 0.0099 0.0000 0.0000 0.0049 0.0010
## 31 0.0000 0.0081 0.0000 0.0000 0.0000
## 32 0.0000 0.0000 0.0088 0.0001 0.0001
## 33 0.0014 0.0000 0.0002 0.0048 0.0036
## 34 0.0001 0.0042 0.0000 0.0000 0.0000
## 35 0.0041 0.0000 0.0003 0.0163 0.0130
## 36 0.0000 0.0459 0.0000 0.0000 0.0000
## 37 0.0436 0.0000 0.0000 0.0164 0.0030
## 38 0.0335 0.0000 0.0000 0.0559 0.0157
## 39 0.0000 0.2091 0.0000 0.0000 0.0000
## 40 0.0001 0.0000 0.0263 0.0005 0.0015
## 41 0.0000 0.0000 0.0755 0.0000 0.0001
## 42 0.0000 0.0259 0.0000 0.0000 0.0000
## 43 0.0895 0.0000 0.0000 0.2030 0.0506
## 44 0.0154 0.0000 0.0004 0.0851 0.0893
## 45 1.0000 0.0000 0.0000 0.1510 0.0312
## 46 0.0000 1.0000 0.0000 0.0000 0.0000
## 47 0.0000 0.0000 1.0000 0.0001 0.0003
## 48 0.1510 0.0000 0.0001 1.0000 0.1761
## 49 0.0312 0.0000 0.0003 0.1761 1.0000#dinormalisasi
diag(W3)<-0
rtot<-rowSums(W3,na.rm=TRUE)
rtot## 1 2 3 4 5 6 7
## 0.09660841 0.21002889 0.18454594 0.42899235 0.33084989 0.08959893 0.17429096
## 8 9 10 11 12 13 14
## 0.58983345 0.25469061 0.17232106 1.29154016 1.59786514 1.40373290 1.57119927
## 15 16 17 18 19 20 21
## 0.75809237 1.21474683 0.31012719 1.35848425 1.51901609 0.31220470 0.14654515
## 22 23 24 25 26 27 28
## 0.54044208 0.27648523 0.69495600 0.90153012 0.75143343 0.79590071 0.88251106
## 29 30 31 32 33 34 35
## 0.99176472 0.83830907 0.27769243 0.27328180 0.88271317 0.24552150 0.78889338
## 36 37 38 39 40 41 42
## 0.38979228 0.60653846 0.93985438 0.26129238 0.21174786 0.23844345 0.27940689
## 43 44 45 46 47 48 49
## 0.94550855 0.62704284 0.39738006 0.29320721 0.11293980 0.73291799 0.39191889W3<-W3/rtot #row-normalized
rowSums(W3,na.rm=TRUE)## 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
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1W3 #matriks bobot Exponential distance dengan alpha=1## 1 2 3 4 5
## 1 0.000000e+00 2.824961e-01 4.830528e-01 1.506400e-01 2.123007e-02
## 2 1.299417e-01 0.000000e+00 6.035225e-02 6.082558e-01 9.649779e-03
## 3 2.528745e-01 6.868597e-02 0.000000e+00 1.835790e-01 2.240661e-01
## 4 3.392388e-02 2.977939e-01 7.897290e-02 0.000000e+00 3.097361e-02
## 5 6.199195e-03 6.125836e-03 1.249826e-01 4.016155e-02 0.000000e+00
## 6 1.138181e-02 1.857431e-03 1.418790e-01 7.134128e-03 2.142604e-01
## 7 2.235263e-03 5.953589e-02 5.083256e-03 1.305432e-01 7.331355e-03
## 8 5.379575e-03 3.686351e-02 3.194914e-02 2.741096e-01 5.798340e-02
## 9 3.319341e-04 1.544761e-04 6.620457e-03 9.506410e-04 7.109826e-02
## 10 4.970269e-05 8.884354e-06 6.293812e-04 4.174247e-05 2.468593e-03
## 11 3.085283e-04 1.192274e-03 4.011983e-03 9.218777e-03 4.955950e-02
## 12 1.855515e-04 9.995426e-04 2.030928e-03 7.346414e-03 2.008125e-02
## 13 1.991373e-04 1.785186e-03 1.519520e-03 1.115707e-02 9.067770e-03
## 14 7.755995e-05 7.226543e-04 6.259913e-04 4.368714e-03 4.665609e-03
## 15 1.338496e-04 2.768937e-04 2.487280e-03 2.160480e-03 5.849395e-02
## 16 3.513377e-05 1.620776e-04 4.839578e-04 1.197161e-03 8.261825e-03
## 17 3.578533e-06 8.169831e-07 5.170885e-05 4.355652e-06 3.030591e-04
## 18 2.046889e-05 1.533239e-04 2.157771e-04 9.921411e-04 2.677288e-03
## 19 3.340080e-05 2.918317e-04 3.038978e-04 1.791680e-03 2.957415e-03
## 20 1.587813e-06 7.509110e-07 3.049482e-05 4.995592e-06 3.689347e-04
## 21 3.643376e-06 3.953523e-05 3.448895e-05 2.106205e-04 4.512126e-04
## 22 1.778790e-05 1.365915e-05 3.775740e-04 9.751095e-05 6.520864e-03
## 23 5.359083e-07 1.437307e-07 8.300454e-06 8.272026e-07 6.100755e-05
## 24 7.382326e-06 4.665850e-05 9.331187e-05 3.138215e-04 1.565886e-03
## 25 1.510717e-05 4.083298e-05 2.721725e-04 3.177941e-04 6.361878e-03
## 26 1.864713e-05 2.830772e-05 3.857766e-04 2.190164e-04 9.080618e-03
## 27 1.753176e-06 1.534634e-06 3.736200e-05 1.135377e-05 6.900384e-04
## 28 1.704793e-06 3.088684e-06 3.492050e-05 2.416746e-05 8.308597e-04
## 29 1.436636e-06 4.008594e-06 2.666374e-05 3.094778e-05 6.378276e-04
## 30 1.300051e-06 5.307614e-06 2.120715e-05 3.907775e-05 4.691413e-04
## 31 6.105824e-08 1.492394e-06 2.671879e-07 4.126707e-06 1.703776e-06
## 32 4.930905e-08 1.823752e-08 8.622573e-07 1.171567e-07 8.777571e-06
## 33 4.861632e-07 4.784059e-07 1.039887e-05 3.612009e-06 2.022727e-04
## 34 6.582494e-08 1.147927e-06 4.418260e-07 4.438654e-06 4.396252e-06
## 35 1.973273e-07 2.407472e-07 4.224738e-06 1.858932e-06 8.928685e-05
## 36 7.824287e-09 1.845970e-07 3.724798e-08 5.334718e-07 2.699693e-07
## 37 3.898261e-07 1.631180e-06 6.454716e-06 1.187919e-05 1.456539e-04
## 38 2.068692e-07 4.664091e-07 4.127493e-06 3.633797e-06 9.950296e-05
## 39 5.482389e-10 1.471360e-08 2.216740e-09 3.624225e-08 1.439804e-08
## 40 3.232730e-08 1.926724e-08 6.444819e-07 1.375803e-07 9.347353e-06
## 41 5.344163e-09 2.202520e-09 9.615461e-08 1.469150e-08 1.081489e-06
## 42 7.097037e-09 1.310092e-07 4.643045e-08 4.821723e-07 4.683302e-07
## 43 5.693440e-08 1.365038e-07 1.130614e-06 1.058372e-06 2.728122e-05
## 44 4.720124e-08 7.425889e-08 9.992184e-07 5.808271e-07 2.259705e-05
## 45 3.052998e-08 1.107297e-07 5.489329e-07 8.185575e-07 1.305010e-05
## 46 4.777872e-10 1.139515e-08 2.338124e-09 3.259100e-08 1.839120e-08
## 47 1.693865e-09 9.500121e-10 3.302444e-08 6.786070e-09 4.619169e-07
## 48 1.495219e-08 3.777814e-08 2.961584e-07 2.912038e-07 7.148292e-06
## 49 8.688866e-09 1.759197e-08 1.797350e-07 1.371826e-07 4.236220e-06
## 6 7 8 9 10
## 1 1.055600e-02 4.032633e-03 3.284448e-02 8.750843e-04 8.865501e-05
## 2 7.923854e-04 4.940543e-02 1.035254e-01 1.873248e-04 7.289289e-06
## 3 6.888372e-02 4.800785e-03 1.021137e-01 9.136847e-03 5.876890e-04
## 4 1.490027e-03 5.303709e-02 3.768808e-01 5.643908e-04 1.676745e-05
## 5 5.802481e-02 3.862141e-03 1.033718e-01 5.473195e-02 1.285751e-03
## 6 0.000000e+00 3.308161e-04 9.555612e-03 3.821816e-01 9.147669e-02
## 7 1.700649e-04 0.000000e+00 1.986174e-01 1.776029e-04 3.235746e-06
## 8 1.451550e-03 5.868983e-02 0.000000e+00 1.162152e-03 2.466699e-05
## 9 1.344496e-01 1.215380e-04 2.691408e-03 0.000000e+00 5.888521e-02
## 10 4.756362e-02 3.272736e-06 8.443202e-05 8.703238e-02 0.000000e+00
## 11 1.042943e-03 5.424109e-03 5.383336e-02 2.854846e-03 4.465358e-05
## 12 4.080187e-04 7.599461e-03 4.538826e-02 1.134191e-03 1.750610e-05
## 13 1.744551e-04 2.684761e-02 6.260303e-02 3.919023e-04 6.149424e-06
## 14 9.281349e-05 1.622171e-02 2.434058e-02 2.806320e-04 4.243672e-06
## 15 3.206021e-03 1.021662e-03 1.107331e-02 2.512274e-02 3.842148e-04
## 16 2.617132e-04 1.921265e-03 7.377676e-03 1.804375e-03 2.825030e-05
## 17 3.507637e-03 4.781018e-07 1.078731e-05 1.541272e-02 3.619969e-01
## 18 6.730022e-05 3.725333e-03 5.922516e-03 3.836659e-04 5.927625e-06
## 19 6.457979e-05 7.671004e-03 1.027553e-02 2.744513e-04 4.126616e-06
## 20 1.129957e-03 1.119976e-06 1.763860e-05 1.790225e-02 3.235607e-02
## 21 1.374580e-05 2.238491e-03 1.139963e-03 1.315806e-04 2.547794e-06
## 22 5.423442e-03 2.614997e-05 3.839162e-04 1.579263e-01 1.050789e-02
## 23 5.193505e-04 1.177086e-07 2.336405e-06 3.354787e-03 5.068851e-02
## 24 5.679600e-05 1.184773e-03 1.916976e-03 5.668674e-04 1.047054e-05
## 25 4.725958e-04 3.171773e-04 1.835659e-03 6.844157e-03 1.400153e-04
## 26 1.678501e-03 1.113505e-04 1.086411e-03 3.583931e-02 9.330388e-04
## 27 5.847511e-04 4.299757e-06 4.957055e-05 1.637850e-02 2.979908e-03
## 28 1.835514e-04 2.017002e-05 1.311700e-04 4.852772e-03 2.609465e-04
## 29 7.297591e-05 4.393619e-05 1.832237e-04 1.579601e-03 5.700631e-05
## 30 3.395229e-05 9.403604e-05 2.407799e-04 6.053952e-04 1.762823e-05
## 31 3.644439e-08 1.433255e-04 1.387792e-05 2.123725e-07 3.547655e-09
## 32 4.417529e-05 2.546268e-08 3.991339e-07 4.615132e-04 3.426298e-03
## 33 1.565494e-04 1.719820e-06 1.677092e-05 4.363157e-03 1.066065e-03
## 34 1.161744e-07 1.019422e-04 1.951184e-05 1.027831e-06 2.026318e-08
## 35 5.206599e-05 1.212051e-06 9.336791e-06 1.491628e-03 3.384205e-04
## 36 6.126147e-09 1.777718e-05 1.892758e-06 4.257911e-08 7.641170e-10
## 37 1.255429e-05 3.374233e-05 7.335487e-05 2.603335e-04 1.010295e-05
## 38 2.076901e-05 4.759311e-06 2.100050e-05 5.696232e-04 4.475084e-05
## 39 3.179755e-10 1.368094e-06 1.158867e-07 2.162016e-09 3.916376e-11
## 40 2.191231e-05 4.997977e-08 5.693430e-07 3.808359e-04 9.320598e-04
## 41 4.673074e-06 3.919123e-09 5.388671e-08 5.403967e-05 3.635767e-04
## 42 1.279905e-08 1.205674e-05 2.071744e-06 1.236906e-07 2.624051e-09
## 43 5.960774e-06 1.636372e-06 6.214756e-06 1.673558e-04 1.668704e-05
## 44 9.983776e-06 5.648111e-07 3.156462e-06 2.899450e-04 6.497563e-05
## 45 1.815932e-06 2.307185e-06 5.033874e-06 4.737761e-05 3.688585e-06
## 46 4.421373e-10 1.095335e-06 1.173021e-07 3.604459e-09 7.105910e-11
## 47 1.277856e-06 2.659559e-09 2.839519e-08 1.958382e-05 7.710737e-05
## 48 1.660760e-06 5.258490e-07 1.731212e-06 4.751684e-05 6.125149e-06
## 49 1.479260e-06 2.007654e-07 7.914260e-07 4.317220e-05 9.706732e-06
## 11 12 13 14 15
## 1 4.124658e-03 3.068949e-03 2.893491e-03 1.261403e-03 1.050326e-03
## 2 7.331705e-03 7.604355e-03 1.193133e-02 5.406084e-03 9.994386e-04
## 3 2.807777e-02 1.758451e-02 1.155810e-02 5.329605e-03 1.021744e-02
## 4 2.775439e-02 2.736314e-02 3.650775e-02 1.600057e-02 3.817885e-03
## 5 1.934657e-01 9.698395e-02 3.847282e-02 2.215688e-02 1.340300e-01
## 6 1.503369e-02 7.276414e-03 2.733162e-03 1.627570e-03 2.712600e-02
## 7 4.019402e-02 6.967036e-02 2.162296e-01 1.462356e-01 4.443801e-03
## 8 1.178772e-01 1.229573e-01 1.489877e-01 6.483848e-02 1.423214e-02
## 9 1.447697e-02 7.115633e-03 2.159978e-03 1.731233e-03 7.477841e-02
## 10 3.346770e-04 1.623271e-04 5.009340e-05 3.869321e-05 1.690277e-03
## 11 0.000000e+00 3.686191e-01 1.155026e-01 8.225691e-02 8.220522e-02
## 12 2.979515e-01 0.000000e+00 1.861068e-01 1.392479e-01 3.963248e-02
## 13 1.062711e-01 2.118448e-01 0.000000e+00 3.098878e-01 1.369388e-02
## 14 6.761594e-02 1.416112e-01 2.768584e-01 0.000000e+00 1.224595e-02
## 15 1.400507e-01 8.353514e-02 2.535647e-02 2.538058e-02 0.000000e+00
## 16 8.816326e-02 1.014718e-01 4.776584e-02 7.337787e-02 9.355890e-02
## 17 6.031972e-05 3.046493e-05 9.098923e-06 7.971339e-06 4.054517e-04
## 18 3.951169e-02 6.622005e-02 6.115002e-02 1.299602e-01 2.013660e-02
## 19 4.620370e-02 8.890490e-02 1.143595e-01 2.569403e-01 1.389572e-02
## 20 1.597681e-04 8.975165e-05 2.716460e-05 2.901199e-05 1.412890e-03
## 21 6.317916e-03 1.062773e-02 1.296963e-02 2.980762e-02 5.500869e-03
## 22 3.726186e-03 2.095562e-03 6.324816e-04 6.566805e-04 3.289182e-02
## 23 1.624354e-05 8.564974e-06 2.549060e-06 2.462801e-06 1.276011e-04
## 24 1.781448e-02 2.470389e-02 1.841052e-02 3.859151e-02 2.326706e-02
## 25 2.638780e-02 2.171847e-02 8.252772e-03 1.179189e-02 1.434637e-01
## 26 1.399874e-02 9.074808e-03 2.924340e-03 3.492055e-03 1.290809e-01
## 27 5.720579e-04 3.533485e-04 1.121020e-04 1.339667e-04 5.386619e-03
## 28 1.857713e-03 1.412525e-03 5.235724e-04 7.629573e-04 1.454535e-02
## 29 2.598955e-03 2.350893e-03 1.032462e-03 1.723034e-03 1.407791e-02
## 30 3.058357e-03 3.315726e-03 1.817312e-03 3.451596e-03 1.034925e-02
## 31 2.637988e-05 5.306940e-05 1.155432e-04 2.264477e-04 1.077236e-05
## 32 3.623469e-06 2.062094e-06 6.289614e-07 6.967345e-07 3.255917e-05
## 33 2.096323e-04 1.375044e-04 4.559812e-05 5.902715e-05 1.951378e-03
## 34 6.697546e-05 1.239760e-04 2.049122e-04 4.492121e-04 4.448076e-05
## 35 1.261658e-04 8.966144e-05 3.183648e-05 4.518305e-05 1.110624e-03
## 36 4.216798e-06 8.305643e-06 1.683949e-05 3.419328e-05 2.046403e-06
## 37 9.166696e-04 1.011451e-03 5.861795e-04 1.157558e-03 3.262898e-03
## 38 3.015430e-04 2.601013e-04 1.114849e-04 1.864174e-04 1.964429e-03
## 39 2.242347e-07 4.468938e-07 9.542915e-07 1.884276e-06 1.039788e-07
## 40 6.485288e-06 4.071091e-06 1.318876e-06 1.672541e-06 6.108321e-05
## 41 5.428357e-07 3.226538e-07 1.009121e-07 1.199501e-07 5.042766e-06
## 42 7.089960e-06 1.305625e-05 2.157544e-05 4.720394e-05 5.038101e-06
## 43 8.864622e-05 7.954805e-05 3.606254e-05 6.310685e-05 5.475105e-04
## 44 4.494298e-05 3.539454e-05 1.396243e-05 2.211729e-05 3.521830e-04
## 45 6.572554e-05 6.923241e-05 3.898592e-05 7.709108e-05 2.911191e-04
## 46 2.867632e-07 5.551747e-07 1.074866e-06 2.223272e-06 1.608789e-07
## 47 3.321133e-07 2.132461e-07 7.053661e-08 9.314202e-08 3.123160e-06
## 48 2.445739e-05 2.269003e-05 1.083085e-05 1.969528e-05 1.447880e-04
## 49 1.136298e-05 9.902528e-06 4.412779e-06 7.717360e-06 7.688470e-05
## 16 17 18 19 20
## 1 4.417694e-04 1.148762e-05 2.878286e-04 5.251754e-04 5.131260e-06
## 2 9.374102e-04 1.206352e-06 9.917114e-04 2.110648e-03 1.116218e-06
## 3 3.185582e-03 8.689609e-05 1.588384e-03 2.501413e-03 5.158946e-05
## 4 3.389916e-03 3.148789e-06 3.141800e-03 6.344146e-03 3.635606e-06
## 5 3.033407e-02 2.840771e-04 1.099306e-02 1.357824e-02 3.481432e-04
## 6 3.548205e-03 1.214092e-02 1.020395e-03 1.094854e-03 3.937300e-03
## 7 1.339054e-02 8.507174e-07 2.903653e-02 6.685589e-02 2.006195e-06
## 8 1.519413e-02 5.671834e-06 1.364054e-02 2.646289e-02 9.336285e-06
## 9 8.605964e-03 1.876749e-02 2.046421e-03 1.636872e-03 2.194493e-02
## 10 1.991455e-04 6.514879e-01 4.673013e-05 3.637626e-05 5.862149e-02
## 11 8.292119e-02 1.448409e-05 4.155969e-02 5.434145e-02 3.862083e-05
## 12 7.714203e-02 5.912892e-06 5.629943e-02 8.451775e-02 1.753645e-05
## 13 4.133507e-02 2.010228e-06 5.917888e-02 1.237514e-01 6.041687e-06
## 14 5.673089e-02 1.573403e-06 1.123657e-01 2.484068e-01 5.764820e-06
## 15 1.499163e-01 1.658658e-04 3.608433e-02 2.784335e-02 5.818696e-04
## 16 0.000000e+00 1.393491e-05 1.914109e-01 1.195783e-01 6.917465e-05
## 17 5.458209e-05 0.000000e+00 1.273761e-05 8.712157e-06 1.381211e-01
## 18 1.711582e-01 2.907858e-06 0.000000e+00 3.189076e-01 1.548551e-05
## 19 9.562598e-02 1.778702e-06 2.852050e-01 0.000000e+00 8.068820e-06
## 20 2.691493e-04 1.372020e-01 6.738150e-05 3.925843e-05 0.000000e+00
## 21 4.252902e-02 1.981070e-06 1.169640e-01 7.134140e-02 2.005514e-05
## 22 5.584010e-03 9.189108e-03 1.334407e-03 8.345109e-04 4.573385e-02
## 23 2.021191e-05 4.507035e-01 4.861407e-06 3.022287e-06 1.848975e-01
## 24 1.551717e-01 7.150879e-06 2.185793e-01 9.633854e-02 5.727225e-05
## 25 1.317953e-01 9.604067e-05 3.798338e-02 1.999329e-02 6.556415e-04
## 26 3.448456e-02 6.890035e-04 8.609057e-03 5.015575e-03 4.306131e-03
## 27 1.380659e-03 5.469464e-03 3.683096e-04 2.012009e-04 7.669964e-02
## 28 8.553813e-03 3.457272e-04 2.868948e-03 1.389576e-03 4.569657e-03
## 29 1.772948e-02 6.506322e-05 7.909036e-03 3.572088e-03 8.121351e-04
## 30 2.686536e-02 1.838318e-05 1.857571e-02 8.056045e-03 2.233902e-04
## 31 9.474694e-05 2.353594e-09 3.773947e-04 3.708796e-04 2.224069e-08
## 32 6.862426e-06 3.010388e-02 1.800822e-06 9.970436e-07 7.206837e-02
## 33 6.460220e-04 2.681879e-03 1.897775e-04 9.705394e-05 5.459012e-02
## 34 3.648298e-04 1.718035e-08 1.205121e-03 9.162265e-04 2.036923e-07
## 35 5.068981e-04 9.561016e-04 1.703808e-04 8.187105e-05 2.146502e-02
## 36 1.774903e-05 5.814562e-10 6.670367e-05 6.006628e-05 6.403102e-09
## 37 8.155490e-03 1.356508e-05 6.457755e-03 2.804667e-03 2.118332e-04
## 38 1.917976e-03 8.456497e-05 8.827658e-04 3.934017e-04 1.609161e-03
## 39 9.036695e-07 3.095529e-11 3.433025e-06 3.182262e-06 3.632293e-10
## 40 1.826156e-05 6.879973e-03 5.451281e-06 2.752599e-06 6.207316e-02
## 41 1.262219e-06 3.221424e-03 3.553412e-07 1.858542e-07 9.389169e-03
## 42 4.023362e-05 2.486121e-09 1.287258e-04 9.640191e-05 3.323745e-08
## 43 6.106046e-04 3.809827e-05 3.183021e-04 1.394158e-04 8.172382e-04
## 44 2.391633e-04 2.110815e-04 9.911275e-05 4.481679e-05 4.925159e-03
## 45 5.614701e-04 8.117461e-06 4.318562e-04 1.879701e-04 1.751999e-04
## 46 1.356474e-06 6.323590e-11 4.813547e-06 4.074288e-06 8.224565e-10
## 47 1.035518e-06 6.594725e-04 3.297461e-07 1.609887e-07 3.413398e-03
## 48 1.785843e-04 1.695621e-05 1.038979e-04 4.511660e-05 3.899159e-04
## 49 7.501468e-05 3.530084e-05 3.929349e-05 1.716084e-05 8.150323e-04
## 21 22 23 24 25
## 1 5.526632e-06 9.950820e-05 1.533725e-06 5.310502e-05 1.409771e-04
## 2 2.758523e-05 3.514744e-05 1.892093e-07 1.543864e-04 1.752719e-04
## 3 2.738716e-05 1.105724e-03 1.243567e-05 3.513902e-04 1.329597e-03
## 4 7.194862e-05 1.228437e-04 5.331314e-07 5.083823e-04 6.678463e-04
## 5 1.998581e-04 1.065181e-02 5.098290e-05 3.289170e-03 1.733543e-02
## 6 2.248219e-05 3.271307e-02 1.602617e-03 4.405267e-04 4.755183e-03
## 7 1.882140e-03 8.108591e-05 1.867262e-07 4.724083e-03 1.640618e-03
## 8 2.832257e-04 3.517679e-04 1.095193e-06 2.258628e-03 2.805711e-03
## 9 7.570951e-05 3.351125e-01 3.641866e-03 1.546770e-03 2.422631e-02
## 10 2.166693e-06 3.295537e-02 8.132856e-02 4.222679e-05 7.325166e-04
## 11 7.168650e-04 1.559214e-03 3.477320e-06 9.585673e-03 1.841940e-02
## 12 9.747018e-04 7.087770e-04 1.482033e-06 1.074441e-02 1.225376e-02
## 13 1.353988e-03 2.435076e-04 5.020737e-07 9.114626e-03 5.300241e-03
## 14 2.780145e-03 2.258770e-04 4.333812e-07 1.706938e-02 6.766005e-03
## 15 1.063361e-03 2.344849e-02 4.653763e-05 2.132930e-02 1.706083e-01
## 16 5.130634e-03 2.484332e-03 4.600377e-06 8.877363e-02 9.781249e-02
## 17 9.361197e-07 1.601337e-02 4.018121e-01 1.602422e-05 2.791872e-04
## 18 1.261737e-02 5.308636e-04 9.894170e-07 1.118180e-01 2.520689e-02
## 19 6.882571e-03 2.969059e-04 5.501045e-07 4.407527e-02 1.186594e-02
## 20 9.413643e-06 7.916761e-02 1.637433e-01 1.274859e-04 1.893247e-03
## 21 0.000000e+00 3.880706e-04 1.047749e-06 3.319727e-01 2.015054e-02
## 22 1.052284e-04 0.000000e+00 3.423861e-03 1.851311e-03 3.218168e-02
## 23 5.553370e-07 6.692577e-03 0.000000e+00 7.996938e-06 1.275525e-04
## 24 7.000298e-02 1.439698e-03 3.181547e-06 0.000000e+00 8.151791e-02
## 25 3.275502e-03 1.929202e-02 3.911837e-05 6.283912e-02 0.000000e+00
## 26 6.955972e-04 1.384886e-01 2.723637e-04 1.272322e-02 2.224126e-01
## 27 6.048005e-05 1.807743e-01 3.979998e-03 7.948327e-04 1.073574e-02
## 28 8.080140e-04 4.238659e-02 2.346306e-04 8.799832e-03 7.036725e-02
## 29 3.567042e-03 1.000969e-02 4.174561e-05 3.231534e-02 1.043007e-01
## 30 1.769961e-02 3.088677e-03 1.147975e-05 1.054764e-01 7.649006e-02
## 31 7.557909e-03 4.724054e-07 1.166911e-09 4.695359e-04 2.661890e-05
## 32 3.963936e-07 1.799046e-03 2.258245e-01 4.170371e-06 5.245898e-05
## 33 5.041302e-05 4.874688e-02 2.947089e-03 5.191622e-04 5.381316e-03
## 34 5.329586e-02 3.228501e-06 1.042707e-08 2.602211e-03 1.588146e-04
## 35 7.771498e-05 1.732546e-02 1.338414e-03 5.895887e-04 4.228117e-03
## 36 1.994257e-03 1.134145e-07 3.290522e-10 1.049029e-04 6.031864e-06
## 37 1.774141e-02 1.732817e-03 1.095221e-05 4.097552e-02 2.494455e-02
## 38 8.345843e-04 5.707914e-03 8.772911e-05 4.186063e-03 1.266623e-02
## 39 1.039321e-04 5.942946e-09 1.858213e-11 5.358387e-06 3.100168e-07
## 40 2.211322e-06 2.605771e-03 3.649820e-02 1.674867e-05 1.523622e-04
## 41 1.212712e-07 2.585760e-04 2.514840e-02 9.935384e-07 1.026330e-05
## 42 6.238488e-03 4.389151e-07 1.702778e-09 3.047685e-04 1.973740e-05
## 43 5.305147e-04 1.773980e-03 4.993979e-05 1.708669e-03 3.680162e-03
## 44 1.013079e-04 3.397033e-03 3.885386e-04 4.497977e-04 1.807658e-03
## 45 2.405462e-03 4.538571e-04 1.107301e-05 2.783089e-03 2.226660e-03
## 46 1.864758e-04 1.152406e-08 4.210252e-11 9.147332e-06 5.548669e-07
## 47 2.031933e-07 1.284513e-04 4.801428e-03 1.161179e-06 8.713801e-06
## 48 2.941937e-04 5.251659e-04 2.850884e-05 6.067930e-04 9.974754e-04
## 49 9.691361e-05 5.044335e-04 8.093528e-05 2.188429e-04 4.774021e-04
## 26 27 28 29 30
## 1 1.450399e-04 1.444340e-05 1.557317e-05 1.474825e-05 1.128105e-05
## 2 1.012783e-04 5.815467e-06 1.297821e-05 1.892874e-05 2.118481e-05
## 3 1.570804e-03 1.611330e-04 1.669922e-04 1.432931e-04 9.633451e-05
## 4 3.836345e-04 2.106443e-05 4.971662e-05 7.154655e-05 7.636321e-05
## 5 2.062409e-02 1.659974e-03 2.216240e-03 1.911969e-03 1.188712e-03
## 6 1.407697e-02 5.194301e-03 1.807903e-03 8.077656e-04 3.176658e-04
## 7 4.800737e-04 1.963487e-05 1.021296e-04 2.500093e-04 4.522969e-04
## 8 1.384061e-03 6.688877e-05 1.962570e-04 3.080781e-04 3.422119e-04
## 9 1.057395e-01 5.118235e-02 1.681501e-02 6.150963e-03 1.992646e-03
## 10 4.068664e-03 1.376333e-02 1.336390e-03 3.280902e-04 8.575799e-05
## 11 8.144636e-03 3.525258e-04 1.269378e-03 1.995719e-03 1.985109e-03
## 12 4.267641e-03 1.760038e-04 7.801465e-04 1.459155e-03 1.739573e-03
## 13 1.565431e-03 6.356055e-05 3.291641e-04 7.294546e-04 1.085298e-03
## 14 1.670092e-03 6.786166e-05 4.285378e-04 1.087605e-03 1.841590e-03
## 15 1.279470e-01 5.655266e-03 1.693254e-02 1.841725e-02 1.144434e-02
## 16 2.133190e-02 9.046062e-04 6.214327e-03 1.447501e-02 1.854006e-02
## 17 1.669445e-03 1.403666e-02 9.838160e-04 2.080676e-04 4.969183e-05
## 18 4.762023e-03 2.157830e-04 1.863752e-03 5.774011e-03 1.146291e-02
## 19 2.481126e-03 1.054208e-04 8.073098e-04 2.332214e-03 4.445941e-03
## 20 1.036426e-02 1.955297e-01 1.291708e-02 2.579868e-03 5.998310e-04
## 21 3.566785e-03 3.284729e-04 4.865950e-03 2.414045e-02 1.012503e-01
## 22 1.925553e-01 2.662235e-01 6.921488e-02 1.836878e-02 4.791014e-03
## 23 7.402319e-04 1.145697e-02 7.489154e-04 1.497433e-04 3.480683e-05
## 24 1.375720e-02 9.102849e-04 1.117473e-02 4.611689e-02 1.272338e-01
## 25 1.853829e-01 9.477871e-03 6.888275e-02 1.147403e-01 7.112609e-02
## 26 0.000000e+00 5.379667e-02 1.406548e-01 7.543859e-02 2.483765e-02
## 27 5.079103e-02 0.000000e+00 8.163262e-02 1.638298e-02 3.809448e-03
## 28 1.197636e-01 7.362113e-02 0.000000e+00 2.262457e-01 5.261835e-02
## 29 5.715779e-02 1.314750e-02 2.013223e-01 0.000000e+00 2.343734e-01
## 30 2.226367e-02 3.616736e-03 5.539279e-02 2.772763e-01 0.000000e+00
## 31 4.493483e-06 3.643292e-07 5.395605e-06 2.685069e-05 1.138272e-04
## 32 2.623106e-04 6.139436e-03 5.217820e-04 1.096342e-04 2.669765e-05
## 33 1.991871e-02 3.347043e-01 6.655738e-02 1.409673e-02 3.394981e-03
## 34 2.877253e-05 3.268178e-06 5.026148e-05 2.504508e-04 1.075351e-03
## 35 1.129118e-02 1.101730e-01 6.670688e-02 1.827511e-02 5.001858e-03
## 36 1.048077e-06 1.037941e-07 1.587686e-06 7.946430e-06 3.418502e-05
## 37 9.528133e-03 3.078216e-03 4.318188e-02 1.659310e-01 3.568974e-01
## 38 1.358354e-02 1.836779e-02 1.255448e-01 1.087442e-01 4.484352e-02
## 39 5.432849e-08 5.814164e-09 8.944226e-08 4.454388e-07 1.912506e-06
## 40 5.965866e-04 1.422114e-02 2.088132e-03 5.110513e-04 1.388461e-04
## 41 4.539996e-05 1.122765e-03 1.272932e-04 2.949517e-05 7.771462e-06
## 42 3.735767e-06 5.148122e-07 7.873094e-06 3.832975e-05 1.613069e-04
## 43 3.719715e-03 7.017520e-03 3.469856e-02 3.408180e-02 1.932383e-02
## 44 3.247260e-03 2.038157e-02 2.720270e-02 1.263173e-02 4.826144e-03
## 45 1.376137e-03 1.553412e-03 1.105266e-02 2.057231e-02 2.488449e-02
## 46 1.011819e-07 1.282196e-08 1.966755e-07 9.643293e-07 4.089935e-06
## 47 3.118188e-05 7.178022e-04 1.302334e-04 3.621975e-05 1.101926e-05
## 48 9.782752e-04 2.423215e-03 9.184128e-03 9.485900e-03 6.659123e-03
## 49 6.192508e-04 2.922342e-03 5.758941e-03 4.237927e-03 2.457652e-03
## 31 32 33 34 35
## 1 1.755066e-07 1.394834e-07 4.442084e-06 1.672881e-07 1.611353e-06
## 2 1.973188e-06 2.372999e-08 2.010653e-06 1.341914e-06 9.042751e-07
## 3 4.020465e-07 1.276859e-06 4.973949e-05 5.878091e-07 1.805983e-05
## 4 2.671272e-06 7.463256e-08 7.432226e-06 2.540336e-06 3.418474e-06
## 5 1.430031e-06 7.250268e-06 5.396669e-04 3.262430e-06 2.128996e-04
## 6 1.129515e-07 1.347371e-04 1.542298e-03 3.183444e-07 4.584264e-04
## 7 2.283561e-04 3.992454e-08 8.710192e-06 1.436046e-04 5.486106e-06
## 8 6.533696e-06 1.849268e-07 2.509846e-05 8.121914e-06 1.248782e-05
## 9 2.315525e-07 4.952015e-04 1.512194e-02 9.908282e-07 4.620255e-03
## 10 5.716985e-09 5.433723e-03 5.460908e-03 2.887080e-08 1.549304e-03
## 11 5.671904e-06 7.667034e-07 1.432748e-04 1.273202e-05 7.706409e-05
## 12 9.222913e-06 3.526785e-07 7.596195e-05 1.904965e-05 4.426739e-05
## 13 2.285725e-05 1.224476e-07 2.867359e-05 3.584040e-05 1.789200e-05
## 14 4.002217e-05 1.211844e-07 3.316196e-05 7.019557e-05 2.268624e-05
## 15 3.945961e-06 1.173713e-05 2.272159e-03 1.440587e-05 1.155748e-03
## 16 2.165925e-05 1.543841e-06 4.694411e-04 7.373846e-05 3.291950e-04
## 17 2.107443e-09 2.652732e-02 7.633415e-03 1.360134e-08 2.432106e-03
## 18 7.714455e-05 3.622654e-07 1.233132e-04 2.178038e-04 9.894286e-05
## 19 6.780076e-05 1.793752e-07 5.639887e-05 1.480915e-04 4.251932e-05
## 20 1.978212e-08 6.308352e-02 1.543456e-01 1.601860e-07 5.423881e-02
## 21 1.432169e-02 7.392068e-07 3.036623e-04 8.929179e-02 4.183614e-04
## 22 2.427335e-07 9.097120e-04 7.961910e-02 1.466700e-06 2.529030e-02
## 23 1.172006e-09 2.232081e-01 9.408945e-03 9.259333e-09 3.818886e-03
## 24 1.876185e-04 1.639941e-06 6.594249e-04 9.193368e-04 6.692836e-04
## 25 8.199244e-06 1.590195e-05 5.268996e-03 4.325135e-05 3.699858e-03
## 26 1.660568e-06 9.539731e-05 2.339862e-02 9.401067e-06 1.185406e-02
## 27 1.271157e-07 2.108047e-03 3.712120e-01 1.008176e-06 1.092030e-01
## 28 1.697790e-06 1.615770e-04 6.657262e-02 1.398314e-05 5.963055e-02
## 29 7.518149e-06 3.020981e-05 1.254670e-02 6.200167e-05 1.453683e-02
## 30 3.770560e-05 8.703213e-06 3.574808e-03 3.149456e-04 4.707014e-03
## 31 0.000000e+00 8.474070e-10 3.421171e-07 2.057667e-01 5.078934e-07
## 32 8.610837e-10 0.000000e+00 8.802732e-03 8.687203e-09 5.775201e-03
## 33 1.076265e-07 2.725264e-03 0.000000e+00 9.951012e-07 3.137545e-01
## 34 2.327285e-01 9.669436e-09 3.577645e-06 0.000000e+00 6.099997e-06
## 35 1.787798e-07 2.000597e-03 3.510680e-01 1.898457e-06 0.000000e+00
## 36 4.362930e-01 2.900295e-10 1.092371e-07 1.214481e-01 1.825086e-07
## 37 4.864363e-05 1.188430e-05 4.119299e-03 5.423555e-04 7.506057e-03
## 38 2.083221e-06 1.141394e-04 3.336207e-02 2.321817e-05 7.223168e-02
## 39 2.971679e-02 1.841996e-11 6.499053e-09 7.243598e-03 1.166558e-08
## 40 5.397366e-09 3.542070e-01 3.202342e-02 6.360049e-08 3.509525e-02
## 41 2.913818e-10 3.829269e-01 2.101637e-03 3.427497e-09 1.966256e-03
## 42 1.143322e-01 1.909946e-09 6.276881e-07 4.277763e-01 1.199768e-06
## 43 1.667708e-06 9.299525e-05 1.607266e-02 2.162938e-05 4.720094e-02
## 44 2.960322e-07 1.019798e-03 6.435117e-02 3.808916e-06 2.319066e-01
## 45 1.452474e-05 2.477442e-05 3.425708e-03 2.261698e-04 1.026252e-02
## 46 2.748908e-02 4.758431e-11 1.546949e-08 1.422673e-02 2.974997e-08
## 47 6.039511e-10 7.778626e-02 1.751520e-03 8.278463e-09 2.434875e-03
## 48 1.324219e-06 7.861522e-05 6.490789e-03 1.975578e-05 2.220696e-02
## 49 4.638209e-07 3.356214e-04 9.204050e-03 7.162956e-06 3.313444e-02
## 36 37 38 39 40
## 1 3.156916e-08 2.447453e-06 2.012526e-06 1.482797e-09 7.085550e-08
## 2 3.425933e-07 4.710655e-06 2.087126e-06 1.830487e-08 1.942494e-08
## 3 7.867404e-08 2.121441e-05 2.102047e-05 3.138607e-09 7.394779e-07
## 4 4.847247e-07 1.679561e-05 7.961074e-06 2.207457e-08 6.790875e-08
## 5 3.180655e-07 2.670235e-04 2.826608e-04 1.137101e-08 5.982417e-06
## 6 2.665127e-08 8.498607e-05 2.178580e-04 9.272942e-10 5.178504e-05
## 7 3.975770e-05 1.174245e-04 2.566432e-05 2.051010e-06 6.072093e-08
## 8 1.250832e-06 7.543240e-05 3.346268e-05 5.133707e-08 2.043919e-07
## 9 6.516537e-08 6.199768e-04 2.102013e-03 2.218058e-09 3.166241e-04
## 10 1.728442e-09 3.556053e-05 2.440751e-04 5.938445e-11 1.145314e-03
## 11 1.272648e-06 4.304902e-04 2.194330e-04 4.536508e-08 1.063262e-06
## 12 2.026126e-06 3.839399e-04 1.529900e-04 7.307872e-08 5.394979e-07
## 13 4.676035e-06 2.532821e-04 7.464352e-05 1.776329e-07 1.989476e-07
## 14 8.482870e-06 4.468582e-04 1.115105e-04 3.133574e-07 2.254055e-07
## 15 1.052210e-06 2.610596e-03 2.435425e-03 3.583846e-08 1.706156e-05
## 16 5.695371e-06 4.072139e-03 1.483946e-03 1.943796e-07 3.183253e-06
## 17 7.308200e-10 2.653023e-05 2.562779e-04 2.608085e-11 4.697490e-03
## 18 1.913940e-05 2.883270e-03 6.107331e-04 6.603118e-07 8.496949e-07
## 19 1.541351e-05 1.119895e-03 2.434078e-04 5.473944e-07 3.837069e-07
## 20 7.994369e-09 4.115408e-04 4.844183e-03 3.039963e-10 4.210013e-02
## 21 5.304481e-03 7.343025e-02 5.352533e-03 1.853127e-04 3.195211e-06
## 22 8.179990e-08 1.944742e-03 9.926334e-03 2.873289e-09 1.020954e-03
## 23 4.639018e-10 2.402638e-05 2.982170e-04 1.756104e-11 2.795236e-02
## 24 5.883877e-05 3.576231e-02 5.661207e-03 2.014668e-06 5.103194e-06
## 25 2.607982e-06 1.678239e-02 1.320467e-02 8.985282e-08 3.578624e-05
## 26 5.436709e-07 7.690873e-03 1.698960e-02 1.889139e-08 1.681133e-04
## 27 5.083317e-08 2.345841e-03 2.168995e-02 1.908777e-09 3.783508e-03
## 28 7.012576e-07 2.967835e-02 1.337023e-01 2.648191e-08 5.010221e-04
## 29 3.123177e-06 1.014792e-01 1.030523e-01 1.173562e-07 1.091126e-04
## 30 1.589516e-05 2.582246e-01 5.027546e-02 5.961085e-07 3.507104e-05
## 31 6.124173e-01 1.062479e-04 7.050693e-06 2.796177e-02 4.115635e-09
## 32 4.136801e-10 2.637674e-05 3.925414e-04 1.761184e-11 2.744514e-01
## 33 4.823738e-08 2.830493e-03 3.552172e-02 1.923788e-09 7.681873e-03
## 34 1.928121e-01 1.339840e-03 8.887899e-05 7.708884e-03 5.485168e-08
## 35 9.017752e-08 5.771011e-03 8.605378e-02 3.863800e-09 9.419959e-03
## 36 0.000000e+00 4.007800e-05 2.653444e-06 8.726512e-02 1.638132e-09
## 37 2.575615e-05 0.000000e+00 1.085757e-01 1.088919e-06 6.384375e-05
## 38 1.100481e-06 7.006970e-02 0.000000e+00 4.773738e-08 6.194829e-04
## 39 1.301809e-01 2.527709e-06 1.717088e-07 0.000000e+00 1.157435e-10
## 40 3.015526e-09 1.828764e-04 2.749609e-03 1.428250e-10 0.000000e+00
## 41 1.625744e-10 9.864387e-06 1.487344e-04 7.782105e-12 2.542604e-01
## 42 3.274435e-01 2.507951e-04 1.765330e-05 3.031290e-02 1.257218e-08
## 43 1.033344e-06 4.896161e-02 2.908530e-01 5.041590e-08 6.707427e-04
## 44 1.821172e-07 9.370353e-03 1.140780e-01 9.023954e-09 8.395834e-03
## 45 1.169830e-05 1.095944e-01 8.418972e-02 7.011594e-07 2.179329e-04
## 46 1.566550e-01 6.180469e-06 4.371878e-07 7.132032e-01 3.240400e-10
## 47 4.058281e-10 1.866312e-05 2.520438e-04 2.293101e-11 2.327970e-01
## 48 9.961416e-07 2.242401e-02 7.620773e-02 5.771973e-08 7.474381e-04
## 49 3.726969e-07 7.604798e-03 4.009403e-02 2.354482e-08 3.876527e-03
## 41 42 43 44 45
## 1 1.319016e-08 2.052576e-08 5.572182e-07 3.063626e-07 1.255792e-07
## 2 2.500496e-09 1.742850e-07 6.145133e-07 2.217005e-07 2.095034e-07
## 3 1.242370e-07 7.029679e-08 5.792625e-06 3.395104e-06 1.182009e-06
## 4 8.165864e-09 3.140435e-07 2.332675e-06 8.489743e-07 7.582383e-07
## 5 7.794291e-07 3.955107e-07 7.796473e-05 4.282703e-05 1.567433e-05
## 6 1.243613e-05 3.991277e-08 6.290212e-05 6.986976e-05 8.053835e-06
## 7 5.361662e-09 1.932824e-05 8.877130e-06 2.032009e-06 5.260337e-06
## 8 2.178400e-08 9.813949e-07 9.962312e-06 3.355586e-06 3.391400e-06
## 9 5.059239e-05 1.356941e-07 6.212886e-04 7.138385e-04 7.392073e-05
## 10 5.030870e-04 4.254721e-09 9.156011e-05 2.364337e-04 8.506042e-06
## 11 1.002180e-07 1.533815e-06 6.489597e-05 2.181982e-05 2.022238e-05
## 12 4.814842e-08 2.283050e-06 4.707116e-05 1.388971e-05 1.721771e-05
## 13 1.714132e-08 4.294496e-06 2.429055e-05 6.236971e-06 1.103645e-05
## 14 1.820349e-08 8.394293e-06 3.797613e-05 8.826688e-06 1.949750e-05
## 15 1.586106e-06 1.856872e-06 6.828664e-04 2.913020e-04 1.526001e-04
## 16 2.477617e-07 9.254233e-06 4.752693e-04 1.234542e-04 1.836737e-04
## 17 2.476814e-03 2.239853e-09 1.161531e-04 4.267834e-04 1.040127e-05
## 18 6.237008e-08 2.647573e-05 2.215391e-04 4.574800e-05 1.263254e-04
## 19 2.917396e-08 1.773211e-05 8.677909e-05 1.850016e-05 4.917364e-05
## 20 7.170891e-03 2.974578e-08 2.474997e-03 9.891861e-03 2.229978e-04
## 21 1.973202e-07 1.189447e-02 3.422878e-03 4.334800e-04 6.522785e-03
## 22 1.140839e-04 2.269178e-07 3.103595e-03 3.941376e-03 3.337153e-04
## 23 2.168822e-02 1.720771e-09 1.707813e-04 8.811695e-04 1.591475e-05
## 24 3.408888e-07 1.225321e-04 2.324696e-03 4.058421e-04 1.591387e-03
## 25 2.714515e-06 6.117118e-06 3.859687e-03 1.257283e-03 9.814760e-04
## 26 1.440623e-05 1.389077e-06 4.680418e-03 2.709716e-03 7.277415e-04
## 27 3.363686e-04 1.807287e-07 8.336624e-03 1.605743e-02 7.755928e-04
## 28 3.439304e-05 2.492656e-06 3.717549e-02 1.932809e-02 4.976828e-03
## 29 7.091329e-06 1.079853e-05 3.249221e-02 7.986404e-03 8.242907e-03
## 30 2.210467e-06 5.376328e-05 2.179488e-02 3.609884e-03 1.179589e-02
## 31 2.501980e-10 1.150381e-01 5.678341e-06 6.684550e-07 2.078501e-05
## 32 3.341108e-01 1.952753e-09 3.217477e-04 2.339919e-03 3.602457e-05
## 33 5.677060e-04 1.986833e-07 1.721605e-02 4.571240e-02 1.542186e-03
## 34 3.328687e-09 4.868154e-01 8.329519e-05 9.727675e-06 3.660591e-04
## 35 5.943018e-04 4.249288e-07 5.657152e-02 1.843283e-01 5.169422e-03
## 36 9.944992e-11 2.347147e-01 2.506555e-06 2.929645e-07 1.192602e-05
## 37 3.877905e-06 1.155308e-04 7.632429e-02 9.687123e-03 7.180190e-02
## 38 3.773430e-05 5.248105e-06 2.926028e-01 7.610948e-02 3.559628e-02
## 39 7.101592e-12 3.241439e-02 1.824342e-07 2.165546e-08 1.066341e-06
## 40 2.863157e-01 1.658932e-08 2.995038e-03 2.486234e-02 4.089874e-04
## 41 0.000000e+00 9.079371e-10 1.631058e-04 1.372183e-03 2.382378e-05
## 42 7.748258e-10 0.000000e+00 1.981306e-05 2.367556e-06 1.173030e-04
## 43 4.113289e-05 5.854950e-06 0.000000e+00 1.234987e-01 9.470833e-02
## 44 5.217953e-04 1.054970e-06 1.862219e-01 0.000000e+00 2.455228e-02
## 45 1.429519e-05 8.247838e-05 2.253448e-01 3.874208e-02 0.000000e+00
## 46 2.015155e-11 8.819749e-02 5.072715e-07 6.144940e-08 3.292601e-06
## 47 6.683695e-01 2.699736e-09 3.804817e-04 3.170507e-03 8.229766e-05
## 48 5.029246e-05 6.802440e-06 2.769827e-01 1.161688e-01 2.059877e-01
## 49 3.060935e-04 2.735781e-06 1.292336e-01 2.278421e-01 7.969836e-02
## 46 47 48 49
## 1 1.450087e-09 1.980208e-09 1.134345e-07 3.524880e-08
## 2 1.590801e-08 5.108544e-10 1.318308e-07 3.282703e-08
## 3 3.714818e-09 2.021054e-08 1.176183e-06 3.817019e-07
## 4 2.227526e-08 1.786553e-09 4.975112e-07 1.253273e-07
## 5 1.629873e-08 1.576812e-07 1.583531e-05 5.018151e-06
## 6 1.446868e-09 1.610743e-06 1.358500e-05 6.470503e-06
## 7 1.842666e-06 1.723383e-09 2.211269e-06 4.514506e-07
## 8 5.831106e-08 5.437038e-09 2.151177e-06 5.258684e-07
## 9 4.149557e-09 8.684234e-06 1.367382e-04 6.643355e-05
## 10 1.209083e-10 5.053643e-05 2.605156e-05 2.207653e-05
## 11 6.510137e-08 2.904192e-08 1.387898e-05 3.448104e-06
## 12 1.018742e-07 1.507259e-08 1.040759e-05 2.428858e-06
## 13 2.245145e-07 5.675147e-09 5.655012e-06 1.232037e-06
## 14 4.148929e-07 6.695167e-09 9.187266e-06 1.925013e-06
## 15 6.222311e-08 4.652851e-07 1.399800e-04 3.974788e-05
## 16 3.274164e-07 9.627622e-08 1.077489e-04 2.420230e-05
## 17 5.978586e-11 2.401617e-04 4.007231e-05 4.461095e-05
## 18 1.038928e-06 2.741398e-08 5.605412e-05 1.133606e-05
## 19 7.864371e-07 1.196961e-08 2.176855e-05 4.427640e-06
## 20 7.724104e-10 1.234794e-03 9.153494e-04 1.023132e-03
## 21 3.731004e-04 1.565975e-07 1.471354e-03 2.591848e-04
## 22 6.252175e-09 2.684333e-05 7.122013e-04 3.658061e-04
## 23 4.464890e-11 1.961307e-03 7.557236e-05 1.147261e-04
## 24 3.859329e-06 1.887073e-07 6.399390e-04 1.234160e-04
## 25 1.804609e-07 1.091627e-06 8.109187e-04 2.075393e-04
## 26 3.948091e-08 4.686610e-06 9.541704e-04 3.229775e-04
## 27 4.723570e-09 1.018575e-04 2.231457e-03 1.439025e-03
## 28 6.534385e-08 1.666669e-05 7.627341e-03 2.557518e-03
## 29 2.850961e-07 4.124619e-06 7.010117e-03 1.674715e-03
## 30 1.430497e-06 1.484552e-06 5.821947e-03 1.148980e-03
## 31 2.902491e-02 2.456319e-10 3.495033e-06 6.546097e-07
## 32 5.105376e-11 3.214691e-02 2.108392e-04 4.813214e-04
## 33 5.138436e-09 2.241003e-04 5.389312e-03 4.086538e-03
## 34 1.698987e-02 3.808090e-09 5.897392e-05 1.143402e-05
## 35 1.105714e-08 3.485823e-04 2.063128e-02 1.646105e-02
## 36 1.178381e-01 1.175861e-10 1.873023e-06 3.747302e-07
## 37 2.987705e-06 3.475146e-06 2.709632e-02 4.913891e-03
## 38 1.363899e-07 3.028744e-05 5.942837e-02 1.671919e-02
## 39 8.003154e-01 9.911594e-12 1.619023e-07 3.531546e-08
## 40 4.486981e-10 1.241668e-01 2.587090e-03 7.174968e-03
## 41 2.477979e-11 3.165762e-01 1.545870e-04 5.031123e-04
## 42 9.255369e-02 1.091267e-09 1.784362e-05 3.837430e-06
## 43 1.573076e-07 4.544805e-05 2.147052e-01 5.356809e-02
## 44 2.873393e-08 5.710558e-04 1.357837e-01 1.424076e-01
## 45 2.429448e-06 2.338990e-05 3.799186e-01 7.860307e-02
## 46 0.000000e+00 3.098652e-11 4.972012e-07 1.137267e-07
## 47 8.044525e-11 0.000000e+00 5.412802e-04 2.372364e-03
## 48 1.989076e-07 8.340916e-05 0.000000e+00 2.403374e-01
## 49 8.508266e-08 6.836475e-04 4.494492e-01 0.000000e+00W3 = mat2listw(W3,style='W')
summary(W3)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 49
## Number of nonzero links: 2352
## Percentage nonzero weights: 97.95918
## Average number of links: 48
## Link number distribution:
##
## 48
## 49
## 49 least connected regions:
## 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 49 with 48 links
## 49 most connected regions:
## 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 49 with 48 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 49 2401 49 21.3021 200.9931matriks bobot Exponential distance dengan alpha=2
alpha2=2
W4<-exp((-alpha2)*D)
round(W4,4)## 1 2 3 4 5 6 7 8 9 10 11
## 1 1.0000 0.0007 0.0022 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 2 0.0007 1.0000 0.0002 0.0163 0.0000 0.0000 0.0001 0.0005 0.0000 0.0000 0.0000
## 3 0.0022 0.0002 1.0000 0.0011 0.0017 0.0002 0.0000 0.0004 0.0000 0.0000 0.0000
## 4 0.0002 0.0163 0.0011 1.0000 0.0002 0.0000 0.0005 0.0261 0.0000 0.0000 0.0001
## 5 0.0000 0.0000 0.0017 0.0002 1.0000 0.0004 0.0000 0.0012 0.0003 0.0000 0.0041
## 6 0.0000 0.0000 0.0002 0.0000 0.0004 1.0000 0.0000 0.0000 0.0012 0.0001 0.0000
## 7 0.0000 0.0001 0.0000 0.0005 0.0000 0.0000 1.0000 0.0012 0.0000 0.0000 0.0000
## 8 0.0000 0.0005 0.0004 0.0261 0.0012 0.0000 0.0012 1.0000 0.0000 0.0000 0.0048
## 9 0.0000 0.0000 0.0000 0.0000 0.0003 0.0012 0.0000 0.0000 1.0000 0.0002 0.0000
## 10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0002 1.0000 0.0000
## 11 0.0000 0.0000 0.0000 0.0001 0.0041 0.0000 0.0000 0.0048 0.0000 0.0000 1.0000
## 12 0.0000 0.0000 0.0000 0.0001 0.0010 0.0000 0.0001 0.0053 0.0000 0.0000 0.2267
## 13 0.0000 0.0000 0.0000 0.0002 0.0002 0.0000 0.0014 0.0077 0.0000 0.0000 0.0223
## 14 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0006 0.0015 0.0000 0.0000 0.0113
## 15 0.0000 0.0000 0.0000 0.0000 0.0020 0.0000 0.0000 0.0001 0.0004 0.0000 0.0113
## 16 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0001 0.0000 0.0000 0.0115
## 17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0126 0.0000
## 18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0029
## 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0002 0.0000 0.0000 0.0049
## 20 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 22 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0073 0.0000 0.0000
## 23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000
## 24 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002
## 25 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0006
## 26 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0007 0.0000 0.0001
## 27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0000
## 28 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 29 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 30 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 33 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 35 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 37 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 38 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 44 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 48 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 12 13 14 15 16 17 18 19 20 21 22
## 1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 4 0.0001 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 5 0.0010 0.0002 0.0001 0.0020 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 7 0.0001 0.0014 0.0006 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000
## 8 0.0053 0.0077 0.0015 0.0001 0.0001 0.0000 0.0001 0.0002 0.0000 0.0000 0.0000
## 9 0.0000 0.0000 0.0000 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0073
## 10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0126 0.0000 0.0000 0.0001 0.0000 0.0000
## 11 0.2267 0.0223 0.0113 0.0113 0.0115 0.0000 0.0029 0.0049 0.0000 0.0000 0.0000
## 12 1.0000 0.0884 0.0495 0.0040 0.0152 0.0000 0.0081 0.0182 0.0000 0.0000 0.0000
## 13 0.0884 1.0000 0.1892 0.0004 0.0034 0.0000 0.0069 0.0302 0.0000 0.0000 0.0000
## 14 0.0495 0.1892 1.0000 0.0004 0.0079 0.0000 0.0312 0.1523 0.0000 0.0000 0.0000
## 15 0.0040 0.0004 0.0004 1.0000 0.0129 0.0000 0.0007 0.0004 0.0000 0.0000 0.0003
## 16 0.0152 0.0034 0.0079 0.0129 1.0000 0.0000 0.0541 0.0211 0.0000 0.0000 0.0000
## 17 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0018 0.0000 0.0000
## 18 0.0081 0.0069 0.0312 0.0007 0.0541 0.0000 1.0000 0.1877 0.0000 0.0003 0.0000
## 19 0.0182 0.0302 0.1523 0.0004 0.0211 0.0000 0.1877 1.0000 0.0000 0.0001 0.0000
## 20 0.0000 0.0000 0.0000 0.0000 0.0000 0.0018 0.0000 0.0000 1.0000 0.0000 0.0006
## 21 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0003 0.0001 0.0000 1.0000 0.0000
## 22 0.0000 0.0000 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0006 0.0000 1.0000
## 23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0155 0.0000 0.0000 0.0026 0.0000 0.0000
## 24 0.0003 0.0002 0.0007 0.0003 0.0116 0.0000 0.0231 0.0045 0.0000 0.0024 0.0000
## 25 0.0004 0.0001 0.0001 0.0167 0.0141 0.0000 0.0012 0.0003 0.0000 0.0000 0.0003
## 26 0.0000 0.0000 0.0000 0.0094 0.0007 0.0000 0.0000 0.0000 0.0000 0.0000 0.0108
## 27 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0037 0.0000 0.0207
## 28 0.0000 0.0000 0.0000 0.0002 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0014
## 29 0.0000 0.0000 0.0000 0.0002 0.0003 0.0000 0.0001 0.0000 0.0000 0.0000 0.0001
## 30 0.0000 0.0000 0.0000 0.0001 0.0005 0.0000 0.0002 0.0000 0.0000 0.0002 0.0000
## 31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0004 0.0000 0.0000
## 33 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0023 0.0000 0.0019
## 34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000
## 35 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0003 0.0000 0.0002
## 36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 37 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 38 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 40 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0000
## 41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 44 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 48 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 23 24 25 26 27 28 29 30 31 32 33
## 1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 9 0.0000 0.0000 0.0000 0.0007 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 10 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 11 0.0000 0.0002 0.0006 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 12 0.0000 0.0003 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 13 0.0000 0.0002 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 14 0.0000 0.0007 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 15 0.0000 0.0003 0.0167 0.0094 0.0000 0.0002 0.0002 0.0001 0.0000 0.0000 0.0000
## 16 0.0000 0.0116 0.0141 0.0007 0.0000 0.0001 0.0003 0.0005 0.0000 0.0000 0.0000
## 17 0.0155 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 18 0.0000 0.0231 0.0012 0.0000 0.0000 0.0000 0.0001 0.0002 0.0000 0.0000 0.0000
## 19 0.0000 0.0045 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 20 0.0026 0.0000 0.0000 0.0000 0.0037 0.0000 0.0000 0.0000 0.0000 0.0004 0.0023
## 21 0.0000 0.0024 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000
## 22 0.0000 0.0000 0.0003 0.0108 0.0207 0.0014 0.0001 0.0000 0.0000 0.0000 0.0019
## 23 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0038 0.0000
## 24 0.0000 1.0000 0.0032 0.0001 0.0000 0.0001 0.0010 0.0078 0.0000 0.0000 0.0000
## 25 0.0000 0.0032 1.0000 0.0279 0.0001 0.0039 0.0107 0.0041 0.0000 0.0000 0.0000
## 26 0.0000 0.0001 0.0279 1.0000 0.0016 0.0112 0.0032 0.0003 0.0000 0.0000 0.0003
## 27 0.0000 0.0000 0.0001 0.0016 1.0000 0.0042 0.0002 0.0000 0.0000 0.0000 0.0873
## 28 0.0000 0.0001 0.0039 0.0112 0.0042 1.0000 0.0399 0.0022 0.0000 0.0000 0.0035
## 29 0.0000 0.0010 0.0107 0.0032 0.0002 0.0399 1.0000 0.0540 0.0000 0.0000 0.0002
## 30 0.0000 0.0078 0.0041 0.0003 0.0000 0.0022 0.0540 1.0000 0.0000 0.0000 0.0000
## 31 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000
## 32 0.0038 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000
## 33 0.0000 0.0000 0.0000 0.0003 0.0873 0.0035 0.0002 0.0000 0.0000 0.0000 1.0000
## 34 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0033 0.0000 0.0000
## 35 0.0000 0.0000 0.0000 0.0001 0.0076 0.0028 0.0002 0.0000 0.0000 0.0000 0.0767
## 36 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0289 0.0000 0.0000
## 37 0.0000 0.0006 0.0002 0.0000 0.0000 0.0007 0.0101 0.0469 0.0000 0.0000 0.0000
## 38 0.0000 0.0000 0.0001 0.0002 0.0003 0.0139 0.0104 0.0018 0.0000 0.0000 0.0010
## 39 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000
## 40 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0056 0.0000
## 41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0083 0.0000
## 42 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0010 0.0000 0.0000
## 43 0.0000 0.0000 0.0000 0.0000 0.0000 0.0011 0.0010 0.0003 0.0000 0.0000 0.0002
## 44 0.0000 0.0000 0.0000 0.0000 0.0002 0.0003 0.0001 0.0000 0.0000 0.0000 0.0016
## 45 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0000
## 46 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000
## 47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
## 48 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 49 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 34 35 36 37 38 39 40 41 42 43 44
## 1 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 3 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 4 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 5 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 6 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 7 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 9 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 10 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 11 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 12 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 13 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 14 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 15 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 16 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 17 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 18 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 19 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 20 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000
## 21 0.0002 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 22 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 23 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000
## 24 0.0000 0.0000 0.0000 0.0006 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 25 0.0000 0.0000 0.0000 0.0002 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 26 0.0000 0.0001 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## 27 0.0000 0.0076 0.0000 0.0000 0.0003 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002
## 28 0.0000 0.0028 0.0000 0.0007 0.0139 0.0000 0.0000 0.0000 0.0000 0.0011 0.0003
## 29 0.0000 0.0002 0.0000 0.0101 0.0104 0.0000 0.0000 0.0000 0.0000 0.0010 0.0001
## 30 0.0000 0.0000 0.0000 0.0469 0.0018 0.0000 0.0000 0.0000 0.0000 0.0003 0.0000
## 31 0.0033 0.0000 0.0289 0.0000 0.0000 0.0001 0.0000 0.0000 0.0010 0.0000 0.0000
## 32 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0056 0.0083 0.0000 0.0000 0.0000
## 33 0.0000 0.0767 0.0000 0.0000 0.0010 0.0000 0.0000 0.0000 0.0000 0.0002 0.0016
## 34 1.0000 0.0000 0.0022 0.0000 0.0000 0.0000 0.0000 0.0000 0.0143 0.0000 0.0000
## 35 0.0000 1.0000 0.0000 0.0000 0.0046 0.0000 0.0001 0.0000 0.0000 0.0020 0.0211
## 36 0.0022 0.0000 1.0000 0.0000 0.0000 0.0012 0.0000 0.0000 0.0084 0.0000 0.0000
## 37 0.0000 0.0000 0.0000 1.0000 0.0043 0.0000 0.0000 0.0000 0.0000 0.0021 0.0000
## 38 0.0000 0.0046 0.0000 0.0043 1.0000 0.0000 0.0000 0.0000 0.0000 0.0756 0.0051
## 39 0.0000 0.0000 0.0012 0.0000 0.0000 1.0000 0.0000 0.0000 0.0001 0.0000 0.0000
## 40 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 1.0000 0.0037 0.0000 0.0000 0.0000
## 41 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0037 1.0000 0.0000 0.0000 0.0000
## 42 0.0143 0.0000 0.0084 0.0000 0.0000 0.0001 0.0000 0.0000 1.0000 0.0000 0.0000
## 43 0.0000 0.0020 0.0000 0.0021 0.0756 0.0000 0.0000 0.0000 0.0000 1.0000 0.0136
## 44 0.0000 0.0211 0.0000 0.0000 0.0051 0.0000 0.0000 0.0000 0.0000 0.0136 1.0000
## 45 0.0000 0.0000 0.0000 0.0019 0.0011 0.0000 0.0000 0.0000 0.0000 0.0080 0.0002
## 46 0.0000 0.0000 0.0021 0.0000 0.0000 0.0437 0.0000 0.0000 0.0007 0.0000 0.0000
## 47 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0007 0.0057 0.0000 0.0000 0.0000
## 48 0.0000 0.0003 0.0000 0.0003 0.0031 0.0000 0.0000 0.0000 0.0000 0.0412 0.0072
## 49 0.0000 0.0002 0.0000 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0026 0.0080
## 45 46 47 48 49
## 1 0.0000 0.0000 0.0000 0.0000 0.0000
## 2 0.0000 0.0000 0.0000 0.0000 0.0000
## 3 0.0000 0.0000 0.0000 0.0000 0.0000
## 4 0.0000 0.0000 0.0000 0.0000 0.0000
## 5 0.0000 0.0000 0.0000 0.0000 0.0000
## 6 0.0000 0.0000 0.0000 0.0000 0.0000
## 7 0.0000 0.0000 0.0000 0.0000 0.0000
## 8 0.0000 0.0000 0.0000 0.0000 0.0000
## 9 0.0000 0.0000 0.0000 0.0000 0.0000
## 10 0.0000 0.0000 0.0000 0.0000 0.0000
## 11 0.0000 0.0000 0.0000 0.0000 0.0000
## 12 0.0000 0.0000 0.0000 0.0000 0.0000
## 13 0.0000 0.0000 0.0000 0.0000 0.0000
## 14 0.0000 0.0000 0.0000 0.0000 0.0000
## 15 0.0000 0.0000 0.0000 0.0000 0.0000
## 16 0.0000 0.0000 0.0000 0.0000 0.0000
## 17 0.0000 0.0000 0.0000 0.0000 0.0000
## 18 0.0000 0.0000 0.0000 0.0000 0.0000
## 19 0.0000 0.0000 0.0000 0.0000 0.0000
## 20 0.0000 0.0000 0.0000 0.0000 0.0000
## 21 0.0000 0.0000 0.0000 0.0000 0.0000
## 22 0.0000 0.0000 0.0000 0.0000 0.0000
## 23 0.0000 0.0000 0.0000 0.0000 0.0000
## 24 0.0000 0.0000 0.0000 0.0000 0.0000
## 25 0.0000 0.0000 0.0000 0.0000 0.0000
## 26 0.0000 0.0000 0.0000 0.0000 0.0000
## 27 0.0000 0.0000 0.0000 0.0000 0.0000
## 28 0.0000 0.0000 0.0000 0.0000 0.0000
## 29 0.0001 0.0000 0.0000 0.0000 0.0000
## 30 0.0001 0.0000 0.0000 0.0000 0.0000
## 31 0.0000 0.0001 0.0000 0.0000 0.0000
## 32 0.0000 0.0000 0.0001 0.0000 0.0000
## 33 0.0000 0.0000 0.0000 0.0000 0.0000
## 34 0.0000 0.0000 0.0000 0.0000 0.0000
## 35 0.0000 0.0000 0.0000 0.0003 0.0002
## 36 0.0000 0.0021 0.0000 0.0000 0.0000
## 37 0.0019 0.0000 0.0000 0.0003 0.0000
## 38 0.0011 0.0000 0.0000 0.0031 0.0002
## 39 0.0000 0.0437 0.0000 0.0000 0.0000
## 40 0.0000 0.0000 0.0007 0.0000 0.0000
## 41 0.0000 0.0000 0.0057 0.0000 0.0000
## 42 0.0000 0.0007 0.0000 0.0000 0.0000
## 43 0.0080 0.0000 0.0000 0.0412 0.0026
## 44 0.0002 0.0000 0.0000 0.0072 0.0080
## 45 1.0000 0.0000 0.0000 0.0228 0.0010
## 46 0.0000 1.0000 0.0000 0.0000 0.0000
## 47 0.0000 0.0000 1.0000 0.0000 0.0000
## 48 0.0228 0.0000 0.0000 1.0000 0.0310
## 49 0.0010 0.0000 0.0000 0.0310 1.0000#dinormalisasi
diag(W4)<-0
rtot<-rowSums(W4,na.rm=TRUE)
rtot## 1 2 3 4 5 6
## 0.003150253 0.017823292 0.005763766 0.045100893 0.011300088 0.001792430
## 7 8 9 10 11 12
## 0.004261037 0.049090821 0.010424809 0.013234614 0.300765245 0.417464829
## 13 14 15 16 17 18
## 0.350515673 0.444919714 0.059724386 0.153617820 0.030111304 0.316560932
## 19 20 21 22 23 24
## 0.420307425 0.012134291 0.003374524 0.043723238 0.022265122 0.055995129
## 25 26 27 28 29 30
## 0.084148018 0.066912227 0.126129961 0.085267602 0.131871874 0.118723352
## 31 32 33 34 35 36
## 0.033336811 0.018316285 0.175084301 0.019984866 0.116094481 0.042800517
## 37 38 39 40 41 42
## 0.067440873 0.121961269 0.045022278 0.010368792 0.017752766 0.024420349
## 43 44 45 46 47 48
## 0.147947913 0.057594408 0.035246053 0.046590528 0.006467291 0.106080820
## 49
## 0.042992874W4<-W4/rtot #row-normalized
rowSums(W4,na.rm=TRUE)## 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
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1W4 #matriks bobot Exponential distance dengan alpha=2## 1 2 3 4 5
## 1 0.000000e+00 2.364338e-01 6.913111e-01 6.723026e-02 1.335326e-03
## 2 4.178948e-02 0.000000e+00 9.014821e-03 9.156778e-01 2.304649e-04
## 3 3.778441e-01 2.787653e-02 0.000000e+00 1.991356e-01 2.966573e-01
## 4 4.695968e-03 3.618641e-01 2.544896e-02 0.000000e+00 3.914693e-03
## 5 3.722638e-04 3.635055e-04 1.513142e-01 1.562432e-02 0.000000e+00
## 6 5.802112e-04 1.545214e-05 9.015709e-02 2.279529e-04 2.056114e-01
## 7 3.561983e-05 2.526923e-02 1.842122e-04 1.214908e-01 3.831801e-04
## 8 2.050947e-04 9.630569e-03 7.233973e-03 5.324844e-01 2.382681e-02
## 9 6.855853e-07 1.484842e-07 2.727305e-04 5.623295e-06 3.145399e-02
## 10 5.542748e-09 1.770994e-10 8.887772e-07 3.909505e-09 1.367302e-05
## 11 5.279322e-07 7.883886e-06 8.927019e-05 4.713405e-04 1.362204e-02
## 12 2.105665e-07 6.110306e-06 2.522607e-05 3.300738e-04 2.466277e-03
## 13 2.229291e-07 1.791548e-05 1.298000e-05 6.997804e-04 4.622347e-04
## 14 3.337767e-08 2.897624e-06 2.174290e-06 1.058981e-04 1.207807e-04
## 15 1.723958e-07 7.377660e-07 5.953081e-05 4.491516e-05 3.292414e-02
## 16 1.185713e-08 2.523340e-07 2.249806e-06 1.376687e-05 6.556649e-04
## 17 4.090348e-11 2.131946e-12 8.540434e-09 6.059776e-11 2.933627e-07
## 18 2.442534e-09 1.370476e-07 2.714331e-07 5.738505e-06 4.178709e-05
## 19 6.124512e-09 4.675443e-07 5.070057e-07 1.762294e-05 4.801558e-05
## 20 2.025179e-11 4.529408e-12 7.469931e-09 2.004649e-10 1.093361e-06
## 21 8.447698e-11 9.947154e-09 7.569904e-09 2.823134e-07 1.295665e-06
## 22 2.113661e-09 1.246331e-09 9.523360e-07 6.351741e-08 2.840510e-04
## 23 9.860520e-13 7.092801e-14 2.365497e-10 2.349322e-12 1.277868e-08
## 24 4.700573e-10 1.877699e-08 7.509969e-08 8.494340e-07 2.114874e-05
## 25 2.204362e-09 1.610418e-08 7.154927e-07 9.754572e-07 3.909194e-04
## 26 2.934267e-09 6.762160e-09 1.255878e-06 4.047891e-07 6.958351e-04
## 27 1.543656e-11 1.182793e-11 7.010674e-09 6.474110e-10 2.391366e-06
## 28 2.654603e-11 8.713713e-11 1.113826e-08 5.334802e-09 6.305386e-06
## 29 1.539425e-11 1.198531e-10 5.302824e-09 7.143717e-09 3.034392e-06
## 30 1.000444e-11 1.667520e-10 2.662176e-09 9.039226e-09 1.302805e-06
## 31 8.623679e-15 5.151943e-12 1.651344e-13 3.939229e-11 6.714734e-12
## 32 9.913734e-15 1.356175e-15 3.031501e-12 5.596524e-14 3.141469e-10
## 33 1.051856e-12 1.018556e-12 4.812431e-10 5.806170e-11 1.820818e-07
## 34 1.306950e-14 3.974719e-12 5.888171e-13 5.942653e-11 5.829658e-11
## 35 2.087370e-13 3.107046e-13 9.568075e-11 1.852474e-11 4.273661e-08
## 36 2.173236e-16 1.209670e-13 4.925189e-15 1.010277e-12 2.587296e-13
## 37 8.289634e-13 1.451435e-11 2.272729e-10 7.697812e-10 1.157278e-07
## 38 3.099495e-13 1.575554e-12 1.233877e-10 9.563580e-11 7.170859e-08
## 39 4.557909e-19 3.282946e-16 7.451703e-18 1.991849e-15 3.143640e-16
## 40 4.519067e-15 1.605274e-15 1.796103e-12 8.185070e-14 3.778219e-10
## 41 9.146695e-17 1.553618e-17 2.961045e-14 6.912535e-16 3.745840e-12
## 42 1.610188e-16 5.486887e-14 6.891729e-15 7.432361e-13 7.011754e-13
## 43 1.958717e-14 1.125931e-13 7.724156e-12 6.768600e-12 4.497276e-09
## 44 1.520971e-14 3.764532e-14 6.816085e-12 2.303074e-12 3.485921e-09
## 45 4.175943e-15 5.493255e-14 1.350020e-12 3.001927e-12 7.630087e-10
## 46 4.212313e-19 2.396029e-16 1.008757e-17 1.959959e-15 6.241253e-16
## 47 5.658860e-18 1.780041e-18 2.151010e-15 9.082560e-17 4.208229e-13
## 48 1.132096e-15 7.226952e-15 4.441422e-13 4.294059e-13 2.587488e-10
## 49 2.697256e-16 1.105668e-15 1.154147e-13 6.723481e-14 6.411401e-11
## 6 7 8 9 10
## 1 3.301284e-04 4.817944e-05 3.196019e-03 2.268737e-06 2.328579e-08
## 2 1.553972e-06 6.041147e-03 2.652555e-02 8.684814e-08 1.315044e-10
## 3 2.803728e-02 1.361844e-04 6.161279e-02 4.932822e-04 2.040788e-06
## 4 9.059459e-06 1.147819e-02 5.795915e-01 1.299792e-06 1.147223e-09
## 5 3.261427e-02 1.444895e-04 1.035105e-01 2.901763e-02 1.601378e-05
## 6 0.000000e+00 4.901587e-07 4.089596e-04 6.541884e-01 3.747868e-02
## 7 2.061881e-07 0.000000e+00 2.812349e-01 2.248716e-07 7.464200e-11
## 8 1.493215e-05 2.441092e-02 0.000000e+00 9.571599e-06 4.312119e-09
## 9 1.124804e-01 9.191404e-08 4.507302e-05 0.000000e+00 2.157597e-02
## 10 5.075925e-03 2.403185e-11 1.599483e-08 1.699523e-02 0.000000e+00
## 11 6.032668e-06 1.631718e-04 1.607278e-02 4.520156e-05 1.105861e-08
## 12 1.018171e-06 3.532043e-04 1.259933e-02 7.867434e-06 1.874299e-09
## 13 1.710917e-07 4.052030e-03 2.203191e-02 8.634100e-07 2.125839e-10
## 14 4.779727e-08 1.460072e-03 3.287326e-03 4.369736e-07 9.992280e-11
## 15 9.890661e-05 1.004401e-05 1.179906e-03 6.073327e-03 1.420499e-06
## 16 6.579325e-07 3.545721e-05 5.228404e-04 3.127399e-05 7.666130e-09
## 17 3.929881e-05 7.301142e-13 3.716861e-10 7.587675e-04 4.185621e-01
## 18 2.640492e-08 8.090623e-05 2.044864e-04 8.581405e-07 2.048393e-10
## 19 2.289554e-08 3.230443e-04 5.796504e-04 4.135123e-07 9.348581e-11
## 20 1.025624e-05 1.007585e-11 2.499152e-09 2.574422e-03 8.409612e-03
## 21 1.202459e-09 3.188901e-05 8.270115e-06 1.101829e-07 4.131037e-11
## 22 1.964880e-04 4.568023e-09 9.845980e-07 1.666076e-01 7.375928e-04
## 23 9.260618e-07 4.757020e-14 1.874196e-11 3.864103e-05 8.821406e-03
## 24 2.782274e-08 1.210694e-05 3.169552e-05 2.771579e-06 9.455895e-10
## 25 2.157229e-06 9.716747e-07 3.254624e-05 4.524354e-04 1.893511e-07
## 26 2.377490e-05 1.046309e-07 9.960106e-06 1.083914e-02 7.346406e-06
## 27 1.717282e-06 9.285117e-11 1.234090e-08 1.347249e-03 4.459697e-05
## 28 3.077314e-07 3.715941e-09 1.571539e-07 2.150979e-04 6.219553e-07
## 29 3.972137e-08 1.439826e-08 2.503966e-07 1.861058e-05 2.423879e-08
## 30 6.823549e-09 5.234327e-08 3.431727e-07 2.169453e-06 1.839457e-09
## 31 3.072313e-15 4.751719e-08 4.455050e-10 1.043279e-13 2.911299e-17
## 32 7.956879e-09 2.643579e-15 6.495630e-13 8.684650e-07 4.786679e-05
## 33 1.090674e-07 1.316309e-11 1.251716e-09 8.472150e-05 5.057771e-06
## 34 4.070980e-14 3.134624e-08 1.148350e-09 3.186554e-12 1.238492e-15
## 35 1.453226e-08 7.875289e-12 4.673264e-10 1.192741e-05 6.139574e-07
## 36 1.332270e-16 1.121872e-09 1.271767e-11 6.435919e-15 2.072703e-18
## 37 8.597615e-10 6.210746e-09 2.935293e-08 3.697035e-07 5.567883e-10
## 38 3.124141e-09 1.640542e-10 3.194172e-09 2.350036e-06 1.450446e-08
## 39 1.533251e-19 2.838297e-12 2.036543e-14 7.088331e-18 2.325920e-21
## 40 2.076281e-09 1.080185e-14 1.401709e-12 6.271706e-07 3.756622e-06
## 41 6.993748e-11 4.919065e-17 9.299681e-15 9.352553e-09 4.233474e-07
## 42 5.236940e-16 4.647105e-10 1.372128e-11 4.890981e-14 2.201238e-17
## 43 2.146977e-10 1.618028e-11 2.333835e-10 1.692403e-07 1.682598e-09
## 44 6.804619e-10 2.177813e-12 6.801666e-11 5.739122e-07 2.882140e-08
## 45 1.477409e-11 2.384879e-11 1.135289e-10 1.005652e-08 6.095668e-11
## 46 3.607165e-19 2.213836e-12 2.539002e-14 2.397352e-17 9.317322e-21
## 47 3.220590e-12 1.395054e-17 1.590234e-15 7.564260e-10 1.172636e-08
## 48 1.396651e-11 1.400219e-12 1.517659e-11 1.143323e-08 1.899797e-10
## 49 7.817812e-12 1.440037e-13 2.237776e-12 6.658928e-09 3.366216e-10
## 11 12 13 14 15
## 1 5.040346e-05 2.790383e-05 2.480440e-05 4.714029e-06 3.268384e-06
## 2 1.330393e-04 1.431182e-04 3.523287e-04 7.233288e-05 2.472193e-06
## 3 4.658304e-03 1.827104e-03 7.893611e-04 1.678390e-04 6.168608e-04
## 4 3.143238e-03 3.055244e-03 5.438562e-03 1.044683e-03 5.947844e-05
## 5 3.625667e-01 9.111291e-02 1.433799e-02 4.755513e-03 1.740140e-01
## 6 1.012266e-03 2.371364e-04 3.345756e-05 1.186431e-05 3.295602e-03
## 7 1.151748e-02 3.460434e-02 3.333227e-01 1.524546e-01 1.407809e-04
## 8 9.847329e-02 1.071438e-01 1.573111e-01 2.979368e-02 1.435485e-03
## 9 1.304106e-03 3.150539e-04 2.903063e-05 1.864956e-05 3.479447e-02
## 10 2.513142e-07 5.912178e-08 5.630235e-09 3.359193e-09 6.410342e-06
## 11 0.000000e+00 7.536050e-01 7.398976e-02 3.752606e-02 3.747892e-02
## 12 5.429396e-01 0.000000e+00 2.118287e-01 1.185872e-01 9.606447e-03
## 13 6.348802e-02 2.522884e-01 0.000000e+00 5.398467e-01 1.054179e-03
## 14 2.536758e-02 1.112694e-01 4.253008e-01 0.000000e+00 8.320810e-04
## 15 1.887396e-01 6.714768e-02 6.186859e-03 6.198628e-03 0.000000e+00
## 16 7.466297e-02 9.890558e-02 2.191618e-02 5.172019e-02 8.408144e-02
## 17 1.162168e-08 2.964494e-09 2.644418e-10 2.029611e-10 5.250835e-07
## 18 9.101295e-03 2.556412e-02 2.179943e-02 9.846298e-02 2.363873e-03
## 19 1.171956e-02 4.339194e-02 7.179637e-02 3.624284e-01 1.060035e-03
## 20 2.050428e-07 6.470671e-08 5.927492e-09 6.761135e-09 1.603545e-05
## 21 2.540259e-04 7.188053e-04 1.070498e-03 5.654380e-03 1.925719e-04
## 22 9.275022e-05 2.933507e-05 2.672279e-06 2.880674e-06 7.227060e-03
## 23 9.058994e-10 2.518668e-10 2.230893e-11 2.082464e-11 5.590205e-08
## 24 2.737227e-03 5.263750e-03 2.923455e-03 1.284541e-02 4.669253e-03
## 25 6.725478e-03 4.555910e-03 6.578339e-04 1.343023e-03 1.987928e-01
## 26 1.653688e-03 6.949451e-04 7.216578e-05 1.029053e-04 1.406045e-01
## 27 1.643537e-06 6.270554e-07 6.311404e-08 9.013496e-08 1.457243e-04
## 28 3.152198e-05 1.822420e-05 2.503859e-06 5.316878e-06 1.932434e-03
## 29 5.038051e-05 4.122218e-05 7.950846e-06 2.214385e-05 1.478229e-03
## 30 5.536668e-05 6.507728e-05 1.954929e-05 7.051998e-05 6.340009e-04
## 31 1.609717e-09 6.514671e-09 3.088111e-08 1.186150e-07 2.684267e-10
## 32 5.353443e-11 1.733807e-11 1.612991e-12 1.979332e-12 4.322456e-09
## 33 1.955727e-07 8.414442e-08 9.253072e-09 1.550585e-08 1.694629e-05
## 34 1.353036e-08 4.636104e-08 1.266524e-07 6.086683e-07 5.967908e-09
## 35 8.533129e-08 4.309595e-08 5.433442e-09 1.094400e-08 6.612400e-06
## 36 6.312235e-11 2.448860e-10 1.006643e-09 4.150487e-09 1.486619e-11
## 37 4.583731e-06 5.580634e-06 1.874367e-06 7.309350e-06 5.807650e-05
## 38 6.585635e-07 4.899865e-07 9.001838e-08 2.516936e-07 2.794936e-05
## 39 7.624859e-14 3.028550e-13 1.380982e-12 5.384122e-12 1.639515e-14
## 40 1.818731e-10 7.166903e-11 7.521734e-12 1.209661e-11 1.613443e-08
## 41 9.437167e-13 3.334097e-13 3.261305e-14 4.607929e-14 8.144088e-11
## 42 1.606978e-10 5.449534e-10 1.488132e-09 7.123262e-09 8.114394e-11
## 43 4.748347e-08 3.823677e-08 7.858411e-09 2.406440e-08 1.811369e-06
## 44 1.378916e-08 8.552371e-09 1.330872e-09 3.339472e-09 8.467416e-07
## 45 1.935396e-08 2.147438e-08 6.809533e-09 2.662624e-08 3.797022e-07
## 46 1.517395e-13 5.687363e-13 2.131867e-12 9.120883e-12 4.775842e-14
## 47 2.175425e-13 8.968785e-14 9.812976e-15 1.711051e-14 1.923801e-11
## 48 3.028965e-09 2.607017e-09 5.940173e-10 1.964257e-09 1.061547e-07
## 49 4.612965e-10 3.503387e-10 6.956973e-11 2.127813e-10 2.111912e-08
## 16 17 18 19 20
## 1 5.781968e-07 3.909709e-10 2.454440e-07 8.171338e-07 7.800679e-11
## 2 2.174851e-06 3.601786e-12 2.434113e-06 1.102559e-05 3.083670e-12
## 3 5.996257e-05 4.461729e-08 1.490781e-05 3.697205e-05 1.572623e-08
## 4 4.689125e-05 4.045768e-11 4.027828e-05 1.642329e-04 5.393463e-11
## 5 8.913365e-03 7.817224e-07 1.170625e-03 1.785942e-03 1.174076e-06
## 6 5.638722e-05 6.601865e-04 4.663371e-06 5.368782e-06 6.943211e-05
## 7 1.278294e-03 5.159470e-12 6.010685e-03 3.186499e-02 2.869331e-11
## 8 1.636102e-03 2.279846e-10 1.318625e-03 4.962870e-03 6.177414e-10
## 9 4.608471e-04 2.191645e-03 2.605839e-05 1.667199e-05 2.996582e-03
## 10 8.898288e-08 9.523096e-01 4.899586e-09 2.968940e-09 7.710438e-03
## 11 3.813460e-02 1.163512e-09 9.579279e-03 1.637761e-02 8.272396e-09
## 12 3.639506e-02 2.138258e-10 1.938511e-02 4.368741e-02 1.880805e-09
## 13 9.605035e-03 2.271706e-11 1.968770e-02 8.609186e-02 2.052003e-10
## 14 1.785747e-02 1.373601e-11 7.005653e-02 3.423794e-01 1.843964e-10
## 15 2.162669e-01 2.647319e-07 1.252939e-02 7.459941e-03 3.257946e-06
## 16 0.000000e+00 1.865256e-09 3.519355e-01 1.373520e-01 4.596464e-08
## 17 9.515913e-09 0.000000e+00 5.182342e-10 2.424384e-10 6.093548e-02
## 18 1.707841e-01 4.929449e-11 0.000000e+00 5.929002e-01 1.397986e-09
## 19 5.020066e-02 1.736857e-11 4.465518e-01 0.000000e+00 3.574191e-10
## 20 5.819036e-07 1.512117e-01 3.647084e-08 1.238028e-08 0.000000e+00
## 21 1.151068e-02 2.497641e-11 8.706318e-02 3.239020e-02 2.559657e-09
## 22 2.082948e-04 5.640696e-04 1.189496e-05 4.652111e-06 1.397210e-02
## 23 1.402598e-09 6.974301e-01 8.114146e-11 3.136102e-11 1.173764e-01
## 24 2.076774e-01 4.410453e-10 4.120810e-01 8.005057e-02 2.829130e-08
## 25 1.677708e-01 8.908958e-08 1.393490e-02 3.860871e-03 4.151925e-06
## 26 1.003518e-02 4.006068e-06 6.254416e-04 2.122839e-04 1.564768e-04
## 27 9.573538e-06 1.502412e-04 6.812798e-07 2.033103e-07 2.954515e-02
## 28 6.683066e-04 1.091750e-06 7.517985e-05 1.763686e-05 1.907319e-04
## 29 2.344536e-03 3.157449e-08 4.665650e-04 9.517204e-05 4.919509e-06
## 30 4.272258e-03 2.000385e-09 2.042505e-03 3.841633e-04 2.953932e-07
## 31 2.076512e-08 1.281348e-17 3.294547e-07 3.181778e-07 1.144196e-15
## 32 1.920169e-10 3.695124e-03 1.322287e-11 4.053333e-12 2.117744e-02
## 33 1.857319e-06 3.200886e-05 1.602808e-07 4.191971e-08 1.326234e-02
## 34 4.014750e-07 8.903109e-16 4.380655e-06 2.532115e-06 1.251489e-13
## 35 1.377420e-06 4.900418e-06 1.556204e-07 3.593236e-08 2.469947e-03
## 36 1.118321e-09 1.200194e-18 1.579490e-08 1.280793e-08 1.455453e-16
## 37 3.628220e-04 1.003780e-09 2.274870e-04 4.290977e-05 2.447831e-07
## 38 2.664316e-05 5.179409e-08 5.644042e-06 1.120912e-06 1.875418e-05
## 39 1.238355e-12 1.453101e-21 1.787228e-11 1.535670e-11 2.000727e-19
## 40 1.442068e-09 2.046837e-04 1.285012e-10 3.276392e-11 1.666163e-02
## 41 5.102393e-12 3.323537e-05 4.043857e-13 1.106240e-13 2.823314e-04
## 42 5.174883e-09 1.975909e-17 5.297284e-08 2.970938e-08 3.531650e-15
## 43 2.252902e-06 8.770665e-09 6.122108e-07 1.174481e-07 4.035705e-06
## 44 3.904840e-07 3.041686e-07 6.706148e-08 1.371184e-08 1.655978e-04
## 45 1.412391e-06 2.952175e-10 8.355645e-07 1.582993e-07 1.375212e-07
## 46 3.395274e-12 7.378687e-21 4.275453e-11 3.063057e-11 1.248181e-18
## 47 2.114887e-12 8.577583e-07 2.144524e-13 5.111668e-14 2.297976e-05
## 48 1.614956e-07 1.455900e-09 5.466225e-08 1.030734e-08 7.698672e-07
## 49 2.010428e-08 4.452112e-09 5.516164e-09 1.052138e-09 2.373261e-06
## 21 22 23 24 25
## 1 9.049101e-11 2.933609e-08 6.969145e-12 8.355176e-09 5.888184e-08
## 2 1.883317e-09 3.057438e-09 8.860433e-14 5.899133e-08 7.603168e-08
## 3 4.431967e-09 7.224307e-06 9.137791e-10 7.295954e-07 1.044583e-05
## 4 2.112316e-08 6.157720e-08 1.159798e-12 1.054617e-06 1.819982e-06
## 5 3.869221e-07 1.099074e-03 2.517846e-08 1.047980e-04 2.911048e-03
## 6 2.263814e-09 4.792985e-03 1.150331e-05 8.691763e-07 1.012740e-04
## 7 2.525447e-05 4.687327e-08 2.485677e-13 1.590997e-04 1.918887e-05
## 8 5.684912e-07 8.769422e-07 8.500408e-12 3.615329e-05 5.578846e-05
## 9 3.566635e-08 6.987777e-01 8.252882e-05 1.488708e-05 3.652013e-03
## 10 1.053320e-11 2.436788e-03 1.484061e-02 4.000752e-09 1.203928e-06
## 11 2.850118e-06 1.348341e-05 6.706214e-11 5.096047e-04 1.881652e-03
## 12 5.810371e-06 3.072413e-06 1.343310e-11 7.060339e-04 9.183307e-04
## 13 1.030602e-05 3.333394e-07 1.417087e-12 4.670240e-04 1.579257e-04
## 14 4.288603e-05 2.830902e-07 1.042128e-12 1.616652e-03 2.540069e-04
## 15 1.088062e-05 5.290812e-03 2.084016e-08 4.377700e-03 2.800869e-01
## 16 2.528553e-04 5.928558e-05 2.032904e-10 7.570035e-02 9.190064e-02
## 17 2.799065e-12 8.190594e-04 5.156989e-01 8.201700e-10 2.489667e-07
## 18 9.280892e-04 1.642925e-06 5.707036e-12 7.289127e-02 3.704165e-03
## 19 2.600513e-04 4.839442e-07 1.661300e-12 1.066468e-02 7.729691e-04
## 20 7.118360e-10 5.034537e-02 2.153731e-01 1.305536e-07 2.879247e-05
## 21 0.000000e+00 9.584113e-07 6.986257e-12 7.013506e-01 2.584067e-03
## 22 7.396940e-08 0.000000e+00 7.831020e-05 2.289522e-05 6.918364e-03
## 23 1.058844e-12 1.537820e-04 0.000000e+00 2.195666e-10 5.585949e-08
## 24 4.226661e-02 1.787751e-05 8.730539e-11 0.000000e+00 5.731528e-02
## 25 1.036269e-04 3.594776e-03 1.478013e-08 3.813966e-02 0.000000e+00
## 26 4.083111e-06 1.618467e-01 6.259996e-07 1.366059e-03 4.174401e-01
## 27 1.837062e-08 1.641243e-01 7.955460e-05 3.172861e-06 5.788471e-04
## 28 5.963402e-06 1.641017e-02 5.028349e-07 7.073022e-04 4.522696e-02
## 29 9.490333e-05 7.473202e-04 1.299830e-08 7.789014e-03 8.114093e-02
## 30 1.854384e-03 5.646992e-05 7.800757e-10 6.585415e-02 3.463237e-02
## 31 1.321318e-04 5.162188e-13 3.149776e-18 5.099666e-07 1.639019e-09
## 32 6.406744e-13 1.319680e-05 2.079343e-01 7.091425e-11 1.122080e-08
## 33 1.131039e-08 1.057513e-02 3.865258e-05 1.199494e-06 1.288751e-04
## 34 8.567710e-03 3.143980e-11 3.279458e-16 2.042503e-05 7.607791e-08
## 35 3.237685e-08 1.609143e-03 9.602974e-06 1.863474e-06 9.583388e-05
## 36 1.411821e-05 4.566199e-14 3.843678e-19 3.906545e-08 1.291578e-10
## 37 1.716998e-03 1.637944e-05 6.543307e-10 9.158884e-03 3.394258e-03
## 38 5.044749e-06 2.359685e-04 5.574253e-08 1.269144e-04 1.161967e-03
## 39 1.638045e-08 5.355864e-17 5.236210e-22 4.354057e-11 1.457459e-13
## 40 2.114530e-11 2.936179e-05 5.760402e-03 1.213027e-09 1.003840e-07
## 41 4.709992e-14 2.141319e-07 2.025469e-03 3.161361e-12 3.373487e-10
## 42 1.244174e-04 6.158621e-13 9.269116e-18 2.969358e-07 1.245381e-09
## 43 1.700658e-06 1.901601e-05 1.507008e-08 1.764161e-05 8.183816e-05
## 44 7.006494e-08 7.877958e-05 1.030582e-06 1.381174e-06 2.230728e-05
## 45 2.592379e-05 9.228687e-07 5.493299e-10 3.470211e-05 2.221311e-05
## 46 6.416476e-08 2.450546e-16 3.270904e-21 1.543976e-10 5.681058e-13
## 47 8.143110e-14 3.254233e-08 4.546868e-05 2.659314e-12 1.497569e-10
## 48 4.382688e-07 1.396584e-06 4.115598e-09 1.864468e-06 5.038233e-06
## 49 3.355564e-08 9.090835e-07 2.340301e-08 1.711043e-07 8.142628e-07
## 26 27 28 29 30
## 1 6.232463e-08 6.180497e-10 7.185190e-10 6.444145e-10 3.770367e-10
## 2 2.538651e-08 8.370266e-11 4.168688e-10 8.867753e-10 1.110757e-09
## 3 1.457963e-05 1.534164e-07 1.647764e-07 1.213258e-07 5.483610e-08
## 4 6.005500e-07 1.810561e-09 1.008596e-08 2.088773e-08 2.379481e-08
## 5 4.120311e-03 2.669209e-05 4.757885e-05 3.541131e-05 1.368780e-05
## 6 8.875276e-04 1.208419e-04 1.463907e-05 2.922363e-06 4.519643e-07
## 7 1.643048e-06 2.748466e-09 7.435968e-08 4.456017e-07 1.458417e-06
## 8 1.357592e-05 3.170772e-08 2.729663e-07 6.726363e-07 8.299436e-07
## 9 6.957162e-02 1.630039e-02 1.759349e-03 2.354203e-04 2.470690e-05
## 10 3.714233e-05 4.250229e-04 4.007116e-06 2.415193e-07 1.650116e-08
## 11 3.679015e-04 6.892394e-07 8.936551e-06 2.208956e-05 2.185531e-05
## 12 1.113874e-04 1.894542e-07 3.722311e-06 1.302157e-05 1.850741e-05
## 13 1.377620e-05 2.271103e-08 6.090969e-07 2.991287e-06 6.621552e-06
## 14 1.547610e-05 2.555229e-08 1.018965e-06 6.563320e-06 1.881770e-05
## 15 1.575263e-01 3.077504e-04 2.758907e-03 3.263939e-03 1.260301e-03
## 16 4.371082e-03 7.860481e-06 3.709524e-04 2.012646e-03 3.301809e-03
## 17 8.902137e-06 6.293290e-04 3.091561e-06 1.382798e-07 7.887150e-09
## 18 1.322011e-04 2.714479e-07 2.025015e-05 1.943601e-04 7.660233e-04
## 19 3.379524e-05 6.101133e-08 3.577982e-06 2.986032e-05 1.085138e-04
## 20 8.628614e-04 3.071072e-01 1.340272e-03 5.346376e-05 2.890162e-06
## 21 8.096255e-05 6.866407e-07 1.506835e-04 3.708695e-03 6.524142e-02
## 22 2.476834e-01 4.734550e-01 3.200257e-02 2.253962e-03 1.533349e-04
## 23 1.881284e-06 4.506698e-04 1.925681e-06 7.698636e-08 4.159564e-09
## 24 1.632393e-03 7.146921e-06 1.077057e-03 1.834359e-02 1.396269e-01
## 25 3.319371e-01 8.676373e-04 4.582870e-02 1.271593e-01 4.886236e-02
## 26 0.000000e+00 2.442229e-02 1.669492e-01 4.802447e-02 5.205907e-03
## 27 1.295608e-02 0.000000e+00 3.346779e-02 1.347986e-03 7.288257e-05
## 28 1.310104e-01 4.950639e-02 0.000000e+00 4.675379e-01 2.528895e-02
## 29 2.436778e-02 1.289293e-03 3.023073e-01 0.000000e+00 4.097149e-01
## 30 2.934038e-03 7.742938e-05 1.816263e-02 4.550905e-01 0.000000e+00
## 31 4.670575e-11 3.070379e-13 6.734174e-11 1.667689e-09 2.997064e-08
## 32 2.805536e-07 1.536884e-04 1.110100e-06 4.900901e-08 2.906231e-09
## 33 1.765689e-03 4.985568e-01 1.971444e-02 8.843594e-04 5.129401e-05
## 34 2.497089e-09 3.221731e-11 7.619885e-09 1.892007e-07 3.488013e-06
## 35 6.834457e-04 6.506923e-02 2.385428e-02 1.790380e-03 1.341182e-04
## 36 3.899458e-12 3.824399e-14 8.948421e-12 2.241618e-10 4.148482e-09
## 37 4.952324e-04 5.168824e-05 1.017177e-02 1.501926e-01 6.948327e-01
## 38 1.336365e-03 2.443503e-03 1.141554e-01 8.564678e-02 1.456460e-02
## 39 4.475910e-15 5.126260e-17 1.213142e-14 3.008861e-13 5.546659e-12
## 40 1.539064e-06 8.745384e-04 1.885497e-05 1.129377e-06 8.336378e-08
## 41 6.601092e-09 4.037222e-06 5.189379e-08 2.786163e-09 1.934239e-10
## 42 4.461510e-11 8.472665e-13 1.981587e-10 4.696716e-09 8.318174e-08
## 43 8.360676e-05 2.975702e-04 7.275200e-03 7.018868e-03 2.256361e-03
## 44 7.198601e-05 2.835891e-03 5.051705e-03 1.089280e-03 1.590064e-04
## 45 8.484471e-06 1.081122e-05 5.473121e-04 1.896128e-03 2.774336e-03
## 46 1.889112e-14 3.033617e-16 7.137599e-14 1.715941e-12 3.086630e-11
## 47 1.917680e-09 1.016205e-06 3.345158e-08 2.587393e-09 2.394840e-10
## 48 4.846141e-06 2.973430e-05 4.271199e-04 4.556495e-04 2.245474e-04
## 49 1.370027e-06 3.051112e-05 1.184899e-04 6.416569e-05 2.157927e-05
## 31 32 33 34 35
## 1 9.125805e-14 5.764070e-14 5.845989e-11 8.291147e-14 7.692466e-12
## 2 9.636231e-12 1.393687e-15 1.000563e-11 4.456765e-12 2.023817e-12
## 3 9.551140e-13 9.633605e-12 1.461859e-08 2.041622e-12 1.927214e-09
## 4 2.911724e-11 2.272849e-14 2.253989e-10 2.633276e-11 4.768465e-11
## 5 1.980939e-11 5.091999e-10 2.821187e-06 1.031009e-10 4.390660e-07
## 6 5.714092e-14 8.130886e-08 1.065369e-05 4.538976e-13 9.412448e-07
## 7 3.717573e-07 1.136356e-14 5.408662e-10 1.470183e-07 2.145669e-10
## 8 3.025355e-10 2.423586e-13 4.464293e-09 4.674932e-10 1.105176e-09
## 9 3.336234e-13 1.525884e-06 1.422895e-03 6.108780e-12 1.328280e-04
## 10 7.333302e-17 6.624612e-05 6.691062e-05 1.870178e-15 5.385655e-06
## 11 1.784210e-10 3.260190e-12 1.138486e-07 8.990478e-10 3.293762e-08
## 12 5.202315e-10 7.607085e-13 3.529008e-08 2.219395e-09 1.198473e-08
## 13 2.937037e-09 8.428725e-14 4.621955e-09 7.221163e-09 1.799613e-09
## 14 8.887552e-09 8.148440e-14 6.101846e-09 2.734011e-08 2.855657e-09
## 15 1.498298e-10 1.325612e-09 4.967869e-05 1.996971e-09 1.285343e-05
## 16 4.506267e-09 2.289471e-11 2.116860e-06 5.222977e-08 1.040966e-06
## 17 1.418606e-17 2.247693e-03 1.861178e-04 5.908991e-16 1.889362e-05
## 18 3.469464e-08 7.650779e-13 8.864847e-08 2.765559e-07 5.707170e-08
## 19 2.523637e-08 1.766374e-13 1.746218e-08 1.203976e-07 9.924995e-09
## 20 3.143476e-15 3.196659e-02 1.913608e-01 2.061170e-13 2.363114e-02
## 21 1.305326e-03 3.477461e-12 5.868299e-07 5.074035e-02 1.113868e-06
## 22 3.935913e-13 5.528328e-06 4.234679e-02 1.437039e-11 4.272616e-03
## 23 4.716052e-18 1.710561e-01 3.039489e-04 2.943596e-16 5.007169e-05
## 24 3.036096e-07 2.319640e-11 3.750551e-06 7.289768e-06 3.863534e-06
## 25 6.493282e-10 2.442402e-09 2.681466e-04 1.806824e-08 1.322168e-04
## 26 2.326960e-11 7.679762e-08 4.620149e-03 7.458128e-10 1.185796e-03
## 27 8.115173e-14 2.231825e-05 6.920598e-01 5.104723e-12 5.989202e-02
## 28 2.632839e-11 2.384600e-07 4.048066e-02 1.785935e-09 3.247834e-02
## 29 4.215867e-10 6.807084e-09 1.174151e-03 2.867291e-08 1.576175e-03
## 30 8.415579e-09 4.483647e-10 7.564456e-05 5.871421e-07 1.311484e-04
## 31 0.000000e+00 1.661071e-18 2.707408e-13 9.793865e-02 5.966907e-13
## 32 3.023255e-18 0.000000e+00 3.159504e-04 3.077117e-16 1.359935e-04
## 33 5.155022e-14 3.305287e-05 0.000000e+00 4.406832e-12 4.380987e-01
## 34 1.633717e-01 2.820201e-16 3.860757e-11 0.000000e+00 1.122373e-10
## 35 1.713412e-13 2.145577e-05 6.607050e-01 1.932087e-11 0.000000e+00
## 36 6.757312e-01 2.986082e-19 4.236015e-14 5.235988e-02 1.182454e-13
## 37 1.290760e-08 7.704430e-10 9.256357e-05 1.604581e-06 3.073387e-04
## 38 3.143184e-11 9.435621e-08 8.061301e-03 3.904409e-09 3.778806e-02
## 39 1.339152e-03 5.145213e-22 6.405104e-17 7.956731e-05 2.063662e-16
## 40 1.259719e-16 5.425304e-01 4.434506e-03 1.749165e-14 5.326065e-03
## 41 2.719129e-19 4.696091e-01 1.414556e-05 3.762347e-17 1.238183e-05
## 42 4.178877e-02 1.166177e-17 1.259535e-12 5.849999e-01 4.601687e-12
## 43 1.680592e-11 5.225690e-08 1.560981e-03 2.826898e-09 1.346243e-02
## 44 5.982630e-13 7.099744e-06 2.827008e-02 9.904142e-11 3.671473e-01
## 45 9.451879e-10 2.749847e-09 5.257780e-05 2.291770e-07 4.718566e-04
## 46 1.394351e-03 4.178104e-21 4.415740e-16 3.734751e-04 1.633145e-15
## 47 7.194079e-19 1.193376e-02 6.050645e-06 1.351671e-16 1.169297e-05
## 48 8.879608e-12 3.129588e-08 2.133384e-04 1.976343e-09 2.497192e-03
## 49 7.685931e-13 4.024346e-07 3.026592e-04 1.833076e-10 3.922430e-03
## 36 37 38 39 40
## 1 2.952641e-15 1.774652e-11 1.199962e-11 6.513999e-18 1.487413e-14
## 2 2.904878e-13 5.492029e-11 1.078120e-11 8.292841e-16 9.338762e-16
## 3 3.657342e-14 2.659283e-09 2.610884e-09 5.820720e-17 3.231120e-12
## 4 9.587475e-13 1.151080e-09 2.586171e-10 1.988377e-15 1.881765e-14
## 5 9.799713e-13 6.906835e-07 7.739472e-07 1.252502e-15 3.466838e-10
## 6 3.181259e-15 3.234886e-08 2.125741e-07 3.851221e-18 1.201080e-08
## 7 1.126878e-08 9.829958e-08 4.695631e-09 2.998955e-11 2.628520e-14
## 8 1.108808e-11 4.032499e-08 7.935603e-09 1.867758e-14 2.960641e-13
## 9 2.642357e-14 2.391711e-06 2.749339e-05 3.061282e-17 6.238006e-07
## 10 6.703087e-18 2.837279e-09 1.336633e-07 7.912449e-21 2.943163e-06
## 11 8.982651e-12 1.027814e-06 2.670496e-07 1.141384e-14 6.270020e-12
## 12 2.510690e-11 9.015439e-07 1.431483e-07 3.266196e-14 1.780081e-12
## 13 1.229184e-10 3.606372e-07 3.132173e-08 1.773814e-13 2.225045e-13
## 14 3.992698e-10 1.107950e-06 6.899418e-08 5.448296e-13 2.819097e-13
## 15 1.065362e-11 6.558008e-05 5.707450e-05 1.235922e-14 2.801109e-09
## 16 3.115831e-10 1.592851e-04 2.115271e-05 3.629369e-13 9.733571e-11
## 17 1.705968e-18 2.248185e-09 2.097841e-07 2.172669e-21 7.048260e-05
## 18 2.135544e-09 4.846436e-05 2.174477e-06 2.541850e-12 4.208990e-12
## 19 1.304250e-09 6.885133e-06 3.252569e-07 1.644971e-12 8.082710e-13
## 20 5.133728e-16 1.360474e-06 1.884975e-04 7.423367e-19 1.423742e-02
## 21 1.790672e-04 3.431471e-02 1.823262e-04 2.185450e-07 6.497248e-11
## 22 4.469836e-14 2.526445e-05 6.582089e-04 5.514990e-17 6.963032e-06
## 23 7.388750e-19 1.981962e-09 3.053399e-07 1.058814e-21 2.682600e-03
## 24 2.986012e-08 1.103102e-02 2.764283e-04 3.500833e-11 2.246200e-10
## 25 6.569399e-11 2.720346e-03 1.684115e-03 7.797942e-14 1.236940e-08
## 26 2.494295e-12 4.991451e-04 2.435800e-03 3.011642e-15 2.384951e-07
## 27 1.297759e-14 2.763737e-05 2.362744e-03 1.829826e-17 7.189337e-05
## 28 4.491707e-12 8.045178e-03 1.632805e-01 6.405529e-15 2.292820e-06
## 29 7.275426e-11 7.681029e-02 7.921014e-02 1.027253e-13 8.880039e-08
## 30 1.495554e-09 3.947002e-01 1.496182e-02 2.103404e-12 7.280638e-09
## 31 8.675588e-01 2.611226e-08 1.149920e-10 1.808562e-03 3.918120e-17
## 32 6.977716e-19 2.836784e-09 6.282826e-07 1.264717e-21 3.071248e-01
## 33 1.035522e-14 3.565464e-05 5.615390e-03 1.647049e-17 2.626191e-04
## 34 1.121364e-01 5.414814e-06 2.382736e-08 1.792507e-04 9.075232e-15
## 35 4.359349e-14 1.785372e-04 3.969767e-02 8.003029e-17 4.756889e-04
## 36 0.000000e+00 5.702030e-09 2.499409e-11 2.703329e-02 9.526102e-18
## 37 3.618723e-09 0.000000e+00 6.430704e-02 6.468214e-12 2.223465e-08
## 38 8.771309e-12 3.555984e-02 0.000000e+00 1.650503e-14 2.779444e-06
## 39 2.569925e-02 9.689025e-12 4.471064e-14 0.000000e+00 2.031511e-20
## 40 3.932204e-17 1.446190e-07 3.269277e-05 8.821015e-20 0.000000e+00
## 41 8.464656e-20 3.116341e-10 7.084801e-08 1.939541e-22 2.070439e-01
## 42 3.427639e-01 2.010761e-07 9.962637e-10 2.937496e-03 5.052931e-16
## 43 6.452262e-12 1.448550e-02 5.111745e-01 1.535879e-14 2.718529e-06
## 44 2.264207e-13 5.994128e-04 8.884199e-02 5.559143e-16 4.812180e-04
## 45 6.131227e-10 5.381186e-02 3.175556e-02 2.202598e-12 2.127881e-07
## 46 4.528353e-02 7.048465e-11 3.526856e-13 9.385949e-01 1.937532e-19
## 47 3.248298e-19 6.869740e-10 1.252921e-07 1.037094e-21 1.068874e-01
## 48 5.024768e-12 2.546247e-03 2.940844e-02 1.687029e-14 2.828944e-06
## 49 4.962580e-13 2.066195e-04 5.743217e-03 1.980554e-15 5.368854e-05
## 41 42 43 44 45
## 1 5.154480e-16 1.248197e-15 9.198882e-13 2.780711e-13 4.672181e-14
## 2 1.547471e-17 7.517786e-14 9.346148e-13 1.216476e-13 1.086306e-13
## 3 9.120207e-14 2.919939e-14 1.982684e-10 6.810970e-11 8.255518e-12
## 4 2.720935e-16 4.024330e-13 2.220356e-11 2.941054e-12 2.345986e-12
## 5 5.884823e-12 1.515293e-12 5.888119e-08 1.776708e-08 2.379897e-09
## 6 6.926818e-10 7.134889e-15 1.772123e-08 2.186462e-08 2.905152e-10
## 7 2.049431e-16 2.663294e-09 5.617972e-10 2.943647e-11 1.972702e-10
## 8 3.363053e-15 6.825683e-12 7.033618e-10 7.979861e-11 8.151107e-11
## 9 1.592678e-08 1.145723e-13 2.401843e-06 3.170719e-06 3.400089e-08
## 10 5.678735e-07 4.061698e-17 1.880953e-08 1.254250e-07 1.623381e-10
## 11 5.570318e-14 1.304771e-11 2.335736e-08 2.640526e-09 2.268051e-09
## 12 1.417831e-14 3.187802e-11 1.355096e-08 1.179905e-09 1.813056e-09
## 13 1.651771e-15 1.036778e-10 3.316929e-09 2.186800e-10 6.847316e-10
## 14 1.838612e-15 3.909751e-10 8.002069e-09 4.322913e-10 2.109302e-09
## 15 2.420788e-11 3.317846e-11 4.487083e-06 8.165439e-07 2.240794e-07
## 16 5.896554e-13 8.226419e-10 2.169749e-06 1.464003e-07 3.240588e-07
## 17 1.959463e-05 1.602468e-17 4.309350e-08 5.817884e-07 3.455596e-10
## 18 2.267799e-14 4.086465e-09 2.861228e-07 1.220102e-08 9.303218e-08
## 19 4.672490e-15 1.726149e-09 4.134166e-08 1.878922e-09 1.327463e-08
## 20 4.130577e-04 7.107471e-15 4.920552e-05 7.859962e-04 3.994530e-07
## 21 2.477842e-13 9.003687e-04 7.456127e-05 1.195827e-06 2.707675e-04
## 22 8.694308e-08 3.439719e-13 6.434518e-05 1.037724e-04 7.439403e-07
## 23 1.614978e-03 1.016635e-17 1.001381e-07 2.665862e-06 8.695982e-10
## 24 1.002282e-12 1.294983e-07 4.661190e-05 1.420622e-06 2.184319e-05
## 25 7.117069e-11 3.614184e-10 1.438867e-04 1.526803e-05 9.304134e-06
## 26 1.751364e-09 1.628277e-11 1.848608e-04 6.196165e-05 4.469200e-06
## 27 5.682381e-07 1.640415e-13 3.490438e-04 1.294946e-03 3.021113e-06
## 28 1.080432e-08 5.675198e-11 1.262321e-02 3.412198e-03 2.262359e-04
## 29 3.750769e-10 8.697492e-10 7.874514e-03 4.757378e-04 5.067876e-04
## 30 2.892278e-11 1.710975e-08 2.811779e-03 7.713630e-05 8.236323e-04
## 31 1.448011e-19 3.061169e-02 7.458422e-11 1.033590e-12 9.993201e-10
## 32 4.551611e-01 1.554815e-17 4.220997e-07 2.232470e-05 5.291535e-09
## 33 1.434297e-06 1.756769e-13 1.319044e-03 9.299511e-03 1.058439e-05
## 34 3.342132e-17 7.148360e-01 2.092752e-08 2.854276e-10 4.041851e-07
## 35 1.893386e-06 9.679598e-13 1.715618e-02 1.821416e-01 1.432547e-04
## 36 3.510964e-20 1.955680e-01 2.230344e-11 3.046824e-13 5.049040e-10
## 37 8.203285e-11 7.280968e-08 3.177745e-02 5.118977e-04 2.812324e-02
## 38 1.031268e-08 1.994822e-10 6.200920e-01 4.195432e-02 9.177160e-03
## 39 7.647817e-23 1.593315e-03 5.047060e-14 7.111491e-16 1.724322e-12
## 40 3.544869e-01 1.190055e-15 3.878954e-05 2.672969e-03 7.233188e-07
## 41 0.000000e+00 2.640075e-18 8.520074e-08 6.030160e-06 1.817716e-09
## 42 1.919245e-18 0.000000e+00 1.254947e-09 1.791938e-11 4.398863e-08
## 43 1.022352e-08 2.071421e-10 0.000000e+00 9.216096e-02 5.419989e-02
## 44 1.858722e-06 7.597916e-12 2.367421e-01 0.000000e+00 4.115264e-03
## 45 9.155488e-10 3.047768e-08 2.275080e-01 6.724616e-03 0.000000e+00
## 46 7.493220e-22 1.435371e-02 4.748239e-13 6.967661e-15 2.000459e-11
## 47 8.810588e-01 1.437521e-17 2.855217e-07 1.982573e-05 1.335816e-08
## 48 1.280795e-08 2.343168e-10 3.884893e-01 6.833648e-02 2.148605e-01
## 49 3.347373e-07 2.673983e-11 5.966873e-02 1.854659e-01 2.269315e-02
## 46 47 48 49
## 1 6.229781e-18 1.161732e-17 3.812191e-14 3.681063e-15
## 2 6.263280e-16 6.458987e-19 4.301343e-14 2.667062e-15
## 3 8.154134e-17 2.413563e-15 8.174338e-12 8.608974e-13
## 4 2.024694e-15 1.302404e-17 1.009996e-12 6.409225e-14
## 5 2.573283e-15 2.408463e-13 2.429033e-09 2.439313e-10
## 6 9.376082e-18 1.162025e-11 8.265752e-10 1.875165e-10
## 7 2.420626e-11 2.117376e-17 3.485921e-11 1.452964e-12
## 8 2.409685e-14 2.094996e-16 3.279524e-11 1.959804e-12
## 9 1.071424e-16 4.692678e-10 1.163423e-07 2.746203e-08
## 10 3.280027e-20 5.730262e-09 1.522765e-09 1.093521e-09
## 11 2.350545e-14 4.677769e-15 1.068325e-09 6.594001e-11
## 12 6.347295e-14 1.389428e-15 6.624618e-10 3.607985e-11
## 13 2.833676e-13 1.810572e-16 1.797747e-10 8.533150e-12
## 14 9.551089e-13 2.487160e-16 4.683316e-10 2.056119e-11
## 15 3.725597e-14 2.083199e-12 1.885490e-07 1.520269e-08
## 16 1.029748e-12 8.903649e-14 1.115208e-07 5.626565e-09
## 17 1.141687e-20 1.842289e-07 5.129074e-09 6.356719e-09
## 18 6.292489e-12 4.381229e-15 1.831753e-08 7.491630e-10
## 19 3.395358e-12 7.865349e-16 2.601456e-09 1.076223e-10
## 20 4.792485e-18 1.224767e-05 6.730360e-06 8.408676e-06
## 21 8.858939e-07 1.560631e-13 1.377733e-05 4.275131e-07
## 22 2.611248e-16 4.813476e-09 3.388376e-06 8.938979e-07
## 23 6.844478e-21 1.320717e-05 1.960852e-08 4.519008e-08
## 24 1.284659e-10 3.071439e-13 3.532170e-06 1.313733e-07
## 25 3.145451e-13 1.150974e-11 6.351426e-06 4.160228e-07
## 26 1.315375e-14 1.853502e-10 7.682939e-06 8.802785e-07
## 27 1.120573e-16 5.210572e-08 2.500785e-05 1.040007e-05
## 28 3.900010e-14 2.537202e-09 5.313768e-04 5.974391e-05
## 29 6.062445e-13 1.268916e-10 3.665352e-04 2.091930e-05
## 30 1.211284e-11 1.304556e-11 2.006359e-04 7.814428e-06
## 31 1.948703e-03 1.395641e-19 2.825573e-11 9.912173e-13
## 32 1.062770e-20 4.213689e-03 1.812536e-07 9.446140e-07
## 33 1.175044e-16 2.234997e-07 1.292583e-04 7.431955e-05
## 34 8.706790e-04 4.374135e-17 1.049054e-08 3.943444e-10
## 35 6.554066e-16 6.513819e-07 2.281798e-03 1.452580e-03
## 36 4.929341e-02 4.908279e-20 1.245386e-11 4.984883e-13
## 37 4.869328e-11 6.587787e-11 4.005107e-03 1.317178e-04
## 38 1.347297e-13 6.643918e-09 2.557919e-02 2.024556e-03
## 39 9.712887e-01 1.489749e-22 3.974952e-14 1.891279e-15
## 40 8.705995e-19 6.666850e-02 2.894230e-05 2.226127e-04
## 41 1.966528e-21 3.209677e-01 7.653332e-08 8.106522e-07
## 42 2.738482e-02 3.807016e-18 1.017861e-09 4.707641e-11
## 43 1.495276e-13 1.248109e-08 2.785525e-01 1.733942e-02
## 44 5.636433e-15 2.226236e-06 1.258662e-01 1.384459e-01
## 45 2.644338e-11 2.451086e-09 6.466702e-01 2.768093e-02
## 46 0.000000e+00 1.771729e-21 4.561588e-13 2.386585e-14
## 47 1.276358e-20 0.000000e+00 5.778509e-07 1.110029e-05
## 48 2.003442e-13 3.522909e-08 0.000000e+00 2.924939e-01
## 49 2.586295e-14 1.669784e-06 7.217007e-01 0.000000e+00W4 = mat2listw(W4,style='W')
summary(W4)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 49
## Number of nonzero links: 2352
## Percentage nonzero weights: 97.95918
## Average number of links: 48
## Link number distribution:
##
## 48
## 49
## 49 least connected regions:
## 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 49 with 48 links
## 49 most connected regions:
## 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 49 with 48 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 49 2401 49 38.80069 206.8219Spatial contiguity weights (Bobot Kedekatan Spasial)
shpcolumbus<-st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
class(shpcolumbus) #bentuknya sf; data.frame## [1] "sf" "data.frame"columbus.map<- readOGR(system.file("shapes/columbus.shp", package="spData"))## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\User\Documents\R\win-library\4.1\spData\shapes\columbus.shp", layer: "columbus"
## with 49 features
## It has 20 fields
## Integer64 fields read as strings: COLUMBUS_ COLUMBUS_I POLYIDclass(columbus.map) #bentuknya spatialPolygonDataFrame## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"spplot(columbus.map,"CRIME",sub="Map of CRIME")
Spatial Autocorrelation (Responsi Pertemuan 9)
Joint Count Statistics
Joint count merupakan metode paling dasar dalam menentukan autokorelasi spasial antar area. Penjelasan lebih lengkap mengenai metode ini dapat dilihat pada Lizazaro (2016). Ilustrasi berikut ini diadaptasi dari Lizazaro (2016). Untuk melakukan analisis joint count, terlebih dulu kita load beberapa package pada R.
library(raster)
library(sp)
library(spdep)Sebagai ilustrasi, kita akan membuat data contoh dengan menggunakan syntax berikut.
pri <- rep(1,12)
seg <- rep(0,4)
ter <- rep(1,2)
cua <- rep(0,4)
qui <- rep(1,2)
sex <- rep(0,12)
A <- matrix(c(pri, seg, ter, cua ,qui, sex), nrow=6, byrow=FALSE)
A## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 1 0 0 0 0
## [2,] 1 1 0 0 0 0
## [3,] 1 1 0 0 0 0
## [4,] 1 1 0 0 0 0
## [5,] 1 1 1 1 0 0
## [6,] 1 1 1 1 0 0Selanjutnya matriks A dikonversi menjadi raster menggunakan fungsi raster().
rA <- raster(A)
rA## class : RasterLayer
## dimensions : 6, 6, 36 (nrow, ncol, ncell)
## resolution : 0.1666667, 0.1666667 (x, y)
## extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax)
## crs : NA
## source : memory
## names : layer
## values : 0, 1 (min, max)Berikut ini adalah plot dari area A.
plot(rA)
text(coordinates(rA), labels=rA[], cex=1.5)
Kriteria ketetanggaan selanjutnya digunakan untuk mengkuantifikasi pola kedekatan data tersebut.
pA <- rasterToPolygons(rA, dissolve=FALSE)
pA## class : SpatialPolygonsDataFrame
## features : 36
## extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax)
## crs : NA
## variables : 1
## names : layer
## min values : 0
## max values : 1Seandainya kita akan menggunakan kriteria Queen Contiguity, maka dapat dilakukan dengan syntax berikut.
#Queen Contiguity
spA <- SpatialPolygons(pA@polygons)
nb1 <- poly2nb(spA, queen = T)
nb1## Neighbour list object:
## Number of regions: 36
## Number of nonzero links: 220
## Percentage nonzero weights: 16.97531
## Average number of links: 6.111111Selanjutnya kita dapat memvisualisasikan link dari ketetanggannya.
par(mai=c(0,0,0,0))
plot(spA, col='gray', border='blue')
xy <- coordinates(spA)
plot(nb1, xy, col='red', lwd=2, add=TRUE)
Seandainya kriteria yang digunakan adalah Rook Contiguity.
nb2 <- poly2nb(spA, queen = F)
nb2## Neighbour list object:
## Number of regions: 36
## Number of nonzero links: 120
## Percentage nonzero weights: 9.259259
## Average number of links: 3.333333Selanjutnya kita dapat memvisualisasikan link dari ketetanggannya
par(mai=c(0,0,0,0))
plot(spA, col='gray', border='blue')
xy <- coordinates(spA)
plot(nb2, xy, col='green', lwd=2, add=TRUE)
Agar dapat mengidentifikasi pola autokorelasi dengan Joint Count Statistics pada data ini, maka matriks bobot perlu disusun dengan tipe biner.
wl1 <- nb2listw(nb1, style='B')
wl2 <- nb2listw(nb2, style='B')
jc_test1 <- joincount.test(as.factor(pA$layer), wl1)
jc_test1##
## Join count test under nonfree sampling
##
## data: as.factor(pA$layer)
## weights: wl1
##
## Std. deviate for 0 = 5.1529, p-value = 1.282e-07
## alternative hypothesis: greater
## sample estimates:
## Same colour statistic Expectation Variance
## 53.00000 33.17460 14.80263
##
##
## Join count test under nonfree sampling
##
## data: as.factor(pA$layer)
## weights: wl1
##
## Std. deviate for 1 = 4.7634, p-value = 9.52e-07
## alternative hypothesis: greater
## sample estimates:
## Same colour statistic Expectation Variance
## 37.00000 20.95238 11.34999jc_test2 <- joincount.test(as.factor(pA$layer), wl2)
jc_test2##
## Join count test under nonfree sampling
##
## data: as.factor(pA$layer)
## weights: wl2
##
## Std. deviate for 0 = 5.4677, p-value = 2.28e-08
## alternative hypothesis: greater
## sample estimates:
## Same colour statistic Expectation Variance
## 30.000000 18.095238 4.740611
##
##
## Join count test under nonfree sampling
##
## data: as.factor(pA$layer)
## weights: wl2
##
## Std. deviate for 1 = 5.1203, p-value = 1.525e-07
## alternative hypothesis: greater
## sample estimates:
## Same colour statistic Expectation Variance
## 22.000000 11.428571 4.262554Berdasarkan kedua output di atas, dengan p-value yang sangat kecil, artinya kita dapat menolak hipotesis nol yang menyatakan bahwa tidak terdapat autokorelasi. Sesuai dengan output di atas, alternative hypothesis: greater, artinya kita dapat menyimpulkan bahwa terdapat cukup bukti untuk menyatakan bahwa terdapat autokorelasi positif pada taraf nyata 5%.
Keterangan: grater : mengecek apakah ada asosiasi spasial positif (+)
less : mengecek apakah ada asosiasi spasial negatif (-)
two.sided : mengecek apakah ada asosiasi spasial atau tidak.
Pengujian hipotesis dapat pula dilakukan dengan melibatkan algoritma monte carlo seperti di bawah ini.
set.seed(123)
jc_test3 <- joincount.mc(as.factor(pA$layer), wl1, nsim=99)
jc_test3##
## Monte-Carlo simulation of join-count statistic
##
## data: as.factor(pA$layer)
## weights: wl1
## number of simulations + 1: 100
##
## Join-count statistic for 0 = 53, rank of observed statistic = 100,
## p-value = 0.01
## alternative hypothesis: greater
## sample estimates:
## mean of simulation variance of simulation
## 33.01010 15.15296
##
##
## Monte-Carlo simulation of join-count statistic
##
## data: as.factor(pA$layer)
## weights: wl1
## number of simulations + 1: 100
##
## Join-count statistic for 1 = 37, rank of observed statistic = 100,
## p-value = 0.01
## alternative hypothesis: greater
## sample estimates:
## mean of simulation variance of simulation
## 20.37374 12.37930Global Autocorrelation
Global Autocorrelation berarti autokorelasi spasial secara umum (global), bukan untuk masing-masing area (lokal)
Moran’s I (Indeks Moran)
Sebagai ilustrasi, kita masih akan menggunakan data yang sama. Seandainya kita ingin menguji autokorelasi menggunakan pendekatan indeks moran, maka kita dapat menggunakan fungsi moran.test().
#cek normalitas data
moran(pA$layer,wl1,n=length(wl1$neighbours), S0=Szero(wl1))#s0 Grand total matrix bobot## $I
## [1] 0.6240909
##
## $K
## [1] 1.05Diperoleh nilai kurtosisnya 1.05 berarti data tidak normal karena untuk data yang normal biasanya sekitar 3 sehingga kurang tepat jika uji Mohran dengan asumsi kenormalan. Secara umum, data count jarang terpenuhi asumsinya normalnya. Jadi, selanjutnya digunakan uji Mohran dengan asumsi data acak (random).
#Asumsi Data Acak (Random)
I1 <- moran.test(pA$layer,wl1)#jika datanya asumsi normal kita tambahkan randomisation=F, untuk pA$layer adalah peubah yang akan dilihat asosiasi spasialnya, wl1 adalah matriks bobot yang digunakan
I1##
## Moran I test under randomisation
##
## data: pA$layer
## weights: wl1
##
## Moran I statistic standard deviate = 7.4654, p-value = 4.151e-14
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.624090909 -0.028571429 0.007643049Sama halnya seperti yang telah diperlihatkan pada uji dengan metode joint count, uji moran juga dapat dilakukan dengan melibatkan simulasi monte carlo.
set.seed(123)
MC<- moran.mc(pA$layer, wl1, nsim=599)
# View results (including p-value)
MC##
## Monte-Carlo simulation of Moran I
##
## data: pA$layer
## weights: wl1
## number of simulations + 1: 600
##
## statistic = 0.62409, observed rank = 600, p-value = 0.001667
## alternative hypothesis: greaterGlobal Geary’s C
Geary’s C merupakan alternatif dari indeks Moran, yang memiliki nilai antara 0 s.d 2. Nilai 0 menunjukkan autokorelasi positif, 1 menunjukkan tidak ada autokorelasi, dan 2 menunjukkan autokorelasi negatif.
C1 <- geary.test(pA$layer,wl1)
C1##
## Geary C test under randomisation
##
## data: pA$layer
## weights: wl1
##
## Geary C statistic standard deviate = 7.4869, p-value = 3.526e-14
## alternative hypothesis: Expectation greater than statistic
## sample estimates:
## Geary C statistic Expectation Variance
## 0.357954545 1.000000000 0.007354046Dengan monte carlo:
GS1 <- geary.mc(pA$layer, wl1, nsim=599)
GS1##
## Monte-Carlo simulation of Geary C
##
## data: pA$layer
## weights: wl1
## number of simulations + 1: 600
##
## statistic = 0.35795, observed rank = 1, p-value = 0.001667
## alternative hypothesis: greaterGlobal G
CG <- globalG.test(pA$layer,wl1)
CG##
## Getis-Ord global G statistic
##
## data: pA$layer
## weights: wl1
##
## standard deviate = 4.7634, p-value = 9.52e-07
## alternative hypothesis: greater
## sample estimates:
## Global G statistic Expectation Variance
## 0.3083333333 0.1746031746 0.0007881941Local Autocorrelation
Local Moran’s I
Pendekatan ini termasuk ke dalam Local Indicators for Spatial Association (LISA), yang mengindentifikasi autokorelasi pada tingkat lokal.
oid <- order(pA$layer)
resI <- localmoran(pA$layer, wl1)
head(resI)## Ii E.Ii Var.Ii Z.Ii Pr(z > 0)
## 1 3.75 -0.08571429 2.817443 2.2851709 0.01115140
## 2 1.75 -0.14285714 4.402701 0.9021074 0.18349991
## 3 0.40 -0.14285714 4.402701 0.2587176 0.39792657
## 4 4.00 -0.14285714 4.402701 1.9744237 0.02416679
## 5 4.00 -0.14285714 4.402701 1.9744237 0.02416679
## 6 2.40 -0.08571429 2.817443 1.4808929 0.06931756pA$z.li <- resI[,4]
pA$pvalue <- resI[,5]
lm.palette <- colorRampPalette(c("white","orange", "red"), space = "rgb")
spplot(pA, zcol="z.li", col.regions=lm.palette(20), main="Local Moran")
Warna yang lebih pekat berarti memiliki nilai Z_Score yang besar berarti cenderung memiliki korelasi positif dengan tetangganya.
moran.plot(pA$layer,wl1)
Terdapat 4 kuadran dalam Indeks Moran Lokal. Tanda seperti diamond pada moral plot di atas berarti pengamatan tersebut memiliki pengaruh yang besar terhadap autokorelasi spasial pada data tesebut. Tanda seperti diamond pada gambar di atas berada pada Kuadran 4, yaitu memiliki nilai yang kecil sedangkan tetangganya memiliki nilai yang besar (biasa disebut Coldspot).
Getis-Ord Gi
Menurut Mendez (2020), pendekatan Getis-ord Gi dapat membantu mengidentifikasi pola penggerombolan berdasarkan ukuran autokorelasi pada level lokal.
local_g <- localG(pA$layer, wl1)
local_g## [1] 2.0615528 0.8246211 -0.2730593 -2.1844747 -2.1844747 -1.6383560
## [7] 2.7487371 1.2598378 -0.5233637 -2.9126330 -2.9126330 -2.1844747
## [13] 2.7487371 1.2598378 -0.5233637 -2.9126330 -2.9126330 -2.1844747
## [19] 2.7487371 2.0615528 1.0694824 -1.3197868 -2.1162099 -2.1844747
## [25] 2.7487371 2.8632678 2.0615528 -0.3435921 -1.3197868 -2.1844747
## [31] 2.0615528 2.7487371 2.7487371 0.8246211 -0.2730593 -1.6383560
## attr(,"gstari")
## [1] FALSE
## attr(,"call")
## localG(x = pA$layer, listw = wl1)
## attr(,"class")
## [1] "localG"Output di atas menghasilkan z-score, yang biasanya disajikan secara visual untuk mengidentifikasi cluster maupun hotspot.
pA$localg <- as.numeric(local_g)
lm.palette <- colorRampPalette(c("white","orange", "red"), space = "rgb")
spplot(pA, zcol="localg", col.regions=lm.palette(20), main="Local Gi")
Exercise (Latihan)
Sebagai latihan, Anda dipersilahkan menggunakan data yang tersedia pada: https://github.com/raoy/SpatialReg . Terdapat dua data yang harus Anda download, yaitu:
Jabar Data (gabung).xlsx
petaJabar2.zip
Data pertama (dengan format Excel) menyimpan data kependudukan yang diperoleh dari BPS. Sedangkan data kedua merupakan data shapefile berisi peta Provinsi Jawa Barat. Silahkan manfaatkan kedua data tersebut untuk mengeksplorasi pola depedensi spasial untuk peubah kemiskinan antar kota/kabupaten di Jawa Barat pada tahun 2015. Data tersebut terdapat pada kolom I dengan nama kolom p.miskin15 pada file Excel.
Input Data
library(readxl)
library(rgdal)datajabar<-read_excel("Jabar Data (gabung).xlsx", sheet = "data")
View(datajabar)petajabar<-readOGR(dsn="petaJabar2", layer="Jabar2") #dsn diisi nama folder #layer diisi nama file dalam folder## OGR data source with driver: ESRI Shapefile
## Source: "D:\Kuliah S2 IPB\Bahan Kuliah\Semester 2 SSD 2020\STA533 Spasial\PRAKTIKUM\R\petaJabar2", layer: "Jabar2"
## with 26 features
## It has 7 fieldsplot(petajabar) #peta kosongan tanpa data
text(petajabar,'KABKOT',cex=0.5) #menambahkan nama wilayah pada peta
Eksplorasi Data
Peta sebaran persentase penduduk miskin di Jabar tahun 2015
library(raster)colfunc<-colorRampPalette(c("green", "yellow","red")) #menentukan warna peta
petajabar$miskin<-datajabar$p.miskin15
spplot(petajabar, "miskin", col.regions=colfunc(16),
main="Peta Persentase Penduduk Miskin di Jawa Barat Tahun 2015")
Membuat Matriks Bobot
Distance Matrix (dengan Matriks Jarak)
# Matriks dengan Distance
longlat<-cbind(datajabar$Long ,datajabar$Lat)
plot(longlat)
gjarak<-pointDistance(longlat,lonlat=TRUE) #hitung jarak dengan memperhitungkan bahwa bumi itu bulat
gjarak## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.000 NA NA NA NA NA NA
## [2,] 57149.459 0.00 NA NA NA NA NA
## [3,] 76541.152 49887.51 0.00 NA NA NA NA
## [4,] 110518.410 99543.90 50180.73 0.00 NA NA NA
## [5,] 143197.373 123243.55 74037.50 34802.83 0.00 NA NA
## [6,] 183595.699 164797.40 115786.77 73201.61 41760.58 0.00 NA
## [7,] 210952.313 197960.73 148247.86 101432.73 75218.13 36513.26 0.00
## [8,] 204023.478 204569.08 155606.85 105436.43 93828.94 71481.73 48691.53
## [9,] 198161.633 206711.56 159866.78 111083.85 108198.38 94637.38 76758.36
## [10,] 167036.578 173410.51 126531.64 78095.76 79366.74 76407.23 72165.40
## [11,] 137167.994 143118.36 97146.53 50959.47 62803.43 76381.49 86098.07
## [12,] 155349.912 175480.14 135009.16 94826.46 109136.36 115966.42 113856.28
## [13,] 106906.216 130498.91 95869.79 69408.16 97012.55 120773.67 132734.13
## [14,] 73472.772 95832.25 66850.17 59229.32 93289.84 126829.28 147193.51
## [15,] 73214.326 115514.13 99889.25 97983.58 131587.54 162871.48 179468.81
## [16,] 54597.832 105335.61 101666.14 111855.55 146540.73 181154.89 200735.43
## [17,] 80531.057 80327.67 38633.99 31171.16 65745.11 104093.89 130421.77
## [18,] 4940.917 54124.36 71674.07 105721.75 138307.67 178763.01 206283.97
## [19,] 45274.233 27968.49 33542.73 77890.69 106185.56 147852.84 178857.05
## [20,] 103795.763 103808.55 57980.71 20200.74 51533.48 84792.23 107892.68
## [21,] 198363.532 207022.55 160209.54 111453.59 108639.65 95105.87 77186.38
## [22,] 38597.128 92629.71 96509.50 114669.42 149425.35 186276.12 208388.89
## [23,] 19015.650 75958.90 89861.53 117321.15 151315.56 190375.02 215420.27
## [24,] 92919.094 94422.17 50642.21 24748.45 58920.45 94418.90 118548.39
## [25,] 183050.783 169658.38 119885.86 73118.27 47530.87 17397.97 28441.52
## [26,] 218155.155 207719.77 157864.79 109880.96 85871.92 48795.41 12808.60
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] NA NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA NA
## [8,] 0.00 NA NA NA NA NA NA
## [9,] 28629.29 0.0000 NA NA NA NA NA
## [10,] 39390.36 33365.0673 0.00 NA NA NA NA
## [11,] 67028.03 63665.6481 30624.58 0.00 NA NA NA
## [12,] 75163.98 53554.1738 41789.16 46520.89 0.00 NA NA
## [13,] 108114.97 95077.0825 68724.91 46661.40 48443.70 0.00 NA
## [14,] 132659.60 124854.1655 94491.77 65783.99 83052.53 35371.07 0.00
## [15,] 157215.51 143246.8115 117825.16 93942.38 92735.84 49102.89 38909.58
## [16,] 181544.48 168762.1632 142235.73 116624.84 118795.95 73910.18 54335.01
## [17,] 127117.18 126715.9837 93585.25 63052.20 96975.10 57573.27 33456.56
## [18,] 199861.84 194415.9377 163088.59 133094.95 152272.40 103850.97 69958.13
## [19,] 180920.89 181115.0541 148019.95 117466.34 147923.92 102557.16 67875.19
## [20,] 102505.64 102912.6544 69602.17 39450.14 78540.59 49247.53 42372.36
## [21,] 29022.74 468.9937 33696.84 63963.19 53502.71 95188.38 125042.99
## [22,] 192646.13 181707.5267 153672.58 126449.75 133298.47 86680.84 61335.05
## [23,] 204162.54 195676.5332 165976.82 137193.50 149667.41 101730.59 71535.31
## [24,] 113105.44 112474.9097 79325.91 48809.32 84392.00 49134.77 34507.17
## [25,] 54478.87 77243.2948 60353.57 64786.40 100970.96 110839.22 121360.27
## [26,] 41153.43 69779.9485 70742.09 88889.68 111613.28 135033.87 152233.27
## [,15] [,16] [,17] [,18] [,19] [,20] [,21]
## [1,] NA NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA NA
## [13,] NA NA NA NA NA NA NA
## [14,] NA NA NA NA NA NA NA
## [15,] 0.00 NA NA NA NA NA NA
## [16,] 26127.07 0.00 NA NA NA NA NA
## [17,] 71654.98 82135.55 0.00 NA NA NA NA
## [18,] 72013.43 54882.88 75868.63 0.00 NA NA NA
## [19,] 89687.08 83037.27 54435.69 40715.15 0.00 NA NA
## [20,] 80128.11 96521.30 24611.92 99294.06 78750.89 0.00 NA
## [21,] 143320.21 168852.14 127011.06 194624.76 181405.14 103227.34 0.00
## [22,] 41967.43 17586.29 83745.56 39740.79 73170.15 101655.53 181828.52
## [23,] 61527.73 39130.55 86281.68 21907.51 61298.60 107530.21 195837.05
## [24,] 73236.93 87747.56 14260.84 88407.00 68696.34 10893.60 112772.33
## [25,] 155480.37 175464.16 102565.59 178339.19 150420.44 80772.87 77711.68
## [26,] 182825.82 205016.76 137797.84 213573.14 187704.44 114485.29 70175.44
## [,22] [,23] [,24] [,25] [,26]
## [1,] NA NA NA NA NA
## [2,] NA NA NA NA NA
## [3,] NA NA NA NA NA
## [4,] NA NA NA NA NA
## [5,] NA NA NA NA NA
## [6,] NA NA NA NA NA
## [7,] NA NA NA NA NA
## [8,] NA NA NA NA NA
## [9,] NA NA NA NA NA
## [10,] NA NA NA NA NA
## [11,] NA NA NA NA NA
## [12,] NA NA NA NA NA
## [13,] NA NA NA NA NA
## [14,] NA NA NA NA NA
## [15,] NA NA NA NA NA
## [16,] NA NA NA NA NA
## [17,] NA NA NA NA NA
## [18,] NA NA NA NA NA
## [19,] NA NA NA NA NA
## [20,] NA NA NA NA NA
## [21,] NA NA NA NA NA
## [22,] 0.0 NA NA NA NA
## [23,] 21755.4 0.00 NA NA NA
## [24,] 91866.8 96953.85 0.00 NA NA
## [25,] 182111.2 188148.14 91203.51 0.00 NA
## [26,] 213568.3 221687.85 125306.42 38363.13 0m.gjarak<-as.matrix(gjarak)
m.gjarak## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 0.000 NA NA NA NA NA NA
## [2,] 57149.459 0.00 NA NA NA NA NA
## [3,] 76541.152 49887.51 0.00 NA NA NA NA
## [4,] 110518.410 99543.90 50180.73 0.00 NA NA NA
## [5,] 143197.373 123243.55 74037.50 34802.83 0.00 NA NA
## [6,] 183595.699 164797.40 115786.77 73201.61 41760.58 0.00 NA
## [7,] 210952.313 197960.73 148247.86 101432.73 75218.13 36513.26 0.00
## [8,] 204023.478 204569.08 155606.85 105436.43 93828.94 71481.73 48691.53
## [9,] 198161.633 206711.56 159866.78 111083.85 108198.38 94637.38 76758.36
## [10,] 167036.578 173410.51 126531.64 78095.76 79366.74 76407.23 72165.40
## [11,] 137167.994 143118.36 97146.53 50959.47 62803.43 76381.49 86098.07
## [12,] 155349.912 175480.14 135009.16 94826.46 109136.36 115966.42 113856.28
## [13,] 106906.216 130498.91 95869.79 69408.16 97012.55 120773.67 132734.13
## [14,] 73472.772 95832.25 66850.17 59229.32 93289.84 126829.28 147193.51
## [15,] 73214.326 115514.13 99889.25 97983.58 131587.54 162871.48 179468.81
## [16,] 54597.832 105335.61 101666.14 111855.55 146540.73 181154.89 200735.43
## [17,] 80531.057 80327.67 38633.99 31171.16 65745.11 104093.89 130421.77
## [18,] 4940.917 54124.36 71674.07 105721.75 138307.67 178763.01 206283.97
## [19,] 45274.233 27968.49 33542.73 77890.69 106185.56 147852.84 178857.05
## [20,] 103795.763 103808.55 57980.71 20200.74 51533.48 84792.23 107892.68
## [21,] 198363.532 207022.55 160209.54 111453.59 108639.65 95105.87 77186.38
## [22,] 38597.128 92629.71 96509.50 114669.42 149425.35 186276.12 208388.89
## [23,] 19015.650 75958.90 89861.53 117321.15 151315.56 190375.02 215420.27
## [24,] 92919.094 94422.17 50642.21 24748.45 58920.45 94418.90 118548.39
## [25,] 183050.783 169658.38 119885.86 73118.27 47530.87 17397.97 28441.52
## [26,] 218155.155 207719.77 157864.79 109880.96 85871.92 48795.41 12808.60
## [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] NA NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA NA
## [8,] 0.00 NA NA NA NA NA NA
## [9,] 28629.29 0.0000 NA NA NA NA NA
## [10,] 39390.36 33365.0673 0.00 NA NA NA NA
## [11,] 67028.03 63665.6481 30624.58 0.00 NA NA NA
## [12,] 75163.98 53554.1738 41789.16 46520.89 0.00 NA NA
## [13,] 108114.97 95077.0825 68724.91 46661.40 48443.70 0.00 NA
## [14,] 132659.60 124854.1655 94491.77 65783.99 83052.53 35371.07 0.00
## [15,] 157215.51 143246.8115 117825.16 93942.38 92735.84 49102.89 38909.58
## [16,] 181544.48 168762.1632 142235.73 116624.84 118795.95 73910.18 54335.01
## [17,] 127117.18 126715.9837 93585.25 63052.20 96975.10 57573.27 33456.56
## [18,] 199861.84 194415.9377 163088.59 133094.95 152272.40 103850.97 69958.13
## [19,] 180920.89 181115.0541 148019.95 117466.34 147923.92 102557.16 67875.19
## [20,] 102505.64 102912.6544 69602.17 39450.14 78540.59 49247.53 42372.36
## [21,] 29022.74 468.9937 33696.84 63963.19 53502.71 95188.38 125042.99
## [22,] 192646.13 181707.5267 153672.58 126449.75 133298.47 86680.84 61335.05
## [23,] 204162.54 195676.5332 165976.82 137193.50 149667.41 101730.59 71535.31
## [24,] 113105.44 112474.9097 79325.91 48809.32 84392.00 49134.77 34507.17
## [25,] 54478.87 77243.2948 60353.57 64786.40 100970.96 110839.22 121360.27
## [26,] 41153.43 69779.9485 70742.09 88889.68 111613.28 135033.87 152233.27
## [,15] [,16] [,17] [,18] [,19] [,20] [,21]
## [1,] NA NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA NA
## [13,] NA NA NA NA NA NA NA
## [14,] NA NA NA NA NA NA NA
## [15,] 0.00 NA NA NA NA NA NA
## [16,] 26127.07 0.00 NA NA NA NA NA
## [17,] 71654.98 82135.55 0.00 NA NA NA NA
## [18,] 72013.43 54882.88 75868.63 0.00 NA NA NA
## [19,] 89687.08 83037.27 54435.69 40715.15 0.00 NA NA
## [20,] 80128.11 96521.30 24611.92 99294.06 78750.89 0.00 NA
## [21,] 143320.21 168852.14 127011.06 194624.76 181405.14 103227.34 0.00
## [22,] 41967.43 17586.29 83745.56 39740.79 73170.15 101655.53 181828.52
## [23,] 61527.73 39130.55 86281.68 21907.51 61298.60 107530.21 195837.05
## [24,] 73236.93 87747.56 14260.84 88407.00 68696.34 10893.60 112772.33
## [25,] 155480.37 175464.16 102565.59 178339.19 150420.44 80772.87 77711.68
## [26,] 182825.82 205016.76 137797.84 213573.14 187704.44 114485.29 70175.44
## [,22] [,23] [,24] [,25] [,26]
## [1,] NA NA NA NA NA
## [2,] NA NA NA NA NA
## [3,] NA NA NA NA NA
## [4,] NA NA NA NA NA
## [5,] NA NA NA NA NA
## [6,] NA NA NA NA NA
## [7,] NA NA NA NA NA
## [8,] NA NA NA NA NA
## [9,] NA NA NA NA NA
## [10,] NA NA NA NA NA
## [11,] NA NA NA NA NA
## [12,] NA NA NA NA NA
## [13,] NA NA NA NA NA
## [14,] NA NA NA NA NA
## [15,] NA NA NA NA NA
## [16,] NA NA NA NA NA
## [17,] NA NA NA NA NA
## [18,] NA NA NA NA NA
## [19,] NA NA NA NA NA
## [20,] NA NA NA NA NA
## [21,] NA NA NA NA NA
## [22,] 0.0 NA NA NA NA
## [23,] 21755.4 0.00 NA NA NA
## [24,] 91866.8 96953.85 0.00 NA NA
## [25,] 182111.2 188148.14 91203.51 0.00 NA
## [26,] 213568.3 221687.85 125306.42 38363.13 0djarak<-dist(longlat) #hitung jarak tanpa memperhitungkan bahwa bumi itu bulat
djarak## 1 2 3 4 5 6
## 2 0.516772258
## 3 0.692234256 0.451582681
## 4 0.999710801 0.901056552 0.454255058
## 5 1.295474351 1.115801663 0.670302784 0.314834237
## 6 1.661198429 1.492194541 1.048413676 0.662554238 0.378204571
## 7 1.908816488 1.792466835 1.342396995 0.918339119 0.681314206 0.330767164
## 8 1.845568470 1.851538493 1.408464171 0.954314221 0.849313673 0.646740976
## 9 1.792104788 1.870382712 1.446562223 1.005077119 0.978948741 0.855962560
## 10 1.510722086 1.569181631 1.145001027 0.706628674 0.717983103 0.690919695
## 11 1.240589598 1.295071942 0.879065743 0.461026756 0.567957420 0.690708574
## 12 1.404516341 1.587251926 1.221164946 0.857602400 0.986950179 1.048610040
## 13 0.966571110 1.180379098 0.867069946 0.627627924 0.877226335 1.092208794
## 14 0.664361990 0.866862898 0.604557539 0.535607592 0.843683694 1.147258964
## 15 0.661854917 1.044596623 0.903253970 0.886027058 1.189943319 1.473006240
## 16 0.493603303 0.952516623 0.919312913 1.011486254 1.325227259 1.638441173
## 17 0.728363707 0.726920938 0.349511624 0.281977527 0.594756430 0.941973947
## 18 0.044677717 0.489422945 0.648219182 0.956349255 1.251269061 1.617516898
## 19 0.409409283 0.253060214 0.303474189 0.704920775 0.961089487 1.338417080
## 20 0.938818049 0.939456635 0.524683865 0.182665201 0.466039026 0.767153988
## 21 1.793924261 1.873189118 1.449657507 1.008417932 0.982937663 0.860199337
## 22 0.348975054 0.837616863 0.872692348 1.036977530 1.351407213 1.684899804
## 23 0.171946726 0.686859256 0.812619830 1.061067742 1.368678132 1.722219171
## 24 0.840414931 0.854467316 0.458212156 0.223801226 0.532904480 0.854300759
## 25 1.656212112 1.536089139 1.085501739 0.661921959 0.430500514 0.157386063
## 26 1.973955976 1.880752822 1.429435243 0.994847937 0.777779650 0.441969442
## 7 8 9 10 11 12
## 2
## 3
## 4
## 5
## 6
## 7
## 8 0.440308634
## 9 0.694084071 0.258877492
## 10 0.652616943 0.356354814 0.301814172
## 11 0.778872504 0.606469434 0.575921001 0.277052826
## 12 1.029594605 0.679730933 0.484268257 0.377879625 0.420672525
## 13 1.200583164 0.977849769 0.859774335 0.621499004 0.421954275 0.437945587
## 14 1.331700318 1.200028401 1.129173971 0.854628364 0.594974531 0.750904910
## 15 1.623245568 1.421707049 1.295113081 1.065352320 0.849429339 0.838191595
## 16 1.815638985 1.641676496 1.525746136 1.286023376 1.054486978 1.073695509
## 17 1.180462972 1.150303873 1.146360883 0.846701729 0.570460665 0.877006613
## 18 1.866634664 1.807983600 1.758287471 1.475064181 1.203791623 1.376737308
## 19 1.619151866 1.637265506 1.638567914 1.339253518 1.062818883 1.337849109
## 20 0.976480238 0.927611643 0.931032242 0.629725442 0.356919412 0.710278787
## 21 0.697954867 0.262435113 0.004241212 0.304813685 0.578610121 0.483801877
## 22 1.885013502 1.742164187 1.642863892 1.389490623 1.143360056 1.204828034
## 23 1.948882544 1.846514436 1.769343833 1.500893726 1.240620378 1.352920490
## 24 1.072915684 1.023494566 1.017514372 0.717683939 0.441593492 0.763195264
## 25 0.257587087 0.492911378 0.698614091 0.545738653 0.585922550 0.913019773
## 26 0.115971471 0.372120653 0.630973109 0.639832260 0.804231613 1.009357036
## 13 14 15 16 17 18
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14 0.319814815
## 15 0.443873350 0.351836337
## 16 0.668069953 0.491254708 0.236094170
## 17 0.520644669 0.302529340 0.647940262 0.742704279
## 18 0.938978322 0.632601931 0.651027336 0.496204722 0.686215667
## 19 0.927547761 0.613912103 0.810999335 0.750863760 0.492564412 0.368179594
## 20 0.445323516 0.383187273 0.724558069 0.872782389 0.222697199 0.898130149
## 21 0.860778053 1.130877751 1.295772169 1.526554194 1.149025665 1.760169755
## 22 0.783513341 0.554510456 0.379251974 0.158936046 0.757269814 0.359331645
## 23 0.919628925 0.646748745 0.556094355 0.353693650 0.780255183 0.198099110
## 24 0.444315265 0.312041612 0.662243300 0.793441782 0.129032921 0.799630923
## 25 1.002416933 1.097847187 1.406166377 1.586946842 0.928228942 1.613632924
## 26 1.221438977 1.377332314 1.653616340 1.854350002 1.247248563 1.932560003
## 19 20 21 22 23 24
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 16
## 17
## 18
## 19
## 20 0.712601791
## 21 1.641185881 0.933875162
## 22 0.661640544 0.919231816 1.643952000
## 23 0.554294202 0.972437946 1.770788938 0.196660364
## 24 0.621596006 0.098561502 1.020200849 0.830702382 0.876768803
## 25 1.361617609 0.730916661 0.702849972 1.647189391 1.702019405 0.825318355
## 26 1.699203338 1.036202526 0.634549179 1.931842715 2.005552824 1.134123145
## 25
## 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 0.347470926m.djarak<-as.matrix(djarak)
m.djarak## 1 2 3 4 5 6 7
## 1 0.00000000 0.5167723 0.6922343 0.9997108 1.2954744 1.6611984 1.9088165
## 2 0.51677226 0.0000000 0.4515827 0.9010566 1.1158017 1.4921945 1.7924668
## 3 0.69223426 0.4515827 0.0000000 0.4542551 0.6703028 1.0484137 1.3423970
## 4 0.99971080 0.9010566 0.4542551 0.0000000 0.3148342 0.6625542 0.9183391
## 5 1.29547435 1.1158017 0.6703028 0.3148342 0.0000000 0.3782046 0.6813142
## 6 1.66119843 1.4921945 1.0484137 0.6625542 0.3782046 0.0000000 0.3307672
## 7 1.90881649 1.7924668 1.3423970 0.9183391 0.6813142 0.3307672 0.0000000
## 8 1.84556847 1.8515385 1.4084642 0.9543142 0.8493137 0.6467410 0.4403086
## 9 1.79210479 1.8703827 1.4465622 1.0050771 0.9789487 0.8559626 0.6940841
## 10 1.51072209 1.5691816 1.1450010 0.7066287 0.7179831 0.6909197 0.6526169
## 11 1.24058960 1.2950719 0.8790657 0.4610268 0.5679574 0.6907086 0.7788725
## 12 1.40451634 1.5872519 1.2211649 0.8576024 0.9869502 1.0486100 1.0295946
## 13 0.96657111 1.1803791 0.8670699 0.6276279 0.8772263 1.0922088 1.2005832
## 14 0.66436199 0.8668629 0.6045575 0.5356076 0.8436837 1.1472590 1.3317003
## 15 0.66185492 1.0445966 0.9032540 0.8860271 1.1899433 1.4730062 1.6232456
## 16 0.49360330 0.9525166 0.9193129 1.0114863 1.3252273 1.6384412 1.8156390
## 17 0.72836371 0.7269209 0.3495116 0.2819775 0.5947564 0.9419739 1.1804630
## 18 0.04467772 0.4894229 0.6482192 0.9563493 1.2512691 1.6175169 1.8666347
## 19 0.40940928 0.2530602 0.3034742 0.7049208 0.9610895 1.3384171 1.6191519
## 20 0.93881805 0.9394566 0.5246839 0.1826652 0.4660390 0.7671540 0.9764802
## 21 1.79392426 1.8731891 1.4496575 1.0084179 0.9829377 0.8601993 0.6979549
## 22 0.34897505 0.8376169 0.8726923 1.0369775 1.3514072 1.6848998 1.8850135
## 23 0.17194673 0.6868593 0.8126198 1.0610677 1.3686781 1.7222192 1.9488825
## 24 0.84041493 0.8544673 0.4582122 0.2238012 0.5329045 0.8543008 1.0729157
## 25 1.65621211 1.5360891 1.0855017 0.6619220 0.4305005 0.1573861 0.2575871
## 26 1.97395598 1.8807528 1.4294352 0.9948479 0.7777797 0.4419694 0.1159715
## 8 9 10 11 12 13 14
## 1 1.8455685 1.792104788 1.5107221 1.2405896 1.4045163 0.9665711 0.6643620
## 2 1.8515385 1.870382712 1.5691816 1.2950719 1.5872519 1.1803791 0.8668629
## 3 1.4084642 1.446562223 1.1450010 0.8790657 1.2211649 0.8670699 0.6045575
## 4 0.9543142 1.005077119 0.7066287 0.4610268 0.8576024 0.6276279 0.5356076
## 5 0.8493137 0.978948741 0.7179831 0.5679574 0.9869502 0.8772263 0.8436837
## 6 0.6467410 0.855962560 0.6909197 0.6907086 1.0486100 1.0922088 1.1472590
## 7 0.4403086 0.694084071 0.6526169 0.7788725 1.0295946 1.2005832 1.3317003
## 8 0.0000000 0.258877492 0.3563548 0.6064694 0.6797309 0.9778498 1.2000284
## 9 0.2588775 0.000000000 0.3018142 0.5759210 0.4842683 0.8597743 1.1291740
## 10 0.3563548 0.301814172 0.0000000 0.2770528 0.3778796 0.6214990 0.8546284
## 11 0.6064694 0.575921001 0.2770528 0.0000000 0.4206725 0.4219543 0.5949745
## 12 0.6797309 0.484268257 0.3778796 0.4206725 0.0000000 0.4379456 0.7509049
## 13 0.9778498 0.859774335 0.6214990 0.4219543 0.4379456 0.0000000 0.3198148
## 14 1.2000284 1.129173971 0.8546284 0.5949745 0.7509049 0.3198148 0.0000000
## 15 1.4217070 1.295113081 1.0653523 0.8494293 0.8381916 0.4438733 0.3518363
## 16 1.6416765 1.525746136 1.2860234 1.0544870 1.0736955 0.6680700 0.4912547
## 17 1.1503039 1.146360883 0.8467017 0.5704607 0.8770066 0.5206447 0.3025293
## 18 1.8079836 1.758287471 1.4750642 1.2037916 1.3767373 0.9389783 0.6326019
## 19 1.6372655 1.638567914 1.3392535 1.0628189 1.3378491 0.9275478 0.6139121
## 20 0.9276116 0.931032242 0.6297254 0.3569194 0.7102788 0.4453235 0.3831873
## 21 0.2624351 0.004241212 0.3048137 0.5786101 0.4838019 0.8607781 1.1308778
## 22 1.7421642 1.642863892 1.3894906 1.1433601 1.2048280 0.7835133 0.5545105
## 23 1.8465144 1.769343833 1.5008937 1.2406204 1.3529205 0.9196289 0.6467487
## 24 1.0234946 1.017514372 0.7176839 0.4415935 0.7631953 0.4443153 0.3120416
## 25 0.4929114 0.698614091 0.5457387 0.5859225 0.9130198 1.0024169 1.0978472
## 26 0.3721207 0.630973109 0.6398323 0.8042316 1.0093570 1.2214390 1.3773323
## 15 16 17 18 19 20 21
## 1 0.6618549 0.4936033 0.7283637 0.04467772 0.4094093 0.9388180 1.793924261
## 2 1.0445966 0.9525166 0.7269209 0.48942294 0.2530602 0.9394566 1.873189118
## 3 0.9032540 0.9193129 0.3495116 0.64821918 0.3034742 0.5246839 1.449657507
## 4 0.8860271 1.0114863 0.2819775 0.95634926 0.7049208 0.1826652 1.008417932
## 5 1.1899433 1.3252273 0.5947564 1.25126906 0.9610895 0.4660390 0.982937663
## 6 1.4730062 1.6384412 0.9419739 1.61751690 1.3384171 0.7671540 0.860199337
## 7 1.6232456 1.8156390 1.1804630 1.86663466 1.6191519 0.9764802 0.697954867
## 8 1.4217070 1.6416765 1.1503039 1.80798360 1.6372655 0.9276116 0.262435113
## 9 1.2951131 1.5257461 1.1463609 1.75828747 1.6385679 0.9310322 0.004241212
## 10 1.0653523 1.2860234 0.8467017 1.47506418 1.3392535 0.6297254 0.304813685
## 11 0.8494293 1.0544870 0.5704607 1.20379162 1.0628189 0.3569194 0.578610121
## 12 0.8381916 1.0736955 0.8770066 1.37673731 1.3378491 0.7102788 0.483801877
## 13 0.4438733 0.6680700 0.5206447 0.93897832 0.9275478 0.4453235 0.860778053
## 14 0.3518363 0.4912547 0.3025293 0.63260193 0.6139121 0.3831873 1.130877751
## 15 0.0000000 0.2360942 0.6479403 0.65102734 0.8109993 0.7245581 1.295772169
## 16 0.2360942 0.0000000 0.7427043 0.49620472 0.7508638 0.8727824 1.526554194
## 17 0.6479403 0.7427043 0.0000000 0.68621567 0.4925644 0.2226972 1.149025665
## 18 0.6510273 0.4962047 0.6862157 0.00000000 0.3681796 0.8981301 1.760169755
## 19 0.8109993 0.7508638 0.4925644 0.36817959 0.0000000 0.7126018 1.641185881
## 20 0.7245581 0.8727824 0.2226972 0.89813015 0.7126018 0.0000000 0.933875162
## 21 1.2957722 1.5265542 1.1490257 1.76016975 1.6411859 0.9338752 0.000000000
## 22 0.3792520 0.1589360 0.7572698 0.35933165 0.6616405 0.9192318 1.643952000
## 23 0.5560944 0.3536937 0.7802552 0.19809911 0.5542942 0.9724379 1.770788938
## 24 0.6622433 0.7934418 0.1290329 0.79963092 0.6215960 0.0985615 1.020200849
## 25 1.4061664 1.5869468 0.9282289 1.61363292 1.3616176 0.7309167 0.702849972
## 26 1.6536163 1.8543500 1.2472486 1.93256000 1.6992033 1.0362025 0.634549179
## 22 23 24 25 26
## 1 0.3489751 0.1719467 0.8404149 1.6562121 1.9739560
## 2 0.8376169 0.6868593 0.8544673 1.5360891 1.8807528
## 3 0.8726923 0.8126198 0.4582122 1.0855017 1.4294352
## 4 1.0369775 1.0610677 0.2238012 0.6619220 0.9948479
## 5 1.3514072 1.3686781 0.5329045 0.4305005 0.7777797
## 6 1.6848998 1.7222192 0.8543008 0.1573861 0.4419694
## 7 1.8850135 1.9488825 1.0729157 0.2575871 0.1159715
## 8 1.7421642 1.8465144 1.0234946 0.4929114 0.3721207
## 9 1.6428639 1.7693438 1.0175144 0.6986141 0.6309731
## 10 1.3894906 1.5008937 0.7176839 0.5457387 0.6398323
## 11 1.1433601 1.2406204 0.4415935 0.5859225 0.8042316
## 12 1.2048280 1.3529205 0.7631953 0.9130198 1.0093570
## 13 0.7835133 0.9196289 0.4443153 1.0024169 1.2214390
## 14 0.5545105 0.6467487 0.3120416 1.0978472 1.3773323
## 15 0.3792520 0.5560944 0.6622433 1.4061664 1.6536163
## 16 0.1589360 0.3536937 0.7934418 1.5869468 1.8543500
## 17 0.7572698 0.7802552 0.1290329 0.9282289 1.2472486
## 18 0.3593316 0.1980991 0.7996309 1.6136329 1.9325600
## 19 0.6616405 0.5542942 0.6215960 1.3616176 1.6992033
## 20 0.9192318 0.9724379 0.0985615 0.7309167 1.0362025
## 21 1.6439520 1.7707889 1.0202008 0.7028500 0.6345492
## 22 0.0000000 0.1966604 0.8307024 1.6471894 1.9318427
## 23 0.1966604 0.0000000 0.8767688 1.7020194 2.0055528
## 24 0.8307024 0.8767688 0.0000000 0.8253184 1.1341231
## 25 1.6471894 1.7020194 0.8253184 0.0000000 0.3474709
## 26 1.9318427 2.0055528 1.1341231 0.3474709 0.0000000K-Nearest Neighbor Weight
#k=5
koord <- coordinates(petajabar)
koord## [,1] [,2]
## 0 106.7687 -6.561184
## 1 106.7101 -7.074623
## 2 107.1578 -7.133713
## 3 107.6108 -7.099969
## 4 107.7889 -7.359586
## 5 108.1413 -7.496892
## 6 108.4661 -7.434347
## 7 108.5603 -7.004233
## 8 108.5513 -6.745512
## 9 108.2578 -6.815865
## 10 107.9809 -6.825066
## 11 108.1687 -6.448640
## 12 107.7322 -6.484194
## 13 107.4322 -6.595016
## 14 107.3539 -6.252003
## 15 107.1207 -6.215149
## 16 107.4150 -6.897056
## 17 106.7996 -6.593453
## 18 106.9243 -6.939872
## 19 107.6366 -6.919135
## 20 108.5535 -6.741886
## 21 106.9757 -6.280231
## 22 106.8168 -6.396102
## 23 107.5436 -6.886495
## 24 108.2194 -7.360251
## 25 108.5665 -7.376302W1<-knn2nb(knearneigh(longlat,k=5,longlat=TRUE)) #matriks bobot dengan knn k=5 #knearneigh(x, k=1, longlat = NULL, use_kd_tree=TRUE)
W1## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 130
## Percentage nonzero weights: 19.23077
## Average number of links: 5
## Non-symmetric neighbours listclass(W1) #nb## [1] "nb"Normalisasi Bobot Spasial dengan standardisasi baris:
W1<- nb2listw(W1,style='W') #W is row standardised (sums over all links to n). Standardisasi Baris
W1## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 130
## Percentage nonzero weights: 19.23077
## Average number of links: 5
## Non-symmetric neighbours list
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 26 676 26 9.76 105.6plot(petajabar, col='gray', border='blue', main ="knn, k=5")
plot(W1, longlat, col='red', lwd=2, add=TRUE)
Radial Distance Weigth
#d=50
W2<-dnearneigh(koord,0,50,longlat=TRUE) #dnearneigh(x, d1, d2, row.names = NULL, longlat = NULL, bounds=c("GE", "LE"), use_kd_tree=TRUE, symtest=FALSE)
W2## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 108
## Percentage nonzero weights: 15.97633
## Average number of links: 4.153846class(W2) #nb## [1] "nb"Normalisasi Bobot Spasial dengan standardisasi baris:
W2 <- nb2listw(W2,style='W') #W is row standardised (sums over all links to n). Standardisasi Baris
W2## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 108
## Percentage nonzero weights: 15.97633
## Average number of links: 4.153846
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 26 676 26 13.04833 105.4128plot(petajabar, col='gray', border='blue', main ="Radial Distance, d=50")
plot(W2, longlat, col='red', lwd=2, add=TRUE)
Power Distance Weigth
Dengan alpha = 1:
#Alpha = 1
alpha1=1
W3a<-1/(m.gjarak^alpha1)
W3a## [,1] [,2] [,3] [,4] [,5]
## [1,] Inf NA NA NA NA
## [2,] 1.749798e-05 Inf NA NA NA
## [3,] 1.306487e-05 2.004510e-05 Inf NA NA
## [4,] 9.048266e-06 1.004582e-05 1.992797e-05 Inf NA
## [5,] 6.983368e-06 8.114015e-06 1.350667e-05 2.873329e-05 Inf
## [6,] 5.446751e-06 6.068057e-06 8.636565e-06 1.366090e-05 2.394603e-05
## [7,] 4.740408e-06 5.051507e-06 6.745460e-06 9.858750e-06 1.329467e-05
## [8,] 4.901397e-06 4.888324e-06 6.426452e-06 9.484388e-06 1.065769e-05
## [9,] 5.046386e-06 4.837659e-06 6.255208e-06 9.002208e-06 9.242283e-06
## [10,] 5.986713e-06 5.766663e-06 7.903161e-06 1.280479e-05 1.259974e-05
## [11,] 7.290330e-06 6.987224e-06 1.029373e-05 1.962344e-05 1.592270e-05
## [12,] 6.437081e-06 5.698650e-06 7.406905e-06 1.054558e-05 9.162849e-06
## [13,] 9.353993e-06 7.662899e-06 1.043081e-05 1.440753e-05 1.030794e-05
## [14,] 1.361048e-05 1.043490e-05 1.495883e-05 1.688353e-05 1.071928e-05
## [15,] 1.365853e-05 8.656950e-06 1.001109e-05 1.020579e-05 7.599504e-06
## [16,] 1.831575e-05 9.493465e-06 9.836116e-06 8.940102e-06 6.824041e-06
## [17,] 1.241757e-05 1.244901e-05 2.588394e-05 3.208093e-05 1.521026e-05
## [18,] 2.023916e-04 1.847597e-05 1.395205e-05 9.458792e-06 7.230257e-06
## [19,] 2.208762e-05 3.575452e-05 2.981272e-05 1.283850e-05 9.417477e-06
## [20,] 9.634305e-06 9.633118e-06 1.724712e-05 4.950314e-05 1.940486e-05
## [21,] 5.041249e-06 4.830392e-06 6.241825e-06 8.972344e-06 9.204742e-06
## [22,] 2.590866e-05 1.079567e-05 1.036167e-05 8.720721e-06 6.692305e-06
## [23,] 5.258826e-05 1.316501e-05 1.112823e-05 8.523613e-06 6.608706e-06
## [24,] 1.076205e-05 1.059073e-05 1.974637e-05 4.040658e-05 1.697204e-05
## [25,] 5.462965e-06 5.894198e-06 8.341268e-06 1.367647e-05 2.103896e-05
## [26,] 4.583894e-06 4.814178e-06 6.334535e-06 9.100758e-06 1.164525e-05
## [,6] [,7] [,8] [,9] [,10]
## [1,] NA NA NA NA NA
## [2,] NA NA NA NA NA
## [3,] NA NA NA NA NA
## [4,] NA NA NA NA NA
## [5,] NA NA NA NA NA
## [6,] Inf NA NA NA NA
## [7,] 2.738731e-05 Inf NA NA NA
## [8,] 1.398959e-05 2.053745e-05 Inf NA NA
## [9,] 1.056665e-05 1.302790e-05 3.492926e-05 Inf NA
## [10,] 1.308777e-05 1.385706e-05 2.538692e-05 2.997147e-05 Inf
## [11,] 1.309218e-05 1.161466e-05 1.491913e-05 1.570706e-05 3.265351e-05
## [12,] 8.623186e-06 8.783003e-06 1.330425e-05 1.867268e-05 2.392965e-05
## [13,] 8.279950e-06 7.533857e-06 9.249412e-06 1.051778e-05 1.455076e-05
## [14,] 7.884615e-06 6.793778e-06 7.538090e-06 8.009344e-06 1.058293e-05
## [15,] 6.139810e-06 5.571999e-06 6.360696e-06 6.980958e-06 8.487152e-06
## [16,] 5.520138e-06 4.981682e-06 5.508292e-06 5.925499e-06 7.030582e-06
## [17,] 9.606712e-06 7.667432e-06 7.866757e-06 7.891664e-06 1.068544e-05
## [18,] 5.593998e-06 4.847686e-06 5.003457e-06 5.143611e-06 6.131637e-06
## [19,] 6.763482e-06 5.591057e-06 5.527278e-06 5.521352e-06 6.755846e-06
## [20,] 1.179353e-05 9.268470e-06 9.755560e-06 9.716978e-06 1.436737e-05
## [21,] 1.051460e-05 1.295565e-05 3.445575e-05 2.132225e-03 2.967638e-05
## [22,] 5.368375e-06 4.798720e-06 5.190865e-06 5.503349e-06 6.507342e-06
## [23,] 5.252790e-06 4.642089e-06 4.898058e-06 5.110475e-06 6.024938e-06
## [24,] 1.059110e-05 8.435374e-06 8.841308e-06 8.890872e-06 1.260622e-05
## [25,] 5.747798e-05 3.515987e-05 1.835574e-05 1.294611e-05 1.656903e-05
## [26,] 2.049373e-05 7.807252e-05 2.429931e-05 1.433076e-05 1.413586e-05
## [,11] [,12] [,13] [,14] [,15]
## [1,] NA NA NA NA NA
## [2,] NA NA NA NA NA
## [3,] NA NA NA NA NA
## [4,] NA NA NA NA NA
## [5,] NA NA NA NA NA
## [6,] NA NA NA NA NA
## [7,] NA NA NA NA NA
## [8,] NA NA NA NA NA
## [9,] NA NA NA NA NA
## [10,] NA NA NA NA NA
## [11,] Inf NA NA NA NA
## [12,] 2.149572e-05 Inf NA NA NA
## [13,] 2.143099e-05 2.064252e-05 Inf NA NA
## [14,] 1.520127e-05 1.204057e-05 2.827169e-05 Inf NA
## [15,] 1.064482e-05 1.078332e-05 2.036540e-05 2.570061e-05 Inf
## [16,] 8.574503e-06 8.417796e-06 1.352994e-05 1.840434e-05 3.827448e-05
## [17,] 1.585987e-05 1.031193e-05 1.736917e-05 2.988950e-05 1.395576e-05
## [18,] 7.513433e-06 6.567178e-06 9.629183e-06 1.429427e-05 1.388630e-05
## [19,] 8.513077e-06 6.760232e-06 9.750660e-06 1.473292e-05 1.114988e-05
## [20,] 2.534845e-05 1.273227e-05 2.030559e-05 2.360029e-05 1.248002e-05
## [21,] 1.563399e-05 1.869064e-05 1.050548e-05 7.997250e-06 6.977383e-06
## [22,] 7.908280e-06 7.501961e-06 1.153657e-05 1.630389e-05 2.382800e-05
## [23,] 7.288975e-06 6.681481e-06 9.829885e-06 1.397911e-05 1.625284e-05
## [24,] 2.048789e-05 1.184946e-05 2.035219e-05 2.897948e-05 1.365431e-05
## [25,] 1.543534e-05 9.903837e-06 9.022077e-06 8.239929e-06 6.431680e-06
## [26,] 1.124990e-05 8.959507e-06 7.405549e-06 6.568866e-06 5.469687e-06
## [,16] [,17] [,18] [,19] [,20]
## [1,] NA NA NA NA NA
## [2,] NA NA NA NA NA
## [3,] NA NA NA NA NA
## [4,] NA NA NA NA NA
## [5,] NA NA NA NA NA
## [6,] NA NA NA NA NA
## [7,] NA NA NA NA NA
## [8,] NA NA NA NA NA
## [9,] NA NA NA NA NA
## [10,] NA NA NA NA NA
## [11,] NA NA NA NA NA
## [12,] NA NA NA NA NA
## [13,] NA NA NA NA NA
## [14,] NA NA NA NA NA
## [15,] NA NA NA NA NA
## [16,] Inf NA NA NA NA
## [17,] 1.217500e-05 Inf NA NA NA
## [18,] 1.822062e-05 1.318068e-05 Inf NA NA
## [19,] 1.204279e-05 1.837030e-05 2.456088e-05 Inf NA
## [20,] 1.036041e-05 4.063072e-05 1.007110e-05 1.269827e-05 Inf
## [21,] 5.922341e-06 7.873330e-06 5.138092e-06 5.512523e-06 9.687356e-06
## [22,] 5.686248e-05 1.194093e-05 2.516306e-05 1.366678e-05 9.837143e-06
## [23,] 2.555548e-05 1.158995e-05 4.564644e-05 1.631359e-05 9.299712e-06
## [24,] 1.139633e-05 7.012209e-05 1.131132e-05 1.455682e-05 9.179706e-05
## [25,] 5.699169e-06 9.749859e-06 5.607292e-06 6.648033e-06 1.238039e-05
## [26,] 4.877650e-06 7.257008e-06 4.682237e-06 5.327524e-06 8.734747e-06
## [,21] [,22] [,23] [,24] [,25] [,26]
## [1,] NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA
## [13,] NA NA NA NA NA NA
## [14,] NA NA NA NA NA NA
## [15,] NA NA NA NA NA NA
## [16,] NA NA NA NA NA NA
## [17,] NA NA NA NA NA NA
## [18,] NA NA NA NA NA NA
## [19,] NA NA NA NA NA NA
## [20,] NA NA NA NA NA NA
## [21,] Inf NA NA NA NA NA
## [22,] 5.499687e-06 Inf NA NA NA NA
## [23,] 5.106286e-06 4.596561e-05 Inf NA NA NA
## [24,] 8.867423e-06 1.088533e-05 1.031419e-05 Inf NA NA
## [25,] 1.286808e-05 5.491149e-06 5.314961e-06 1.096449e-05 Inf NA
## [26,] 1.425000e-05 4.682344e-06 4.510847e-06 7.980437e-06 2.60667e-05 Inf#dinormalisasi
diag(W3a) <-0
rtot<-rowSums(W3a,na.rm=TRUE)
rtot## [1] 0.000000e+00 1.749798e-05 3.310996e-05 3.902205e-05 5.733735e-05
## [6] 5.775830e-05 6.707810e-05 7.088530e-05 9.290755e-05 1.273643e-04
## [11] 1.481040e-04 1.340596e-04 1.443685e-04 1.629293e-04 1.511666e-04
## [16] 1.695767e-04 2.413210e-04 3.615207e-04 2.459506e-04 3.285516e-04
## [21] 2.348056e-03 2.798965e-04 3.314515e-04 4.724165e-04 3.186789e-04
## [26] 3.158338e-04W3a<-W3a/rtot #row-normalized
rowSums(W3a,na.rm=TRUE) #baris 1 totalnya nol (0)## [1] 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1W3a #matriks bobot power distance dengan alpha=1## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] NaN NA NA NA NA NA
## [2,] 1.000000000 0.000000000 NA NA NA NA
## [3,] 0.394590205 0.605409795 0.000000000 NA NA NA
## [4,] 0.231875714 0.257439545 0.510684742 0.00000000 NA NA
## [5,] 0.121794410 0.141513607 0.235564952 0.50112703 0.000000000 NA
## [6,] 0.094302472 0.105059474 0.149529415 0.23651841 0.414590226 0.000000000
## [7,] 0.070669975 0.075307837 0.100561278 0.14697420 0.198196832 0.408289882
## [8,] 0.069145464 0.068961049 0.090659881 0.13379908 0.150351240 0.197355290
## [9,] 0.054316204 0.052069598 0.067327229 0.09689426 0.099478274 0.113732938
## [10,] 0.047004646 0.045276929 0.062051634 0.10053677 0.098926773 0.102758542
## [11,] 0.049224414 0.047177835 0.069503402 0.13249773 0.107510282 0.088398564
## [12,] 0.048016580 0.042508351 0.055250855 0.07866340 0.068349097 0.064323545
## [13,] 0.064792498 0.053078762 0.072251343 0.09979693 0.071400256 0.057352904
## [14,] 0.083536131 0.064045567 0.091811752 0.10362487 0.065790994 0.048392856
## [15,] 0.090354131 0.057267600 0.066225513 0.06751353 0.050272364 0.040616177
## [16,] 0.108008612 0.055983308 0.058003933 0.05272010 0.040241618 0.032552453
## [17,] 0.051456657 0.051586942 0.107259410 0.13293886 0.063029159 0.039808859
## [18,] 0.559834003 0.051106257 0.038592666 0.02616390 0.019999567 0.015473523
## [19,] 0.089805107 0.145372779 0.121214246 0.05219953 0.038290117 0.027499352
## [20,] 0.029323570 0.029319957 0.052494397 0.15067086 0.059061847 0.035895534
## [21,] 0.002146988 0.002057187 0.002658295 0.00382118 0.003920154 0.004478001
## [22,] 0.092565166 0.038570232 0.037019665 0.03115695 0.023909929 0.019179858
## [23,] 0.158660494 0.039719274 0.033574238 0.02571602 0.019938679 0.015847838
## [24,] 0.022780851 0.022418209 0.041798653 0.08553167 0.035926001 0.022418986
## [25,] 0.017142539 0.018495728 0.026174523 0.04291616 0.066019308 0.180363325
## [26,] 0.014513626 0.015242758 0.020056544 0.02881502 0.036871452 0.064887710
## [,7] [,8] [,9] [,10] [,11] [,12]
## [1,] NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA
## [7,] 0.000000000 NA NA NA NA NA
## [8,] 0.289727992 0.00000000 NA NA NA NA
## [9,] 0.140224302 0.37595720 0.00000000 NA NA NA
## [10,] 0.108798605 0.19932528 0.23532083 0.00000000 NA NA
## [11,] 0.078422365 0.10073419 0.10605428 0.22047695 0.00000000 NA
## [12,] 0.065515681 0.09924131 0.13928646 0.17850016 0.16034456 0.000000000
## [13,] 0.052184927 0.06406810 0.07285374 0.10078909 0.14844649 0.142984975
## [14,] 0.041697701 0.04626601 0.04915840 0.06495414 0.09329978 0.073900591
## [15,] 0.036859980 0.04207738 0.04618055 0.05614435 0.07041781 0.071333981
## [16,] 0.029377156 0.03248260 0.03494288 0.04145960 0.05056415 0.049640044
## [17,] 0.031772755 0.03259873 0.03270194 0.04427897 0.06572108 0.042731166
## [18,] 0.013409154 0.01384003 0.01422771 0.01696068 0.02078286 0.018165429
## [19,] 0.022732441 0.02247312 0.02244903 0.02746831 0.03461296 0.027486139
## [20,] 0.028210092 0.02969263 0.02957520 0.04372942 0.07715213 0.038752732
## [21,] 0.005517608 0.01467416 0.90808085 0.01263870 0.00665827 0.007960049
## [22,] 0.017144626 0.01854566 0.01966209 0.02324910 0.02825430 0.026802629
## [23,] 0.014005332 0.01477760 0.01541847 0.01817743 0.02199107 0.020158247
## [24,] 0.017855798 0.01871507 0.01881998 0.02668455 0.04336828 0.025082664
## [25,] 0.110330091 0.05759949 0.04062430 0.05199287 0.04843540 0.031077797
## [26,] 0.247194956 0.07693701 0.04537438 0.04475726 0.03561968 0.028367792
## [,13] [,14] [,15] [,16] [,17] [,18]
## [1,] NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA
## [13,] 0.00000000 NA NA NA NA NA
## [14,] 0.17352121 0.000000000 NA NA NA NA
## [15,] 0.13472153 0.170015105 0.000000000 NA NA NA
## [16,] 0.07978652 0.108531055 0.225705972 0.000000000 NA NA
## [17,] 0.07197539 0.123857876 0.057830718 0.050451464 0.000000000 NA
## [18,] 0.02663522 0.039539274 0.038410800 0.050399935 0.036458990 0.000000000
## [19,] 0.03964479 0.059901966 0.045333812 0.048964246 0.074691025 0.099861037
## [20,] 0.06180335 0.071831318 0.037984952 0.031533581 0.123666183 0.030653016
## [21,] 0.00447412 0.003405902 0.002971557 0.002522232 0.003353127 0.002188232
## [22,] 0.04121729 0.058249719 0.085131474 0.203155404 0.042661956 0.089901325
## [23,] 0.02965708 0.042175432 0.049035332 0.077101714 0.034967241 0.137716801
## [24,] 0.04308102 0.061343073 0.028903126 0.024123476 0.148432759 0.023943535
## [25,] 0.02831087 0.025856526 0.020182323 0.017883738 0.030594619 0.017595432
## [26,] 0.02344761 0.020798491 0.017318244 0.015443724 0.022977300 0.014825002
## [,19] [,20] [,21] [,22] [,23] [,24]
## [1,] NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA
## [13,] NA NA NA NA NA NA
## [14,] NA NA NA NA NA NA
## [15,] NA NA NA NA NA NA
## [16,] NA NA NA NA NA NA
## [17,] NA NA NA NA NA NA
## [18,] NA NA NA NA NA NA
## [19,] 0.000000000 NA NA NA NA NA
## [20,] 0.038649242 0.000000000 NA NA NA NA
## [21,] 0.002347696 0.004125692 0.00000000 NA NA NA
## [22,] 0.048827967 0.035145647 0.01964901 0.00000000 NA NA
## [23,] 0.049218616 0.028057533 0.01540583 0.13867972 0.00000000 NA
## [24,] 0.030813519 0.194313811 0.01877035 0.02304180 0.02183282 0.00000000
## [25,] 0.020861228 0.038849123 0.04037945 0.01723098 0.01667811 0.03440608
## [26,] 0.016868126 0.027656150 0.04511867 0.01482534 0.01428234 0.02526784
## [,25] [,26]
## [1,] NA NA
## [2,] NA NA
## [3,] NA NA
## [4,] NA NA
## [5,] NA NA
## [6,] NA NA
## [7,] NA NA
## [8,] NA NA
## [9,] NA NA
## [10,] NA NA
## [11,] NA NA
## [12,] NA NA
## [13,] NA NA
## [14,] NA NA
## [15,] NA NA
## [16,] NA NA
## [17,] NA NA
## [18,] NA NA
## [19,] NA NA
## [20,] NA NA
## [21,] NA NA
## [22,] NA NA
## [23,] NA NA
## [24,] NA NA
## [25,] 0.00000000 NA
## [26,] 0.08253296 0##Menggunakan jarak tanpa memperhatikan bentuk bumi
W3a_1<-1/(m.djarak^alpha1)
W3a_1## 1 2 3 4 5 6 7
## 1 Inf 1.9350884 1.4445977 1.0002893 0.7719180 0.6019750 0.5238848
## 2 1.9350884 Inf 2.2144339 1.1098083 0.8962166 0.6701539 0.5578904
## 3 1.4445977 2.2144339 Inf 2.2014064 1.4918631 0.9538220 0.7449361
## 4 1.0002893 1.1098083 2.2014064 Inf 3.1762746 1.5093104 1.0889224
## 5 0.7719180 0.8962166 1.4918631 3.1762746 Inf 2.6440717 1.4677516
## 6 0.6019750 0.6701539 0.9538220 1.5093104 2.6440717 Inf 3.0232747
## 7 0.5238848 0.5578904 0.7449361 1.0889224 1.4677516 3.0232747 Inf
## 8 0.5418385 0.5400914 0.7099932 1.0478729 1.1774213 1.5462141 2.2711342
## 9 0.5580031 0.5346499 0.6912941 0.9949485 1.0215039 1.1682754 1.4407477
## 10 0.6619351 0.6372749 0.8733617 1.4151704 1.3927904 1.4473462 1.5322924
## 11 0.8060683 0.7721579 1.1375713 2.1690715 1.7606954 1.4477886 1.2839072
## 12 0.7119889 0.6300197 0.8188902 1.1660415 1.0132224 0.9536434 0.9712561
## 13 1.0345850 0.8471855 1.1533095 1.5933007 1.1399567 0.9155759 0.8329286
## 14 1.5052035 1.1535850 1.6541023 1.8670385 1.1852783 0.8716428 0.7509197
## 15 1.5109051 0.9573073 1.1071083 1.1286337 0.8403762 0.6788838 0.6160497
## 16 2.0259184 1.0498504 1.0877689 0.9886442 0.7545876 0.6103362 0.5507703
## 17 1.3729405 1.3756654 2.8611352 3.5463819 1.6813606 1.0616005 0.8471253
## 18 22.3825226 2.0432226 1.5426881 1.0456431 0.7991886 0.6182316 0.5357235
## 19 2.4425435 3.9516287 3.2951732 1.4185991 1.0404858 0.7471513 0.6176073
## 20 1.0651691 1.0644451 1.9059096 5.4744965 2.1457430 1.3035193 1.0240863
## 21 0.5574371 0.5338489 0.6898181 0.9916523 1.0173585 1.1625212 1.4327574
## 22 2.8655343 1.1938633 1.1458792 0.9643410 0.7399694 0.5935071 0.5305002
## 23 5.8157548 1.4559023 1.2305877 0.9424469 0.7306320 0.5806462 0.5131146
## 24 1.1898884 1.1703198 2.1823952 4.4682508 1.8765089 1.1705479 0.9320397
## 25 0.6037874 0.6510039 0.9212330 1.5107521 2.3228776 6.3538027 3.8821822
## 26 0.5065969 0.5317020 0.6995770 1.0051787 1.2857112 2.2625999 8.6228104
## 8 9 10 11 12 13 14
## 1 0.5418385 0.5580031 0.6619351 0.8060683 0.7119889 1.0345850 1.5052035
## 2 0.5400914 0.5346499 0.6372749 0.7721579 0.6300197 0.8471855 1.1535850
## 3 0.7099932 0.6912941 0.8733617 1.1375713 0.8188902 1.1533095 1.6541023
## 4 1.0478729 0.9949485 1.4151704 2.1690715 1.1660415 1.5933007 1.8670385
## 5 1.1774213 1.0215039 1.3927904 1.7606954 1.0132224 1.1399567 1.1852783
## 6 1.5462141 1.1682754 1.4473462 1.4477886 0.9536434 0.9155759 0.8716428
## 7 2.2711342 1.4407477 1.5322924 1.2839072 0.9712561 0.8329286 0.7509197
## 8 Inf 3.8628310 2.8061919 1.6488877 1.4711704 1.0226520 0.8333136
## 9 3.8628310 Inf 3.3132970 1.7363493 2.0649712 1.1630959 0.8856031
## 10 2.8061919 3.3132970 Inf 3.6094200 2.6463454 1.6090130 1.1700992
## 11 1.6488877 1.7363493 3.6094200 Inf 2.3771460 2.3699250 1.6807442
## 12 1.4711704 2.0649712 2.6463454 2.3771460 Inf 2.2833887 1.3317265
## 13 1.0226520 1.1630959 1.6090130 2.3699250 2.2833887 Inf 3.1268095
## 14 0.8333136 0.8856031 1.1700992 1.6807442 1.3317265 3.1268095 Inf
## 15 0.7033798 0.7721333 0.9386566 1.1772610 1.1930447 2.2528949 2.8422306
## 16 0.6091334 0.6554170 0.7775908 0.9483284 0.9313627 1.4968492 2.0356039
## 17 0.8693355 0.8723256 1.1810535 1.7529692 1.1402423 1.9206957 3.3054645
## 18 0.5531024 0.5687352 0.6779366 0.8307086 0.7263550 1.0649873 1.5807729
## 19 0.6107745 0.6102890 0.7466846 0.9408941 0.7474685 1.0781116 1.6288977
## 20 1.0780374 1.0740767 1.5879936 2.8017529 1.4078979 2.2455585 2.6096900
## 21 3.8104657 235.7816798 3.2806926 1.7282795 2.0669618 1.1617397 0.8842689
## 22 0.5739987 0.6086932 0.7196882 0.8746151 0.8299940 1.2763025 1.8033925
## 23 0.5415609 0.5651813 0.6662697 0.8060483 0.7391417 1.0873951 1.5461955
## 24 0.9770448 0.9827871 1.3933710 2.2645261 1.3102807 2.2506542 3.2047008
## 25 2.0287623 1.4314054 1.8323789 1.7067102 1.0952665 0.9975889 0.9108736
## 26 2.6873005 1.5848536 1.5629096 1.2434229 0.9907297 0.8187065 0.7260412
## 15 16 17 18 19 20 21
## 1 1.5109051 2.0259184 1.3729405 22.3825226 2.4425435 1.0651691 0.5574371
## 2 0.9573073 1.0498504 1.3756654 2.0432226 3.9516287 1.0644451 0.5338489
## 3 1.1071083 1.0877689 2.8611352 1.5426881 3.2951732 1.9059096 0.6898181
## 4 1.1286337 0.9886442 3.5463819 1.0456431 1.4185991 5.4744965 0.9916523
## 5 0.8403762 0.7545876 1.6813606 0.7991886 1.0404858 2.1457430 1.0173585
## 6 0.6788838 0.6103362 1.0616005 0.6182316 0.7471513 1.3035193 1.1625212
## 7 0.6160497 0.5507703 0.8471253 0.5357235 0.6176073 1.0240863 1.4327574
## 8 0.7033798 0.6091334 0.8693355 0.5531024 0.6107745 1.0780374 3.8104657
## 9 0.7721333 0.6554170 0.8723256 0.5687352 0.6102890 1.0740767 235.7816798
## 10 0.9386566 0.7775908 1.1810535 0.6779366 0.7466846 1.5879936 3.2806926
## 11 1.1772610 0.9483284 1.7529692 0.8307086 0.9408941 2.8017529 1.7282795
## 12 1.1930447 0.9313627 1.1402423 0.7263550 0.7474685 1.4078979 2.0669618
## 13 2.2528949 1.4968492 1.9206957 1.0649873 1.0781116 2.2455585 1.1617397
## 14 2.8422306 2.0356039 3.3054645 1.5807729 1.6288977 2.6096900 0.8842689
## 15 Inf 4.2355980 1.5433522 1.5360338 1.2330466 1.3801516 0.7717406
## 16 4.2355980 Inf 1.3464309 2.0152972 1.3317995 1.1457610 0.6550701
## 17 1.5433522 1.3464309 Inf 1.4572678 2.0301913 4.4904022 0.8703026
## 18 1.5360338 2.0152972 1.4572678 Inf 2.7160658 1.1134244 0.5681270
## 19 1.2330466 1.3317995 2.0301913 2.7160658 Inf 1.4033083 0.6093155
## 20 1.3801516 1.1457610 4.4904022 1.1134244 1.4033083 Inf 1.0708069
## 21 0.7717406 0.6550701 0.8703026 0.5681270 0.6093155 1.0708069 Inf
## 22 2.6367694 6.2918389 1.3205333 2.7829444 1.5113947 1.0878649 0.6082903
## 23 1.7982560 2.8273055 1.2816320 5.0479783 1.8040961 1.0283433 0.5647200
## 24 1.5100191 1.2603319 7.7499602 1.2505769 1.6087619 10.1459493 0.9801991
## 25 0.7111534 0.6301408 1.0773204 0.6197196 0.7344206 1.3681450 1.4227787
## 26 0.6047352 0.5392725 0.8017648 0.5174484 0.5885111 0.9650623 1.5759220
## 22 23 24 25 26
## 1 2.8655343 5.8157548 1.1898884 0.6037874 0.5065969
## 2 1.1938633 1.4559023 1.1703198 0.6510039 0.5317020
## 3 1.1458792 1.2305877 2.1823952 0.9212330 0.6995770
## 4 0.9643410 0.9424469 4.4682508 1.5107521 1.0051787
## 5 0.7399694 0.7306320 1.8765089 2.3228776 1.2857112
## 6 0.5935071 0.5806462 1.1705479 6.3538027 2.2625999
## 7 0.5305002 0.5131146 0.9320397 3.8821822 8.6228104
## 8 0.5739987 0.5415609 0.9770448 2.0287623 2.6873005
## 9 0.6086932 0.5651813 0.9827871 1.4314054 1.5848536
## 10 0.7196882 0.6662697 1.3933710 1.8323789 1.5629096
## 11 0.8746151 0.8060483 2.2645261 1.7067102 1.2434229
## 12 0.8299940 0.7391417 1.3102807 1.0952665 0.9907297
## 13 1.2763025 1.0873951 2.2506542 0.9975889 0.8187065
## 14 1.8033925 1.5461955 3.2047008 0.9108736 0.7260412
## 15 2.6367694 1.7982560 1.5100191 0.7111534 0.6047352
## 16 6.2918389 2.8273055 1.2603319 0.6301408 0.5392725
## 17 1.3205333 1.2816320 7.7499602 1.0773204 0.8017648
## 18 2.7829444 5.0479783 1.2505769 0.6197196 0.5174484
## 19 1.5113947 1.8040961 1.6087619 0.7344206 0.5885111
## 20 1.0878649 1.0283433 10.1459493 1.3681450 0.9650623
## 21 0.6082903 0.5647200 0.9801991 1.4227787 1.5759220
## 22 Inf 5.0849087 1.2038006 0.6070947 0.5176405
## 23 5.0849087 Inf 1.1405515 0.5875374 0.4986156
## 24 1.2038006 1.1405515 Inf 1.2116537 0.8817385
## 25 0.6070947 0.5875374 1.2116537 Inf 2.8779386
## 26 0.5176405 0.4986156 0.8817385 2.8779386 Inf#dinormalisasi
diag(W3a_1) <-0
rtot<-rowSums(W3a_1,na.rm=TRUE)
rtot## 1 2 3 4 5 6 7 8
## 54.43637 28.47732 34.75885 43.82448 34.37376 34.89644 36.59461 34.52251
## 9 10 11 12 13 14 15 16
## 264.94315 38.47976 39.87525 31.61856 36.74321 41.09420 34.67972 36.80101
## 17 18 19 20 21 22 23 24
## 47.75745 54.59470 35.44722 51.98760 264.79675 38.37336 38.88482 54.31686
## 25 26
## 38.09653 34.89679W3a_1<-W3a_1/rtot #row-normalized
rowSums(W3a_1,na.rm=TRUE)## 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
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1W3a_1 #matriks bobot power distance dengan alpha=1 dengan distance tanpa memperhatikan bentuk bumi## 1 2 3 4 5 6
## 1 0.000000000 0.035547709 0.026537361 0.018375384 0.014180188 0.011058324
## 2 0.067951925 0.000000000 0.077761330 0.038971660 0.031471247 0.023532903
## 3 0.041560566 0.063708483 0.000000000 0.063333686 0.042920376 0.027441122
## 4 0.022824900 0.025323937 0.050232350 0.000000000 0.072477185 0.034439896
## 5 0.022456605 0.026072694 0.043401216 0.092404041 0.000000000 0.076921216
## 6 0.017250327 0.019204076 0.027332929 0.043251126 0.075769092 0.000000000
## 7 0.014315900 0.015245151 0.020356442 0.029756357 0.040108406 0.082615295
## 8 0.015695223 0.015644616 0.020566096 0.030353325 0.034105903 0.044788579
## 9 0.002106124 0.002017980 0.002609217 0.003755328 0.003855559 0.004409532
## 10 0.017202162 0.016561299 0.022696648 0.036777004 0.036195400 0.037613178
## 11 0.020214754 0.019364340 0.028528257 0.054396438 0.044155094 0.036307952
## 12 0.022518071 0.019925632 0.025899039 0.036878393 0.032045182 0.030160877
## 13 0.028157176 0.023056925 0.031388371 0.043363133 0.031024961 0.024918233
## 14 0.036628127 0.028071723 0.040251479 0.045433141 0.028842960 0.021210848
## 15 0.043567396 0.027604240 0.031923796 0.032544486 0.024232495 0.019575814
## 16 0.055050622 0.028527763 0.029558128 0.026864595 0.020504535 0.016584769
## 17 0.028748192 0.028805251 0.059909710 0.074258187 0.035206244 0.022229001
## 18 0.409976095 0.037425290 0.028257102 0.019152831 0.014638575 0.011324021
## 19 0.068906490 0.111479226 0.092959988 0.040020039 0.029353101 0.021077852
## 20 0.020488909 0.020474982 0.036660853 0.105303900 0.041274136 0.025073660
## 21 0.002105151 0.002016071 0.002605085 0.003744957 0.003842035 0.004390240
## 22 0.074675098 0.031111774 0.029861321 0.025130483 0.019283415 0.015466645
## 23 0.149563624 0.037441405 0.031646995 0.024236884 0.018789644 0.014932464
## 24 0.021906430 0.021546161 0.040178966 0.082262688 0.034547449 0.021550362
## 25 0.015848883 0.017088274 0.024181547 0.039655901 0.060973473 0.166781676
## 26 0.014517006 0.015236415 0.020047030 0.028804333 0.036843251 0.064836906
## 7 8 9 10 11 12
## 1 0.009623801 0.009953611 0.01025056 0.01215979 0.014807532 0.013079285
## 2 0.019590694 0.018965671 0.01877459 0.02237833 0.027114841 0.022123564
## 3 0.021431549 0.020426254 0.01988829 0.02512631 0.032727526 0.023559182
## 4 0.024847356 0.023910677 0.02270303 0.03229178 0.049494523 0.026607084
## 5 0.042699764 0.034253488 0.02971755 0.04051900 0.051222072 0.029476620
## 6 0.086635616 0.044308646 0.03347835 0.04147547 0.041488144 0.027327811
## 7 0.000000000 0.062061983 0.03937049 0.04187208 0.035084596 0.026540958
## 8 0.065787058 0.000000000 0.11189312 0.08128587 0.047762686 0.042614817
## 9 0.005437950 0.014579849 0.00000000 0.01250569 0.006553667 0.007794016
## 10 0.039820734 0.072926434 0.08610492 0.00000000 0.093800471 0.068772392
## 11 0.032198098 0.041351158 0.04354454 0.09051780 0.000000000 0.059614574
## 12 0.030717914 0.046528702 0.06530884 0.08369596 0.075181991 0.000000000
## 13 0.022668911 0.027832407 0.03165472 0.04379076 0.064499673 0.062144508
## 14 0.018273132 0.020278133 0.02155056 0.02847359 0.040899793 0.032406680
## 15 0.017763976 0.020282164 0.02226469 0.02706644 0.033946668 0.034401795
## 16 0.014966174 0.016552085 0.01780976 0.02112961 0.025769089 0.025308077
## 17 0.017738074 0.018203138 0.01826575 0.02473024 0.036705668 0.023875692
## 18 0.009812737 0.010131063 0.01041741 0.01241763 0.015215919 0.013304496
## 19 0.017423293 0.017230533 0.01721684 0.02106469 0.026543522 0.021086800
## 20 0.019698666 0.020736434 0.02066025 0.03054563 0.053892720 0.027081420
## 21 0.005410782 0.014390153 0.89042511 0.01238947 0.006526815 0.007805843
## 22 0.013824700 0.014958261 0.01586239 0.01875489 0.022792248 0.021629432
## 23 0.013195754 0.013927308 0.01453475 0.01713444 0.020729125 0.019008490
## 24 0.017159308 0.017987873 0.01809359 0.02565264 0.041691037 0.024122910
## 25 0.101903833 0.053253206 0.03757312 0.04809832 0.044799626 0.028749773
## 26 0.247094661 0.077007097 0.04541546 0.04478663 0.035631441 0.028390282
## 13 14 15 16 17 18
## 1 0.019005399 0.027650694 0.027755433 0.037216262 0.025221012 0.411168507
## 2 0.029749485 0.040508908 0.033616488 0.036866201 0.048307413 0.071749128
## 3 0.033180307 0.047587939 0.031851116 0.031294728 0.082313850 0.044382592
## 4 0.036356412 0.042602643 0.025753501 0.022559179 0.080922403 0.023859797
## 5 0.033163568 0.034482065 0.024448186 0.021952428 0.048914067 0.023249960
## 6 0.026236940 0.024977985 0.019454240 0.017489927 0.030421454 0.017716178
## 7 0.022760961 0.020519952 0.016834438 0.015050584 0.023148906 0.014639408
## 8 0.029622760 0.024138270 0.020374528 0.017644530 0.025181702 0.016021500
## 9 0.004389983 0.003342616 0.002914336 0.002473803 0.003292501 0.002146631
## 10 0.041814525 0.030408171 0.024393513 0.020207786 0.030692846 0.017618003
## 11 0.059433486 0.042150062 0.029523602 0.023782383 0.043961337 0.020832686
## 12 0.072216730 0.042118513 0.037732421 0.029456208 0.036062440 0.022972428
## 13 0.000000000 0.085098975 0.061314591 0.040738118 0.052273488 0.028984602
## 14 0.076088831 0.000000000 0.069163792 0.049535068 0.080436282 0.038467058
## 15 0.064962890 0.081956559 0.000000000 0.122134720 0.044503016 0.044291989
## 16 0.040674137 0.055313808 0.115094620 0.000000000 0.036586794 0.054762012
## 17 0.040217717 0.069213584 0.032316467 0.028193105 0.000000000 0.030513935
## 18 0.019507155 0.028954695 0.028135218 0.036913788 0.026692477 0.000000000
## 19 0.030414560 0.045952762 0.034785425 0.037571339 0.057273640 0.076622815
## 20 0.043194121 0.050198322 0.026547711 0.022039122 0.086374495 0.021417116
## 21 0.004387288 0.003339425 0.002914464 0.002473860 0.003286681 0.002145521
## 22 0.033260118 0.046995951 0.068713542 0.163963726 0.034412763 0.072522826
## 23 0.027964514 0.039763472 0.046245705 0.072709746 0.032959698 0.129818734
## 24 0.041435647 0.059000114 0.027800191 0.023203329 0.142680568 0.023023735
## 25 0.026185822 0.023909622 0.018667145 0.016540637 0.028278704 0.016267090
## 26 0.023460797 0.020805387 0.017329250 0.015453356 0.022975317 0.014827965
## 19 20 21 22 23 24
## 1 0.044869696 0.019567231 0.01024016 0.052640066 0.106835823 0.021858334
## 2 0.138764088 0.037378702 0.01874646 0.041923308 0.051124986 0.041096563
## 3 0.094800970 0.054832346 0.01984582 0.032966540 0.035403575 0.062786740
## 4 0.032370019 0.124918699 0.02262782 0.022004623 0.021505035 0.101957883
## 5 0.030269768 0.062423862 0.02959695 0.021527157 0.021255513 0.054591314
## 6 0.021410528 0.037353931 0.03331346 0.017007668 0.016639123 0.033543476
## 7 0.016877001 0.027984618 0.03915214 0.014496674 0.014021587 0.025469314
## 8 0.017692066 0.031227087 0.11037627 0.016626797 0.015687183 0.028301674
## 9 0.002303472 0.004053989 0.88993311 0.002297448 0.002133217 0.003709426
## 10 0.019404605 0.041268280 0.08525761 0.018703031 0.017314807 0.036210488
## 11 0.023595943 0.070262957 0.04334216 0.021933785 0.020214252 0.056790270
## 12 0.023640183 0.044527584 0.06537180 0.026250218 0.023376834 0.041440244
## 13 0.029341791 0.061114924 0.03161781 0.034735736 0.029594451 0.061253608
## 14 0.039638142 0.063505072 0.02151810 0.043884358 0.037625640 0.077984263
## 15 0.035555264 0.039797080 0.02225337 0.076032025 0.051853243 0.043541846
## 16 0.036189213 0.031133956 0.01780033 0.170969201 0.076826849 0.034247212
## 17 0.042510460 0.094025161 0.01822339 0.027650831 0.026836272 0.162277501
## 18 0.049749622 0.020394367 0.01040627 0.050974625 0.092462787 0.022906563
## 19 0.000000000 0.039588669 0.01718937 0.042637890 0.050895277 0.045384714
## 20 0.026993137 0.000000000 0.02059735 0.020925470 0.019780551 0.195160967
## 21 0.002301069 0.004043882 0.00000000 0.002297197 0.002132655 0.003701704
## 22 0.039386562 0.028349483 0.01585189 0.000000000 0.132511431 0.031370737
## 23 0.046395895 0.026445878 0.01452289 0.130768473 0.000000000 0.029331536
## 24 0.029618097 0.186791904 0.01804595 0.022162559 0.020998113 0.000000000
## 25 0.019277888 0.035912591 0.03734668 0.015935697 0.015422334 0.031804832
## 26 0.016864333 0.027654759 0.04515951 0.014833470 0.014288295 0.025267037
## 25 26
## 1 0.011091617 0.009306221
## 2 0.022860437 0.018671071
## 3 0.026503548 0.020126583
## 4 0.034472794 0.022936469
## 5 0.067577052 0.037403853
## 6 0.182075950 0.064837552
## 7 0.106086168 0.235630597
## 8 0.058766364 0.077841983
## 9 0.005402689 0.005981863
## 10 0.047619287 0.040616404
## 11 0.042801243 0.031182825
## 12 0.034639993 0.031333807
## 13 0.027150292 0.022281844
## 14 0.022165503 0.017667730
## 15 0.020506318 0.017437718
## 16 0.017122923 0.014653743
## 17 0.022558163 0.016788265
## 18 0.011351278 0.009477996
## 19 0.020718707 0.016602461
## 20 0.026316759 0.018563319
## 21 0.005373097 0.005951440
## 22 0.015820735 0.013489580
## 23 0.015109684 0.012822886
## 24 0.022307138 0.016233238
## 25 0.000000000 0.075543332
## 26 0.082470011 0.000000000class(W3a_1) #matriks array## [1] "matrix" "array"summary matriks:
W3a_1 = mat2listw(W3a_1,style='W')
summary(W3a_1)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 650
## Percentage nonzero weights: 96.15385
## Average number of links: 25
## Link number distribution:
##
## 25
## 26
## 26 least connected regions:
## 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 with 25 links
## 26 most connected regions:
## 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 with 25 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 26 676 26 6.638168 105.5718plot(petajabar, col='gray', border='blue', main ="Power Distance Weigth alpha=1, Euclidean")
plot(W3a_1, longlat, col='red', lwd=2, add=TRUE)
Power distance weigth dengan alpha=2
#alpha=2
alpha2=2
W3b<-1/(m.gjarak^alpha2)
#dinormalisasi
W3b[!is.finite(W3b)]<-NA
rtot<-rowSums(W3b,na.rm=TRUE)
rtot## [1] 0.000000e+00 3.061793e-10 5.724966e-10 5.799135e-10 1.122637e-09
## [6] 9.011112e-10 1.117498e-09 9.102543e-10 1.755890e-09 2.360363e-09
## [11] 2.688392e-09 2.035807e-09 1.987467e-09 2.434491e-09 2.058462e-09
## [16] 2.950101e-09 4.525048e-09 4.287917e-08 4.732606e-09 7.881756e-09
## [21] 4.550046e-06 6.575840e-09 9.331060e-09 1.915577e-08 7.310506e-09
## [26] 9.340007e-09W3b<-W3b/rtot #row-normalized
rowSums(W3b,na.rm=TRUE)## [1] 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1W3b #matriks bobot power distance dengan alpha=2## [,1] [,2] [,3] [,4] [,5]
## [1,] NA NA NA NA NA
## [2,] 1.000000e+00 NA NA NA NA
## [3,] 2.981515e-01 7.018485e-01 NA NA NA
## [4,] 1.411782e-01 1.740234e-01 6.847985e-01 NA NA
## [5,] 4.344007e-02 5.864517e-02 1.625014e-01 7.354133e-01 NA
## [6,] 3.292279e-02 4.086212e-02 8.277587e-02 2.071001e-01 6.363391e-01
## [7,] 2.010872e-02 2.283468e-02 4.071704e-02 8.697548e-02 1.581642e-01
## [8,] 2.639228e-02 2.625169e-02 4.537116e-02 9.882250e-02 1.247853e-01
## [9,] 1.450319e-02 1.332825e-02 2.228365e-02 4.615310e-02 4.864759e-02
## [10,] 1.518441e-02 1.408868e-02 2.646201e-02 6.946503e-02 6.725802e-02
## [11,] 1.976978e-02 1.816004e-02 3.941421e-02 1.432378e-01 9.430631e-02
## [12,] 2.035361e-02 1.595172e-02 2.694865e-02 5.462663e-02 4.124056e-02
## [13,] 4.402446e-02 2.954515e-02 5.474399e-02 1.044429e-01 5.346187e-02
## [14,] 7.609199e-02 4.472686e-02 9.191508e-02 1.170896e-01 4.719795e-02
## [15,] 9.062854e-02 3.640717e-02 4.868774e-02 5.060000e-02 2.805612e-02
## [16,] 1.137136e-01 3.055010e-02 3.279521e-02 2.709243e-02 1.578506e-02
## [17,] 3.407611e-02 3.424889e-02 1.480600e-01 2.274421e-01 5.112696e-02
## [18,] 9.552971e-01 7.961008e-03 4.539724e-03 2.086532e-03 1.219161e-03
## [19,] 1.030855e-01 2.701230e-01 1.878031e-01 3.482800e-02 1.873997e-02
## [20,] 1.177654e-02 1.177364e-02 3.774070e-02 3.109157e-01 4.777472e-02
## [21,] 5.585481e-06 5.128010e-06 8.562635e-06 1.769278e-05 1.862119e-05
## [22,] 1.020796e-01 1.772345e-02 1.632708e-02 1.156521e-02 6.810833e-03
## [23,] 2.963785e-01 1.857427e-02 1.327154e-02 7.786036e-03 4.680603e-03
## [24,] 6.046312e-03 5.855345e-03 2.035519e-02 8.523237e-02 1.503725e-02
## [25,] 4.082342e-03 4.752279e-03 9.517363e-03 2.558590e-02 6.054817e-02
## [26,] 2.249686e-03 2.481402e-03 4.296178e-03 8.867638e-03 1.451946e-02
## [,6] [,7] [,8] [,9] [,10]
## [1,] NA NA NA NA NA
## [2,] NA NA NA NA NA
## [3,] NA NA NA NA NA
## [4,] NA NA NA NA NA
## [5,] NA NA NA NA NA
## [6,] NA NA NA NA NA
## [7,] 6.711999e-01 NA NA NA NA
## [8,] 2.150043e-01 4.633727e-01 NA NA NA
## [9,] 6.358832e-02 9.666102e-02 0.6948348804 NA NA
## [10,] 7.256919e-02 8.135103e-02 0.2730493603 0.3805722656 NA
## [11,] 6.375748e-02 5.017884e-02 0.0827931701 0.0917692324 0.3966131501
## [12,] 3.652574e-02 3.789218e-02 0.0869448735 0.1712682446 0.2812783093
## [13,] 3.449494e-02 2.855846e-02 0.0430455516 0.0556606518 0.1065299262
## [14,] 2.553599e-02 1.895896e-02 0.0233407284 0.0263503090 0.0460048739
## [15,] 1.831332e-02 1.508270e-02 0.0196546961 0.0236748477 0.0349929911
## [16,] 1.032911e-02 8.412305e-03 0.0102848261 0.0119018065 0.0167550474
## [17,] 2.039513e-02 1.299202e-02 0.0136762914 0.0137630297 0.0252325999
## [18,] 7.297906e-04 5.480531e-04 0.0005838400 0.0006170067 0.0008768119
## [19,] 9.665856e-03 6.605224e-03 0.0064553864 0.0064415527 0.0096440438
## [20,] 1.764676e-02 1.089916e-02 0.0120748421 0.0119795211 0.0261897580
## [21,] 2.429795e-05 3.688951e-05 0.0002609201 0.9991948011 0.0001935557
## [22,] 4.382626e-03 3.501867e-03 0.0040975873 0.0046057775 0.0064395571
## [23,] 2.956985e-03 2.309383e-03 0.0025710876 0.0027989267 0.0038902195
## [24,] 5.855751e-03 3.714576e-03 0.0040806889 0.0041265696 0.0082960302
## [25,] 4.519138e-01 1.691013e-01 0.0460888958 0.0229261417 0.0375531697
## [26,] 4.496711e-02 6.526033e-01 0.0632179914 0.0219882937 0.0213942503
## [,11] [,12] [,13] [,14] [,15]
## [1,] NA NA NA NA NA
## [2,] NA NA NA NA NA
## [3,] NA NA NA NA NA
## [4,] NA NA NA NA NA
## [5,] NA NA NA NA NA
## [6,] NA NA NA NA NA
## [7,] NA NA NA NA NA
## [8,] NA NA NA NA NA
## [9,] NA NA NA NA NA
## [10,] NA NA NA NA NA
## [11,] NA NA NA NA NA
## [12,] 2.269695e-01 NA NA NA NA
## [13,] 2.310918e-01 2.144003e-01 NA NA NA
## [14,] 9.491862e-02 5.955059e-02 3.283185e-01 NA NA
## [15,] 5.504704e-02 5.648874e-02 2.014851e-01 3.208810e-01 NA
## [16,] 2.492189e-02 2.401927e-02 6.205182e-02 1.148163e-01 4.965713e-01
## [17,] 5.558740e-02 2.349938e-02 6.667070e-02 1.974305e-01 4.304117e-02
## [18,] 1.316529e-03 1.005799e-03 2.162383e-03 4.765158e-03 4.497038e-03
## [19,] 1.531344e-02 9.656570e-03 2.008943e-02 4.586459e-02 2.626878e-02
## [20,] 8.152295e-02 2.056784e-02 5.231281e-02 7.066620e-02 1.976092e-02
## [21,] 5.371851e-05 7.677726e-05 2.425584e-05 1.405612e-05 1.069964e-05
## [22,] 9.510708e-03 8.558515e-03 2.023963e-02 4.042326e-02 8.634237e-02
## [23,] 5.693796e-03 4.784258e-03 1.035538e-02 2.094248e-02 2.830918e-02
## [24,] 2.191265e-02 7.329898e-03 2.162333e-02 4.384113e-02 9.732855e-03
## [25,] 3.259004e-02 1.341713e-02 1.113437e-02 9.287514e-03 5.658501e-03
## [26,] 1.355034e-02 8.594509e-03 5.871747e-03 4.619912e-03 3.203154e-03
## [,16] [,17] [,18] [,19] [,20]
## [1,] NA NA NA NA NA
## [2,] NA NA NA NA NA
## [3,] NA NA NA NA NA
## [4,] NA NA NA NA NA
## [5,] NA NA NA NA NA
## [6,] NA NA NA NA NA
## [7,] NA NA NA NA NA
## [8,] NA NA NA NA NA
## [9,] NA NA NA NA NA
## [10,] NA NA NA NA NA
## [11,] NA NA NA NA NA
## [12,] NA NA NA NA NA
## [13,] NA NA NA NA NA
## [14,] NA NA NA NA NA
## [15,] NA NA NA NA NA
## [16,] NA NA NA NA NA
## [17,] 3.275778e-02 NA NA NA NA
## [18,] 7.742476e-03 4.051624e-03 NA NA NA
## [19,] 3.064457e-02 7.130701e-02 1.274640e-01 NA NA
## [20,] 1.361854e-02 2.094527e-01 1.286858e-02 2.045814e-02 NA
## [21,] 7.708521e-06 1.362389e-05 5.802137e-06 6.678594e-06 2.062504e-05
## [22,] 4.917002e-01 2.168329e-02 9.628881e-02 2.840409e-02 1.471590e-02
## [23,] 6.999018e-02 1.439567e-02 2.232970e-01 2.852120e-02 9.268469e-03
## [24,] 6.780011e-03 2.566907e-01 6.679242e-03 1.106199e-02 4.399041e-01
## [25,] 4.442994e-03 1.300317e-02 4.300896e-03 6.045592e-03 2.096629e-02
## [26,] 2.547265e-03 5.638557e-03 2.347251e-03 3.038811e-03 8.168710e-03
## [,21] [,22] [,23] [,24] [,25] [,26]
## [1,] NA NA NA NA NA NA
## [2,] NA NA NA NA NA NA
## [3,] NA NA NA NA NA NA
## [4,] NA NA NA NA NA NA
## [5,] NA NA NA NA NA NA
## [6,] NA NA NA NA NA NA
## [7,] NA NA NA NA NA NA
## [8,] NA NA NA NA NA NA
## [9,] NA NA NA NA NA NA
## [10,] NA NA NA NA NA NA
## [11,] NA NA NA NA NA NA
## [12,] NA NA NA NA NA NA
## [13,] NA NA NA NA NA NA
## [14,] NA NA NA NA NA NA
## [15,] NA NA NA NA NA NA
## [16,] NA NA NA NA NA NA
## [17,] NA NA NA NA NA NA
## [18,] NA NA NA NA NA NA
## [19,] NA NA NA NA NA NA
## [20,] NA NA NA NA NA NA
## [21,] NA NA NA NA NA NA
## [22,] 0.004599650 NA NA NA NA NA
## [23,] 0.002794340 0.226430529 NA NA NA NA
## [24,] 0.004104832 0.006185621 0.005553546 NA NA NA
## [25,] 0.022650610 0.004124574 0.003864139 0.016444833 NA NA
## [26,] 0.021741154 0.002347358 0.002178557 0.006818772 0.07274863 NA#Matriks jarak tanpa memperhatikan bentuk bumi
W3b_1<-1/(m.djarak^alpha2)
W3b_1## 1 2 3 4 5 6
## 1 Inf 3.7445671 2.0868625 1.0005786 0.5958574 0.3623740
## 2 3.7445671 Inf 4.9037175 1.2316744 0.8032042 0.4491063
## 3 2.0868625 4.9037175 Inf 4.8461902 2.2256556 0.9097764
## 4 1.0005786 1.2316744 4.8461902 Inf 10.0887205 2.2780179
## 5 0.5958574 0.8032042 2.2256556 10.0887205 Inf 6.9911151
## 6 0.3623740 0.4491063 0.9097764 2.2780179 6.9911151 Inf
## 7 0.2744553 0.3112417 0.5549298 1.1857519 2.1542947 9.1401899
## 8 0.2935889 0.2916987 0.5040904 1.0980376 1.3863209 2.3907780
## 9 0.3113674 0.2858506 0.4778876 0.9899226 1.0434703 1.3648674
## 10 0.4381581 0.4061192 0.7627606 2.0027073 1.9398652 2.0948110
## 11 0.6497462 0.5962278 1.2940686 4.7048712 3.1000482 2.0960918
## 12 0.5069281 0.3969248 0.6705812 1.3596528 1.0266196 0.9094357
## 13 1.0703662 0.7177232 1.3301228 2.5386071 1.2995012 0.8382791
## 14 2.2656376 1.3307583 2.7360543 3.4858328 1.4048847 0.7597611
## 15 2.2828344 0.9164373 1.2256889 1.2738140 0.7062321 0.4608831
## 16 4.1043452 1.1021860 1.1832412 0.9774173 0.5694024 0.3725103
## 17 1.8849655 1.8924554 8.1860944 12.5768247 2.8269733 1.1269956
## 18 500.9773163 4.1747584 2.3798865 1.0933695 0.6387025 0.3822103
## 19 5.9660189 15.6153692 10.8581661 2.0124235 1.0826108 0.5582350
## 20 1.1345853 1.1330433 3.6324913 29.9701118 4.6042132 1.6991625
## 21 0.3107362 0.2849947 0.4758490 0.9833744 1.0350183 1.3514556
## 22 8.2112870 1.4253095 1.3130391 0.9299537 0.5475547 0.3522507
## 23 33.8230039 2.1196516 1.5143462 0.8882062 0.5338231 0.3371500
## 24 1.4158345 1.3696484 4.7628487 19.9652650 3.5212857 1.3701825
## 25 0.3645592 0.4238061 0.8486702 2.2823720 5.3957603 40.3708093
## 26 0.2566404 0.2827070 0.4894080 1.0103843 1.6530533 5.1193581
## 7 8 9 10 11 12 13
## 1 0.2744553 0.2935889 3.113674e-01 0.4381581 0.6497462 0.5069281 1.0703662
## 2 0.3112417 0.2916987 2.858506e-01 0.4061192 0.5962278 0.3969248 0.7177232
## 3 0.5549298 0.5040904 4.778876e-01 0.7627606 1.2940686 0.6705812 1.3301228
## 4 1.1857519 1.0980376 9.899226e-01 2.0027073 4.7048712 1.3596528 2.5386071
## 5 2.1542947 1.3863209 1.043470e+00 1.9398652 3.1000482 1.0266196 1.2995012
## 6 9.1401899 2.3907780 1.364867e+00 2.0948110 2.0960918 0.9094357 0.8382791
## 7 Inf 5.1580506 2.075754e+00 2.3479201 1.6484176 0.9433383 0.6937700
## 8 5.1580506 Inf 1.492146e+01 7.8747131 2.7188307 2.1643422 1.0458171
## 9 2.0757538 14.9214633 Inf 10.9779372 3.0149087 4.2641060 1.3527921
## 10 2.3479201 7.8747131 1.097794e+01 Inf 13.0279124 7.0031439 2.5889230
## 11 1.6484176 2.7188307 3.014909e+00 13.0279124 Inf 5.6508230 5.6165447
## 12 0.9433383 2.1643422 4.264106e+00 7.0031439 5.6508230 Inf 5.2138639
## 13 0.6937700 1.0458171 1.352792e+00 2.5889230 5.6165447 5.2138639 Inf
## 14 0.5638804 0.6944116 7.842929e-01 1.3691322 2.8249011 1.7734956 9.7769376
## 15 0.3795173 0.4947431 5.961899e-01 0.8810762 1.3859434 1.4233555 5.0755354
## 16 0.3033479 0.3710435 4.295715e-01 0.6046475 0.8993268 0.8674366 2.2405576
## 17 0.7176212 0.7557442 7.609520e-01 1.3948873 3.0729012 1.3001524 3.6890721
## 18 0.2869996 0.3059222 3.234597e-01 0.4595980 0.6900767 0.5275916 1.1341980
## 19 0.3814388 0.3730455 3.724527e-01 0.5575379 0.8852817 0.5587091 1.1623246
## 20 1.0487527 1.1621645 1.153641e+00 2.5217238 7.8498193 1.9821764 5.0425329
## 21 2.0527938 14.5196490 5.559300e+04 10.7629439 2.9869500 4.2723311 1.3496390
## 22 0.2814304 0.3294745 3.705074e-01 0.5179511 0.7649516 0.6888900 1.6289480
## 23 0.2632865 0.2932882 3.194299e-01 0.4439153 0.6497139 0.5463305 1.1824281
## 24 0.8686980 0.9546165 9.658705e-01 1.9414828 5.1280786 1.7168354 5.0654442
## 25 15.0713385 4.1158763 2.048921e+00 3.3576125 2.9128598 1.1996088 0.9951836
## 26 74.3528586 7.2215837 2.511761e+00 2.4426865 1.5461005 0.9815454 0.6702803
## 14 15 16 17 18 19
## 1 2.2656376 2.2828344 4.1043452 1.8849655 500.9773163 5.9660189
## 2 1.3307583 0.9164373 1.1021860 1.8924554 4.1747584 15.6153692
## 3 2.7360543 1.2256889 1.1832412 8.1860944 2.3798865 10.8581661
## 4 3.4858328 1.2738140 0.9774173 12.5768247 1.0933695 2.0124235
## 5 1.4048847 0.7062321 0.5694024 2.8269733 0.6387025 1.0826108
## 6 0.7597611 0.4608831 0.3725103 1.1269956 0.3822103 0.5582350
## 7 0.5638804 0.3795173 0.3033479 0.7176212 0.2869996 0.3814388
## 8 0.6944116 0.4947431 0.3710435 0.7557442 0.3059222 0.3730455
## 9 0.7842929 0.5961899 0.4295715 0.7609520 0.3234597 0.3724527
## 10 1.3691322 0.8810762 0.6046475 1.3948873 0.4595980 0.5575379
## 11 2.8249011 1.3859434 0.8993268 3.0729012 0.6900767 0.8852817
## 12 1.7734956 1.4233555 0.8674366 1.3001524 0.5275916 0.5587091
## 13 9.7769376 5.0755354 2.2405576 3.6890721 1.1341980 1.1623246
## 14 Inf 8.0782747 4.1436832 10.9260957 2.4988430 2.6533076
## 15 8.0782747 Inf 17.9402906 2.3819359 2.3593999 1.5204040
## 16 4.1436832 17.9402906 Inf 1.8128762 4.0614229 1.7736900
## 17 10.9260957 2.3819359 1.8128762 Inf 2.1236295 4.1216768
## 18 2.4988430 2.3593999 4.0614229 2.1236295 Inf 7.3770134
## 19 2.6533076 1.5204040 1.7736900 4.1216768 7.3770134 Inf
## 20 6.8104820 1.9048185 1.3127682 20.1637123 1.2397138 1.9692742
## 21 0.7819314 0.5955836 0.4291168 0.7574266 0.3227683 0.3712654
## 22 3.2522245 6.9525529 39.5872370 1.7438082 7.7447797 2.2843139
## 23 2.3907205 3.2337246 7.9936563 1.6425805 25.4820846 3.2547627
## 24 10.2701072 2.2801575 1.5884365 60.0618828 1.5639427 2.5881150
## 25 0.8296907 0.5057392 0.3970775 1.1606193 0.3840524 0.5393736
## 26 0.5271358 0.3657047 0.2908149 0.6428268 0.2677528 0.3463453
## 20 21 22 23 24 25
## 1 1.1345853 3.107362e-01 8.2112870 33.8230039 1.4158345 0.3645592
## 2 1.1330433 2.849947e-01 1.4253095 2.1196516 1.3696484 0.4238061
## 3 3.6324913 4.758490e-01 1.3130391 1.5143462 4.7628487 0.8486702
## 4 29.9701118 9.833744e-01 0.9299537 0.8882062 19.9652650 2.2823720
## 5 4.6042132 1.035018e+00 0.5475547 0.5338231 3.5212857 5.3957603
## 6 1.6991625 1.351456e+00 0.3522507 0.3371500 1.3701825 40.3708093
## 7 1.0487527 2.052794e+00 0.2814304 0.2632865 0.8686980 15.0713385
## 8 1.1621645 1.451965e+01 0.3294745 0.2932882 0.9546165 4.1158763
## 9 1.1536407 5.559300e+04 0.3705074 0.3194299 0.9658705 2.0489215
## 10 2.5217238 1.076294e+01 0.5179511 0.4439153 1.9414828 3.3576125
## 11 7.8498193 2.986950e+00 0.7649516 0.6497139 5.1280786 2.9128598
## 12 1.9821764 4.272331e+00 0.6888900 0.5463305 1.7168354 1.1996088
## 13 5.0425329 1.349639e+00 1.6289480 1.1824281 5.0654442 0.9951836
## 14 6.8104820 7.819314e-01 3.2522245 2.3907205 10.2701072 0.8296907
## 15 1.9048185 5.955836e-01 6.9525529 3.2337246 2.2801575 0.5057392
## 16 1.3127682 4.291168e-01 39.5872370 7.9936563 1.5884365 0.3970775
## 17 20.1637123 7.574266e-01 1.7438082 1.6425805 60.0618828 1.1606193
## 18 1.2397138 3.227683e-01 7.7447797 25.4820846 1.5639427 0.3840524
## 19 1.9692742 3.712654e-01 2.2843139 3.2547627 2.5881150 0.5393736
## 20 Inf 1.146627e+00 1.1834500 1.0574898 102.9402876 1.8718208
## 21 1.1466275 Inf 0.3700170 0.3189087 0.9607904 2.0242994
## 22 1.1834500 3.700170e-01 Inf 25.8562968 1.4491358 0.3685640
## 23 1.0574898 3.189087e-01 25.8562968 Inf 1.3008578 0.3452002
## 24 102.9402876 9.607904e-01 1.4491358 1.3008578 Inf 1.4681046
## 25 1.8718208 2.024299e+00 0.3685640 0.3452002 1.4681046 Inf
## 26 0.9313453 2.483530e+00 0.2679517 0.2486176 0.7774627 8.2825307
## 26
## 1 0.2566404
## 2 0.2827070
## 3 0.4894080
## 4 1.0103843
## 5 1.6530533
## 6 5.1193581
## 7 74.3528586
## 8 7.2215837
## 9 2.5117609
## 10 2.4426865
## 11 1.5461005
## 12 0.9815454
## 13 0.6702803
## 14 0.5271358
## 15 0.3657047
## 16 0.2908149
## 17 0.6428268
## 18 0.2677528
## 19 0.3463453
## 20 0.9313453
## 21 2.4835301
## 22 0.2679517
## 23 0.2486176
## 24 0.7774627
## 25 8.2825307
## 26 Inf#dinormalisasi
diag(W3b_1) <-0
rtot<-rowSums(W3b_1,na.rm=TRUE)
rtot## 1 2 3 4 5 6
## 574.33261 46.20918 60.17243 110.77408 57.17419 84.08581
## 7 8 9 10 11 12
## 123.06008 71.43929 55644.71791 78.72017 75.71540 47.94822
## 13 14 15 16 17 18
## 63.31939 82.93248 65.22084 95.35610 147.72471 568.79949
## 19 20 21 22 23 24
## 69.18316 205.46621 55643.94853 108.42188 116.03947 236.29537
## 25 26
## 97.56445 113.97039W3b_1<-W3b_1/rtot #row-normalized
rowSums(W3b_1,na.rm=TRUE)## 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
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1W3b_1 #matriks bobot power distance dengan alpha=2 tanpa memperhatikan bentuk bumi## 1 2 3 4 5
## 1 0.000000e+00 6.519858e-03 3.633543e-03 1.742159e-03 1.037478e-03
## 2 8.103513e-02 0.000000e+00 1.061200e-01 2.665432e-02 1.738192e-02
## 3 3.468137e-02 8.149443e-02 0.000000e+00 8.053839e-02 3.698796e-02
## 4 9.032606e-03 1.111880e-02 4.374841e-02 0.000000e+00 9.107474e-02
## 5 1.042179e-02 1.404837e-02 3.892763e-02 1.764559e-01 0.000000e+00
## 6 4.309573e-03 5.341047e-03 1.081962e-02 2.709159e-02 8.314263e-02
## 7 2.230255e-03 2.529185e-03 4.509422e-03 9.635553e-03 1.750604e-02
## 8 4.109628e-03 4.083169e-03 7.056206e-03 1.537022e-02 1.940558e-02
## 9 5.595633e-06 5.137065e-06 8.588193e-06 1.779005e-05 1.875237e-05
## 10 5.566021e-03 5.159024e-03 9.689520e-03 2.544084e-02 2.464254e-02
## 11 8.581427e-03 7.874591e-03 1.709122e-02 6.213890e-02 4.094343e-02
## 12 1.057241e-02 8.278198e-03 1.398553e-02 2.835669e-02 2.141101e-02
## 13 1.690424e-02 1.133497e-02 2.100656e-02 4.009210e-02 2.052296e-02
## 14 2.731906e-02 1.604629e-02 3.299135e-02 4.203218e-02 1.694010e-02
## 15 3.500161e-02 1.405130e-02 1.879290e-02 1.953078e-02 1.082832e-02
## 16 4.304229e-02 1.155863e-02 1.240866e-02 1.025018e-02 5.971326e-03
## 17 1.275999e-02 1.281069e-02 5.541452e-02 8.513691e-02 1.913677e-02
## 18 8.807626e-01 7.339596e-03 4.184052e-03 1.922241e-03 1.122896e-03
## 19 8.623514e-02 2.257106e-01 1.569481e-01 2.908834e-02 1.564847e-02
## 20 5.522004e-03 5.514500e-03 1.767926e-02 1.458639e-01 2.240862e-02
## 21 5.584366e-06 5.121755e-06 8.551676e-06 1.767262e-05 1.860074e-05
## 22 7.573459e-02 1.314596e-02 1.211046e-02 8.577177e-03 5.050224e-03
## 23 2.914784e-01 1.826664e-02 1.305027e-02 7.654345e-03 4.600358e-03
## 24 5.991799e-03 5.796340e-03 2.015634e-02 8.449283e-02 1.490205e-02
## 25 3.736599e-03 4.343857e-03 8.698560e-03 2.339348e-02 5.530457e-02
## 26 2.251817e-03 2.480530e-03 4.294168e-03 8.865323e-03 1.450424e-02
## 6 7 8 9 10
## 1 6.309479e-04 4.778682e-04 0.0005111828 0.0005421378 0.0007628996
## 2 9.718983e-03 6.735494e-03 0.0063125705 0.0061860122 0.0087887134
## 3 1.511949e-02 9.222327e-03 0.0083774311 0.0079419698 0.0126762478
## 4 2.056454e-02 1.070424e-02 0.0099124053 0.0089364097 0.0180792051
## 5 1.222775e-01 3.767950e-02 0.0242473214 0.0182507239 0.0339290385
## 6 0.000000e+00 1.087007e-01 0.0284325983 0.0162318405 0.0249127781
## 7 7.427421e-02 0.000000e+00 0.0419148979 0.0168678085 0.0190794621
## 8 3.346587e-02 7.220187e-02 0.0000000000 0.2088691308 0.1102294358
## 9 2.452825e-05 3.730370e-05 0.0002681560 0.0000000000 0.0001972862
## 10 2.661086e-02 2.982616e-02 0.1000342538 0.1394552083 0.0000000000
## 11 2.768383e-02 2.177123e-02 0.0359085584 0.0398189657 0.1720642461
## 12 1.896704e-02 1.967411e-02 0.0451391588 0.0889314811 0.1460563958
## 13 1.323890e-02 1.095667e-02 0.0165165369 0.0213645777 0.0408867313
## 14 9.161202e-03 6.799271e-03 0.0083732164 0.0094570056 0.0165089997
## 15 7.066502e-03 5.818957e-03 0.0075856605 0.0091410957 0.0135091220
## 16 3.906518e-03 3.181211e-03 0.0038911354 0.0045049186 0.0063409419
## 17 7.629026e-03 4.857828e-03 0.0051158958 0.0051511493 0.0094424775
## 18 6.719596e-04 5.045708e-04 0.0005378384 0.0005686709 0.0008080141
## 19 8.068944e-03 5.513463e-03 0.0053921431 0.0053835747 0.0080588680
## 20 8.269790e-03 5.104259e-03 0.0056562320 0.0056147465 0.0122731803
## 21 2.428756e-05 3.689159e-05 0.0002609385 0.9990843928 0.0001934252
## 22 3.248889e-03 2.595698e-03 0.0030388195 0.0034172748 0.0047771825
## 23 2.905477e-03 2.268940e-03 0.0025274864 0.0027527691 0.0038255543
## 24 5.798601e-03 3.676322e-03 0.0040399287 0.0040875557 0.0082163386
## 25 4.137861e-01 1.544757e-01 0.0421862296 0.0210006973 0.0344143023
## 26 4.491832e-02 6.523875e-01 0.0633636861 0.0220387157 0.0214326424
## 11 12 13 14 15
## 1 1.131306e-03 8.826386e-04 1.863670e-03 3.944818e-03 3.974760e-03
## 2 1.290280e-02 8.589740e-03 1.553205e-02 2.879857e-02 1.983236e-02
## 3 2.150601e-02 1.114433e-02 2.210519e-02 4.547023e-02 2.036961e-02
## 4 4.247267e-02 1.227411e-02 2.291698e-02 3.146795e-02 1.149921e-02
## 5 5.422112e-02 1.795600e-02 2.272881e-02 2.457201e-02 1.235229e-02
## 6 2.492801e-02 1.081557e-02 9.969330e-03 9.035546e-03 5.481105e-03
## 7 1.339523e-02 7.665673e-03 5.637653e-03 4.582155e-03 3.084000e-03
## 8 3.805792e-02 3.029624e-02 1.463924e-02 9.720303e-03 6.925364e-03
## 9 5.418140e-05 7.663092e-05 2.431124e-05 1.409465e-05 1.071422e-05
## 10 1.654965e-01 8.896251e-02 3.288767e-02 1.739240e-02 1.119251e-02
## 11 0.000000e+00 7.463242e-02 7.417969e-02 3.730947e-02 1.830464e-02
## 12 1.178526e-01 0.000000e+00 1.087395e-01 3.698773e-02 2.968526e-02
## 13 8.870181e-02 8.234229e-02 0.000000e+00 1.544067e-01 8.015768e-02
## 14 3.406266e-02 2.138481e-02 1.178903e-01 0.000000e+00 9.740786e-02
## 15 2.125001e-02 2.182363e-02 7.782076e-02 1.238603e-01 0.000000e+00
## 16 9.431246e-03 9.096812e-03 2.349674e-02 4.345483e-02 1.881399e-01
## 17 2.080154e-02 8.801184e-03 2.497261e-02 7.396255e-02 1.612415e-02
## 18 1.213216e-03 9.275528e-04 1.994021e-03 4.393188e-03 4.148034e-03
## 19 1.279620e-02 8.075796e-03 1.680069e-02 3.835193e-02 2.197651e-02
## 20 3.820492e-02 9.647214e-03 2.454191e-02 3.314648e-02 9.270714e-03
## 21 5.367969e-05 7.677980e-05 2.425491e-05 1.405241e-05 1.070347e-05
## 22 7.055325e-03 6.353791e-03 1.502416e-02 2.999602e-02 6.412500e-02
## 23 5.599077e-03 4.708144e-03 1.018988e-02 2.060265e-02 2.786745e-02
## 24 2.170198e-02 7.265633e-03 2.143692e-02 4.346301e-02 9.649607e-03
## 25 2.985575e-02 1.229555e-02 1.020027e-02 8.504027e-03 5.183642e-03
## 26 1.356581e-02 8.612284e-03 5.881180e-03 4.625200e-03 3.208769e-03
## 16 17 18 19 20
## 1 7.146286e-03 3.282010e-03 8.722773e-01 1.038774e-02 1.975485e-03
## 2 2.385210e-02 4.095410e-02 9.034478e-02 3.379279e-01 2.451988e-02
## 3 1.966418e-02 1.360439e-01 3.955111e-02 1.804509e-01 6.036804e-02
## 4 8.823520e-03 1.135358e-01 9.870265e-03 1.816692e-02 2.705517e-01
## 5 9.959081e-03 4.944492e-02 1.117117e-02 1.893531e-02 8.052958e-02
## 6 4.430121e-03 1.340292e-02 4.545479e-03 6.638873e-03 2.020748e-02
## 7 2.465039e-03 5.831470e-03 2.332191e-03 3.099614e-03 8.522282e-03
## 8 5.193829e-03 1.057883e-02 4.282268e-03 5.221853e-03 1.626786e-02
## 9 7.719897e-06 1.367519e-05 5.812946e-06 6.693406e-06 2.073226e-05
## 10 7.680974e-03 1.771957e-02 5.838377e-03 7.082530e-03 3.203403e-02
## 11 1.187773e-02 4.058489e-02 9.114087e-03 1.169223e-02 1.036753e-01
## 12 1.809111e-02 2.711576e-02 1.100336e-02 1.165234e-02 4.133994e-02
## 13 3.538502e-02 5.826133e-02 1.791233e-02 1.835653e-02 7.963647e-02
## 14 4.996454e-02 1.317469e-01 3.013105e-02 3.199359e-02 8.212081e-02
## 15 2.750699e-01 3.652109e-02 3.617555e-02 2.331163e-02 2.920567e-02
## 16 0.000000e+00 1.901164e-02 4.259216e-02 1.860070e-02 1.376701e-02
## 17 1.227199e-02 0.000000e+00 1.437559e-02 2.790107e-02 1.364952e-01
## 18 7.140342e-03 3.733529e-03 0.000000e+00 1.296944e-02 2.179527e-03
## 19 2.563760e-02 5.957631e-02 1.066302e-01 0.000000e+00 2.846465e-02
## 20 6.389217e-03 9.813639e-02 6.033663e-03 9.584419e-03 0.000000e+00
## 21 7.711833e-06 1.361202e-05 5.800600e-06 6.672161e-06 2.060651e-05
## 22 3.651222e-01 1.608355e-02 7.143189e-02 2.106875e-02 1.091523e-02
## 23 6.888739e-02 1.415536e-02 2.195984e-01 2.804875e-02 9.113191e-03
## 24 6.722250e-03 2.541814e-01 6.618592e-03 1.095288e-02 4.356424e-01
## 25 4.069899e-03 1.189592e-02 3.936397e-03 5.528382e-03 1.918548e-02
## 26 2.551670e-03 5.640297e-03 2.349319e-03 3.038906e-03 8.171818e-03
## 21 22 23 24 25
## 1 0.0005410387 1.429709e-02 5.889097e-02 2.465182e-03 6.347528e-04
## 2 0.0061674904 3.084473e-02 4.587079e-02 2.964018e-02 9.171469e-03
## 3 0.0079080909 2.182128e-02 2.516678e-02 7.915334e-02 1.410397e-02
## 4 0.0088772965 8.395047e-03 8.018177e-03 1.802341e-01 2.060384e-02
## 5 0.0181028956 9.576957e-03 9.336785e-03 6.158873e-02 9.437406e-02
## 6 0.0160723395 4.189181e-03 4.009595e-03 1.629505e-02 4.801144e-01
## 7 0.0166812325 2.286935e-03 2.139496e-03 7.059137e-03 1.224714e-01
## 8 0.2032445747 4.611951e-03 4.105418e-03 1.336262e-02 5.761362e-02
## 9 0.9990705789 6.658446e-06 5.740525e-06 1.735781e-05 3.682149e-05
## 10 0.1367241006 6.579649e-03 5.639156e-03 2.466309e-02 4.265251e-02
## 11 0.0394497043 1.010299e-02 8.581002e-03 6.772835e-02 3.847117e-02
## 12 0.0891030215 1.436737e-02 1.139418e-02 3.580603e-02 2.501884e-02
## 13 0.0213147820 2.572589e-02 1.867403e-02 7.999831e-02 1.571689e-02
## 14 0.0094285313 3.921533e-02 2.882731e-02 1.238370e-01 1.000441e-02
## 15 0.0091317989 1.066002e-01 4.958116e-02 3.496057e-02 7.754257e-03
## 16 0.0045001506 4.151516e-01 8.382952e-02 1.665794e-02 4.164154e-03
## 17 0.0051272843 1.180444e-02 1.111920e-02 4.065798e-01 7.856636e-03
## 18 0.0005674553 1.361601e-02 4.479977e-02 2.749550e-03 6.751982e-04
## 19 0.0053664129 3.301835e-02 4.704559e-02 3.740961e-02 7.796314e-03
## 20 0.0055806135 5.759828e-03 5.146782e-03 5.010084e-01 9.110115e-03
## 21 0.0000000000 6.649727e-06 5.731238e-06 1.726675e-05 3.637951e-05
## 22 0.0034127526 0.000000e+00 2.384786e-01 1.336571e-02 3.399351e-03
## 23 0.0027482779 2.228233e-01 0.000000e+00 1.121048e-02 2.974851e-03
## 24 0.0040660566 6.132730e-03 5.505219e-03 0.000000e+00 6.213006e-03
## 25 0.0207483294 3.777647e-03 3.538176e-03 1.504754e-02 0.000000e+00
## 26 0.0217910125 2.351064e-03 2.181422e-03 6.821621e-03 7.267266e-02
## 26
## 1 4.468498e-04
## 2 6.117983e-03
## 3 8.133426e-03
## 4 9.121126e-03
## 5 2.891258e-02
## 6 6.088255e-02
## 7 6.041997e-01
## 8 1.010870e-01
## 9 4.513925e-05
## 10 3.103000e-02
## 11 2.041990e-02
## 12 2.047095e-02
## 13 1.058570e-02
## 14 6.356205e-03
## 15 5.607175e-03
## 16 3.049777e-03
## 17 4.351518e-03
## 18 4.707332e-04
## 19 5.006208e-03
## 20 4.532839e-03
## 21 4.463253e-05
## 22 2.471380e-03
## 23 2.142526e-03
## 24 3.290216e-03
## 25 8.489292e-02
## 26 0.000000e+00class(W3b_1) #matriks array## [1] "matrix" "array"summary matriks:
W3b_1 = mat2listw(W3b_1,style='W')
summary(W3b_1)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 650
## Percentage nonzero weights: 96.15385
## Average number of links: 25
## Link number distribution:
##
## 25
## 26
## 26 least connected regions:
## 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 with 25 links
## 26 most connected regions:
## 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 with 25 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 26 676 26 14.35111 108.236plot(petajabar, col='gray', border='blue', main ="Power Distance Weigth alpha=2, Euclidean")
plot(W3b_1, longlat, col='red', lwd=2, add=TRUE)
Exponential Distance Weigth
alpha=1
W4<-exp((-alpha)*m.djarak)
W4## 1 2 3 4 5 6 7
## 1 1.0000000 0.5964426 0.5004567 0.3679858 0.2737680 0.1899112 0.1482557
## 2 0.5964426 1.0000000 0.6366198 0.4061403 0.3276525 0.2248786 0.1665488
## 3 0.5004567 0.6366198 1.0000000 0.6349208 0.5115537 0.3504933 0.2612188
## 4 0.3679858 0.4061403 0.6349208 1.0000000 0.7299099 0.5155329 0.3991815
## 5 0.2737680 0.3276525 0.5115537 0.7299099 1.0000000 0.6850903 0.5059516
## 6 0.1899112 0.2248786 0.3504933 0.5155329 0.6850903 1.0000000 0.7183724
## 7 0.1482557 0.1665488 0.2612188 0.3991815 0.5059516 0.7183724 1.0000000
## 8 0.1579355 0.1569954 0.2445185 0.3850761 0.4277084 0.5237499 0.6438377
## 9 0.1666091 0.1540647 0.2353781 0.3660164 0.3757059 0.4248740 0.4995318
## 10 0.2207505 0.2082155 0.3182236 0.4933045 0.4877350 0.5011150 0.5206814
## 11 0.2892136 0.2738782 0.4151706 0.6306358 0.5666818 0.5012208 0.4589232
## 12 0.2454858 0.2044868 0.2948864 0.4241779 0.3727117 0.3504245 0.3571517
## 13 0.3803851 0.3071623 0.4201809 0.5338566 0.4159350 0.3354747 0.3010186
## 14 0.5146017 0.4202679 0.5463161 0.5853136 0.4301232 0.3175059 0.2640279
## 15 0.5158935 0.3518337 0.4052488 0.4122905 0.3042385 0.2292353 0.1972574
## 16 0.6104229 0.3857690 0.3987930 0.3636781 0.2657426 0.1942827 0.1627339
## 17 0.4826982 0.4833951 0.7050323 0.7542906 0.5516969 0.3898575 0.3071365
## 18 0.9563056 0.6129800 0.5229763 0.3842933 0.2861414 0.1983907 0.1546432
## 19 0.6640424 0.7764211 0.7382489 0.4941477 0.3824760 0.2622605 0.1980666
## 20 0.3910898 0.3908401 0.5917424 0.8330470 0.6274828 0.4643327 0.3766344
## 21 0.1663063 0.1536329 0.2346506 0.3647957 0.3742102 0.4230777 0.4976019
## 22 0.7054107 0.4327406 0.4178251 0.3545246 0.2588757 0.1854630 0.1518270
## 23 0.8420240 0.5031539 0.4436941 0.3460861 0.2544431 0.1786692 0.1424331
## 24 0.4315314 0.4255098 0.6324133 0.7994740 0.5868979 0.4255807 0.3420099
## 25 0.1908606 0.2152212 0.3377323 0.5158589 0.6501836 0.8543742 0.7729143
## 26 0.1389063 0.1524753 0.2394441 0.3697797 0.4594250 0.6427693 0.8905006
## 8 9 10 11 12 13 14
## 1 0.1579355 0.1666091 0.2207505 0.2892136 0.2454858 0.3803851 0.5146017
## 2 0.1569954 0.1540647 0.2082155 0.2738782 0.2044868 0.3071623 0.4202679
## 3 0.2445185 0.2353781 0.3182236 0.4151706 0.2948864 0.4201809 0.5463161
## 4 0.3850761 0.3660164 0.4933045 0.6306358 0.4241779 0.5338566 0.5853136
## 5 0.4277084 0.3757059 0.4877350 0.5666818 0.3727117 0.4159350 0.4301232
## 6 0.5237499 0.4248740 0.5011150 0.5012208 0.3504245 0.3354747 0.3175059
## 7 0.6438377 0.4995318 0.5206814 0.4589232 0.3571517 0.3010186 0.2640279
## 8 1.0000000 0.7719176 0.7002241 0.5452726 0.5067533 0.3761190 0.3011857
## 9 0.7719176 1.0000000 0.7394755 0.5621869 0.6161479 0.4232576 0.3233002
## 10 0.7002241 0.7394755 1.0000000 0.7580145 0.6853130 0.5371387 0.4254413
## 11 0.5452726 0.5621869 0.7580145 1.0000000 0.6566051 0.6557640 0.5515766
## 12 0.5067533 0.6161479 0.6853130 0.6566051 1.0000000 0.6453609 0.4719393
## 13 0.3761190 0.4232576 0.5371387 0.6557640 0.6453609 1.0000000 0.7262835
## 14 0.3011857 0.3233002 0.4254413 0.5515766 0.4719393 0.7262835 1.0000000
## 15 0.2413018 0.2738669 0.3446064 0.4276589 0.4324919 0.6415467 0.7033952
## 16 0.1936551 0.2174587 0.2763676 0.3483711 0.3417433 0.5126972 0.6118582
## 17 0.3165406 0.3177911 0.4288270 0.5652650 0.4160264 0.5941374 0.7389468
## 18 0.1639845 0.1723397 0.2287640 0.3000544 0.2524007 0.3910271 0.5312078
## 19 0.1945112 0.1942580 0.2620412 0.3454806 0.2624095 0.3955224 0.5412294
## 20 0.3954972 0.3941466 0.5327380 0.6998289 0.4915072 0.6406170 0.6816852
## 21 0.7691763 0.9957678 0.7372607 0.5606771 0.6164353 0.4228330 0.3227498
## 22 0.1751410 0.1934253 0.2492022 0.3187462 0.2997435 0.4567983 0.5743534
## 23 0.1577862 0.1704448 0.2229308 0.2892047 0.2584843 0.3986669 0.5237458
## 24 0.3593370 0.3614924 0.4878809 0.6430110 0.4661745 0.6412632 0.7319511
## 25 0.6108454 0.4972740 0.5794136 0.5565921 0.4013105 0.3669914 0.3335885
## 26 0.6892711 0.5320738 0.5273809 0.4474316 0.3644532 0.2948056 0.2522506
## 15 16 17 18 19 20 21
## 1 0.5158935 0.6104229 0.4826982 0.9563056 0.6640424 0.3910898 0.1663063
## 2 0.3518337 0.3857690 0.4833951 0.6129800 0.7764211 0.3908401 0.1536329
## 3 0.4052488 0.3987930 0.7050323 0.5229763 0.7382489 0.5917424 0.2346506
## 4 0.4122905 0.3636781 0.7542906 0.3842933 0.4941477 0.8330470 0.3647957
## 5 0.3042385 0.2657426 0.5516969 0.2861414 0.3824760 0.6274828 0.3742102
## 6 0.2292353 0.1942827 0.3898575 0.1983907 0.2622605 0.4643327 0.4230777
## 7 0.1972574 0.1627339 0.3071365 0.1546432 0.1980666 0.3766344 0.4976019
## 8 0.2413018 0.1936551 0.3165406 0.1639845 0.1945112 0.3954972 0.7691763
## 9 0.2738669 0.2174587 0.3177911 0.1723397 0.1942580 0.3941466 0.9957678
## 10 0.3446064 0.2763676 0.4288270 0.2287640 0.2620412 0.5327380 0.7372607
## 11 0.4276589 0.3483711 0.5652650 0.3000544 0.3454806 0.6998289 0.5606771
## 12 0.4324919 0.3417433 0.4160264 0.2524007 0.2624095 0.4915072 0.6164353
## 13 0.6415467 0.5126972 0.5941374 0.3910271 0.3955224 0.6406170 0.4228330
## 14 0.7033952 0.6118582 0.7389468 0.5312078 0.5412294 0.6816852 0.3227498
## 15 1.0000000 0.7897063 0.5231222 0.5215097 0.4444137 0.4845387 0.2736865
## 16 0.7897063 1.0000000 0.4758254 0.6088370 0.4719587 0.4177875 0.2172831
## 17 0.5231222 0.4758254 1.0000000 0.5034778 0.6110574 0.8003572 0.3169454
## 18 0.5215097 0.6088370 0.5034778 1.0000000 0.6919929 0.4073306 0.1720157
## 19 0.4444137 0.4719587 0.6110574 0.6919929 1.0000000 0.4903667 0.1937501
## 20 0.4845387 0.4177875 0.8003572 0.4073306 0.4903667 1.0000000 0.3930277
## 21 0.2736865 0.2172831 0.3169454 0.1720157 0.1937501 0.3930277 1.0000000
## 22 0.6843731 0.8530509 0.4689450 0.6981428 0.5160041 0.3988253 0.1932149
## 23 0.5734444 0.7020900 0.4582890 0.8202886 0.5744776 0.3781600 0.1701987
## 24 0.5156932 0.4522854 0.8789450 0.4494948 0.5370866 0.9061400 0.3605225
## 25 0.2450810 0.2045492 0.3952531 0.1991628 0.2562459 0.4814674 0.4951721
## 26 0.1913566 0.1565547 0.2872942 0.1447771 0.1828291 0.3547995 0.5301744
## 22 23 24 25 26
## 1 0.7054107 0.8420240 0.4315314 0.1908606 0.1389063
## 2 0.4327406 0.5031539 0.4255098 0.2152212 0.1524753
## 3 0.4178251 0.4436941 0.6324133 0.3377323 0.2394441
## 4 0.3545246 0.3460861 0.7994740 0.5158589 0.3697797
## 5 0.2588757 0.2544431 0.5868979 0.6501836 0.4594250
## 6 0.1854630 0.1786692 0.4255807 0.8543742 0.6427693
## 7 0.1518270 0.1424331 0.3420099 0.7729143 0.8905006
## 8 0.1751410 0.1577862 0.3593370 0.6108454 0.6892711
## 9 0.1934253 0.1704448 0.3614924 0.4972740 0.5320738
## 10 0.2492022 0.2229308 0.4878809 0.5794136 0.5273809
## 11 0.3187462 0.2892047 0.6430110 0.5565921 0.4474316
## 12 0.2997435 0.2584843 0.4661745 0.4013105 0.3644532
## 13 0.4567983 0.3986669 0.6412632 0.3669914 0.2948056
## 14 0.5743534 0.5237458 0.7319511 0.3335885 0.2522506
## 15 0.6843731 0.5734444 0.5156932 0.2450810 0.1913566
## 16 0.8530509 0.7020900 0.4522854 0.2045492 0.1565547
## 17 0.4689450 0.4582890 0.8789450 0.3952531 0.2872942
## 18 0.6981428 0.8202886 0.4494948 0.1991628 0.1447771
## 19 0.5160041 0.5744776 0.5370866 0.2562459 0.1828291
## 20 0.3988253 0.3781600 0.9061400 0.4814674 0.3547995
## 21 0.1932149 0.1701987 0.3605225 0.4951721 0.5301744
## 22 1.0000000 0.8214696 0.4357431 0.1925904 0.1448810
## 23 0.8214696 1.0000000 0.4161253 0.1823150 0.1345859
## 24 0.4357431 0.4161253 1.0000000 0.4380955 0.3217041
## 25 0.1925904 0.1823150 0.4380955 1.0000000 0.7064726
## 26 0.1448810 0.1345859 0.3217041 0.7064726 1.0000000round(W4,4)## 1 2 3 4 5 6 7 8 9 10 11
## 1 1.0000 0.5964 0.5005 0.3680 0.2738 0.1899 0.1483 0.1579 0.1666 0.2208 0.2892
## 2 0.5964 1.0000 0.6366 0.4061 0.3277 0.2249 0.1665 0.1570 0.1541 0.2082 0.2739
## 3 0.5005 0.6366 1.0000 0.6349 0.5116 0.3505 0.2612 0.2445 0.2354 0.3182 0.4152
## 4 0.3680 0.4061 0.6349 1.0000 0.7299 0.5155 0.3992 0.3851 0.3660 0.4933 0.6306
## 5 0.2738 0.3277 0.5116 0.7299 1.0000 0.6851 0.5060 0.4277 0.3757 0.4877 0.5667
## 6 0.1899 0.2249 0.3505 0.5155 0.6851 1.0000 0.7184 0.5237 0.4249 0.5011 0.5012
## 7 0.1483 0.1665 0.2612 0.3992 0.5060 0.7184 1.0000 0.6438 0.4995 0.5207 0.4589
## 8 0.1579 0.1570 0.2445 0.3851 0.4277 0.5237 0.6438 1.0000 0.7719 0.7002 0.5453
## 9 0.1666 0.1541 0.2354 0.3660 0.3757 0.4249 0.4995 0.7719 1.0000 0.7395 0.5622
## 10 0.2208 0.2082 0.3182 0.4933 0.4877 0.5011 0.5207 0.7002 0.7395 1.0000 0.7580
## 11 0.2892 0.2739 0.4152 0.6306 0.5667 0.5012 0.4589 0.5453 0.5622 0.7580 1.0000
## 12 0.2455 0.2045 0.2949 0.4242 0.3727 0.3504 0.3572 0.5068 0.6161 0.6853 0.6566
## 13 0.3804 0.3072 0.4202 0.5339 0.4159 0.3355 0.3010 0.3761 0.4233 0.5371 0.6558
## 14 0.5146 0.4203 0.5463 0.5853 0.4301 0.3175 0.2640 0.3012 0.3233 0.4254 0.5516
## 15 0.5159 0.3518 0.4052 0.4123 0.3042 0.2292 0.1973 0.2413 0.2739 0.3446 0.4277
## 16 0.6104 0.3858 0.3988 0.3637 0.2657 0.1943 0.1627 0.1937 0.2175 0.2764 0.3484
## 17 0.4827 0.4834 0.7050 0.7543 0.5517 0.3899 0.3071 0.3165 0.3178 0.4288 0.5653
## 18 0.9563 0.6130 0.5230 0.3843 0.2861 0.1984 0.1546 0.1640 0.1723 0.2288 0.3001
## 19 0.6640 0.7764 0.7382 0.4941 0.3825 0.2623 0.1981 0.1945 0.1943 0.2620 0.3455
## 20 0.3911 0.3908 0.5917 0.8330 0.6275 0.4643 0.3766 0.3955 0.3941 0.5327 0.6998
## 21 0.1663 0.1536 0.2347 0.3648 0.3742 0.4231 0.4976 0.7692 0.9958 0.7373 0.5607
## 22 0.7054 0.4327 0.4178 0.3545 0.2589 0.1855 0.1518 0.1751 0.1934 0.2492 0.3187
## 23 0.8420 0.5032 0.4437 0.3461 0.2544 0.1787 0.1424 0.1578 0.1704 0.2229 0.2892
## 24 0.4315 0.4255 0.6324 0.7995 0.5869 0.4256 0.3420 0.3593 0.3615 0.4879 0.6430
## 25 0.1909 0.2152 0.3377 0.5159 0.6502 0.8544 0.7729 0.6108 0.4973 0.5794 0.5566
## 26 0.1389 0.1525 0.2394 0.3698 0.4594 0.6428 0.8905 0.6893 0.5321 0.5274 0.4474
## 12 13 14 15 16 17 18 19 20 21 22
## 1 0.2455 0.3804 0.5146 0.5159 0.6104 0.4827 0.9563 0.6640 0.3911 0.1663 0.7054
## 2 0.2045 0.3072 0.4203 0.3518 0.3858 0.4834 0.6130 0.7764 0.3908 0.1536 0.4327
## 3 0.2949 0.4202 0.5463 0.4052 0.3988 0.7050 0.5230 0.7382 0.5917 0.2347 0.4178
## 4 0.4242 0.5339 0.5853 0.4123 0.3637 0.7543 0.3843 0.4941 0.8330 0.3648 0.3545
## 5 0.3727 0.4159 0.4301 0.3042 0.2657 0.5517 0.2861 0.3825 0.6275 0.3742 0.2589
## 6 0.3504 0.3355 0.3175 0.2292 0.1943 0.3899 0.1984 0.2623 0.4643 0.4231 0.1855
## 7 0.3572 0.3010 0.2640 0.1973 0.1627 0.3071 0.1546 0.1981 0.3766 0.4976 0.1518
## 8 0.5068 0.3761 0.3012 0.2413 0.1937 0.3165 0.1640 0.1945 0.3955 0.7692 0.1751
## 9 0.6161 0.4233 0.3233 0.2739 0.2175 0.3178 0.1723 0.1943 0.3941 0.9958 0.1934
## 10 0.6853 0.5371 0.4254 0.3446 0.2764 0.4288 0.2288 0.2620 0.5327 0.7373 0.2492
## 11 0.6566 0.6558 0.5516 0.4277 0.3484 0.5653 0.3001 0.3455 0.6998 0.5607 0.3187
## 12 1.0000 0.6454 0.4719 0.4325 0.3417 0.4160 0.2524 0.2624 0.4915 0.6164 0.2997
## 13 0.6454 1.0000 0.7263 0.6415 0.5127 0.5941 0.3910 0.3955 0.6406 0.4228 0.4568
## 14 0.4719 0.7263 1.0000 0.7034 0.6119 0.7389 0.5312 0.5412 0.6817 0.3227 0.5744
## 15 0.4325 0.6415 0.7034 1.0000 0.7897 0.5231 0.5215 0.4444 0.4845 0.2737 0.6844
## 16 0.3417 0.5127 0.6119 0.7897 1.0000 0.4758 0.6088 0.4720 0.4178 0.2173 0.8531
## 17 0.4160 0.5941 0.7389 0.5231 0.4758 1.0000 0.5035 0.6111 0.8004 0.3169 0.4689
## 18 0.2524 0.3910 0.5312 0.5215 0.6088 0.5035 1.0000 0.6920 0.4073 0.1720 0.6981
## 19 0.2624 0.3955 0.5412 0.4444 0.4720 0.6111 0.6920 1.0000 0.4904 0.1938 0.5160
## 20 0.4915 0.6406 0.6817 0.4845 0.4178 0.8004 0.4073 0.4904 1.0000 0.3930 0.3988
## 21 0.6164 0.4228 0.3227 0.2737 0.2173 0.3169 0.1720 0.1938 0.3930 1.0000 0.1932
## 22 0.2997 0.4568 0.5744 0.6844 0.8531 0.4689 0.6981 0.5160 0.3988 0.1932 1.0000
## 23 0.2585 0.3987 0.5237 0.5734 0.7021 0.4583 0.8203 0.5745 0.3782 0.1702 0.8215
## 24 0.4662 0.6413 0.7320 0.5157 0.4523 0.8789 0.4495 0.5371 0.9061 0.3605 0.4357
## 25 0.4013 0.3670 0.3336 0.2451 0.2045 0.3953 0.1992 0.2562 0.4815 0.4952 0.1926
## 26 0.3645 0.2948 0.2523 0.1914 0.1566 0.2873 0.1448 0.1828 0.3548 0.5302 0.1449
## 23 24 25 26
## 1 0.8420 0.4315 0.1909 0.1389
## 2 0.5032 0.4255 0.2152 0.1525
## 3 0.4437 0.6324 0.3377 0.2394
## 4 0.3461 0.7995 0.5159 0.3698
## 5 0.2544 0.5869 0.6502 0.4594
## 6 0.1787 0.4256 0.8544 0.6428
## 7 0.1424 0.3420 0.7729 0.8905
## 8 0.1578 0.3593 0.6108 0.6893
## 9 0.1704 0.3615 0.4973 0.5321
## 10 0.2229 0.4879 0.5794 0.5274
## 11 0.2892 0.6430 0.5566 0.4474
## 12 0.2585 0.4662 0.4013 0.3645
## 13 0.3987 0.6413 0.3670 0.2948
## 14 0.5237 0.7320 0.3336 0.2523
## 15 0.5734 0.5157 0.2451 0.1914
## 16 0.7021 0.4523 0.2045 0.1566
## 17 0.4583 0.8789 0.3953 0.2873
## 18 0.8203 0.4495 0.1992 0.1448
## 19 0.5745 0.5371 0.2562 0.1828
## 20 0.3782 0.9061 0.4815 0.3548
## 21 0.1702 0.3605 0.4952 0.5302
## 22 0.8215 0.4357 0.1926 0.1449
## 23 1.0000 0.4161 0.1823 0.1346
## 24 0.4161 1.0000 0.4381 0.3217
## 25 0.1823 0.4381 1.0000 0.7065
## 26 0.1346 0.3217 0.7065 1.0000#dinormalisasi
diag(W4) <-0
rtot<-rowSums(W4,na.rm=TRUE)
rtot## 1 2 3 4 5 6 7 8
## 10.147293 8.967326 11.037739 12.464318 11.112340 10.086937 9.438460 10.008341
## 9 10 11 12 13 14 15 16
## 9.978805 11.473046 12.367465 10.434625 11.814843 12.424845 10.727791 10.233501
## 17 18 19 20 21 22 23 24
## 12.771153 10.372539 10.681298 13.023990 9.955166 10.181317 9.963211 13.046363
## 25 26
## 10.679566 9.156396W4<-W4/rtot #row-normalized
rowSums(W4,na.rm=TRUE)## 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
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1W4 #matriks bobot Exponential distance dengan alpha=1## 1 2 3 4 5 6 7
## 1 0.00000000 0.05877849 0.04931923 0.03626443 0.02697941 0.01871546 0.01461037
## 2 0.06651287 0.00000000 0.07099327 0.04529113 0.03653848 0.02507755 0.01857285
## 3 0.04534051 0.05767665 0.00000000 0.05752272 0.04634588 0.03175409 0.02366597
## 4 0.02952314 0.03258424 0.05093907 0.00000000 0.05855995 0.04136069 0.03202594
## 5 0.02463639 0.02948546 0.04603474 0.06568462 0.00000000 0.06165131 0.04553061
## 6 0.01882744 0.02229404 0.03474725 0.05110896 0.06791857 0.00000000 0.07121809
## 7 0.01570762 0.01764576 0.02767600 0.04229307 0.05360532 0.07611119 0.00000000
## 8 0.01578039 0.01568646 0.02443148 0.03847552 0.04273519 0.05233134 0.06433011
## 9 0.01669630 0.01543919 0.02358780 0.03667938 0.03765039 0.04257765 0.05005928
## 10 0.01924079 0.01814823 0.02773663 0.04299682 0.04251138 0.04367759 0.04538301
## 11 0.02338504 0.02214505 0.03356958 0.05099152 0.04582036 0.04052737 0.03710729
## 12 0.02352608 0.01959695 0.02826038 0.04065100 0.03571874 0.03358286 0.03422756
## 13 0.03219553 0.02599800 0.03556382 0.04518525 0.03520444 0.02839434 0.02547800
## 14 0.04141716 0.03382480 0.04396965 0.04710832 0.03461799 0.02555411 0.02125000
## 15 0.04808944 0.03279647 0.03777561 0.03843200 0.02835985 0.02136836 0.01838752
## 16 0.05964947 0.03769667 0.03896936 0.03553799 0.02596790 0.01898496 0.01590207
## 17 0.03779597 0.03785054 0.05520506 0.05906206 0.04319868 0.03052641 0.02404924
## 18 0.09219591 0.05909643 0.05041931 0.03704911 0.02758644 0.01912653 0.01490891
## 19 0.06216870 0.07268977 0.06911603 0.04626289 0.03580800 0.02455324 0.01854331
## 20 0.03002842 0.03000925 0.04543480 0.06396250 0.04817900 0.03565211 0.02891851
## 21 0.01670552 0.01543248 0.02357074 0.03664385 0.03758955 0.04249831 0.04998429
## 22 0.06928482 0.04250340 0.04103841 0.03482109 0.02542654 0.01821601 0.01491232
## 23 0.08451332 0.05050117 0.04453325 0.03473640 0.02553826 0.01793289 0.01429591
## 24 0.03307676 0.03261521 0.04847430 0.06127946 0.04498555 0.03262064 0.02621496
## 25 0.01787157 0.02015261 0.03162416 0.04830336 0.06088109 0.08000083 0.07237320
## 26 0.01517041 0.01665232 0.02615048 0.04038485 0.05017530 0.07019894 0.09725449
## 8 9 10 11 12 13 14
## 1 0.01556430 0.01641907 0.02175462 0.02850156 0.02419224 0.03748636 0.05071320
## 2 0.01750750 0.01718067 0.02321935 0.03054179 0.02280354 0.03425350 0.04686658
## 3 0.02215296 0.02132485 0.02883051 0.03761374 0.02671620 0.03806766 0.04949529
## 4 0.03089428 0.02936514 0.03957733 0.05059529 0.03403137 0.04283079 0.04695913
## 5 0.03848950 0.03380979 0.04389129 0.05099572 0.03354034 0.03743001 0.03870680
## 6 0.05192358 0.04212121 0.04967960 0.04969009 0.03474043 0.03325833 0.03147694
## 7 0.06821427 0.05292513 0.05516593 0.04862267 0.03784004 0.03189277 0.02797363
## 8 0.00000000 0.07712743 0.06996406 0.05448182 0.05063310 0.03758055 0.03009346
## 9 0.07735572 0.00000000 0.07410461 0.05633810 0.06174566 0.04241566 0.03239869
## 10 0.06103210 0.06445328 0.00000000 0.06606916 0.05973244 0.04681744 0.03708181
## 11 0.04408928 0.04545692 0.06129101 0.00000000 0.05309132 0.05302332 0.04459900
## 12 0.04856460 0.05904840 0.06567682 0.06292561 0.00000000 0.06184802 0.04522820
## 13 0.03183444 0.03582422 0.04546304 0.05550341 0.05462289 0.00000000 0.06147213
## 14 0.02424060 0.02602046 0.03424117 0.04439304 0.03798352 0.05845413 0.00000000
## 15 0.02249314 0.02552873 0.03212278 0.03986458 0.04031510 0.05980231 0.06556757
## 16 0.01892364 0.02124969 0.02700616 0.03404222 0.03339456 0.05009988 0.05978972
## 17 0.02478559 0.02488351 0.03357778 0.04426108 0.03257547 0.04652183 0.05786062
## 18 0.01580948 0.01661500 0.02205478 0.02892777 0.02433355 0.03769831 0.05121291
## 19 0.01821045 0.01818674 0.02453271 0.03234444 0.02456719 0.03702943 0.05067075
## 20 0.03036682 0.03026313 0.04090437 0.05373383 0.03773860 0.04918746 0.05234074
## 21 0.07726403 0.10002523 0.07405810 0.05632021 0.06192115 0.04247372 0.03242034
## 22 0.01720219 0.01899806 0.02447642 0.03130697 0.02944055 0.04486633 0.05641248
## 23 0.01583688 0.01710742 0.02237540 0.02902726 0.02594387 0.04001390 0.05256798
## 24 0.02754308 0.02770829 0.03739593 0.04928661 0.03573214 0.04915264 0.05610384
## 25 0.05719759 0.04656313 0.05425442 0.05211749 0.03757742 0.03436389 0.03123615
## 26 0.07527755 0.05810952 0.05759699 0.04886547 0.03980313 0.03219669 0.02754911
## 15 16 17 18 19 20 21
## 1 0.05084050 0.06015623 0.04756916 0.09424244 0.06544035 0.03854129 0.01638922
## 2 0.03923508 0.04301940 0.05390627 0.06835706 0.08658335 0.04358492 0.01713252
## 3 0.03671484 0.03612995 0.06387471 0.04738074 0.06688408 0.05361084 0.02125894
## 4 0.03307766 0.02917753 0.06051600 0.03083147 0.03964499 0.06683454 0.02926720
## 5 0.02737844 0.02391418 0.04964723 0.02574988 0.03441903 0.05646720 0.03367519
## 6 0.02272596 0.01926082 0.03864974 0.01966808 0.02600001 0.04603307 0.04194313
## 7 0.02089933 0.01724157 0.03254096 0.01638437 0.02098506 0.03990422 0.05272067
## 8 0.02411006 0.01934937 0.03162768 0.01638478 0.01943491 0.03951676 0.07685352
## 9 0.02744486 0.02179206 0.03184661 0.01727058 0.01946706 0.03949838 0.09978828
## 10 0.03003618 0.02408843 0.03737691 0.01993926 0.02283972 0.04643388 0.06426024
## 11 0.03457935 0.02816835 0.04570581 0.02426159 0.02793463 0.05658628 0.04533484
## 12 0.04144777 0.03275089 0.03986980 0.02418877 0.02514796 0.04710348 0.05907595
## 13 0.05430006 0.04339433 0.05028737 0.03309626 0.03347674 0.05422137 0.03578828
## 14 0.05661199 0.04924474 0.05947332 0.04275368 0.04356025 0.05486469 0.02597617
## 15 0.00000000 0.07361313 0.04876327 0.04861297 0.04142640 0.04516668 0.02551191
## 16 0.07716873 0.00000000 0.04649683 0.05949449 0.04611899 0.04082547 0.02123253
## 17 0.04096123 0.03725783 0.00000000 0.03942305 0.04784669 0.06266914 0.02481729
## 18 0.05027793 0.05869701 0.04853950 0.00000000 0.06671394 0.03927010 0.01658376
## 19 0.04160671 0.04418552 0.05720816 0.06478547 0.00000000 0.04590890 0.01813919
## 20 0.03720355 0.03207830 0.06145253 0.03127541 0.03765104 0.00000000 0.03017721
## 21 0.02749190 0.02182616 0.03183728 0.01727903 0.01946227 0.03947977 0.00000000
## 22 0.06721853 0.08378591 0.04605936 0.06857097 0.05068147 0.03917227 0.01897740
## 23 0.05755618 0.07046825 0.04599813 0.08233174 0.05765988 0.03795563 0.01708271
## 24 0.03952774 0.03466755 0.06737089 0.03445365 0.04116753 0.06945537 0.02763395
## 25 0.02294860 0.01915332 0.03701022 0.01864896 0.02399404 0.04508306 0.04636631
## 26 0.02089869 0.01709785 0.03137634 0.01581158 0.01996737 0.03874881 0.05790209
## 22 23 24 25 26
## 1 0.06951713 0.08298016 0.04252675 0.01880901 0.01368900
## 2 0.04825748 0.05610969 0.04745113 0.02400059 0.01700343
## 3 0.03785423 0.04019792 0.05729555 0.03059796 0.02169322
## 4 0.02844316 0.02776615 0.06414102 0.04138685 0.02966706
## 5 0.02329624 0.02289734 0.05281496 0.05851005 0.04134367
## 6 0.01838646 0.01771293 0.04219127 0.08470105 0.06372294
## 7 0.01608599 0.01509072 0.03623577 0.08188987 0.09434808
## 8 0.01749950 0.01576547 0.03590375 0.06103363 0.06886966
## 9 0.01938361 0.01708068 0.03622602 0.04983302 0.05332039
## 10 0.02172067 0.01943083 0.04252410 0.05050216 0.04596695
## 11 0.02577296 0.02338432 0.05199214 0.04500455 0.03617812
## 12 0.02872586 0.02477178 0.04467573 0.03845951 0.03492730
## 13 0.03866309 0.03374289 0.05427607 0.03106189 0.02495214
## 14 0.04622620 0.04215311 0.05891028 0.02684850 0.02030211
## 15 0.06379441 0.05345410 0.04807077 0.02284543 0.01783747
## 16 0.08335866 0.06860702 0.04419655 0.01998819 0.01529825
## 17 0.03671908 0.03588470 0.06882268 0.03094890 0.02249556
## 18 0.06730684 0.07908272 0.04333508 0.01920097 0.01395773
## 19 0.04830912 0.05378350 0.05028289 0.02399015 0.01711675
## 20 0.03062236 0.02903565 0.06957468 0.03696774 0.02724200
## 21 0.01940851 0.01709652 0.03621462 0.04974021 0.05325621
## 22 0.00000000 0.08068402 0.04279831 0.01891606 0.01423008
## 23 0.08245028 0.00000000 0.04176619 0.01829882 0.01350828
## 24 0.03339959 0.03189589 0.00000000 0.03357990 0.02465853
## 25 0.01803355 0.01707139 0.04102185 0.00000000 0.06615181
## 26 0.01582293 0.01469856 0.03513436 0.07715618 0.00000000class(W4) #matriks array## [1] "matrix" "array"summary matriks:
W4 = mat2listw(W4,style='W')
summary(W4)## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 650
## Percentage nonzero weights: 96.15385
## Average number of links: 25
## Link number distribution:
##
## 25
## 26
## 26 least connected regions:
## 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 with 25 links
## 26 most connected regions:
## 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 with 25 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 26 676 26 2.459963 104.2758plot(petajabar, col='gray', border='blue', main ="Exponential Alpha=1")
plot(W4, longlat, col='red', lwd=2, add=TRUE)
Spatial Contiguity Weigth
petajabar<-readOGR(dsn="petaJabar2", layer="Jabar2") #dsn diisi nama folder #layer diisi nama file dalam folder## OGR data source with driver: ESRI Shapefile
## Source: "D:\Kuliah S2 IPB\Bahan Kuliah\Semester 2 SSD 2020\STA533 Spasial\PRAKTIKUM\R\petaJabar2", layer: "Jabar2"
## with 26 features
## It has 7 fieldsplot(petajabar) #peta kosongan tanpa data
text(petajabar,'KABKOT',cex=0.5) #menambahkan nama wilayah pada peta
#peta sebaran persentase penduduk miskin di Jabar tahun 2015
library(raster)
colfunc<-colorRampPalette(c("white", "pink","red")) #menentukan warna peta
petajabar$miskin<-datajabar$p.miskin15
spplot(petajabar, "miskin", col.regions=colfunc(16),
main="Peta Persentase Penduduk Miskin di Jawa Barat Tahun 2015")
class(petajabar) #spatialPolygonsDataFrame## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"Rook
#Rook
W6<-poly2nb(petajabar,queen=FALSE)
W6 #matriks bobot Rook## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 102
## Percentage nonzero weights: 15.08876
## Average number of links: 3.923077class(W6) #nb## [1] "nb"par(mfrow=c(1,2))
#memetakan jabar dengan matriks bobot Rook
par(mai=c(0,0,0,0))
plot(petajabar,col='skyblue',border='white',main="Peta Persentase Kemiskinan Jabar \n Tahun 2015 dengan Rook")
xy<-coordinates(petajabar)
plot(W6,xy,col='red',lwd=2,add=TRUE)
Queen
#Queen
W7<-poly2nb(petajabar,queen=TRUE)
W7 #matriks bobot Queen## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 102
## Percentage nonzero weights: 15.08876
## Average number of links: 3.923077#memetakan jabar dengan matriks bobot Queen
par(mai=c(0,0,0,0))
plot(petajabar,col='skyblue',border='white',main="Peta Persentase Kemiskinan Jabar \n Tahun 2015 dengan Queen")
xy<-coordinates(petajabar)
plot(W7,xy,col='orange',lwd=2,add=TRUE)
Queen Vs Rook
par(mfrow=c(1,1))
plot(petajabar, col='skyblue',border="white",main="Matriks Kontiguty Queen vs Rook")
plot(W7, xy, add = TRUE, col = "orange")
plot(W6, xy, add = TRUE, col = "red")
Global Autocorrelation
Moran’s I
Digunakan K-Nearest Neighbor Weight dengan k=5:
WL1<-W1Cek normalitas:
moran(petajabar$miskin,WL1,n=length(WL1$neighbours),S0=Szero(WL1))## $I
## [1] 0.328805
##
## $K
## [1] 2.259555Karena kurtosisnya tidak mendekati 3, maka data tidak mengikuti sebaran normal sehingga Indeks Moran yang digunakan selanjutnya dengan asumsi acak (randomisasi)
Indeks Moran dengan Asumsi Randomisasi:
MI1 <- moran.test(petajabar$miskin,WL1,randomisation=TRUE)
MI1 ##
## Moran I test under randomisation
##
## data: petajabar$miskin
## weights: WL1
##
## Moran I statistic standard deviate = 3.4303, p-value = 0.0003015
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.3288050 -0.0400000 0.0115593Karena p-value = 0.0003015 < 0.05 berarti tolak Ho. alternative hypothesis: greater berarti pada taraf nyata 5%, terdapat autokorelasi spasial positif.
Uji moran juga dapat dilakukan dengan melibatkan simulasi monte carlo.
set.seed(123)
MMC<- moran.mc(petajabar$miskin,WL1, nsim=599)
# View results (including p-value)
MMC##
## Monte-Carlo simulation of Moran I
##
## data: petajabar$miskin
## weights: WL1
## number of simulations + 1: 600
##
## statistic = 0.32881, observed rank = 596, p-value = 0.006667
## alternative hypothesis: greaterGeary
Global Geary’s C
Geary’s C merupakan alternatif dari indeks Moran, yang memiliki nilai antara 0 s.d 2. Nilai 0 menunjukkan autokorelasi positif, 1 menunjukkan tidak ada autokorelasi, dan 2 menunjukkan autokorelasi negatif.
C1 <- geary.test(petajabar$miskin,WL1)
C1##
## Geary C test under randomisation
##
## data: petajabar$miskin
## weights: WL1
##
## Geary C statistic standard deviate = 3.0292, p-value = 0.001226
## alternative hypothesis: Expectation greater than statistic
## sample estimates:
## Geary C statistic Expectation Variance
## 0.67482267 1.00000000 0.01152329Dengan monte carlo:
GS1 <- geary.mc(petajabar$miskin,WL1, nsim=599)
GS1##
## Monte-Carlo simulation of Geary C
##
## data: petajabar$miskin
## weights: WL1
## number of simulations + 1: 600
##
## statistic = 0.67482, observed rank = 5, p-value = 0.008333
## alternative hypothesis: greaterLocal Autocorrelation
Local Moran’s I
Pendekatan ini termasuk ke dalam Local Indicators for Spatial Association (LISA), yang mengindentifikasi autokorelasi pada tingkat lokal.
petajabar## class : SpatialPolygonsDataFrame
## features : 26
## extent : 106.3705, 108.8338, -7.823398, -5.91377 (xmin, xmax, ymin, ymax)
## crs : NA
## variables : 8
## names : PROVNO, KABKOTNO, KODE2010, PROVINSI, KABKOT, SUMBER, IDSP2010, miskin
## min values : 32, 01, 3201, JAWA BARAT, BANDUNG, SP2010_BADAN PUSAT STATISTIK, 3201, 2.39749571489967
## max values : 32, 79, 3279, JAWA BARAT, TASIKMALAYA, SP2010_BADAN PUSAT STATISTIK, 3279, 16.2801622356294oid <- order(petajabar$miskin)
resI <- localmoran(petajabar$miskin,WL1)
head(resI)## Ii E.Ii Var.Ii Z.Ii Pr(z > 0)
## 1 0.34018182 -0.04 0.1596824 0.9513994 0.1707008
## 2 0.16607406 -0.04 0.1596824 0.5156973 0.3030329
## 3 -0.20510875 -0.04 0.1596824 -0.4131822 0.6602634
## 4 0.05778174 -0.04 0.1596824 0.2446974 0.4033454
## 5 -0.14671090 -0.04 0.1596824 -0.2670424 0.6052818
## 6 0.30991181 -0.04 0.1596824 0.8756491 0.1906104petajabar$z.li <- resI[,4]
petajabar$pvalue <- resI[,5]
lm.palette <- colorRampPalette(c("white","orange", "red"), space = "rgb")
spplot(petajabar, zcol="z.li", col.regions=lm.palette(20), main="Local Moran")
Warna yang lebih pekat berarti memiliki nilai Z_Score yang besar berarti cenderung memiliki korelasi positif dengan tetangganya.
moran.plot(petajabar$miskin,WL1)
Terdapat 4 kuadran dalam Indeks Moran Lokal. Tanda seperti diamond pada moral plot di atas berarti pengamatan tersebut memiliki pengaruh yang besar terhadap autokorelasi spasial pada data tesebut.
Getis-Ord Gi
Menurut Mendez (2020), pendekatan Getis-ord Gi dapat membantu mengidentifikasi pola penggerombolan berdasarkan ukuran autokorelasi pada level lokal.
local_g <- localG(petajabar$miskin,WL1)
local_g## [1] -2.82707329 -1.39534118 -0.72139195 -0.30142760 -0.36647984 1.36557202
## [7] 1.87842906 0.92463532 2.25953114 2.30507206 0.30640742 2.00075509
## [13] 0.30583182 -0.59520707 -2.09731098 -2.06698919 -1.32798436 -2.18837784
## [19] 0.03126852 -0.57504181 2.65719825 -2.30058195 -2.33323973 -0.70182887
## [25] 0.95071873 2.15101471
## attr(,"gstari")
## [1] FALSE
## attr(,"call")
## localG(x = petajabar$miskin, listw = WL1)
## attr(,"class")
## [1] "localG"Output di atas menghasilkan z-score, yang biasanya disajikan secara visual untuk mengidentifikasi cluster maupun hotspot.
petajabar$localg <- as.numeric(local_g)
lm.palette <- colorRampPalette(c("white","orange", "red"), space = "rgb")
spplot(petajabar, zcol="localg", col.regions=lm.palette(20), main="Local Gi")
Spatial Regression (Responsi Pertemuan 10)
Model Spasial Global
Tahapan Regresi Spasial
Tahapan regresi spasial, yaitu:
Eksplorasi Data
Regresi Klasik & Uji Asumsi
Matriks Pembobot Spasial
Uji Lagrange Multiplier
Regresi Spasial & Uji Asumsi
Kebaikan Model
Data Import
Silahkan download files yang ada pada link berikut ini:
https://github.com/raoy/SpatialReg
Data yang Anda download terdiri dari dua jenis data:
Data polygon (peta Jawa Barat, dengan extension .shp)
Dataframe (data pendidikan dan kemiskinan, diperoleh dari BPS)
library(openxlsx)
library(spdep)
library(rgdal)
library(raster)data.jabar = read.xlsx("Jabar Data (gabung).xlsx")
head(data.jabar)## PROVNO KABKOTNO KODE2010 PROVINSI KABKOT IDSP2010 Long Lat
## 1 32 1 3201 JAWA BARAT BOGOR 3201 106.7687 -6.561184
## 2 32 2 3202 JAWA BARAT SUKABUMI 3202 106.7101 -7.074623
## 3 32 3 3203 JAWA BARAT CIANJUR 3203 107.1578 -7.133713
## 4 32 4 3204 JAWA BARAT BANDUNG 3204 107.6108 -7.099969
## 5 32 5 3205 JAWA BARAT GARUT 3205 107.7889 -7.359586
## 6 32 6 3206 JAWA BARAT TASIKMALAYA 3206 108.1413 -7.496892
## p.miskin15 p.miskin16 j.miskin15 j.miskin16 AHH2015 AHH2016 EYS2015 EYS2016
## 1 8.959759 8.834574 487.10 490.80 70.59 70.65 11.83 12.05
## 2 8.960361 8.134848 217.86 198.66 70.03 70.14 12.13 12.18
## 3 12.214160 11.621474 273.90 261.39 69.28 69.39 11.83 11.88
## 4 7.998184 7.613293 281.04 272.65 73.07 73.10 12.13 12.42
## 5 12.805808 11.640370 325.67 298.52 70.69 70.76 11.65 11.69
## 6 11.994455 11.237185 208.12 195.61 68.36 68.54 12.44 12.46
## MYS2015 MYS2016 EXP2015 EXP2016 APM.SD15 APM.SMP15 APM.SMA15 APM.PT15
## 1 7.75 7.83 9368 9537 96.29364 72.97053 55.10088 11.835186
## 2 6.51 6.74 7849 8077 99.65285 75.76340 43.93547 9.924350
## 3 6.54 6.61 6877 7074 99.88696 78.40315 37.18006 3.862863
## 4 8.41 8.50 9375 9580 98.00367 82.13540 55.49216 17.042698
## 5 6.84 6.88 6875 7079 98.09334 75.28081 44.60243 5.708659
## 6 6.88 6.94 6934 7081 99.08656 76.97621 54.79954 12.158104
## APK.SD15 APK.SMP15 APK.SMA15 APK.PT15 APS.USIA15 APS.USIA2 APS.USIA3
## 1 108.8195 84.63248 67.93291 12.636548 99.04861 89.24145 62.22669
## 2 113.0554 82.99804 54.93650 10.445696 99.65285 93.29009 53.65847
## 3 109.4963 86.03816 43.86501 4.122956 100.00000 94.00525 46.19371
## 4 110.0396 89.63486 66.20417 19.736955 99.91440 95.00882 60.55318
## 5 111.7212 82.16636 51.52615 6.168872 98.77839 87.05195 51.70852
## 6 107.0913 91.74347 65.10568 13.011988 99.78970 94.22934 72.26013
## APS.USIA4
## 1 14.643598
## 2 14.866231
## 3 5.850893
## 4 19.991477
## 5 8.091359
## 6 15.309082Studi Kasus: Kemiskinan di Jawa Barat
Y : Persentase Penduduk Miskin Tahun 2016
X : Angka Melek Huruf Tahun 2016
jabar2 <- readOGR(dsn="petaJabar2", layer="Jabar2")## OGR data source with driver: ESRI Shapefile
## Source: "D:\Kuliah S2 IPB\Bahan Kuliah\Semester 2 SSD 2020\STA533 Spasial\PRAKTIKUM\R\petaJabar2", layer: "Jabar2"
## with 26 features
## It has 7 fields#dsn diisi nama folder #layer diisi nama file dalam folderEksplorasi Data
plot(data.jabar$EYS2016, data.jabar$p.miskin16,
xlab="Angka Melek Huruf Thn.2016",
ylab="Persentase Penduduk Miskin Thn.2016",
pch=20, col="orange", cex=2)
reg.klasik = lm(p.miskin16~EYS2016, data = data.jabar)
library(DescTools)## Registered S3 method overwritten by 'DescTools':
## method from
## reorder.factor gdatalines.lm(reg.klasik, col=2, add=T)
Plot tersebut memperlihatkan adanya pola hubungan linear negatif antara angka melek huruf terhadap persentase penduduk miskin di Jawa Barat pada tahun 2016.
Plot Persentase Penduduk Miskin Tahun 2016
k=16
colfunc <- colorRampPalette(c("green", "yellow","red"))
color <- colfunc(k)
library(sp)
jabar2$miskin2<- data.jabar$p.miskin16
spplot(jabar2, "miskin2", col.regions=color)
Berdasarkan plot di atas, dapat dilihat adanya kecenderungan pola bergerombol pada data persentase kemiskinan di kabupaten/kota di Jawa Barat. Hal ini tampak dari gradasi warna yang cenderung mengumpul, seperti pada warna merah dan oranye.
Moran Test
w<-poly2nb(jabar2)
ww<-nb2listw(w)
moran(data.jabar$p.miskin16, ww, n=length(ww$neighbours),
S0=Szero(ww))## $I
## [1] 0.3932657
##
## $K
## [1] 2.403804moran.test(data.jabar$p.miskin16, ww,randomisation=T,
alternative="greater")##
## Moran I test under randomisation
##
## data: data.jabar$p.miskin16
## weights: ww
##
## Moran I statistic standard deviate = 3.0168, p-value = 0.001277
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.3932657 -0.0400000 0.0206265p-value = 0.001277 < alpha=0.05 berarti tolak Ho.Ada autokorelasi antar daerah tersebut.
Moran Plot
moran.plot(data.jabar$p.miskin16, ww, labels=data.jabar$KABKOT)
Classical Regression Modelling
OLS Method
reg.klasik = lm(p.miskin16~EYS2016, data = data.jabar)
err.regklasik<-residuals(reg.klasik)
summary(reg.klasik)##
## Call:
## lm(formula = p.miskin16 ~ EYS2016, data = data.jabar)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3627 -1.9604 -0.3899 1.5238 8.1125
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.5732 9.0973 4.350 0.000217 ***
## EYS2016 -2.3948 0.7209 -3.322 0.002854 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.788 on 24 degrees of freedom
## Multiple R-squared: 0.315, Adjusted R-squared: 0.2865
## F-statistic: 11.04 on 1 and 24 DF, p-value: 0.002854Model Diagnostics
library(nortest)
library(car)
library(DescTools)
library(lmtest)Uji Kenormalan Galat
H0: galat model menyebar normal
H1: galat model tidak menyebar normal
ad.test(err.regklasik)##
## Anderson-Darling normality test
##
## data: err.regklasik
## A = 0.39412, p-value = 0.3495P_Value > alpha=0.05 tidak tolak HO. Galat model menyebar normal.
hist(err.regklasik)
qqnorm(err.regklasik,datax=T)
qqline(rnorm(length(err.regklasik),mean(err.regklasik),sd(err.regklasik)),datax=T, col="red")
Tidak persis garis sebaran titik2nya.
Heteroscedastics
H0: ragam galat homogen
H1: ragam galat tidak homogen
bptest(reg.klasik)##
## studentized Breusch-Pagan test
##
## data: reg.klasik
## BP = 1.0644, df = 1, p-value = 0.3022P_Value > alpha=0.05 tidak tolak HO. ragam galat homogen.
Explore for Spatial Autocorrelation
Uji kebebasan sisaan pada data spasial dapat dilakukan dengan uji moran menggunakan fungsi berikut:
w<-poly2nb(jabar2)
ww<-nb2listw(w)
lm.morantest(reg.klasik, ww, alternative="two.sided")##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = p.miskin16 ~ EYS2016, data = data.jabar)
## weights: ww
##
## Moran I statistic standard deviate = 3.4736, p-value = 0.0005135
## alternative hypothesis: two.sided
## sample estimates:
## Observed Moran I Expectation Variance
## 0.44799554 -0.05028275 0.02057696Selain menggunakan fungsi lm.morantest, uji moran dapat dilakukan menggunakan fungsi moran.test seperti yang dibahas pada modul pertemuan sebelumnya. Perbedaannya adalah pada fungsi pertama, input yang digunakan adalah objek lm, sedangkan pada fungsi kedua, yang digunakan sebagai input adalah data sisaan model.
moran.test(err.regklasik, ww,randomisation=F, alternative="two.sided")##
## Moran I test under normality
##
## data: err.regklasik
## weights: ww
##
## Moran I statistic standard deviate = 3.4266, p-value = 0.0006112
## alternative hypothesis: two.sided
## sample estimates:
## Moran I statistic Expectation Variance
## 0.44799554 -0.04000000 0.02028183Terlihat pada output bahwa hasil kedua tes menunjukkan kesimpulan yang sama, yaitu tolak H0 yang menyatakan bahwa tidak terdapat autokorelasi pada sisaan model regresi klasik pada taraf nyata 5%. Oleh karenanya, untuk mencari model yang lebih baik, kita dapat melakukan uji LM (lagrange multiplier) untuk mengidentifikasi model dependensi spasial yang dapat digunakan pada kasus ini.
Lagrange Multiplier Test
lm.LMtests tests against various alternative models, which helps determine more/less desirable alternatives:
LMerr: simple LM test for error dependence
LMlag: simple LM test for a missing spatially lagged dependent variable
RLMerr: robust test for error dependence in the possible presence of a missing lagged dependent variable
RLMlag: robust test for a missing lagged dependent variable in the possible presence of error dependence
SARMA: a portmanteau test for seasonal autoregressive moving average (SARMA, in fact LMerr + RLMlag)
LM<-lm.LMtests(reg.klasik, nb2listw(w, style="W"),
test=c("LMerr", "LMlag","RLMerr","RLMlag","SARMA"))
summary(LM)## Lagrange multiplier diagnostics for spatial dependence
## data:
## model: lm(formula = p.miskin16 ~ EYS2016, data = data.jabar)
## weights: nb2listw(w, style = "W")
##
## statistic parameter p.value
## LMerr 8.375484 1 0.003803 **
## LMlag 6.642323 1 0.009958 **
## RLMerr 1.822499 1 0.177016
## RLMlag 0.089338 1 0.765020
## SARMA 8.464822 2 0.014517 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Output memperlihatkan bahwa hasil uji model SEM dan SAR sama-sama signifikan pada taraf 5%. Selanjutnya, hasil uji robust keduanya ternyata sama-sama tidak signifikan. Berdasarkan skema tersebut, kita dapat mencoba kandidat model SARMA atau GSM. Namun demikian, ada pula pendapat yang menyarankan agar kita mengambil kandidat model dengan p-value terkecil, dalam hal ini adalah model SEM ( p-value = 0.003803 ).
Pada modul ini, untuk kepentingan pembelajaran, kita akan mencoba ketiga model, SEM, SAR, dan SARMA, meskipun pada prakteknya, Anda hanya perlu memodelkan yang menurut Anda terbaik saja.
Spatial Regression Modelling
Model SEM
sem <-errorsarlm(p.miskin16~EYS2016,data=data.jabar,nb2listw(w))
summary(sem)##
## Call:errorsarlm(formula = p.miskin16 ~ EYS2016, data = data.jabar,
## listw = nb2listw(w))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.24699 -1.36122 -0.13809 1.15579 7.03019
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 36.88515 7.80246 4.7274 2.274e-06
## EYS2016 -2.17498 0.60042 -3.6224 0.0002919
##
## Lambda: 0.61793, LR test value: 8.7676, p-value: 0.0030663
## Asymptotic standard error: 0.1576
## z-value: 3.9208, p-value: 8.8267e-05
## Wald statistic: 15.372, p-value: 8.8267e-05
##
## Log likelihood: -58.12466 for error model
## ML residual variance (sigma squared): 4.5459, (sigma: 2.1321)
## Number of observations: 26
## Number of parameters estimated: 4
## AIC: 124.25, (AIC for lm: 131.02)Output di atas menunjukkan bahwa koefisien Lambda signifikan pada taraf nyata 5% ( p-value = 0.0030663 ). Berarti kita memasukkan komponen error tersebut ke dalam model sudah benar.
AIC model SEM adalah sebesar 124.25. Selanjutnya kita akan coba memeriksa sisaan model SEM ini.
err.sem<-residuals(sem)
ad.test(err.sem)##
## Anderson-Darling normality test
##
## data: err.sem
## A = 0.63011, p-value = 0.08968sem <- errorsarlm(p.miskin16~EYS2016,data=data.jabar,nb2listw(w))
bptest.Sarlm(sem)##
## studentized Breusch-Pagan test
##
## data:
## BP = 0.38918, df = 1, p-value = 0.5327moran.test(err.sem, ww, alternative="two.sided")##
## Moran I test under randomisation
##
## data: err.sem
## weights: ww
##
## Moran I statistic standard deviate = -0.096995, p-value = 0.9227
## alternative hypothesis: two.sided
## sample estimates:
## Moran I statistic Expectation Variance
## -0.05288671 -0.04000000 0.01765155Terlihat pada output di atas bahwa sisaan telah memenuhi asumsi kenormalan, kehomogenan ragam, serta kebebasan.
Model SAR
sar<-lagsarlm(p.miskin16~EYS2016,data=data.jabar,nb2listw(w))
summary(sar)##
## Call:
## lagsarlm(formula = p.miskin16 ~ EYS2016, data = data.jabar, listw = nb2listw(w))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.17670 -0.93185 -0.11318 0.91353 7.69131
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 28.22898 7.66250 3.6840 0.0002296
## EYS2016 -1.94997 0.57325 -3.4016 0.0006700
##
## Rho: 0.59078, LR test value: 7.9343, p-value: 0.0048507
## Asymptotic standard error: 0.1559
## z-value: 3.7894, p-value: 0.00015101
## Wald statistic: 14.36, p-value: 0.00015101
##
## Log likelihood: -58.54132 for lag model
## ML residual variance (sigma squared): 4.7513, (sigma: 2.1798)
## Number of observations: 26
## Number of parameters estimated: 4
## AIC: 125.08, (AIC for lm: 131.02)
## LM test for residual autocorrelation
## test value: 0.036687, p-value: 0.8481Output di atas memperlihatkan bahwa koefisien Rho pada model SAR signifikan, dengan nilai AIC sebesar 125.08. Selain itu, terlihat pula hasil uji autokorelasi pada sisaan model memperlihatkan nilai p-value sebesar 0.8481, artinya tidak terdapat autokorelasi pada sisaan.
err.sar<-residuals(sar)
ad.test(err.sar)##
## Anderson-Darling normality test
##
## data: err.sar
## A = 0.70904, p-value = 0.05644sar<-lagsarlm(p.miskin16~EYS2016,data=data.jabar,nb2listw(w))
bptest.Sarlm(sar)##
## studentized Breusch-Pagan test
##
## data:
## BP = 0.97078, df = 1, p-value = 0.3245Berdasarkan output di atas, pada taraf 5% dapat disimpulkan bahwa sisaan model telah memenuhi asumsi kenormalan dan kehomogenan ragam.
Model GSM/SARMA
gsm<-sacsarlm(p.miskin16~EYS2016,data=data.jabar,nb2listw(w))
summary(gsm)##
## Call:
## sacsarlm(formula = p.miskin16 ~ EYS2016, data = data.jabar, listw = nb2listw(w))
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0817 -1.2815 -0.1627 1.1317 6.7128
##
## Type: sac
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 38.02312 8.19667 4.6388 3.504e-06
## EYS2016 -2.15311 0.59378 -3.6261 0.0002877
##
## Rho: -0.14689
## Asymptotic standard error: 0.38983
## z-value: -0.3768, p-value: 0.70633
## Lambda: 0.69352
## Asymptotic standard error: 0.23014
## z-value: 3.0135, p-value: 0.0025829
##
## LR test value: 8.8931, p-value: 0.011719
##
## Log likelihood: -58.06189 for sac model
## ML residual variance (sigma squared): 4.3254, (sigma: 2.0797)
## Number of observations: 26
## Number of parameters estimated: 5
## AIC: 126.12, (AIC for lm: 131.02)Output di atas memperlihatkan bahwa hanya salah satu koefisien dependensi spasial yang signifikan, yaitu Lambda. AIC model SARMA adalah sebesar 126.12.
err.gsm<-residuals(gsm)
ad.test(err.gsm)##
## Anderson-Darling normality test
##
## data: err.gsm
## A = 0.55307, p-value = 0.1388gsm<-sacsarlm(p.miskin16~EYS2016,data=data.jabar,nb2listw(w))
bptest.Sarlm(gsm)##
## studentized Breusch-Pagan test
##
## data:
## BP = 0.31097, df = 1, p-value = 0.5771moran.test(err.gsm, ww, alternative="two.sided")##
## Moran I test under randomisation
##
## data: err.gsm
## weights: ww
##
## Moran I statistic standard deviate = -0.0061095, p-value = 0.9951
## alternative hypothesis: two.sided
## sample estimates:
## Moran I statistic Expectation Variance
## -0.04081914 -0.04000000 0.01797655Berdasarkan output di atas, terlihat bahwa sisaan model SARMA telah memenuhi asumsi kenormalan, kehomogenan ragam, dan kebebasan.
Mohon diingat bahwa pada ilustrasi yang kita lakukan saat ini, kita hanya menggunakan satu peubah bebas sehingga kita tidak perlu mengkhawatirkan masalah multikolinieritas. Pada saat Anda memiliki lebih dari satu peubah bebas, pastikan Anda juga memperhatikan multikolinieritas pada model. Pemeriksaan dapat dilakukan dengan fungsi vif() pada package car.
Goodness of Fits
Akhirnya, kita akan coba merangkum hasil pemodelan yang telah dilakukan sepanjang ilustrasi pada modul ini.
Ilustrasi pada kasus ini memperlihatkan bahwa ternyata SEM merupakan model terbaik berdasarkan nilai AIC-nya. Hal ini ternyata sejalan dengan p-value nya yang juga terkecil pada uji LM.
Exercise
Sebagai latihan, silahkan Anda mencoba memodelkan dengan peubah lain yang terdapat pada data Jabar ini. Diskusikan hasilnya bersama rekan Anda. Anda juga dapat mencoba memodelkan lebih dari satu peubah penjelas.
Peubah yang saya gunakan:
Y:p.miskin15 (persentase penduduk miskin 2015)
X1: AHH2015(angka harapan hidup tahun 2015)
X2: EXP2015 (pengeluaran per kapita riil tahun 2015)
X3: MYS2015 (rata-rata lama sekolah tahun 2015)
str(data.jabar)## 'data.frame': 26 obs. of 32 variables:
## $ PROVNO : num 32 32 32 32 32 32 32 32 32 32 ...
## $ KABKOTNO : num 1 2 3 4 5 6 7 8 9 10 ...
## $ KODE2010 : num 3201 3202 3203 3204 3205 ...
## $ PROVINSI : chr "JAWA BARAT" "JAWA BARAT" "JAWA BARAT" "JAWA BARAT" ...
## $ KABKOT : chr "BOGOR" "SUKABUMI" "CIANJUR" "BANDUNG" ...
## $ IDSP2010 : num 3201 3202 3203 3204 3205 ...
## $ Long : num 107 107 107 108 108 ...
## $ Lat : num -6.56 -7.07 -7.13 -7.1 -7.36 ...
## $ p.miskin15: num 8.96 8.96 12.21 8 12.81 ...
## $ p.miskin16: num 8.83 8.13 11.62 7.61 11.64 ...
## $ j.miskin15: num 487 218 274 281 326 ...
## $ j.miskin16: num 491 199 261 273 299 ...
## $ AHH2015 : num 70.6 70 69.3 73.1 70.7 ...
## $ AHH2016 : num 70.7 70.1 69.4 73.1 70.8 ...
## $ EYS2015 : num 11.8 12.1 11.8 12.1 11.6 ...
## $ EYS2016 : num 12.1 12.2 11.9 12.4 11.7 ...
## $ MYS2015 : num 7.75 6.51 6.54 8.41 6.84 6.88 7.45 7.2 6.32 6.8 ...
## $ MYS2016 : num 7.83 6.74 6.61 8.5 6.88 6.94 7.55 7.34 6.41 6.89 ...
## $ EXP2015 : num 9368 7849 6877 9375 6875 ...
## $ EXP2016 : num 9537 8077 7074 9580 7079 ...
## $ APM.SD15 : num 96.3 99.7 99.9 98 98.1 ...
## $ APM.SMP15 : num 73 75.8 78.4 82.1 75.3 ...
## $ APM.SMA15 : num 55.1 43.9 37.2 55.5 44.6 ...
## $ APM.PT15 : num 11.84 9.92 3.86 17.04 5.71 ...
## $ APK.SD15 : num 109 113 109 110 112 ...
## $ APK.SMP15 : num 84.6 83 86 89.6 82.2 ...
## $ APK.SMA15 : num 67.9 54.9 43.9 66.2 51.5 ...
## $ APK.PT15 : num 12.64 10.45 4.12 19.74 6.17 ...
## $ APS.USIA15: num 99 99.7 100 99.9 98.8 ...
## $ APS.USIA2 : num 89.2 93.3 94 95 87.1 ...
## $ APS.USIA3 : num 62.2 53.7 46.2 60.6 51.7 ...
## $ APS.USIA4 : num 14.64 14.87 5.85 19.99 8.09 ...Eksplorasi Data
Persentase Penduduk Miskin Tahun 2015
#Plot Persentase Penduduk Miskin Tahun 2015
k=16
colfunc <- colorRampPalette(c("green", "yellow","red"))
color <- colfunc(k)
library(sp)
jabar2$miskin15<- data.jabar$p.miskin15
spplot(jabar2, "miskin15", col.regions=color)
Berdasarkan plot di atas, dapat dilihat adanya kecenderungan pola bergerombol pada data persentase kemiskinan di kabupaten/kota di Jawa Barat Tahun 2015. Hal ini tampak dari gradasi warna yang cenderung mengumpul, seperti pada warna merah dan oranye.
Keterkaitan Peubah Y dengan Peubah X
#Y:p.miskin15 vs X1: AHH2015
plot(data.jabar$AHH2015, data.jabar$p.miskin15,
xlab="Angka Harapan Hidup Thn.2015",
ylab="Persentase Penduduk Miskin Thn.2015",
pch=20, col="orange", cex=2)
reg.klasik = lm(p.miskin15~AHH2015, data = data.jabar)
lines.lm(reg.klasik, col=2, add=T)
Plot tersebut memperlihatkan adanya pola hubungan linear negatif antara Angka Harapan Hidup terhadap persentase penduduk miskin di Jawa Barat pada tahun 2015.
#Y:p.miskin15 vs X2: EXP2015
plot(data.jabar$EXP2015, data.jabar$p.miskin15,
xlab="Pengeluaran per Kapita Riil Tahun 2015",
ylab="Persentase Penduduk Miskin Thn.2015",
pch=20, col="orange", cex=2)
reg.klasik = lm(p.miskin15~EXP2015, data = data.jabar)
lines.lm(reg.klasik, col=2, add=T)
Plot tersebut memperlihatkan adanya pola hubungan linear negatif antara Pengeluaran per Kapita Riil terhadap persentase penduduk miskin di Jawa Barat pada tahun 2015.
#Y:p.miskin15 vs X3: MYS2015
plot(data.jabar$MYS2015, data.jabar$p.miskin15,
xlab="Rata-rata Lama Sekolah Tahun 2015",
ylab="Persentase Penduduk Miskin Thn.2015",
pch=20, col="orange", cex=2)
reg.klasik = lm(p.miskin15~MYS2015, data = data.jabar)
lines.lm(reg.klasik, col=2, add=T)
Plot tersebut memperlihatkan adanya pola hubungan linear negatif antara Rata-rata Lama Sekolah terhadap persentase penduduk miskin di Jawa Barat pada tahun 2015.
Moran Test
w<-poly2nb(jabar2) #pembobot spasial
ww<-nb2listw(w)
moran(data.jabar$p.miskin15, ww, n=length(ww$neighbours),
S0=Szero(ww)) ## $I
## [1] 0.4218876
##
## $K
## [1] 2.259555Nilai Kurtosis kurang dari 3 berarti data tidak menyebar normal. Selanjutnya dilakukan Moran Test dengan asumsi randomisasi.
moran.test(data.jabar$p.miskin15, ww,randomisation=T,
alternative="greater") #p.value=0.0006736 tolak Ho berarti ada hubungan ##
## Moran I test under randomisation
##
## data: data.jabar$p.miskin15
## weights: ww
##
## Moran I statistic standard deviate = 3.2057, p-value = 0.0006736
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.42188758 -0.04000000 0.02075945p-value = 0.0006736 < alpha=0.05 berarti tolak Ho.Ada autokorelasi antar daerah tersebut.
Moran Plot
moran.plot(data.jabar$p.miskin15, ww, labels=data.jabar$KABKOT)
Classical Regression Modelling
OLS Method
reg.klasik15 = lm(p.miskin15~AHH2015+MYS2015+EXP2015, data = data.jabar)
err.regklasik15<-residuals(reg.klasik15)
summary(reg.klasik15)##
## Call:
## lm(formula = p.miskin15 ~ AHH2015 + MYS2015 + EXP2015, data = data.jabar)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9510 -1.3005 -0.1107 1.1326 6.3622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.2459753 33.7548467 0.363 0.7202
## AHH2015 0.1810378 0.5165348 0.350 0.7293
## MYS2015 -1.1587370 0.5224354 -2.218 0.0372 *
## EXP2015 -0.0006044 0.0003615 -1.672 0.1086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.393 on 22 degrees of freedom
## Multiple R-squared: 0.6069, Adjusted R-squared: 0.5533
## F-statistic: 11.32 on 3 and 22 DF, p-value: 0.0001072Dengan menggunakan regresi linier berganda, pada taraf nyata 5%, peubah yang signifikan mempengaruhi persentase penduduk miskin di Jawa Barat pada tahun 2015 adalah Rata-rata Lama Sekolah Tahun 2015 (pvalue=0.0372 < alpha=0.05). Model yang terbentuk secara bersama-sama signifikan (pvalue=0.0001072 < alpha=0.05) memengaruhi persentase penduduk miskin di Jawa Barat pada tahun 2015
Model Diagnostics
Uji Multikolinieritas
vif(reg.klasik15) ## AHH2015 MYS2015 EXP2015
## 2.912880 2.954451 2.974447Nilai VIF dibawah 10 berarti tidak mengandung multikolinieritas antar peubah bebas.
Uji Kenormalan Galat
H0: galat model menyebar normal
H1: galat model tidak menyebar normal
ad.test(err.regklasik15) ##
## Anderson-Darling normality test
##
## data: err.regklasik15
## A = 0.24013, p-value = 0.7516Dengan taraf nyata 5% dapat disimpulkan bahwa (p.value=0.7516>alpha=0.05) Terima H0. Maka, Galat menyebar normal.
hist(err.regklasik15)
qqnorm(err.regklasik15,datax=T)
qqline(rnorm(length(err.regklasik15),mean(err.regklasik15),sd(err.regklasik15)),datax=T, col="red")
Tidak persis ada di garis sebaran titik2nya.
Heteroscedastics
H0: ragam galat homogen
H1: ragam galat tidak homogen
bptest(reg.klasik15)##
## studentized Breusch-Pagan test
##
## data: reg.klasik15
## BP = 1.0368, df = 3, p-value = 0.7924pvalue=0.7924>alpha=0.05 berarti terima Ho maka ragam galat homogen.
Explore for Spatial Autocorrelation
Uji kebebasan sisaan pada data spasial dapat dilakukan dengan uji moran.
w<-poly2nb(jabar2)
ww<-nb2listw(w)
lm.morantest(reg.klasik15, ww, alternative="two.sided") ##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = p.miskin15 ~ AHH2015 + MYS2015 + EXP2015, data =
## data.jabar)
## weights: ww
##
## Moran I statistic standard deviate = 1.2932, p-value = 0.1959
## alternative hypothesis: two.sided
## sample estimates:
## Observed Moran I Expectation Variance
## 0.12395156 -0.06279679 0.02085440moran.test(err.regklasik15, ww,randomisation=F, alternative="two.sided") ##
## Moran I test under normality
##
## data: err.regklasik15
## weights: ww
##
## Moran I statistic standard deviate = 1.1512, p-value = 0.2496
## alternative hypothesis: two.sided
## sample estimates:
## Moran I statistic Expectation Variance
## 0.12395156 -0.04000000 0.02028183Terlihat pada output bahwa hasil kedua tes menunjukkan kesimpulan yang sama, yaitu tidak tolak H0 yang menyatakan bahwa terdapat autokorelasi pada sisaan model regresi klasik pada taraf nyata 5%. Oleh karenanya, untuk mencari model yang lebih baik, kita dapat melakukan uji LM (lagrange multiplier) untuk mengidentifikasi model dependensi spasial yang dapat digunakan pada kasus ini.
Lagrange Multiplier Test
LM15<-lm.LMtests(reg.klasik15, nb2listw(w, style="W"),
test=c("LMerr", "LMlag","RLMerr","RLMlag","SARMA"))
summary(LM15)## Lagrange multiplier diagnostics for spatial dependence
## data:
## model: lm(formula = p.miskin15 ~ AHH2015 + MYS2015 + EXP2015, data =
## data.jabar)
## weights: nb2listw(w, style = "W")
##
## statistic parameter p.value
## LMerr 0.64116 1 0.42329
## LMlag 4.25684 1 0.03909 *
## RLMerr 3.31348 1 0.06871 .
## RLMlag 6.92917 1 0.00848 **
## SARMA 7.57033 2 0.02271 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Skema Efek Dependensi Spasial:
Output memperlihatkan bahwa hasil uji model SAR signifikan pada taraf 5% sedangkan SEM tidak nyata. Berdasarkan Skema Efek Dependensi Spasial di atas, kita dapat mencoba kandidat model SAR.
Model SAR
#Model SAR
sar15<-lagsarlm(p.miskin15~AHH2015+MYS2015+EXP2015,data=data.jabar,nb2listw(w))
summary(sar15) ##
## Call:lagsarlm(formula = p.miskin15 ~ AHH2015 + MYS2015 + EXP2015,
## data = data.jabar, listw = nb2listw(w))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.395063 -1.029469 0.062131 0.965811 6.297157
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.97002945 28.63712456 -0.0339 0.97298
## AHH2015 0.27725828 0.43033901 0.6443 0.51939
## MYS2015 -0.97635839 0.43142970 -2.2631 0.02363
## EXP2015 -0.00054327 0.00029996 -1.8112 0.07012
##
## Rho: 0.41245, LR test value: 4.4444, p-value: 0.035015
## Asymptotic standard error: 0.17337
## z-value: 2.379, p-value: 0.017361
## Wald statistic: 5.6595, p-value: 0.017361
##
## Log likelihood: -55.18792 for lag model
## ML residual variance (sigma squared): 3.8955, (sigma: 1.9737)
## Number of observations: 26
## Number of parameters estimated: 6
## AIC: 122.38, (AIC for lm: 124.82)
## LM test for residual autocorrelation
## test value: 4.5189, p-value: 0.033523Output di atas memperlihatkan bahwa koefisien Rho pada model SAR signifikan (p-value: 0.035015< ALPHA=0.05) Artinya Ada dependensi Spasial, dengan nilai AIC sebesar 122.38.
Uji Normalitas Galat
err.sar15<-residuals(sar15)
ad.test(err.sar15) ##
## Anderson-Darling normality test
##
## data: err.sar15
## A = 0.52033, p-value = 0.1689p-value = 0.1689 > alpha= 0.05 berarti terima H0 berarti sisaan menyebar normal.
Uji Kehomogenan Ragam Galat
bptest.Sarlm(sar15) ##
## studentized Breusch-Pagan test
##
## data:
## BP = 1.1533, df = 3, p-value = 0.7642p-value = 0.7642 > alpha= 0.05 berarti terima H0 berarti ragam sisaan homogen
Berdasarkan output di atas, pada taraf 5% dapat disimpulkan bahwa sisaan model telah memenuhi asumsi kenormalan dan kehomogenan ragam.
Spatial Durbin Model (Responsi Pertemuan 11)
Berikut ini adalah penjelasan tentang Spatial Durbin Model yang dirujuk dari Zhukov (2010):
Application in R
library(rgdal)
library (spdep)
library(spatialreg)rm(list=ls())
data(columbus)
col.listw <- nb2listw(col.gal.nb)OLS Regression
columbus.lm<- lm(CRIME ~ INC + HOVAL,data=columbus)
summary(columbus.lm)##
## Call:
## lm(formula = CRIME ~ INC + HOVAL, data = columbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.418 -6.388 -1.580 9.052 28.649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 68.6190 4.7355 14.490 < 2e-16 ***
## INC -1.5973 0.3341 -4.780 1.83e-05 ***
## HOVAL -0.2739 0.1032 -2.654 0.0109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.43 on 46 degrees of freedom
## Multiple R-squared: 0.5524, Adjusted R-squared: 0.5329
## F-statistic: 28.39 on 2 and 46 DF, p-value: 9.341e-09Moran Test
col.moran <- lm.morantest(columbus.lm, col.listw)
col.moran##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = CRIME ~ INC + HOVAL, data = columbus)
## weights: col.listw
##
## Moran I statistic standard deviate = 2.681, p-value = 0.00367
## alternative hypothesis: greater
## sample estimates:
## Observed Moran I Expectation Variance
## 0.212374153 -0.033268284 0.008394853LM-Test
columbus.lagrange <- lm.LMtests(columbus.lm, col.listw, test=c("LMerr","RLMerr","LMlag","RLMlag","SARMA"))
summary(columbus.lagrange)## Lagrange multiplier diagnostics for spatial dependence
## data:
## model: lm(formula = CRIME ~ INC + HOVAL, data = columbus)
## weights: col.listw
##
## statistic parameter p.value
## LMerr 4.611126 1 0.031765 *
## RLMerr 0.033514 1 0.854744
## LMlag 7.855675 1 0.005066 **
## RLMlag 3.278064 1 0.070212 .
## SARMA 7.889190 2 0.019359 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Spatial Lag Model
columbus.lag <- lagsarlm(CRIME ~ INC + HOVAL,data=columbus, col.listw)
summary(columbus.lag)##
## Call:
## lagsarlm(formula = CRIME ~ INC + HOVAL, data = columbus, listw = col.listw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.4497093 -5.4565567 0.0016387 6.7159553 24.7107978
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 46.851431 7.314754 6.4051 1.503e-10
## INC -1.073533 0.310872 -3.4533 0.0005538
## HOVAL -0.269997 0.090128 -2.9957 0.0027381
##
## Rho: 0.40389, LR test value: 8.4179, p-value: 0.0037154
## Asymptotic standard error: 0.12071
## z-value: 3.3459, p-value: 0.00082027
## Wald statistic: 11.195, p-value: 0.00082027
##
## Log likelihood: -183.1683 for lag model
## ML residual variance (sigma squared): 99.164, (sigma: 9.9581)
## Number of observations: 49
## Number of parameters estimated: 5
## AIC: 376.34, (AIC for lm: 382.75)
## LM test for residual autocorrelation
## test value: 0.19184, p-value: 0.66139Spatial Error Model
columbus.err <- errorsarlm(CRIME ~ INC + HOVAL,data=columbus,col.listw)
summary(columbus.err)##
## Call:
## errorsarlm(formula = CRIME ~ INC + HOVAL, data = columbus, listw = col.listw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.45950 -6.21730 -0.69775 7.65256 24.23631
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 61.053618 5.314875 11.4873 < 2.2e-16
## INC -0.995473 0.337025 -2.9537 0.0031398
## HOVAL -0.307979 0.092584 -3.3265 0.0008794
##
## Lambda: 0.52089, LR test value: 6.4441, p-value: 0.011132
## Asymptotic standard error: 0.14129
## z-value: 3.6868, p-value: 0.00022713
## Wald statistic: 13.592, p-value: 0.00022713
##
## Log likelihood: -184.1552 for error model
## ML residual variance (sigma squared): 99.98, (sigma: 9.999)
## Number of observations: 49
## Number of parameters estimated: 5
## AIC: 378.31, (AIC for lm: 382.75)SARMA
columbus.sarma <- sacsarlm(CRIME ~ INC + HOVAL, data=columbus,col.listw)
summary(columbus.sarma)##
## Call:
## sacsarlm(formula = CRIME ~ INC + HOVAL, data = columbus, listw = col.listw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.1121 -4.6324 -0.3040 7.0306 24.6929
##
## Type: sac
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 49.051431 10.054986 4.8783 1.07e-06
## INC -1.068781 0.332839 -3.2111 0.001322
## HOVAL -0.283114 0.091526 -3.0933 0.001980
##
## Rho: 0.35326
## Asymptotic standard error: 0.19669
## z-value: 1.796, p-value: 0.072494
## Lambda: 0.13199
## Asymptotic standard error: 0.29905
## z-value: 0.44138, p-value: 0.65894
##
## LR test value: 8.6082, p-value: 0.013513
##
## Log likelihood: -183.0731 for sac model
## ML residual variance (sigma squared): 99.423, (sigma: 9.9711)
## Number of observations: 49
## Number of parameters estimated: 6
## AIC: 378.15, (AIC for lm: 382.75)Spatial Durbin Model
Model:
columbus.durbin <- lagsarlm(CRIME ~ INC+HOVAL, data=columbus, col.listw, type="mixed");
summary(columbus.durbin)##
## Call:
## lagsarlm(formula = CRIME ~ INC + HOVAL, data = columbus, listw = col.listw,
## type = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.15904 -6.62594 -0.39823 6.57561 23.62757
##
## Type: mixed
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 45.592893 13.128679 3.4728 0.0005151
## INC -0.939088 0.338229 -2.7765 0.0054950
## HOVAL -0.299605 0.090843 -3.2980 0.0009736
## lag.INC -0.618375 0.577052 -1.0716 0.2838954
## lag.HOVAL 0.266615 0.183971 1.4492 0.1472760
##
## Rho: 0.38251, LR test value: 4.1648, p-value: 0.041272
## Asymptotic standard error: 0.16237
## z-value: 2.3557, p-value: 0.018488
## Wald statistic: 5.5493, p-value: 0.018488
##
## Log likelihood: -182.0161 for mixed model
## ML residual variance (sigma squared): 95.051, (sigma: 9.7494)
## Number of observations: 49
## Number of parameters estimated: 7
## AIC: 378.03, (AIC for lm: 380.2)
## LM test for residual autocorrelation
## test value: 0.101, p-value: 0.75063SLX Model
Model: 
SLX model dapat dilakukan dengan fungsi lmSLX() pada package spatialreg. Perhatikan bahwa model SLX adalah bentuk khusus dari model Spatial Durbin.
columbus.SLX <- lmSLX(CRIME ~ INC+HOVAL, data=columbus, col.listw, Durbin = TRUE);
summary(columbus.SLX)##
## Call:
## lm(formula = formula(paste("y ~ ", paste(colnames(x)[-1], collapse = "+"))),
## data = as.data.frame(x), weights = weights)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.245 -7.613 0.188 7.863 25.982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 74.0290 6.7218 11.013 3.13e-14 ***
## INC -1.1081 0.3750 -2.955 0.00501 **
## HOVAL -0.2949 0.1014 -2.910 0.00565 **
## lag.INC -1.3834 0.5592 -2.474 0.01729 *
## lag.HOVAL 0.2262 0.2026 1.116 0.27041
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.94 on 44 degrees of freedom
## Multiple R-squared: 0.6085, Adjusted R-squared: 0.5729
## F-statistic: 17.09 on 4 and 44 DF, p-value: 1.581e-08Dengan mengatur argumen Durbin pada fungsi tersebut, kita dapat memodifikasi model menjadi model SLX.
columbus.SLX <- lmSLX(CRIME ~ INC+HOVAL, data=columbus, col.listw, Durbin = ~INC);
summary(columbus.SLX)##
## Call:
## lm(formula = formula(paste("y ~ ", paste(colnames(x)[-1], collapse = "+"))),
## data = as.data.frame(x), weights = weights)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.708 -7.019 1.415 8.467 26.791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 77.43354 6.00623 12.892 < 2e-16 ***
## INC -1.18157 0.37018 -3.192 0.00258 **
## HOVAL -0.26925 0.09898 -2.720 0.00924 **
## lag.INC -1.01554 0.45293 -2.242 0.02993 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.97 on 45 degrees of freedom
## Multiple R-squared: 0.5974, Adjusted R-squared: 0.5705
## F-statistic: 22.26 on 3 and 45 DF, p-value: 5.495e-09Spatial Durbin Error Model (SDEM)
columbus.errX <- errorsarlm(CRIME ~ INC+HOVAL, data=columbus, col.listw, etype="mixed");
summary(columbus.errX)##
## Call:
## errorsarlm(formula = CRIME ~ INC + HOVAL, data = columbus, listw = col.listw,
## etype = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.02060 -6.68585 -0.15142 6.51557 24.18199
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 73.258655 8.528043 8.5903 < 2.2e-16
## INC -1.069530 0.324719 -3.2937 0.0009887
## HOVAL -0.280344 0.091809 -3.0535 0.0022615
## lag.INC -1.196774 0.568968 -2.1034 0.0354297
## lag.HOVAL 0.146758 0.200872 0.7306 0.4650196
##
## Lambda: 0.37613, LR test value: 3.7313, p-value: 0.053403
## Asymptotic standard error: 0.16554
## z-value: 2.2721, p-value: 0.023079
## Wald statistic: 5.1626, p-value: 0.023079
##
## Log likelihood: -182.2329 for error model
## ML residual variance (sigma squared): 96.022, (sigma: 9.7991)
## Number of observations: 49
## Number of parameters estimated: 7
## AIC: 378.47, (AIC for lm: 380.2)Ilustrasi dan penjelasan tentang SLX dan SDEM dirujuk dari Mendez (2020).
Exercises 1
Lakukan pemodelan untuk memprediksi GRP pada data yang tersedia pada: https://github.com/raoy/Spatial-Statistics .
Data yang digunakan adalah:
China29.zip
Data China (spasial).xlsx
library("openxlsx")data.china = read.xlsx("Data China (spasial).xlsx")
head(data.china)## Region Long Lat Gross.Regional.Product Level.of.Urbanization
## 1 Beijing 116.4107 40.18491 87475 86.20
## 2 Tianjin 117.3330 39.31040 93173 81.55
## 3 Hebei 116.1241 39.54362 36584 46.80
## 4 Shanxi 112.2920 37.57590 33628 51.26
## 5 Nei Mongol 113.9145 44.08640 63886 57.74
## 6 Liaoning 122.6090 41.30373 56649 65.65China <- readOGR(dsn="China29", layer="China29")## OGR data source with driver: ESRI Shapefile
## Source: "D:\Kuliah S2 IPB\Bahan Kuliah\Semester 2 SSD 2020\STA533 Spasial\PRAKTIKUM\R\China29", layer: "China29"
## with 29 features
## It has 12 fieldsOLS Regression
china.lm<- lm(Gross.Regional.Product ~ Level.of.Urbanization,data=data.china)
summary(china.lm)##
## Call:
## lm(formula = Gross.Regional.Product ~ Level.of.Urbanization,
## data = data.china)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12050.9 -4410.4 -374.8 2961.6 14948.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -31895.5 5197.6 -6.137 1.48e-06 ***
## Level.of.Urbanization 1400.0 92.6 15.118 1.06e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6595 on 27 degrees of freedom
## Multiple R-squared: 0.8944, Adjusted R-squared: 0.8904
## F-statistic: 228.6 on 1 and 27 DF, p-value: 1.064e-14Moran Test
wc<-poly2nb(China)
wwc<-nb2listw(wc)
china.moran <- lm.morantest(china.lm, wwc)
china.moran##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = Gross.Regional.Product ~ Level.of.Urbanization,
## data = data.china)
## weights: wwc
##
## Moran I statistic standard deviate = -1.2198, p-value = 0.8887
## alternative hypothesis: greater
## sample estimates:
## Observed Moran I Expectation Variance
## -0.17902561 -0.03179058 0.01456927LM-Test
china.lagrange <- lm.LMtests(china.lm, wwc, test=c("LMerr","RLMerr","LMlag","RLMlag","SARMA"))
summary(china.lagrange)## Lagrange multiplier diagnostics for spatial dependence
## data:
## model: lm(formula = Gross.Regional.Product ~ Level.of.Urbanization,
## data = data.china)
## weights: wwc
##
## statistic parameter p.value
## LMerr 1.849327 1 0.1739
## RLMerr 1.824640 1 0.1768
## LMlag 0.089740 1 0.7645
## RLMlag 0.065053 1 0.7987
## SARMA 1.914380 2 0.3840Spatial Lag Model
china.lag <- lagsarlm(Gross.Regional.Product ~ Level.of.Urbanization,data=data.china, wwc)## Warning in lagsarlm(Gross.Regional.Product ~ Level.of.Urbanization, data = data.china, : inversion of asymptotic covariance matrix failed for tol.solve = 2.22044604925031e-16
## reciprocal condition number = 5.32741e-18 - using numerical Hessian.summary(china.lag)##
## Call:lagsarlm(formula = Gross.Regional.Product ~ Level.of.Urbanization,
## data = data.china, listw = wwc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12217.317 -4168.833 -43.335 2630.869 14651.322
##
## Type: lag
## Coefficients: (numerical Hessian approximate standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -29848.311 8129.994 -3.6714 0.0002412
## Level.of.Urbanization 1392.795 91.969 15.1441 < 2.2e-16
##
## Rho: -0.035466, LR test value: 0.10342, p-value: 0.74776
## Approximate (numerical Hessian) standard error: 0.1109
## z-value: -0.31981, p-value: 0.74911
## Wald statistic: 0.10228, p-value: 0.74911
##
## Log likelihood: -295.0898 for lag model
## ML residual variance (sigma squared): 40340000, (sigma: 6351.4)
## Number of observations: 29
## Number of parameters estimated: 4
## AIC: 598.18, (AIC for lm: 596.28)Spatial Error Model
china.err <- errorsarlm(Gross.Regional.Product ~ Level.of.Urbanization,data=data.china, wwc)
summary(china.err)##
## Call:errorsarlm(formula = Gross.Regional.Product ~ Level.of.Urbanization,
## data = data.china, listw = wwc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11830.519 -4410.878 -84.567 1889.525 13482.672
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -31812.667 4801.782 -6.6252 3.468e-11
## Level.of.Urbanization 1398.152 86.112 16.2365 < 2.2e-16
##
## Lambda: -0.46483, LR test value: 2.4782, p-value: 0.11543
## Asymptotic standard error: 0.27434
## z-value: -1.6944, p-value: 0.090194
## Wald statistic: 2.8709, p-value: 0.090194
##
## Log likelihood: -293.9024 for error model
## ML residual variance (sigma squared): 35469000, (sigma: 5955.6)
## Number of observations: 29
## Number of parameters estimated: 4
## AIC: 595.8, (AIC for lm: 596.28)SARMA
china.sarma <- sacsarlm(Gross.Regional.Product ~ Level.of.Urbanization,data=data.china, wwc)## Warning in sacsarlm(Gross.Regional.Product ~ Level.of.Urbanization, data = data.china, : inversion of asymptotic covariance matrix failed for tol.solve = 2.22044604925031e-16
## reciprocal condition number = 2.7894e-18 - using numerical Hessian.summary(china.sarma)##
## Call:sacsarlm(formula = Gross.Regional.Product ~ Level.of.Urbanization,
## data = data.china, listw = wwc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11858.37 -4434.38 -111.84 1872.83 13453.12
##
## Type: sac
## Coefficients: (numerical Hessian approximate standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -31678.647 5635.189 -5.6216 1.892e-08
## Level.of.Urbanization 1398.757 87.195 16.0418 < 2.2e-16
##
## Rho: -0.0035985
## Approximate (numerical Hessian) standard error: 0.078511
## z-value: -0.045835, p-value: 0.96344
## Lambda: -0.4631
## Approximate (numerical Hessian) standard error: 0.28849
## z-value: -1.6052, p-value: 0.10844
##
## LR test value: 2.4796, p-value: 0.28945
##
## Log likelihood: -293.9017 for sac model
## ML residual variance (sigma squared): 35479000, (sigma: 5956.4)
## Number of observations: 29
## Number of parameters estimated: 5
## AIC: 597.8, (AIC for lm: 596.28)Spatial Durbin Model
Model:
china.durbin <- lagsarlm(Gross.Regional.Product ~ Level.of.Urbanization,data=data.china, wwc, type="mixed");## Warning in lagsarlm(Gross.Regional.Product ~ Level.of.Urbanization, data = data.china, : inversion of asymptotic covariance matrix failed for tol.solve = 2.22044604925031e-16
## reciprocal condition number = 5.8045e-18 - using numerical Hessian.summary(china.durbin)##
## Call:lagsarlm(formula = Gross.Regional.Product ~ Level.of.Urbanization,
## data = data.china, listw = wwc, type = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -11831.096 -4411.438 -85.211 1889.050 13481.899
##
## Type: mixed
## Coefficients: (numerical Hessian approximate standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -46593.508 12921.002 -3.6060 0.0003109
## Level.of.Urbanization 1398.154 86.285 16.2039 < 2.2e-16
## lag.Level.of.Urbanization 649.823 407.020 1.5965 0.1103685
##
## Rho: -0.46488, LR test value: 2.4131, p-value: 0.12032
## Approximate (numerical Hessian) standard error: 0.28945
## z-value: -1.6061, p-value: 0.10826
## Wald statistic: 2.5795, p-value: 0.10826
##
## Log likelihood: -293.9024 for mixed model
## ML residual variance (sigma squared): 35468000, (sigma: 5955.5)
## Number of observations: 29
## Number of parameters estimated: 5
## AIC: 597.8, (AIC for lm: 598.22)SLX Model
Model:
SLX model dapat dilakukan dengan fungsi lmSLX() pada package spatialreg. Perhatikan bahwa model SLX adalah bentuk khusus dari model Spatial Durbin.
china.SLX <- lmSLX(Gross.Regional.Product ~ Level.of.Urbanization,data=data.china, wwc, Durbin = TRUE);
summary(china.SLX)##
## Call:
## lm(formula = formula(paste("y ~ ", paste(colnames(x)[-1], collapse = "+"))),
## data = as.data.frame(x), weights = weights)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11903.4 -4551.9 -852.3 3092.8 15097.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -34448.49 11810.70 -2.917 0.0072 **
## Level.of.Urbanization 1405.61 97.12 14.473 5.93e-14 ***
## lag.Level.of.Urbanization 39.94 165.22 0.242 0.8109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6713 on 26 degrees of freedom
## Multiple R-squared: 0.8946, Adjusted R-squared: 0.8865
## F-statistic: 110.3 on 2 and 26 DF, p-value: 1.984e-13Dengan mengatur argumen Durbin pada fungsi tersebut, kita dapat memodifikasi model menjadi model SLX.
china.SLX <- lmSLX(Gross.Regional.Product ~ Level.of.Urbanization,data=data.china, wwc, Durbin = ~Level.of.Urbanization);
summary(china.SLX)##
## Call:
## lm(formula = formula(paste("y ~ ", paste(colnames(x)[-1], collapse = "+"))),
## data = as.data.frame(x), weights = weights)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11903.4 -4551.9 -852.3 3092.8 15097.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -34448.49 11810.70 -2.917 0.0072 **
## Level.of.Urbanization 1405.61 97.12 14.473 5.93e-14 ***
## lag.Level.of.Urbanization 39.94 165.22 0.242 0.8109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6713 on 26 degrees of freedom
## Multiple R-squared: 0.8946, Adjusted R-squared: 0.8865
## F-statistic: 110.3 on 2 and 26 DF, p-value: 1.984e-13Spatial Durbin Error Model (SDEM)
china.errX <- errorsarlm(Gross.Regional.Product ~ Level.of.Urbanization,data=data.china, wwc, etype="mixed");
summary(china.errX)##
## Call:errorsarlm(formula = Gross.Regional.Product ~ Level.of.Urbanization,
## data = data.china, listw = wwc, etype = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -11801.265 -4386.965 -52.759 1916.147 13523.071
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -32123.1217 8211.3010 -3.9121 9.151e-05
## Level.of.Urbanization 1397.3594 87.9373 15.8904 < 2.2e-16
## lag.Level.of.Urbanization 6.3185 136.2782 0.0464 0.963
##
## Lambda: -0.46309, LR test value: 2.4152, p-value: 0.12016
## Asymptotic standard error: 0.27439
## z-value: -1.6877, p-value: 0.091462
## Wald statistic: 2.8484, p-value: 0.091462
##
## Log likelihood: -293.9014 for error model
## ML residual variance (sigma squared): 35478000, (sigma: 5956.4)
## Number of observations: 29
## Number of parameters estimated: 5
## AIC: 597.8, (AIC for lm: 598.22)Ilustrasi dan penjelasan tentang SLX dan SDEM dirujuk dari Mendez (2020).
Exercises 2
Silahkan coba lakukan pemodelan dependensi spasial pada data Jawa Barat yang tersedia di modul 10. Atau silahkan akses pada: https://github.com/raoy/SpatialReg
Peubah yang saya gunakan:
Y:p.miskin15 (persentase penduduk miskin 2015)
X1: EXP2015 (pengeluaran per kapita riil tahun 2015)
X2: MYS2015 (rata-rata lama sekolah tahun 2015)
library(openxlsx)data.jabar3 = read.xlsx("Jabar Data (gabung).xlsx")
head(data.jabar3)## PROVNO KABKOTNO KODE2010 PROVINSI KABKOT IDSP2010 Long Lat
## 1 32 1 3201 JAWA BARAT BOGOR 3201 106.7687 -6.561184
## 2 32 2 3202 JAWA BARAT SUKABUMI 3202 106.7101 -7.074623
## 3 32 3 3203 JAWA BARAT CIANJUR 3203 107.1578 -7.133713
## 4 32 4 3204 JAWA BARAT BANDUNG 3204 107.6108 -7.099969
## 5 32 5 3205 JAWA BARAT GARUT 3205 107.7889 -7.359586
## 6 32 6 3206 JAWA BARAT TASIKMALAYA 3206 108.1413 -7.496892
## p.miskin15 p.miskin16 j.miskin15 j.miskin16 AHH2015 AHH2016 EYS2015 EYS2016
## 1 8.959759 8.834574 487.10 490.80 70.59 70.65 11.83 12.05
## 2 8.960361 8.134848 217.86 198.66 70.03 70.14 12.13 12.18
## 3 12.214160 11.621474 273.90 261.39 69.28 69.39 11.83 11.88
## 4 7.998184 7.613293 281.04 272.65 73.07 73.10 12.13 12.42
## 5 12.805808 11.640370 325.67 298.52 70.69 70.76 11.65 11.69
## 6 11.994455 11.237185 208.12 195.61 68.36 68.54 12.44 12.46
## MYS2015 MYS2016 EXP2015 EXP2016 APM.SD15 APM.SMP15 APM.SMA15 APM.PT15
## 1 7.75 7.83 9368 9537 96.29364 72.97053 55.10088 11.835186
## 2 6.51 6.74 7849 8077 99.65285 75.76340 43.93547 9.924350
## 3 6.54 6.61 6877 7074 99.88696 78.40315 37.18006 3.862863
## 4 8.41 8.50 9375 9580 98.00367 82.13540 55.49216 17.042698
## 5 6.84 6.88 6875 7079 98.09334 75.28081 44.60243 5.708659
## 6 6.88 6.94 6934 7081 99.08656 76.97621 54.79954 12.158104
## APK.SD15 APK.SMP15 APK.SMA15 APK.PT15 APS.USIA15 APS.USIA2 APS.USIA3
## 1 108.8195 84.63248 67.93291 12.636548 99.04861 89.24145 62.22669
## 2 113.0554 82.99804 54.93650 10.445696 99.65285 93.29009 53.65847
## 3 109.4963 86.03816 43.86501 4.122956 100.00000 94.00525 46.19371
## 4 110.0396 89.63486 66.20417 19.736955 99.91440 95.00882 60.55318
## 5 111.7212 82.16636 51.52615 6.168872 98.77839 87.05195 51.70852
## 6 107.0913 91.74347 65.10568 13.011988 99.78970 94.22934 72.26013
## APS.USIA4
## 1 14.643598
## 2 14.866231
## 3 5.850893
## 4 19.991477
## 5 8.091359
## 6 15.309082jabar3 <- readOGR(dsn="petaJabar2", layer="Jabar2")## OGR data source with driver: ESRI Shapefile
## Source: "D:\Kuliah S2 IPB\Bahan Kuliah\Semester 2 SSD 2020\STA533 Spasial\PRAKTIKUM\R\petaJabar2", layer: "Jabar2"
## with 26 features
## It has 7 fieldsw3<-poly2nb(jabar3)
ww3<-nb2listw(w3)OLS Regression
jabar.lm3 <- lm(p.miskin15~MYS2015+EXP2015, data = data.jabar3)
summary(jabar.lm3)##
## Call:
## lm(formula = p.miskin15 ~ MYS2015 + EXP2015, data = data.jabar3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7364 -1.4553 -0.0763 1.2041 6.3209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.0439900 2.4543253 9.797 1.12e-09 ***
## MYS2015 -1.0810720 0.4640032 -2.330 0.0289 *
## EXP2015 -0.0005499 0.0003200 -1.719 0.0991 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.347 on 23 degrees of freedom
## Multiple R-squared: 0.6047, Adjusted R-squared: 0.5703
## F-statistic: 17.59 on 2 and 23 DF, p-value: 2.318e-05Moran Test
jabar.moran3 <- lm.morantest(jabar.lm3, ww3)
jabar.moran3##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = p.miskin15 ~ MYS2015 + EXP2015, data = data.jabar3)
## weights: ww3
##
## Moran I statistic standard deviate = 1.3289, p-value = 0.09194
## alternative hypothesis: greater
## sample estimates:
## Observed Moran I Expectation Variance
## 0.12696672 -0.06136791 0.02008556LM-Test
jabar.lagrange3 <- lm.LMtests(jabar.lm3, ww3, test=c("LMerr","RLMerr","LMlag","RLMlag","SARMA"))
summary(jabar.lagrange3)## Lagrange multiplier diagnostics for spatial dependence
## data:
## model: lm(formula = p.miskin15 ~ MYS2015 + EXP2015, data = data.jabar3)
## weights: ww3
##
## statistic parameter p.value
## LMerr 0.67273 1 0.41210
## RLMerr 2.52129 1 0.11232
## LMlag 3.96387 1 0.04649 *
## RLMlag 5.81243 1 0.01591 *
## SARMA 6.48516 2 0.03906 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Spatial Lag Model
jabar.lag3 <- lagsarlm(p.miskin15~MYS2015+EXP2015, data = data.jabar3, ww3)
summary(jabar.lag3)##
## Call:lagsarlm(formula = p.miskin15 ~ MYS2015 + EXP2015, data = data.jabar3,
## listw = ww3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.157708 -1.404794 0.092556 1.055019 6.236624
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 17.31688597 3.46170899 5.0024 5.662e-07
## MYS2015 -0.86444557 0.39636776 -2.1809 0.02919
## EXP2015 -0.00046256 0.00027774 -1.6654 0.09583
##
## Rho: 0.39948, LR test value: 4.1742, p-value: 0.041044
## Asymptotic standard error: 0.17361
## z-value: 2.3011, p-value: 0.021387
## Wald statistic: 5.295, p-value: 0.021387
##
## Log likelihood: -55.39543 for lag model
## ML residual variance (sigma squared): 3.9708, (sigma: 1.9927)
## Number of observations: 26
## Number of parameters estimated: 5
## AIC: 120.79, (AIC for lm: 122.97)
## LM test for residual autocorrelation
## test value: 3.6549, p-value: 0.055905Spatial Error Model
jabar.err3 <- errorsarlm(p.miskin15~MYS2015+EXP2015, data = data.jabar3, ww3)
summary(jabar.err3)##
## Call:errorsarlm(formula = p.miskin15 ~ MYS2015 + EXP2015, data = data.jabar3,
## listw = ww3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.79715 -1.20385 0.21676 1.36881 6.54281
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 22.98077283 2.47265221 9.2940 < 2e-16
## MYS2015 -0.94326874 0.43502338 -2.1683 0.03013
## EXP2015 -0.00054627 0.00030131 -1.8130 0.06983
##
## Lambda: 0.23511, LR test value: 0.79569, p-value: 0.37239
## Asymptotic standard error: 0.22879
## z-value: 1.0276, p-value: 0.30413
## Wald statistic: 1.056, p-value: 0.30413
##
## Log likelihood: -57.08469 for error model
## ML residual variance (sigma squared): 4.6587, (sigma: 2.1584)
## Number of observations: 26
## Number of parameters estimated: 5
## AIC: 124.17, (AIC for lm: 122.97)SARMA
jabar.sarma3 <- sacsarlm(p.miskin15~MYS2015+EXP2015, data = data.jabar3, ww3)
summary(jabar.sarma3)##
## Call:sacsarlm(formula = p.miskin15 ~ MYS2015 + EXP2015, data = data.jabar3,
## listw = ww3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.483347 -0.946984 -0.051623 1.049559 5.228132
##
## Type: sac
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 14.05816462 3.65829263 3.8428 0.0001216
## MYS2015 -0.89515532 0.33550264 -2.6681 0.0076281
## EXP2015 -0.00032724 0.00023650 -1.3837 0.1664561
##
## Rho: 0.60378
## Asymptotic standard error: 0.17494
## z-value: 3.4513, p-value: 0.00055793
## Lambda: -0.50797
## Asymptotic standard error: 0.3243
## z-value: -1.5664, p-value: 0.11726
##
## LR test value: 6.7768, p-value: 0.033763
##
## Log likelihood: -54.09413 for sac model
## ML residual variance (sigma squared): 3.1497, (sigma: 1.7747)
## Number of observations: 26
## Number of parameters estimated: 6
## AIC: 120.19, (AIC for lm: 122.97)Spatial Durbin Model
Model:
jabar.durbin3 <- lagsarlm(p.miskin15~MYS2015+EXP2015, data = data.jabar3, ww3, type="mixed");
summary(jabar.durbin3)##
## Call:lagsarlm(formula = p.miskin15 ~ MYS2015 + EXP2015, data = data.jabar3,
## listw = ww3, type = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.807627 -1.205796 -0.088521 0.970854 5.528544
##
## Type: mixed
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 28.32435029 7.21474153 3.9259 8.641e-05
## MYS2015 -0.80456534 0.39042550 -2.0607 0.03933
## EXP2015 -0.00031910 0.00027839 -1.1462 0.25171
## lag.MYS2015 -1.16263589 0.84575833 -1.3747 0.16923
## lag.EXP2015 -0.00018041 0.00053211 -0.3391 0.73457
##
## Rho: 0.17369, LR test value: 0.58218, p-value: 0.44546
## Asymptotic standard error: 0.22612
## z-value: 0.76813, p-value: 0.44241
## Wald statistic: 0.59002, p-value: 0.44241
##
## Log likelihood: -53.67477 for mixed model
## ML residual variance (sigma squared): 3.608, (sigma: 1.8995)
## Number of observations: 26
## Number of parameters estimated: 7
## AIC: 121.35, (AIC for lm: 119.93)
## LM test for residual autocorrelation
## test value: 0.6977, p-value: 0.40356SLX Model
Model:
SLX model dapat dilakukan dengan fungsi lmSLX() pada package spatialreg. Perhatikan bahwa model SLX adalah bentuk khusus dari model Spatial Durbin.
jabar.SLX3 <- lmSLX(p.miskin15~MYS2015+EXP2015, data = data.jabar3, ww3, Durbin = TRUE);
summary(jabar.SLX3)##
## Call:
## lm(formula = formula(paste("y ~ ", paste(colnames(x)[-1], collapse = "+"))),
## data = as.data.frame(x), weights = weights)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1042 -1.0743 -0.0182 1.0916 5.4222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.0202219 4.1695551 7.919 9.68e-08 ***
## MYS2015 -0.8496651 0.4375982 -1.942 0.0657 .
## EXP2015 -0.0003147 0.0003143 -1.001 0.3281
## lag.MYS2015 -1.4984529 0.8985287 -1.668 0.1102
## lag.EXP2015 -0.0001822 0.0005837 -0.312 0.7580
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.146 on 21 degrees of freedom
## Multiple R-squared: 0.6984, Adjusted R-squared: 0.6409
## F-statistic: 12.16 on 4 and 21 DF, p-value: 2.853e-05Dengan mengatur argumen Durbin pada fungsi tersebut, kita dapat memodifikasi model menjadi model SLX.
jabar.SLX3 <- lmSLX(p.miskin15~MYS2015+EXP2015, data = data.jabar3, ww3, Durbin = ~MYS2015);
summary(jabar.SLX3)##
## Call:
## lm(formula = formula(paste("y ~ ", paste(colnames(x)[-1], collapse = "+"))),
## data = as.data.frame(x), weights = weights)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9562 -1.0968 -0.0822 1.0526 5.6161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.8836949 4.0606174 8.098 4.82e-08 ***
## MYS2015 -0.8351395 0.4260995 -1.960 0.0628 .
## EXP2015 -0.0003397 0.0002977 -1.141 0.2662
## lag.MYS2015 -1.6869477 0.6516866 -2.589 0.0168 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.101 on 22 degrees of freedom
## Multiple R-squared: 0.697, Adjusted R-squared: 0.6556
## F-statistic: 16.87 on 3 and 22 DF, p-value: 6.504e-06Spatial Durbin Error Model (SDEM)
jabar.errX3 <- errorsarlm(p.miskin15~MYS2015+EXP2015, data = data.jabar3, ww3, etype="mixed");
summary(jabar.errX3)##
## Call:errorsarlm(formula = p.miskin15 ~ MYS2015 + EXP2015, data = data.jabar3,
## listw = ww3, etype = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.856370 -1.171595 -0.052347 1.032852 5.516811
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 32.82270348 3.99974306 8.2062 2.22e-16
## MYS2015 -0.85061052 0.38786127 -2.1931 0.0283
## EXP2015 -0.00032669 0.00027865 -1.1724 0.2410
## lag.MYS2015 -1.35683188 0.81684911 -1.6611 0.0967
## lag.EXP2015 -0.00026333 0.00053600 -0.4913 0.6232
##
## Lambda: 0.13018, LR test value: 0.23852, p-value: 0.62527
## Asymptotic standard error: 0.23959
## z-value: 0.54332, p-value: 0.58691
## Wald statistic: 0.2952, p-value: 0.58691
##
## Log likelihood: -53.8466 for error model
## ML residual variance (sigma squared): 3.6687, (sigma: 1.9154)
## Number of observations: 26
## Number of parameters estimated: 7
## AIC: 121.69, (AIC for lm: 119.93)Ilustrasi dan penjelasan tentang SLX dan SDEM dirujuk dari Mendez (2020).
Marginal Effects (Spill-over) on the Spatial Regression Modeling (Responsi Pertemuan 12)
Marginal Effects
Definisi yang diambil dari materi kuliah yang disusun oleh Dr. Anik Djuraidah menyatakan bahwa efek marginal atau limpahan (spill-over) adalah besarnya dampak perubahan pada peubah dependen pada wilayah-i, akibat perubahan prediktor di wilayah-j.
Efek marginal terdapat pada model dependensi spasial SAR, GSM, SDM, SDEM, dan SLX. Efek ini dapat dibedakan menjadi tiga, yaitu efek langsung (direct effect), efek tidak langsung (indirect effect), dan efek total (total effect).
Application in R
library(rgdal)
library (spdep)
library(spatialreg)data(columbus)
col.listw <- nb2listw(col.gal.nb)OLS Regression
columbus.lm<- lm(CRIME ~ INC + HOVAL, data=columbus)
summary(columbus.lm)##
## Call:
## lm(formula = CRIME ~ INC + HOVAL, data = columbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.418 -6.388 -1.580 9.052 28.649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 68.6190 4.7355 14.490 < 2e-16 ***
## INC -1.5973 0.3341 -4.780 1.83e-05 ***
## HOVAL -0.2739 0.1032 -2.654 0.0109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.43 on 46 degrees of freedom
## Multiple R-squared: 0.5524, Adjusted R-squared: 0.5329
## F-statistic: 28.39 on 2 and 46 DF, p-value: 9.341e-09Moran Test
col.moran <- lm.morantest(columbus.lm, col.listw)
col.moran##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = CRIME ~ INC + HOVAL, data = columbus)
## weights: col.listw
##
## Moran I statistic standard deviate = 2.681, p-value = 0.00367
## alternative hypothesis: greater
## sample estimates:
## Observed Moran I Expectation Variance
## 0.212374153 -0.033268284 0.008394853LM Test
columbus.lagrange <- lm.LMtests(columbus.lm, col.listw, test=c("LMerr","RLMerr","LMlag","RLMlag","SARMA"))
summary(columbus.lagrange)## Lagrange multiplier diagnostics for spatial dependence
## data:
## model: lm(formula = CRIME ~ INC + HOVAL, data = columbus)
## weights: col.listw
##
## statistic parameter p.value
## LMerr 4.611126 1 0.031765 *
## RLMerr 0.033514 1 0.854744
## LMlag 7.855675 1 0.005066 **
## RLMlag 3.278064 1 0.070212 .
## SARMA 7.889190 2 0.019359 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Spatial Lag Model
columbus.lag <- lagsarlm(CRIME ~ INC + HOVAL,data=columbus, col.listw)
summary(columbus.lag)##
## Call:
## lagsarlm(formula = CRIME ~ INC + HOVAL, data = columbus, listw = col.listw)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.4497093 -5.4565567 0.0016387 6.7159553 24.7107978
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 46.851431 7.314754 6.4051 1.503e-10
## INC -1.073533 0.310872 -3.4533 0.0005538
## HOVAL -0.269997 0.090128 -2.9957 0.0027381
##
## Rho: 0.40389, LR test value: 8.4179, p-value: 0.0037154
## Asymptotic standard error: 0.12071
## z-value: 3.3459, p-value: 0.00082027
## Wald statistic: 11.195, p-value: 0.00082027
##
## Log likelihood: -183.1683 for lag model
## ML residual variance (sigma squared): 99.164, (sigma: 9.9581)
## Number of observations: 49
## Number of parameters estimated: 5
## AIC: 376.34, (AIC for lm: 382.75)
## LM test for residual autocorrelation
## test value: 0.19184, p-value: 0.66139Terlihat pada output di atas bahwa koefisien ρ signifikan pada model SAR. Selanjutnya, marginal effect dapat diperoleh dengan fungsi impacts() seperti pada syntax berikut ini.
Interpretasi Efek Marginal
impacts(columbus.lag, listw = col.listw)## Impact measures (lag, exact):
## Direct Indirect Total
## INC -1.1225156 -0.6783818 -1.8008973
## HOVAL -0.2823163 -0.1706152 -0.4529315Terlihat bahwa pengaruh langsung dari peubah INC adalah sebesar -1.12, artinya jika rata-rata pendapatan rumah tangga di wilayah-i meningkat 1,000 USD, maka rata-rata kejadian kriminal di wilayah tersebut akan berkurang sebesar 11.2 per 100 rumah tangga, jika nilai rumahnya tetap. Sedangkan efek tak langsung dari peubah tersebut bernilai -0.678. Artinya, jika rata-rata pendapatan rumah tangga di wilayah-i meningkat sebesar 1,000 USD, maka rata-rata kejadian kriminal di wilayah-j akan berkurang sebesar 6.78 per 100 rumah tangga, jika nilai rumahnya tetap. Interpretasi serupa juga dapat dilakukan terhadap peubah HOVAL.
Ilustration 2
Ilustrasi ini diambil dari materi workshop yang disusun oleh Sarmiento-Barbieri (2016). Data yang digunakan terdapat pada http://www.econ.uiuc.edu/~lab/workshop/foreclosures/. Silahkan download semua data yang terdapat pada link tersebut.
Data Import
Impor data shapefile menggunakan fungsi readOGR() pada package rgdal. Setelah itu, kita dapat menggunakan fungsi str() untuk melihat struktur datanya.
chi.poly<-readOGR(dsn="foreclosures", layer="foreclosures")## OGR data source with driver: ESRI Shapefile
## Source: "D:\Kuliah S2 IPB\Bahan Kuliah\Semester 2 SSD 2020\STA533 Spasial\PRAKTIKUM\R\foreclosures", layer: "foreclosures"
## with 897 features
## It has 16 fieldsstr(slot(chi.poly,"data"))## 'data.frame': 897 obs. of 16 variables:
## $ SP_ID : chr "1" "2" "3" "4" ...
## $ fips : chr "17031010100" "17031010200" "17031010300" "17031010400" ...
## $ est_fcs : int 43 129 55 21 64 56 107 43 7 51 ...
## $ est_mtgs : int 904 2122 1151 574 1427 1241 1959 830 208 928 ...
## $ est_fcs_rt: num 4.76 6.08 4.78 3.66 4.48 4.51 5.46 5.18 3.37 5.5 ...
## $ res_addr : int 2530 3947 3204 2306 5485 2994 3701 1694 443 1552 ...
## $ est_90d_va: num 12.61 12.36 10.46 5.03 8.44 ...
## $ bls_unemp : num 8.16 8.16 8.16 8.16 8.16 8.16 8.16 8.16 8.16 8.16 ...
## $ county : chr "Cook County" "Cook County" "Cook County" "Cook County" ...
## $ fips_num : num 1.7e+10 1.7e+10 1.7e+10 1.7e+10 1.7e+10 ...
## $ totpop : int 5391 10706 6649 5325 10944 7178 10799 5403 1089 3634 ...
## $ tothu : int 2557 3981 3281 2464 5843 3136 3875 1768 453 1555 ...
## $ huage : int 61 53 56 60 54 58 48 57 61 48 ...
## $ oomedval : int 169900 147000 119800 151500 143600 145900 153400 170500 215900 114700 ...
## $ property : num 646 914 478 509 641 612 678 332 147 351 ...
## $ violent : num 433 421 235 159 240 266 272 146 78 84 ...Berikut adalah penjelasan mengenai peubah yang ada pada data tersebut:
est_fcs: estimated count of foreclosure starts from Jan. 2007 through June 2008
est_mtgs: estimated number of active mortgages from Jan. 2007 through June 2008
est_fcs_rt: number of foreclosure starts divided by number of mortgages times 100
bls_unemp: June 2008 place or county unemployment rate
totpop: total population from 2000 Census
violent: number of violent crimes reported between Jan. 2007 through December 2008
property: number of property crimes reported between Jan. 2007 through December 2008
(Sarmiento-Barbieri, 2016)
Visualisasi Data
plot(chi.poly)
library(leaflet)## Warning: package 'leaflet' was built under R version 4.1.2leaflet(chi.poly) %>%
addPolygons(stroke = FALSE, fillOpacity = 0.5, smoothFactor = 0.5) %>%
addTiles()require(RColorBrewer)## Loading required package: RColorBrewerqpal<-colorQuantile("OrRd", chi.poly@data$violent, n=9)
leaflet(chi.poly) %>%
addPolygons(stroke = FALSE, fillOpacity = .8, smoothFactor = 0.2, color = ~qpal(violent)
) %>%
addTiles()OLS
chi.ols<-lm(violent~est_fcs_rt+bls_unemp, data=chi.poly@data)
summary(chi.ols)##
## Call:
## lm(formula = violent ~ est_fcs_rt + bls_unemp, data = chi.poly@data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -892.02 -77.02 -23.73 41.90 1238.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -18.627 45.366 -0.411 0.681
## est_fcs_rt 28.298 1.435 19.720 <2e-16 ***
## bls_unemp -0.308 5.770 -0.053 0.957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 157.3 on 894 degrees of freedom
## Multiple R-squared: 0.3141, Adjusted R-squared: 0.3126
## F-statistic: 204.7 on 2 and 894 DF, p-value: < 2.2e-16Modeling Spatial Dependence
list.queen<-poly2nb(chi.poly, queen=TRUE)
W<-nb2listw(list.queen, style="W", zero.policy=TRUE)
W## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 897
## Number of nonzero links: 6140
## Percentage nonzero weights: 0.7631036
## Average number of links: 6.845039
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 897 804609 897 274.4893 3640.864coords<-coordinates(chi.poly)
W_dist<-dnearneigh(coords,0,1,longlat = TRUE)Checking the Spatial Autocorrelation
moran.lm<-lm.morantest(chi.ols, W, alternative="two.sided")
print(moran.lm)##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = violent ~ est_fcs_rt + bls_unemp, data =
## chi.poly@data)
## weights: W
##
## Moran I statistic standard deviate = 11.785, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## Observed Moran I Expectation Variance
## 0.2142252370 -0.0020099108 0.0003366648LM Test
LM<-lm.LMtests(chi.ols, W, test="all")
summary(LM)## Lagrange multiplier diagnostics for spatial dependence
## data:
## model: lm(formula = violent ~ est_fcs_rt + bls_unemp, data =
## chi.poly@data)
## weights: W
##
## statistic parameter p.value
## LMerr 1.3452e+02 1 < 2.2e-16 ***
## LMlag 1.8218e+02 1 < 2.2e-16 ***
## RLMerr 6.6762e-04 1 0.9794
## RLMlag 4.7653e+01 1 5.089e-12 ***
## SARMA 1.8218e+02 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fitting Spatial Regressions
SAR
sar.chi<-lagsarlm(violent~est_fcs_rt+bls_unemp, data=chi.poly@data, W)
summary(sar.chi)##
## Call:
## lagsarlm(formula = violent ~ est_fcs_rt + bls_unemp, data = chi.poly@data,
## listw = W)
##
## Residuals:
## Min 1Q Median 3Q Max
## -519.127 -65.003 -15.226 36.423 1184.193
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -93.7885 41.3162 -2.270 0.02321
## est_fcs_rt 15.6822 1.5600 10.053 < 2e-16
## bls_unemp 8.8949 5.2447 1.696 0.08989
##
## Rho: 0.49037, LR test value: 141.33, p-value: < 2.22e-16
## Asymptotic standard error: 0.039524
## z-value: 12.407, p-value: < 2.22e-16
## Wald statistic: 153.93, p-value: < 2.22e-16
##
## Log likelihood: -5738.047 for lag model
## ML residual variance (sigma squared): 20200, (sigma: 142.13)
## Number of observations: 897
## Number of parameters estimated: 5
## AIC: 11486, (AIC for lm: 11625)
## LM test for residual autocorrelation
## test value: 8.1464, p-value: 0.0043146impacts(sar.chi, listw=W)## Impact measures (lag, exact):
## Direct Indirect Total
## est_fcs_rt 16.434479 14.336896 30.77137
## bls_unemp 9.321585 8.131842 17.45343SEM
errorsalm.chi<-errorsarlm(violent~est_fcs_rt+bls_unemp, data=chi.poly@data, W)
summary(errorsalm.chi)##
## Call:
## errorsarlm(formula = violent ~ est_fcs_rt + bls_unemp, data = chi.poly@data,
## listw = W)
##
## Residuals:
## Min 1Q Median 3Q Max
## -650.506 -64.355 -22.646 35.461 1206.346
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2624 43.0509 -0.0293 0.9766
## est_fcs_rt 19.4620 1.9450 10.0062 <2e-16
## bls_unemp 4.0380 5.5134 0.7324 0.4639
##
## Lambda: 0.52056, LR test value: 109.68, p-value: < 2.22e-16
## Asymptotic standard error: 0.042291
## z-value: 12.309, p-value: < 2.22e-16
## Wald statistic: 151.51, p-value: < 2.22e-16
##
## Log likelihood: -5753.875 for error model
## ML residual variance (sigma squared): 20796, (sigma: 144.21)
## Number of observations: 897
## Number of parameters estimated: 5
## AIC: 11518, (AIC for lm: 11625)Excercise (1)
Lakukan pemodelan menggunakan data chi.poly:
- periksa multikolineritas antar peubah bebas yang digunakan berdasarkan VIF
hi.ols<-lm(violent~est_fcs_rt+bls_unemp, data=chi.poly@data)
summary(chi.ols)##
## Call:
## lm(formula = violent ~ est_fcs_rt + bls_unemp, data = chi.poly@data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -892.02 -77.02 -23.73 41.90 1238.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -18.627 45.366 -0.411 0.681
## est_fcs_rt 28.298 1.435 19.720 <2e-16 ***
## bls_unemp -0.308 5.770 -0.053 0.957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 157.3 on 894 degrees of freedom
## Multiple R-squared: 0.3141, Adjusted R-squared: 0.3126
## F-statistic: 204.7 on 2 and 894 DF, p-value: < 2.2e-16vif(chi.ols)## est_fcs_rt bls_unemp
## 1.054007 1.054007nilai VIF < 5 berarti tidak ada multikolinieritas.
- eksplorasi autokorelasi spasial pada model menggunakan jarak W_dist
coords<-coordinates(chi.poly)
W_dist<-dnearneigh(coords,0,1,longlat = TRUE)
summary(W_dist)## Neighbour list object:
## Number of regions: 897
## Number of nonzero links: 5446
## Percentage nonzero weights: 0.6768505
## Average number of links: 6.071349
## 55 regions with no links:
## 141 142 143 145 153 154 155 158 462 631 637 638 642 643 644 645 655 656 657 658 659 758 759 769 820 821 822 823 824 855 856 857 861 862 864 865 866 867 868 870 871 872 873 876 877 880 885 886 887 888 889 890 892 896 897
## Link number distribution:
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 55 56 76 82 89 85 79 54 71 65 42 47 33 29 22 6 4 2
## 56 least connected regions:
## 11 15 17 41 138 139 140 144 146 148 156 157 174 198 199 343 344 456 463 477 485 605 607 621 630 632 633 639 641 646 647 648 649 650 651 654 667 668 751 752 753 754 757 764 770 841 846 854 860 869 875 879 884 891 893 895 with 1 link
## 2 most connected regions:
## 364 381 with 17 linksDari output di atas terlihat bahwa ada wilayah yang tidak memiliki tetangga sehingga akan dilakukan modifikasi terlebih dahulu nilai dmax nya.
#dmax=2
W_dist2<-dnearneigh(coords,0,2,longlat = TRUE)
summary(W_dist2)## Neighbour list object:
## Number of regions: 897
## Number of nonzero links: 21762
## Percentage nonzero weights: 2.704668
## Average number of links: 24.26087
## 5 regions with no links:
## 643 658 659 865 866
## Link number distribution:
##
## 0 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
## 5 5 5 11 7 20 20 16 20 16 22 15 27 21 17 26 32 25 18 19 34 21 12 21 18 19
## 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 51 52
## 24 34 24 15 28 12 29 12 27 17 18 15 18 21 19 29 19 12 13 22 6 3 2 3 2 1
## 5 least connected regions:
## 657 867 886 892 896 with 1 link
## 1 most connected region:
## 418 with 52 linksDari output di atas terlihat bahwa ada wilayah yang tidak memiliki tetangga sehingga akan dilakukan modifikasi terlebih dahulu nilai dmax nya.
#dmax=3
W_dist3<-dnearneigh(coords,0,3,longlat = TRUE)
summary(W_dist3)## Neighbour list object:
## Number of regions: 897
## Number of nonzero links: 45734
## Percentage nonzero weights: 5.684003
## Average number of links: 50.98551
## 2 regions with no links:
## 865 866
## Link number distribution:
##
## 0 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
## 2 2 1 1 4 1 4 5 9 8 2 4 8 5 9 9 12 11 6 9 9 11 9 14 8 16
## 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
## 5 8 8 12 10 12 10 10 10 7 11 10 11 12 11 12 11 13 10 8 8 12 7 10 14 7
## 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77
## 10 8 15 12 12 12 11 16 7 15 14 13 16 9 17 12 11 8 9 16 11 11 9 10 10 16
## 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
## 11 10 7 7 9 9 5 8 13 9 11 11 7 9 6 5 6 2 3 1
## 2 least connected regions:
## 659 867 with 1 link
## 1 most connected region:
## 373 with 97 linksDari output di atas terlihat bahwa ada wilayah yang tidak memiliki tetangga sehingga akan dilakukan modifikasi terlebih dahulu nilai dmax nya.
#dmax=4
W_dist4<-dnearneigh(coords,0,4,longlat = TRUE)
summary(W_dist4)## Neighbour list object:
## Number of regions: 897
## Number of nonzero links: 76556
## Percentage nonzero weights: 9.514684
## Average number of links: 85.34671
## 1 region with no links:
## 866
## Link number distribution:
##
## 0 1 3 4 6 8 9 10 12 13 14 15 16 17 18 19 20 21 22 23
## 1 1 1 1 1 1 1 6 2 4 9 3 2 3 2 4 4 2 4 7
## 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
## 4 8 7 7 2 7 1 8 3 12 3 1 5 3 9 3 8 8 4 6
## 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63
## 4 8 6 5 6 6 5 6 6 4 7 8 3 8 3 7 9 4 2 2
## 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
## 7 3 4 6 7 2 7 11 4 8 4 10 6 8 8 2 15 5 5 8
## 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103
## 3 8 18 12 9 12 5 8 6 9 6 7 5 9 6 13 10 12 5 8
## 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123
## 8 6 8 13 8 6 4 10 6 4 11 7 9 6 10 6 2 7 11 6
## 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
## 5 5 8 7 7 6 6 2 5 6 5 4 6 5 4 6 2 7 5 6
## 144 145 146 147 148 149 150 151 152 153 154 155 156 158 159
## 3 6 6 5 7 4 1 7 4 5 2 6 4 2 2
## 1 least connected region:
## 865 with 1 link
## 2 most connected regions:
## 355 380 with 159 linksDari output di atas terlihat bahwa ada wilayah yang tidak memiliki tetangga sehingga akan dilakukan modifikasi terlebih dahulu nilai dmax nya.
#dmax=5
W_dist5<-dnearneigh(coords,0,5,longlat = TRUE)
summary(W_dist5)## Neighbour list object:
## Number of regions: 897
## Number of nonzero links: 114028
## Percentage nonzero weights: 14.17185
## Average number of links: 127.1215
## Link number distribution:
##
## 1 2 5 7 12 15 16 17 19 20 21 22 23 24 25 26 27 28 29 30
## 1 1 1 1 2 3 2 1 2 3 4 3 3 1 6 2 2 2 3 1
## 31 32 33 34 35 36 37 38 39 40 42 43 44 45 46 47 48 49 50 51
## 2 2 4 2 7 3 3 4 2 7 3 2 2 4 5 4 2 9 4 5
## 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
## 1 1 4 4 3 4 2 4 3 3 5 3 1 1 2 5 4 8 2 1
## 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91
## 4 8 5 1 1 4 3 4 2 2 4 4 5 3 4 1 5 2 5 5
## 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111
## 5 2 2 2 7 2 3 2 2 6 5 1 3 5 6 3 6 5 5 2
## 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131
## 4 3 8 5 5 8 5 7 6 8 6 4 4 9 6 8 5 8 5 8
## 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151
## 3 4 4 7 8 7 6 7 8 4 8 6 7 6 5 5 6 2 4 7
## 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171
## 4 6 5 4 8 9 8 7 6 11 9 2 3 7 7 2 6 8 4 5
## 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191
## 3 1 6 6 7 3 5 7 8 3 4 6 4 2 7 4 5 3 3 3
## 192 193 194 195 197 198 199 200 201 202 203 204 205 206 207 208 210 211 212 213
## 3 1 3 7 3 5 2 4 2 3 7 5 2 2 2 3 7 3 1 4
## 214 215 216 217 218 219 220 221 222 223 224 225 226 228 229 230 233
## 1 5 2 5 9 2 1 6 5 3 2 3 2 3 3 1 1
## 1 least connected region:
## 866 with 1 link
## 1 most connected region:
## 313 with 233 linksDari output di atas terlihat bahwa sudah tidak ada wilayah yang tidak memiliki tetangga sehingga nilai dmax yang digunakan adalah 5.
W_dist5.s <- nb2listw(W_dist5,style='W') #W is row standardised (sums over all links to n). Standardisasi Baris
W_dist5.s## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 897
## Number of nonzero links: 114028
## Percentage nonzero weights: 14.17185
## Average number of links: 127.1215
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 897 804609 897 22.16899 3612.328moran.lmC<-lm.morantest(chi.ols,W_dist5.s, alternative="two.sided")
print(moran.lm)##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = violent ~ est_fcs_rt + bls_unemp, data =
## chi.poly@data)
## weights: W
##
## Moran I statistic standard deviate = 11.785, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## Observed Moran I Expectation Variance
## 0.2142252370 -0.0020099108 0.0003366648Dari output di atas telihat p_value < alpha sehingga tolak Ho. Berarti ada autokorelasi spasial pada data.
- lakukan pemodelan yang menurut Anda paling tepat, interpretasikan.
Lagrange Multiplier Test
LM<-lm.LMtests(chi.ols, W_dist5.s,
test=c("LMerr", "LMlag","RLMerr","RLMlag","SARMA"))
summary(LM)## Lagrange multiplier diagnostics for spatial dependence
## data:
## model: lm(formula = violent ~ est_fcs_rt + bls_unemp, data =
## chi.poly@data)
## weights: W_dist5.s
##
## statistic parameter p.value
## LMerr 189.13 1 < 2.2e-16 ***
## LMlag 120.52 1 < 2.2e-16 ***
## RLMerr 92.15 1 < 2.2e-16 ***
## RLMlag 23.54 1 1.224e-06 ***
## SARMA 212.67 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Output memperlihatkan bahwa hasil uji model SEM dan SAR sama-sama signifikan pada taraf 5%. Selanjutnya, hasil uji robust ternyata keduanya juga signifikan. Berdasarkan skema tersebut, kita dapat mencoba kandidat model SARMA/GSM,model SEM, dan Model SAR. Namun demikian, ada pula pendapat yang menyarankan agar kita mengambil kandidat model dengan p-value terkecil,namun karena ada beberapa model yang memiliki p_value terkecil, tetap akan dilakukan pemodelan untuk SAR,SEM,GSM. Lalu, dipilih model terbaik dengan melihat nilai AIC dan Pseudo-Rsquare nya.
Model SEM
W.opt <- W_dist5.s
sem <-errorsarlm(chi.ols,data=chi.poly@data,W.opt)
summary(sem,Nagelkerke=T)##
## Call:errorsarlm(formula = chi.ols, data = chi.poly@data, listw = W.opt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -639.622 -70.323 -24.571 41.019 1169.084
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 17.2975 69.2263 0.2499 0.8027
## est_fcs_rt 20.3372 2.0567 9.8884 <2e-16
## bls_unemp 3.3716 5.6850 0.5931 0.5531
##
## Lambda: 0.9091, LR test value: 78.606, p-value: < 2.22e-16
## Asymptotic standard error: 0.044869
## z-value: 20.261, p-value: < 2.22e-16
## Wald statistic: 410.52, p-value: < 2.22e-16
##
## Log likelihood: -5769.412 for error model
## ML residual variance (sigma squared): 22195, (sigma: 148.98)
## Nagelkerke pseudo-R-squared: 0.37165
## Number of observations: 897
## Number of parameters estimated: 5
## AIC: 11549, (AIC for lm: 11625)Output di atas menunjukkan bahwa koefisien Lambda signifikan pada taraf nyata 5% ( p-value < alpha). Berarti kita memasukkan komponen error tersebut ke dalam model sudah benar.
AIC model SEM adalah sebesar 11549. Selanjutnya kita akan coba memeriksa sisaan model SEM ini.
Uji Asumsi Normal
Ho: Sisaan menyebar normal H1: Sisaan tidak menyebar normal
err.sem<-residuals(sem)
ad.test(err.sem)##
## Anderson-Darling normality test
##
## data: err.sem
## A = 37.053, p-value < 2.2e-16Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
Uji Asumsi Kehomogenan Ragam Sisaan
Ho: Ragam Sisaan homogen H1: Ragam Sisaan tidak homogen
bptest.Sarlm(sem)##
## studentized Breusch-Pagan test
##
## data:
## BP = 43.42, df = 2, p-value = 3.728e-10Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang tidak homogen (tolak Ho karena p_value < alpha=5%)
Uji Kebebasan Sisaan
Ho: tidak terdapat autokorelasi spasial pada galat (galat bebas) H1: terdapat autokorelasi spasial positif pada galat (galat tidak bebas)
moran.test(err.sem, W.opt, randomisation=F,alternative="greater")##
## Moran I test under normality
##
## data: err.sem
## weights: W.opt
##
## Moran I statistic standard deviate = 2.9593, p-value = 0.001542
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 1.368933e-02 -1.116071e-03 2.503038e-05Hasil uji asumsi kebebasan sisaan dengan Moran I test menunjukkan bahwa sisaan bersifat tidak bebas (tolak Ho karena p_value < alpha=5%)
Terlihat pada ketiga output di atas bahwa sisaan tidak memenuhi asumsi kenormalan, kehomogenan ragam, serta kebebasan.
Spatial Durbin Error Model (SDEM)
sdem <- errorsarlm(chi.ols, data=chi.poly@data, W.opt, etype="mixed");
summary(sdem, Nagelkerke=T)##
## Call:errorsarlm(formula = chi.ols, data = chi.poly@data, listw = W.opt,
## etype = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -540.830 -72.362 -16.830 45.385 1147.050
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 38.6552 446.9228 0.0865 0.9311
## est_fcs_rt 17.3184 2.1596 8.0193 1.11e-15
## bls_unemp 6.1884 5.7097 1.0838 0.2784
## lag.est_fcs_rt 30.8778 7.2693 4.2477 2.16e-05
## lag.bls_unemp -28.8953 56.3539 -0.5127 0.6081
##
## Lambda: 0.88674, LR test value: 64.05, p-value: 1.2212e-15
## Asymptotic standard error: 0.052655
## z-value: 16.841, p-value: < 2.22e-16
## Wald statistic: 283.6, p-value: < 2.22e-16
##
## Log likelihood: -5760.29 for error model
## ML residual variance (sigma squared): 21782, (sigma: 147.59)
## Nagelkerke pseudo-R-squared: 0.3843
## Number of observations: 897
## Number of parameters estimated: 7
## AIC: 11535, (AIC for lm: 11597)Output di atas menunjukkan bahwa koefisien Lambda signifikan pada taraf nyata 5% ( p-value < alpha). Berarti kita memasukkan komponen error tersebut ke dalam model sudah benar.Namun, untuk lag.x nya hanya lag.x3 yang signifikan.
AIC model SDEM adalah sebesar 11535. Selanjutnya kita akan coba memeriksa sisaan model SDEM ini.
Uji Asumsi Normal
Ho: Sisaan menyebar normal H1: Sisaan tidak menyebar normal
err.sdem<-residuals(sdem)
ad.test(err.sdem)##
## Anderson-Darling normality test
##
## data: err.sdem
## A = 28.572, p-value < 2.2e-16Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
Uji Asumsi Kehomogenan Ragam Sisaan
Ho: Ragam Sisaan homogen H1: Ragam Sisaan tidak homogen
bptest.Sarlm(sdem)##
## studentized Breusch-Pagan test
##
## data:
## BP = 71.222, df = 4, p-value = 1.255e-14Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang tidak homogen (tolak Ho karena p_value < alpha=5%)
Uji Kebebasan Sisaan
Ho: tidak terdapat autokorelasi spasial pada galat (galat bebas) H1: terdapat autokorelasi spasial positif pada galat (galat tidak bebas)
moran.test(err.sdem, W.opt, randomisation=F,alternative="greater")##
## Moran I test under normality
##
## data: err.sdem
## weights: W.opt
##
## Moran I statistic standard deviate = 1.7305, p-value = 0.04177
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 7.541435e-03 -1.116071e-03 2.503038e-05Hasil uji asumsi kebebasan sisaan dengan Moran I test menunjukkan bahwa sisaan bersifat tidak bebas (tolak Ho karena p_value < alpha=5%)
Terlihat pada ketiga output di atas bahwa sisaan tidak memenuhi asumsi kenormalan, kehomogenan ragam, serta kebebasan.
Likelihood Ratio Test (Model SEM VS SDEM)
Untuk mengecek apakah lag x signifikan atau tidak dilakukan Uji Likelihood Rasio.
LR.Sarlm(sdem,sem)##
## Likelihood ratio for spatial linear models
##
## data:
## Likelihood ratio = 18.243, df = 2, p-value = 0.0001093
## sample estimates:
## Log likelihood of sdem Log likelihood of sem
## -5760.290 -5769.412Dari hasil likelihood ratio test diperoleh P_value < alpha=0.05 berarti lag x signifikan. Ada dependensi spasial di lax x sehingga model SDEM lebih mencerminkan data dibandingkan model SEM.
Model SAR
sar <-lagsarlm(chi.ols,data=chi.poly@data,W.opt)
summary(sar,Nagelkerke=T)##
## Call:lagsarlm(formula = chi.ols, data = chi.poly@data, listw = W.opt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -537.721 -75.361 -17.774 46.656 1153.634
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -122.8671 44.2691 -2.7755 0.005512
## est_fcs_rt 16.3168 1.7844 9.1440 < 2.2e-16
## bls_unemp 8.1010 5.5765 1.4527 0.146307
##
## Rho: 0.69401, LR test value: 78.457, p-value: < 2.22e-16
## Asymptotic standard error: 0.056085
## z-value: 12.374, p-value: < 2.22e-16
## Wald statistic: 153.12, p-value: < 2.22e-16
##
## Log likelihood: -5769.486 for lag model
## ML residual variance (sigma squared): 22426, (sigma: 149.75)
## Nagelkerke pseudo-R-squared: 0.37154
## Number of observations: 897
## Number of parameters estimated: 5
## AIC: 11549, (AIC for lm: 11625)
## LM test for residual autocorrelation
## test value: 40.747, p-value: 1.7324e-10Output di atas memperlihatkan bahwa koefisien Rho pada model SAR signifikan, dengan nilai AIC sebesar 11549. Selain itu, terlihat pula hasil uji autokorelasi pada sisaan model dengan Ho: tidak terdapat autokorelasi spasial pada galat (galat bebas) dan H1: terdapat autokorelasi spasial positif pada galat (galat tidak bebas) memperlihatkan nilai p-value lebih kecil dari galat (tolak Ho), artinya terdapat autokorelasi pada sisaan.
Uji Asumsi Normal
Ho: Sisaan menyebar normal H1: Sisaan tidak menyebar normal
err.sar<-residuals(sar)
ad.test(err.sar)##
## Anderson-Darling normality test
##
## data: err.sar
## A = 29.673, p-value < 2.2e-16Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
Uji Asumsi Kehomogenan Ragam Sisaan
Ho: Ragam Sisaan homogen H1: Ragam Sisaan tidak homogen
bptest.Sarlm(sar)##
## studentized Breusch-Pagan test
##
## data:
## BP = 71.349, df = 2, p-value = 3.331e-16Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang tidak homogen (tolak Ho karena p_value < alpha=5%)
Terlihat pada ketiga output di atas bahwa sisaan tidak memenuhi asumsi kenormalan, kehomogenan ragam, serta kebebasan.
Spatial Durbin Model (SDM)
Berikut ini adalah penjelasan tentang Spatial Durbin Model yang dirujuk dari Zhukov (2010):
Model:
sdm <- lagsarlm(chi.ols,data=chi.poly@data, W.opt, type="mixed");
summary(sdm,Nagelkerke=T)##
## Call:lagsarlm(formula = chi.ols, data = chi.poly@data, listw = W.opt,
## type = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -607.785 -71.287 -20.416 42.880 1153.426
##
## Type: mixed
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -263.6752 222.6254 -1.1844 0.2363
## est_fcs_rt 19.1791 2.1336 8.9892 <2e-16
## bls_unemp 4.5850 5.7072 0.8034 0.4218
## lag.est_fcs_rt -10.3202 4.2595 -2.4229 0.0154
## lag.bls_unemp 24.4867 28.0252 0.8737 0.3823
##
## Rho: 0.83036, LR test value: 52.459, p-value: 4.3943e-13
## Asymptotic standard error: 0.065907
## z-value: 12.599, p-value: < 2.22e-16
## Wald statistic: 158.73, p-value: < 2.22e-16
##
## Log likelihood: -5766.086 for mixed model
## ML residual variance (sigma squared): 22137, (sigma: 148.79)
## Nagelkerke pseudo-R-squared: 0.37629
## Number of observations: 897
## Number of parameters estimated: 7
## AIC: 11546, (AIC for lm: 11597)
## LM test for residual autocorrelation
## test value: 24.755, p-value: 6.5101e-07Output di atas memperlihatkan bahwa koefisien Rho pada model SDM signifikan, dengan nilai AIC sebesar 11546. Namun, lag.x1,lag.x3 tidak nyata. Selain itu, terlihat pula hasil uji autokorelasi pada sisaan model dengan Ho: tidak terdapat autokorelasi spasial pada galat (galat bebas) dan H1: terdapat autokorelasi spasial positif pada galat (galat tidak bebas) memperlihatkan nilai p-value lebih kecil dari alpha (tolak Ho), artinya terdapat autokorelasi pada sisaan.
Uji Asumsi Normal
Ho: Sisaan menyebar normal H1: Sisaan tidak menyebar normal
err.sdm<-residuals(sdm)
ad.test(err.sdm)##
## Anderson-Darling normality test
##
## data: err.sdm
## A = 32.265, p-value < 2.2e-16Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
Uji Asumsi Kehomogenan Ragam Sisaan
Ho: Ragam Sisaan homogen H1: Ragam Sisaan tidak homogen
bptest.Sarlm(sdm)##
## studentized Breusch-Pagan test
##
## data:
## BP = 78.954, df = 4, p-value = 3.331e-16Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang tidak homogen (tolak Ho karena p_value < alpha=5%)
Terlihat pada ketiga output di atas bahwa sisaan tidak memenuhi asumsi kenormalan, kehomogenan ragam, serta kebebasan.
Likelihood Ratio Test (Model SAR vs SDM)
Untuk mengecek apakah lag x signifikan atau tidak dilakukan Uji Likelihood Rasio.
LR.Sarlm(sdm,sar)##
## Likelihood ratio for spatial linear models
##
## data:
## Likelihood ratio = 6.8003, df = 2, p-value = 0.03337
## sample estimates:
## Log likelihood of sdm Log likelihood of sar
## -5766.086 -5769.486Dari hasil likelihood ratio test diperoleh P_value < alpha=0.05 berarti lag x signifikan. Ada dependensi spasial di lag x sehingga model SDM lebih mencerminkan data dibandingkan model SAR.
Model GSM/SARMA
gsm<-sacsarlm(chi.ols,data=chi.poly@data,W.opt)
summary(gsm,Nagelkerke=T)##
## Call:sacsarlm(formula = chi.ols, data = chi.poly@data, listw = W.opt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -573.305 -70.769 -18.829 41.325 1147.976
##
## Type: sac
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -97.8309 51.1284 -1.9134 0.05569
## est_fcs_rt 18.1773 2.1221 8.5659 < 2e-16
## bls_unemp 5.8036 5.6720 1.0232 0.30621
##
## Rho: 0.59331
## Asymptotic standard error: 0.13706
## z-value: 4.3288, p-value: 1.4991e-05
## Lambda: 0.62838
## Asymptotic standard error: 0.1744
## z-value: 3.6031, p-value: 0.00031447
##
## LR test value: 96.184, p-value: < 2.22e-16
##
## Log likelihood: -5760.623 for sac model
## ML residual variance (sigma squared): 21907, (sigma: 148.01)
## Nagelkerke pseudo-R-squared: 0.38384
## Number of observations: 897
## Number of parameters estimated: 6
## AIC: 11533, (AIC for lm: 11625)Output di atas memperlihatkan bahwa KEDUA koefisien dependensi spasial tidak signifikan.AIC model SARMA adalah sebesar 11533.
Uji Asumsi Normal
Ho: Sisaan menyebar normal H1: Sisaan tidak menyebar normal
err.gsm<-residuals(gsm)
ad.test(err.gsm)##
## Anderson-Darling normality test
##
## data: err.gsm
## A = 30.976, p-value < 2.2e-16Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
Uji Asumsi Kehomogenan Ragam Sisaan
Ho: Ragam Sisaan homogen H1: Ragam Sisaan tidak homogen
bptest.Sarlm(gsm)##
## studentized Breusch-Pagan test
##
## data:
## BP = 56.487, df = 2, p-value = 5.421e-13Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang tidak homogen (tolak Ho karena p_value < alpha=5%)
Uji Kebebasan Sisaan
Ho: tidak terdapat autokorelasi spasial pada galat (galat bebas) H1: terdapat autokorelasi spasial positif pada galat (galat tidak bebas)
moran.test(err.gsm, W.opt, randomisation=F,alternative="greater")##
## Moran I test under normality
##
## data: err.gsm
## weights: W.opt
##
## Moran I statistic standard deviate = 0.6849, p-value = 0.2467
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 2.310519e-03 -1.116071e-03 2.503038e-05Hasil uji asumsi kebebasan sisaan dengan Moran I test menunjukkan bahwa sisaan bersifat bebas (tolak Ho karena p_value >alpha=5%)
Terlihat pada ketiga output di atas bahwa sisaan tidak memenuhi asumsi kenormalan dan kehomogenan ragam. Namun memenuhi asumsi kebebasan.
GNS Model
Model GNS memasukkan komponen lag x pada model GSM/SARMA.
gns <-sacsarlm(chi.ols,data=chi.poly@data,W.opt,type="mixed")
summary(gns,Nagelkerke=T)##
## Call:sacsarlm(formula = chi.ols, data = chi.poly@data, listw = W.opt,
## type = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -542.770 -71.703 -16.357 43.224 1143.992
##
## Type: sacmixed
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -152.11733 415.45639 -0.3661 0.7143
## est_fcs_rt 17.28594 2.16217 7.9947 1.332e-15
## bls_unemp 6.57363 5.69907 1.1535 0.2487
## lag.est_fcs_rt 16.59626 14.89837 1.1140 0.2653
## lag.bls_unemp -0.72957 52.58518 -0.0139 0.9889
##
## Rho: 0.30708
## Asymptotic standard error: 0.37477
## z-value: 0.81939, p-value: 0.41257
## Lambda: 0.79695
## Asymptotic standard error: 0.17773
## z-value: 4.4839, p-value: 7.3282e-06
##
## LR test value: 98.266, p-value: < 2.22e-16
##
## Log likelihood: -5759.582 for sacmixed model
## ML residual variance (sigma squared): 21826, (sigma: 147.74)
## Nagelkerke pseudo-R-squared: 0.38527
## Number of observations: 897
## Number of parameters estimated: 8
## AIC: 11535, (AIC for lm: 11625)Output di atas memperlihatkan bahwa kedua koefisien dependensi spasial tidak signifikan, yaitu Rho dan lambda. AIC model GNS adalah sebesar 11535.
Uji Asumsi Normal
Ho: Sisaan menyebar normal H1: Sisaan tidak menyebar normal
err.gns<-residuals(gns)
ad.test(err.gns)##
## Anderson-Darling normality test
##
## data: err.gns
## A = 28.928, p-value < 2.2e-16Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
Uji Asumsi Kehomogenan Ragam Sisaan
Ho: Ragam Sisaan homogen H1: Ragam Sisaan tidak homogen
bptest.Sarlm(gns)##
## studentized Breusch-Pagan test
##
## data:
## BP = 76.368, df = 4, p-value = 9.992e-16Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang tidak homogen (tolak Ho karena p_value < alpha=5%)
Uji Kebebasan Sisaan
Ho: tidak terdapat autokorelasi spasial pada galat (galat bebas) H1: terdapat autokorelasi spasial positif pada galat (galat tidak bebas)
moran.test(err.gns, W.opt, randomisation=F,alternative="greater")##
## Moran I test under normality
##
## data: err.gns
## weights: W.opt
##
## Moran I statistic standard deviate = 1.0539, p-value = 0.146
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 4.156482e-03 -1.116071e-03 2.503038e-05Hasil uji asumsi kebebasan sisaan dengan Moran I test menunjukkan bahwa sisaan bersifat bebas (tidak tolak Ho karena p_value > alpha=5%)
Terlihat pada ketiga output di atas bahwa sisaan telah memenuhi asumsi kebebasan namun tidak asumsi kenormalan dan kehomogenan ragam.
Likelihood Ratio Test (Model GSM vs GNS)
Untuk mengecek apakah lag x signifikan atau tidak dilakukan Uji Likelihood Rasio.
LR.Sarlm(gns,gsm)##
## Likelihood ratio for spatial linear models
##
## data:
## Likelihood ratio = 2.0826, df = 2, p-value = 0.353
## sample estimates:
## Log likelihood of gns Log likelihood of gsm
## -5759.582 -5760.623Dari hasil likelihood ratio test diperoleh P_value > alpha=0.05 berarti lag x tidak signifikan. Tidak Ada dependensi spasial di laxg x sehingga model GSM lebih mencerminkan data dibandingkan model GSN.
SLX Model
Model:
SLX model dapat dilakukan dengan fungsi lmSLX() pada package spatialreg. Perhatikan bahwa model SLX adalah bentuk khusus dari model Spatial Durbin.
slx <- lmSLX(chi.ols, data=chi.poly@data, W.opt, Durbin = TRUE);
summary(slx,Nagelkerke=T)##
## Call:
## lm(formula = formula(paste("y ~ ", paste(colnames(x)[-1], collapse = "+"))),
## data = as.data.frame(x), weights = weights)
##
## Residuals:
## Min 1Q Median 3Q Max
## -663.82 -83.88 -22.20 42.81 1198.81
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -801.026 230.063 -3.482 0.000522 ***
## est_fcs_rt 20.192 2.211 9.134 < 2e-16 ***
## bls_unemp 4.062 5.928 0.685 0.493421
## lag.est_fcs_rt 15.588 3.347 4.658 3.68e-06 ***
## lag.bls_unemp 86.698 29.056 2.984 0.002925 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 154.7 on 892 degrees of freedom
## Multiple R-squared: 0.3387, Adjusted R-squared: 0.3358
## F-statistic: 114.2 on 4 and 892 DF, p-value: < 2.2e-16AIC(slx)## [1] 11596.63Output di atas memperlihatkan bahwa hanya lag.x2 dan lag.x3 yang signifikan, sedangkan lag.x1 tidak signifikan. AIC model SLX adalah sebesar 11596.63.
Uji Asumsi Normal
Ho: Sisaan menyebar normal H1: Sisaan tidak menyebar normal
err.slx<-residuals(slx)
ad.test(err.slx)##
## Anderson-Darling normality test
##
## data: err.slx
## A = 31.562, p-value < 2.2e-16Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
Uji Asumsi Kehomogenan Ragam Sisaan
Ho: Ragam Sisaan homogen H1: Ragam Sisaan tidak homogen
bptest(slx)##
## studentized Breusch-Pagan test
##
## data: slx
## BP = 77.974, df = 4, p-value = 4.678e-16Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang tidak homogen (tolak Ho karena p_value < alpha=5%)
Uji Kebebasan Sisaan
Ho: tidak terdapat autokorelasi spasial pada galat (galat bebas) H1: terdapat autokorelasi spasial positif pada galat (galat tidak bebas)
moran.test(err.slx, W.opt, randomisation=F,alternative="greater")##
## Moran I test under normality
##
## data: err.slx
## weights: W.opt
##
## Moran I statistic standard deviate = 13.095, p-value < 2.2e-16
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 6.439811e-02 -1.116071e-03 2.503038e-05Hasil uji asumsi kebebasan sisaan dengan Moran I test menunjukkan bahwa sisaan bersifat tidak bebas (tolak Ho karena p_value < alpha=5%)
Terlihat pada ketiga output di atas bahwa sisaan tidak memenuhi asumsi kenormalan dan kehomogenan ragam namun tidak memenuhi asumsi kebebasan.
Likelihood Ratio Test (Model SLX vs Regresi Linier Klasik)
Untuk mengecek apakah lag x signifikan atau tidak dilakukan Uji Likelihood Rasio.
LR.Sarlm(slx,chi.ols)##
## Likelihood ratio for spatial linear models
##
## data:
## Likelihood ratio = 32.799, df = 2, p-value = 7.547e-08
## sample estimates:
## Log likelihood of slx Log likelihood of chi.ols
## -5792.315 -5808.715Dari hasil likelihood ratio test diperoleh P_value < alpha=0.05 berarti lag x signifikan. Ada dependensi spasial di laxg x sehingga model SLX lebih mencerminkan data dibandingkan model Regresi Linier Klasik.
Goodness of Fits
Dari model yang digunakan, model terbaik adalah model SAR dengan nilai AIC terendah. selanjutnya model terbaik ini yang akan ditafsirkan hasil dan nilai spillover nya.
AIC(sem)## [1] 11548.82AIC(sdem)## [1] 11534.58AIC(sar)## [1] 11548.97AIC(sdm)## [1] 11546.17AIC(gsm)## [1] 11533.25AIC(gns)## [1] 11535.16AIC(chi.ols)## [1] 11625.43AIC(slx)## [1] 11596.63Model terbaik jika dilihat dari AIC adalah model GSM (AIC terendah) dengan R-Square sekitar 38% .Namun sisaan model ini tidak memenuhi asumsi kenormalan dan kehomogenan ragam. Namun memenuhi asumsi kebebasan. Penulis rekomendasikan lebih baik mencoba menggunakan model GWR atau Regresi Terboboti Geografis yang akan dibahas pada pertemuan 13.
Marginal Effects (Spill-over) on the Spatial Regression Modeling
- Marginal Effects*
Definisi yang diambil dari materi kuliah yang disusun oleh Dr. Anik Djuraidah menyatakan bahwa efek marginal atau limpahan (spill-over) adalah besarnya dampak perubahan pada peubah dependen pada wilayah-i, akibat perubahan prediktor di wilayah-j.
Efek marginal terdapat pada model dependensi spasial SAR, GSM, SDM, SDEM, dan SLX. Efek ini dapat dibedakan menjadi tiga, yaitu efek langsung (direct effect), efek tidak langsung (indirect effect), dan efek total (total effect).
marginal effect dapat diperoleh dengan fungsi impacts() seperti pada syntax berikut ini.
Interpretasi Efek Marginal
impacts(gsm, listw = W.opt)## Impact measures (sac, exact):
## Direct Indirect Total
## est_fcs_rt 18.298542 26.396766 44.69531
## bls_unemp 5.842272 8.427835 14.27011Terlihat bahwa pengaruh langsung dari peubah est_fcs_rt adalah sebesar 18.298542, artinya jika nilai est_fcs_rt di wilayah-i meningkat 1 satuan, maka nilai violent di wilayah tersebut akan bertambah sebesar 18.298542 satuan, jika peubah bls_unemp tetap. Sedangkan efek tak langsung dari peubah tersebut bernilai 1.435. Artinya, jika nilai est_fcs_rt di wilayah-i meningkat sebesar satu satuan, maka nilai violent di wilayah-j akan berkurang sebesar 1.435 satuan, jika nilai bls_unemp nya tetap. Interpretasi serupa juga dapat dilakukan terhadap peubah bls_unemp.
Exercise (2)
Pelajari artikel yang ditulis oleh Guliyev (2020), yang tersedia pada link berikut: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7139267/. Jika memungkinkan, silahkan lakukan pemodelan regresi spasial dan interpretasikan efek marjinal pada kasus ini, berdasarkan data yang tersedia pada artikel tersebut. Catatan: peta China dapat diakses pada https://data.humdata.org/dataset/china-administrative-boundaries.
Jawab
These data are up to 10 March 2020. The rates are multiplied by 100,000. the rate of confirmed cases (Rc) of COVID-19, the rate of deaths (Rd), the rate of recovered cases (Rr) due to treatment
Data Import
Impor data shapefile menggunakan fungsi readOGR() pada package rgdal. Setelah itu, kita dapat menggunakan fungsi str() untuk melihat struktur datanya.
covid <- read_xlsx("CovidChina.xlsx")
covid## # A tibble: 34 x 12
## ADM1_EN ADM1_ZH ADM1_PCODE ADM0_EN ADM0_ZH ADM0_PCODE Rc Rr Rd
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Shaanxi P~ <U+9655><U+897F><U+7701> CN061 China <U+4E2D><U+56FD> CN 0.0387 0.0359 0.0359
## 2 Shanghai ~ <U+4E0A><U+6D77><U+5E02> CN031 China <U+4E2D><U+56FD> CN 0.0544 0.0504 0.0504
## 3 Chongqing~ <U+91CD><U+5E86><U+5E02> CN050 China <U+4E2D><U+56FD> CN 0.0911 0.0865 0.0865
## 4 Zhejiang ~ <U+6D59><U+6C5F><U+7701> CN033 China <U+4E2D><U+56FD> CN 0.192 0.188 0.188
## 5 Jiangxi P~ <U+6C5F><U+897F><U+7701> CN036 China <U+4E2D><U+56FD> CN 0.148 0.147 0.147
## 6 Yunnan Pr~ <U+4E91><U+5357><U+7701> CN053 China <U+4E2D><U+56FD> CN 0.0275 0.0269 0.0269
## 7 Shandong ~ <U+5C71><U+4E1C><U+7701> CN037 China <U+4E2D><U+56FD> CN 0.120 0.114 0.114
## 8 Liaoning ~ <U+8FBD><U+5B81><U+7701> CN021 China <U+4E2D><U+56FD> CN 0.0198 0.0176 0.0176
## 9 Tibet Aut~ <U+897F><U+85CF><U+81EA>~ CN054 China <U+4E2D><U+56FD> CN 0.0002 0.0002 0.0002
## 10 Gansu pro~ <U+7518><U+8083><U+7701> CN062 China <U+4E2D><U+56FD> CN 0.0198 0.0139 0.0139
## # ... with 24 more rows, and 3 more variables: Avg.Rc <dbl>, Avg.Rr <dbl>,
## # Avg.Rd <dbl>covid.map <-readOGR(dsn="chn_adm_ocha_2020_shp", layer="chn_admbnda_adm1_ocha_2020")## OGR data source with driver: ESRI Shapefile
## Source: "D:\Kuliah S2 IPB\Bahan Kuliah\Semester 2 SSD 2020\STA533 Spasial\PRAKTIKUM\R\chn_adm_ocha_2020_shp", layer: "chn_admbnda_adm1_ocha_2020"
## with 34 features
## It has 6 fieldscovid.map@data## ADM1_EN ADM1_ZH ADM1_PCODE
## 0 Shaanxi Province é\231•è¥¿çœ\201 CN061
## 1 Shanghai Municipality 上海市 CN031
## 2 Chongqing Municipality é‡\215庆市 CN050
## 3 Zhejiang Province æµ\231江çœ\201 CN033
## 4 Jiangxi Province 江西çœ\201 CN036
## 5 Yunnan Province 云å\215—çœ\201 CN053
## 6 Shandong Province 山东çœ\201 CN037
## 7 Liaoning Province è¾½å®\201çœ\201 CN021
## 8 Tibet Autonomous Region 西è—\217自治区 CN054
## 9 Gansu province ç”\230肃çœ\201 CN062
## 10 Hong Kong Special Administrative Region é¦\231港特å\210«è¡Œæ”¿åŒº CN081
## 11 Qinghai Province é\235’æµ·çœ\201 CN063
## 12 Beijing Municipality 北京市 CN011
## 13 Macao Special Administrative Region 澳门特å\210«è¡Œæ”¿åŒº CN082
## 14 Inner Mongolia Autonomous Region 内è’\231å\217¤è‡ªæ²»åŒº CN015
## 15 Hubei Province 湖北çœ\201 CN042
## 16 Anhui Province 安徽çœ\201 CN034
## 17 Guizhou Province 贵州çœ\201 CN052
## 18 Ningxia Hui Autonomous Region å®\201å¤\217回æ—\217自治区 CN064
## 19 Jiangsu Province 江è‹\217çœ\201 CN032
## 20 Xinjiang Uygur Autonomous Region 新疆维å\220¾å°”自治区 CN065
## 21 Shanxi Province 山西çœ\201 CN014
## 22 Hunan Province æ¹–å\215—çœ\201 CN043
## 23 Sichuan Province å››å·\235çœ\201 CN051
## 24 Guangxi Zhuang Autonomous Region 广西壮æ—\217自治区 CN045
## 25 Jilin Province å\220‰æž—çœ\201 CN022
## 26 Taiwan Province å\217°æ¹¾çœ\201 CN071
## 27 Hebei Province 河北çœ\201 CN013
## 28 Tianjin Municipality 天津市 CN012
## 29 Guangdong Province 广东çœ\201 CN044
## 30 Fujian Province ç¦\217建çœ\201 CN035
## 31 Heilongjiang Province 黑é¾\231江çœ\201 CN023
## 32 Henan Province æ²³å\215—çœ\201 CN041
## 33 Hainan Province æµ·å\215—çœ\201 CN046
## ADM0_EN ADM0_ZH ADM0_PCODE
## 0 China ä¸å›½ CN
## 1 China ä¸å›½ CN
## 2 China ä¸å›½ CN
## 3 China ä¸å›½ CN
## 4 China ä¸å›½ CN
## 5 China ä¸å›½ CN
## 6 China ä¸å›½ CN
## 7 China ä¸å›½ CN
## 8 China ä¸å›½ CN
## 9 China ä¸å›½ CN
## 10 China ä¸å›½ CN
## 11 China ä¸å›½ CN
## 12 China ä¸å›½ CN
## 13 China ä¸å›½ CN
## 14 China ä¸å›½ CN
## 15 China ä¸å›½ CN
## 16 China ä¸å›½ CN
## 17 China ä¸å›½ CN
## 18 China ä¸å›½ CN
## 19 China ä¸å›½ CN
## 20 China ä¸å›½ CN
## 21 China ä¸å›½ CN
## 22 China ä¸å›½ CN
## 23 China ä¸å›½ CN
## 24 China ä¸å›½ CN
## 25 China ä¸å›½ CN
## 26 China ä¸å›½ CN
## 27 China ä¸å›½ CN
## 28 China ä¸å›½ CN
## 29 China ä¸å›½ CN
## 30 China ä¸å›½ CN
## 31 China ä¸å›½ CN
## 32 China ä¸å›½ CN
## 33 China ä¸å›½ CNDari data covid di Cina di atas yang bersumber dari Tabel A.1 pada link website https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7139267/ terlihat bahwa data Rr dan Rd sama persis sedangkan pada rata-ratanya tidak sama persis.Diasumsikan terdapat kekeliruan input data diantara kedua peubah tersebut pada data sumber. Oleh karena itu, dalam analisis selanjutnya kami akan menggunakan data rata2nya.
str(slot(covid.map,"data"))## 'data.frame': 34 obs. of 6 variables:
## $ ADM1_EN : chr "Shaanxi Province" "Shanghai Municipality" "Chongqing Municipality" "Zhejiang Province" ...
## $ ADM1_ZH : chr "é\231•è¥¿çœ\201" "上海市" "é‡\215庆市" "æµ\231江çœ\201" ...
## $ ADM1_PCODE: chr "CN061" "CN031" "CN050" "CN033" ...
## $ ADM0_EN : chr "China" "China" "China" "China" ...
## $ ADM0_ZH : chr "ä¸å›½" "ä¸å›½" "ä¸å›½" "ä¸å›½" ...
## $ ADM0_PCODE: chr "CN" "CN" "CN" "CN" ...Visualisasi Data
plot(covid.map)
library(leaflet)
leaflet(covid.map) %>%
addPolygons(stroke = FALSE, fillOpacity = 0.5, smoothFactor = 0.5) %>%
addTiles()covid.map@data$Avg.Rc <- covid$Avg.Rc
require(RColorBrewer)
qpal<-colorQuantile("OrRd", covid$Avg.Rc, n=9)
leaflet(covid.map) %>%
addPolygons(stroke = FALSE, fillOpacity = .8, smoothFactor = 0.2, color = ~qpal(Avg.Rc)
) %>%
addTiles()OLS
covid.ols<-lm(Avg.Rc~Avg.Rr+Avg.Rd, data=covid)
summary(covid.ols)##
## Call:
## lm(formula = Avg.Rc ~ Avg.Rr + Avg.Rd, data = covid)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0228528 -0.0017805 -0.0000281 0.0021846 0.0141642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.808e-05 1.420e-03 0.02 0.984
## Avg.Rr 1.769e+00 4.482e-02 39.48 <2e-16 ***
## Avg.Rd 1.137e+01 3.930e-01 28.94 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.006091 on 31 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 5.456e+05 on 2 and 31 DF, p-value: < 2.2e-16vif(covid.ols)## Avg.Rr Avg.Rd
## 233.3868 233.3868VIF > 5 berarti ada multikolinieritas sehingga akan dipilih salah satu peubah.
covid.cor <- cor(covid[,10:12])
covid.cor## Avg.Rc Avg.Rr Avg.Rd
## Avg.Rc 1.0000000 0.9996020 0.9992715
## Avg.Rr 0.9996020 1.0000000 0.9978553
## Avg.Rd 0.9992715 0.9978553 1.0000000corrplot(covid.cor)
Rr memiliki korelasi yang lebih tinggi dengan Rc sehingga Rr yang akan digunakan dalam pemodelan
covid.ols1<-lm(Avg.Rc~Avg.Rr, data=covid)
summary(covid.ols1)##
## Call:
## lm(formula = Avg.Rc ~ Avg.Rr, data = covid)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.084108 -0.003953 0.013792 0.019576 0.026831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.026831 0.005596 -4.795 3.61e-05 ***
## Avg.Rr 3.063637 0.015284 200.452 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03173 on 32 degrees of freedom
## Multiple R-squared: 0.9992, Adjusted R-squared: 0.9992
## F-statistic: 4.018e+04 on 1 and 32 DF, p-value: < 2.2e-16Modeling Spatial Dependence
Bobot Rook:
list.queen<-poly2nb(covid.map, queen=FALSE)
summary(list.queen)## Neighbour list object:
## Number of regions: 34
## Number of nonzero links: 140
## Percentage nonzero weights: 12.11073
## Average number of links: 4.117647
## 2 regions with no links:
## 26 33
## Link number distribution:
##
## 0 1 2 3 4 5 6 7 8
## 2 2 4 5 7 3 7 2 2
## 2 least connected regions:
## 10 13 with 1 link
## 2 most connected regions:
## 0 14 with 8 linksDari output dengan menggunakan Rook di atas, terlihat dua wilayah tidak ada tetangganya sehingga akan dicoba penimbang lainnya.
Bobot Queen:
list.queen2<-poly2nb(covid.map, queen=T)
summary(list.queen2)## Neighbour list object:
## Number of regions: 34
## Number of nonzero links: 140
## Percentage nonzero weights: 12.11073
## Average number of links: 4.117647
## 2 regions with no links:
## 26 33
## Link number distribution:
##
## 0 1 2 3 4 5 6 7 8
## 2 2 4 5 7 3 7 2 2
## 2 least connected regions:
## 10 13 with 1 link
## 2 most connected regions:
## 0 14 with 8 linksDari output dengan menggunakan Rook di atas, terlihat dua wilayah tidak ada tetangganya sehingga akan dicoba penimbang lainnya.
menggunakan Bobot Jarak Ambang d=5
coords2<-coordinates(covid.map)
dmax11<-dnearneigh(coords2,0,1100,longlat = TRUE)
summary(dmax11)## Neighbour list object:
## Number of regions: 34
## Number of nonzero links: 404
## Percentage nonzero weights: 34.9481
## Average number of links: 11.88235
## Link number distribution:
##
## 1 2 4 6 7 9 10 11 12 13 14 16 17 18 19 22
## 1 2 1 2 2 2 1 1 3 6 4 2 3 2 1 1
## 1 least connected region:
## 21 with 1 link
## 1 most connected region:
## 16 with 22 linksNormalisasi Bobot Spasial dengan standardisasi baris:
W.dmax.s1 <- nb2listw(dmax11,style='W') #W is row standardised (sums over all links to n). Standardisasi Baris
W.dmax.s1## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 34
## Number of nonzero links: 404
## Percentage nonzero weights: 34.9481
## Average number of links: 11.88235
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 34 1156 34 8.704045 138.7575plot(covid.map, col='gray', border='blue', main ="dmax=1100")
plot(W.dmax.s1, coords2, col='red', lwd=2, add=TRUE)
Checking the Spatial Autocorrelation
moran.lm11<-lm.morantest(covid.ols1, W.dmax.s1, alternative="two.sided")
print(moran.lm11)##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = Avg.Rc ~ Avg.Rr, data = covid)
## weights: W.dmax.s1
##
## Moran I statistic standard deviate = 2.4096, p-value = 0.01597
## alternative hypothesis: two.sided
## sample estimates:
## Observed Moran I Expectation Variance
## 0.155478475 -0.030056786 0.005928619P-value < alpha=0.05 berarti tolak Ho.Tidak Autokorelasi Spasial antar lokasi. Nilai indeks Moran dengan Matriks Bobot Jarak ambang batas dmax=1100 adalah 0.155478475 .
LM Test
LM1<-lm.LMtests(covid.ols1, W.dmax.s1, test="all")
summary(LM1)## Lagrange multiplier diagnostics for spatial dependence
## data:
## model: lm(formula = Avg.Rc ~ Avg.Rr, data = covid)
## weights: W.dmax.s1
##
## statistic parameter p.value
## LMerr 3.2105 1 0.07317 .
## LMlag 3.9829 1 0.04596 *
## RLMerr 3.7056 1 0.05423 .
## RLMlag 4.4780 1 0.03433 *
## SARMA 7.6885 2 0.02140 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Jika berdasarkan skema dan output di atas, karena pada tahap awal LMlag signifikan sedangkan LMerr tidak signifikan, maka selanjutnya akan digunakan model SAR.
Fitting Spatial Regressions
SAR
sar.covid<-lagsarlm(covid.ols1, W.dmax.s1, data=covid)
summary(sar.covid,Nagelkerke=T)##
## Call:lagsarlm(formula = covid.ols1, data = covid, listw = W.dmax.s1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0806893 -0.0043123 0.0077386 0.0163550 0.0384004
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0101181 0.0089637 -1.1288 0.259
## Avg.Rr 3.0579033 0.0140826 217.1401 <2e-16
##
## Rho: -0.047153, LR test value: 4.5856, p-value: 0.032241
## Asymptotic standard error: 0.020766
## z-value: -2.2708, p-value: 0.023162
## Wald statistic: 5.1563, p-value: 0.023162
##
## Log likelihood: 72.39553 for lag model
## ML residual variance (sigma squared): 0.00082784, (sigma: 0.028772)
## Nagelkerke pseudo-R-squared: 0.9993
## Number of observations: 34
## Number of parameters estimated: 4
## AIC: -136.79, (AIC for lm: -134.21)
## LM test for residual autocorrelation
## test value: 0.12541, p-value: 0.72324Output di atas memperlihatkan bahwa koefisien Rho pada model SAR signifikan, dengan nilai AIC sebesar -136.79. Selain itu, terlihat pula hasil uji autokorelasi pada sisaan model dengan Ho: tidak terdapat autokorelasi spasial pada galat (galat bebas) dan H1: terdapat autokorelasi spasial positif pada galat (galat tidak bebas) memperlihatkan nilai p-value lebih besar dari galat (tidak tolak Ho), artinya tidak terdapat autokorelasi pada sisaan.Kebebasan galat terpenuhi.
Uji Asumsi Normal
Ho: Sisaan menyebar normal H1: Sisaan tidak menyebar normal
err.sar.covid<-residuals(sar.covid)
ad.test(err.sar.covid)##
## Anderson-Darling normality test
##
## data: err.sar.covid
## A = 1.8689, p-value = 7.023e-05Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
Uji Asumsi Kehomogenan Ragam Sisaan
Ho: Ragam Sisaan homogen H1: Ragam Sisaan tidak homogen
bptest.Sarlm(sar.covid)##
## studentized Breusch-Pagan test
##
## data:
## BP = 0.050387, df = 1, p-value = 0.8224Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang homogen (tidak tolak Ho karena p_value > alpha=5%)
Terlihat pada ketiga output di atas bahwa sisaan tidak memenuhi asumsi kenormalan namun memenuhi asumsi kehomogenan ragam dan kebebasan galat.
Spatial Durbin Model (SDM)
Berikut ini adalah penjelasan tentang Spatial Durbin Model yang dirujuk dari Zhukov (2010):
Model:
sdm.covid <- lagsarlm(covid.ols1,data=covid, W.dmax.s1, type="mixed");
summary(sdm.covid,Nagelkerke=T)##
## Call:lagsarlm(formula = covid.ols1, data = covid, listw = W.dmax.s1,
## type = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0797613 -0.0038850 0.0052392 0.0204098 0.0381009
##
## Type: mixed
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0046494 0.0111230 -0.418 0.6759
## Avg.Rr 3.0606285 0.0138927 220.305 <2e-16
## lag.Avg.Rr -0.9571317 0.8492776 -1.127 0.2597
##
## Rho: 0.27582, LR test value: 0.6009, p-value: 0.43824
## Asymptotic standard error: 0.2801
## z-value: 0.9847, p-value: 0.32477
## Wald statistic: 0.96963, p-value: 0.32477
##
## Log likelihood: 72.80399 for mixed model
## ML residual variance (sigma squared): 0.00080071, (sigma: 0.028297)
## Nagelkerke pseudo-R-squared: 0.99932
## Number of observations: 34
## Number of parameters estimated: 5
## AIC: -135.61, (AIC for lm: -137.01)
## LM test for residual autocorrelation
## test value: 0.00423, p-value: 0.94814Output di atas memperlihatkan bahwa koefisien Rho pada model SDM tidak signifikan, dengan nilai AIC sebesar -135.61. lag.x1 tidak nyata. Selain itu, terlihat pula hasil uji autokorelasi pada sisaan model dengan Ho: tidak terdapat autokorelasi spasial pada galat (galat bebas) dan H1: terdapat autokorelasi spasial positif pada galat (galat tidak bebas) memperlihatkan nilai p-value lebih besar dari alpha (tidak tolak Ho), artinya tidak terdapat autokorelasi pada sisaan.
Uji Asumsi Normal
Ho: Sisaan menyebar normal H1: Sisaan tidak menyebar normal
err.sdm.covid <-residuals(sdm.covid)
ad.test(err.sdm.covid)##
## Anderson-Darling normality test
##
## data: err.sdm.covid
## A = 1.7731, p-value = 0.0001221Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
Uji Asumsi Kehomogenan Ragam Sisaan
Ho: Ragam Sisaan homogen H1: Ragam Sisaan tidak homogen
bptest.Sarlm(sdm.covid)##
## studentized Breusch-Pagan test
##
## data:
## BP = 3.6685, df = 2, p-value = 0.1597Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang homogen (tidak tolak Ho karena p_value > alpha=5%)
Terlihat pada ketiga output di atas bahwa sisaan tidak memenuhi asumsi kenormalan namun memenuhi asumsi kehomogenan ragam dan kebebasan galat.
Likelihood Ratio Test (Model SAR vs SDM)
Untuk mengecek apakah lag x signifikan atau tidak dilakukan Uji Likelihood Rasio.
LR.Sarlm(sdm.covid,sar.covid)##
## Likelihood ratio for spatial linear models
##
## data:
## Likelihood ratio = 0.81693, df = 1, p-value = 0.3661
## sample estimates:
## Log likelihood of sdm.covid Log likelihood of sar.covid
## 72.80399 72.39553Dari hasil likelihood ratio test diperoleh P_value < alpha=0.05 berarti lag x tidak signifikan. Tidak Ada dependensi spasial di lag x sehingga model SAR lebih mencerminkan data dibandingkan model SDM.
AIC(sar.covid)## [1] -136.7911AIC(sdm.covid)## [1] -135.608AIC(covid.ols1)## [1] -134.2054Model terbaik jika dilihat dari AIC adalah model SAR (AIC terendah) dengan R-Square sekitar 99.93%.Namun sisaan model ini tidak memenuhi asumsi kenormalan . Namun memenuhi asumsi kebebasan dan kehomogenan ragam. Penulis rekomendasikan untuk pengujian pada penelitian selanjutnya mungkin bisa ditangani dahulu masalah asumsi kenormalan ini.Selanjutnya penulis akan memaparkan terkait efek marginal model SAR.
Marginal Effects (Spill-over) on the Spatial Regression Modeling
- Marginal Effects*
Definisi yang diambil dari materi kuliah yang disusun oleh Dr. Anik Djuraidah menyatakan bahwa efek marginal atau limpahan (spill-over) adalah besarnya dampak perubahan pada peubah dependen pada wilayah-i, akibat perubahan prediktor di wilayah-j.
Efek marginal terdapat pada model dependensi spasial SAR, GSM, SDM, SDEM, dan SLX. Efek ini dapat dibedakan menjadi tiga, yaitu efek langsung (direct effect), efek tidak langsung (indirect effect), dan efek total (total effect).
marginal effect dapat diperoleh dengan fungsi impacts() seperti pada syntax berikut ini.
Interpretasi Efek Marginal
impacts(sar.covid, listw = W.dmax.s1)## Impact measures (lag, exact):
## Direct Indirect Total
## Avg.Rr 3.058674 -0.1384683 2.920205Terlihat bahwa pengaruh langsung dari peubah Avg.Rr adalah sebesar 3.058674, artinya jika nilai rata-rata Rr di wilayah-i meningkat sebesar 1 persen, maka nilai rata-rata Rc di wilayah tersebut akan bertambah sebesar 3.058674 persen. Sedangkan efek tak langsung dari peubah tersebut bernilai -0.1384683. Artinya, jika nilai rata-rata Rr di wilayah-i meningkat sebesar satu persen, maka nilai rata-rata Rc di wilayah-j akan berkurang sebesar 0.1384683 persen.
Geographically Weighted Regression (GWR) (Responsi Pertemuan 13)
Introduction
Suatu pemodelan dapat bersifat global maupun lokal. Regresi linier klasik merupakan salah satu model global. Dikatakan global karena terdapat satu model yang berlaku umum untuk semua pengamatan.
Suatu model lokal bersifat lebih fleksibel, yang dalam konteks spasial, artinya setiap daerah/lokasi dapat memiliki model masing-masing.
Geographically Weighted Regression (GWR) merupakan salah satu model yang bersifat lokal. Beberapa keuntungan dengan menggunakan model ini, diantaranya adalah kita dapat:
menduga galat baku lokal
menghitung ukuran leverage lokal
melakukan pengujian terhadap signifikansi keragaman spasial pada penduga parameter lokal
menguji apakah model lokal lebih baik daripada model global
Terdapat salah satu stand-alone software untuk melakukan GWR, yaitu software GWR yang dapat diakses melalui http://ncg.nuim.ie/ncg/GWR/. Selain itu, pada R software, terdapat beberapa package yang dapat digunakan untuk membangun model GWR, yaitu:
GWmodel
spgwr
gwrr
Pada modul ini akan dibahas pemodelan GWR menggunakan package spgwr.
Ilustrasi menggunakan Data Columbus
library(spgwr)data(columbus)
attach(columbus)Standard Regression
colex0 <- lm(CRIME ~ (INC + HOVAL))
summary(colex0)##
## Call:
## lm(formula = CRIME ~ (INC + HOVAL))
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.418 -6.388 -1.580 9.052 28.649
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 68.6190 4.7355 14.490 < 2e-16 ***
## INC -1.5973 0.3341 -4.780 1.83e-05 ***
## HOVAL -0.2739 0.1032 -2.654 0.0109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.43 on 46 degrees of freedom
## Multiple R-squared: 0.5524, Adjusted R-squared: 0.5329
## F-statistic: 28.39 on 2 and 46 DF, p-value: 9.341e-09Diagnostik Model
Berikut ini apabila pemeriksaan sisaan dilakukan secara visual.
resid<-residuals(colex0)
par(mfrow=c(2,2))
qqnorm(resid); qqline(resid, col="red");
plot(resid~fitted(colex0),xlab = "Predicted Values",ylab = "Residuals")
abline(h=0, col="red")
hist(resid) #histogram utk residual
plot(1:nrow(columbus), resid, pch=20,type="b")
abline(h=0, col="red")
Berikut ini salah satu contoh untuk memeriksa asumsi pada sisaan menggunakan uji formal.
shapiro.test(resid)##
## Shapiro-Wilk normality test
##
## data: resid
## W = 0.97708, p-value = 0.4497lmtest::bptest(colex0)##
## studentized Breusch-Pagan test
##
## data: colex0
## BP = 7.2166, df = 2, p-value = 0.0271library(spdep)coords<-columbus[c("X","Y")]
jarak<-as.matrix(1/dist(coords))
lm.morantest(colex0,listw=mat2listw(jarak), alternative="two.sided")##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = CRIME ~ (INC + HOVAL))
## weights: mat2listw(jarak)
##
## Moran I statistic standard deviate = 5.3215, p-value = 1.029e-07
## alternative hypothesis: two.sided
## sample estimates:
## Observed Moran I Expectation Variance
## 0.090950110 -0.024788597 0.000473023Spatially Dissagregated Model
colex <- lm(CRIME ~ (INC + HOVAL)*(X + Y))
summary(colex)##
## Call:
## lm(formula = CRIME ~ (INC + HOVAL) * (X + Y))
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.5556 -7.6351 -0.6181 7.8363 30.1948
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 108.97559 69.36676 1.571 0.1241
## INC -5.82949 3.84408 -1.516 0.1373
## HOVAL 0.27337 0.82049 0.333 0.7407
## X -0.76287 1.13692 -0.671 0.5061
## Y -0.26332 1.21420 -0.217 0.8294
## INC:X -0.01854 0.05396 -0.344 0.7329
## INC:Y 0.13949 0.08004 1.743 0.0891 .
## HOVAL:X 0.03159 0.01549 2.040 0.0480 *
## HOVAL:Y -0.05034 0.02196 -2.293 0.0272 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.04 on 40 degrees of freedom
## Multiple R-squared: 0.6375, Adjusted R-squared: 0.5649
## F-statistic: 8.791 on 8 and 40 DF, p-value: 7.663e-07Perhatikan bahwa R-square pada model ini sudah lebih baik dibandingkan dengan model regresi klasik, namun perhatikan pula bahwa peubah yang signifikan hanyalah sedikit.
Selanjutnya, misalkan kita ingin mengekstrak koefisien pada model tersebut.
b <- colex$coefficients
b[3]## HOVAL
## 0.2733714b[8]## HOVAL:X
## 0.03159375b[9]## HOVAL:Y
## -0.05034417Koefisien pada setiap titik lokasi.
bihoval <- b[3] + b[8] * X + b[9] * Y
bihoval## [1] -0.71945869 -0.73484522 -0.54173838 -0.61340248 -0.37564117 -0.32192096
## [7] -0.60638062 -0.51564482 -0.16255859 -0.05598467 -0.35829853 -0.37198398
## [13] -0.42336113 -0.39035215 -0.23271508 -0.25443306 0.07333582 -0.29218860
## [19] -0.34176508 0.14326490 -0.18138317 -0.04092354 0.19700015 -0.18584181
## [25] -0.13841490 -0.09558335 0.07053751 0.02287442 -0.01427011 -0.04484941
## [31] -0.34667265 0.35074015 0.13619297 -0.19143232 0.18497546 -0.28527600
## [37] 0.03312495 0.12107370 -0.30416609 0.39765999 0.49266738 -0.14792599
## [43] 0.18759738 0.26427001 0.21424089 -0.21628436 0.61049050 0.27143584
## [49] 0.36488625summary(bihoval)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.7348 -0.3418 -0.1479 -0.1096 0.1362 0.6105Berikut ini kita akan coba memvisualisasikan koefisien tersebut. Sebelumnya Anda harus mendownload data peta columbus melalui link berikut: https://github.com/raoy/Spatial-Statistics/blob/master/columbus.rar. Silahkan simpan pada directory yang Anda inginkan, dan impor data shp tersebut dengan fungsi berikut:
library(rgdal)col.shp <- readOGR( dsn="columbus", layer="columbus")## OGR data source with driver: ESRI Shapefile
## Source: "D:\Kuliah S2 IPB\Bahan Kuliah\Semester 2 SSD 2020\STA533 Spasial\PRAKTIKUM\R\columbus", layer: "columbus"
## with 49 features
## It has 20 fields
## Integer64 fields read as strings: COLUMBUS_ COLUMBUS_I POLYIDcol.shp@data$bi<-bihoval
spplot(col.shp, zcol="bi")
Basic GWR
library(spgwr)colg1 <- gwr(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y),bandwidth=20)
colg1## Call:
## gwr(formula = CRIME ~ INC + HOVAL, data = columbus, coords = cbind(X,
## Y), bandwidth = 20)
## Kernel function: gwr.Gauss
## Fixed bandwidth: 20
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max. Global
## X.Intercept. 68.41172 68.84934 69.06064 69.24627 69.95574 68.6190
## INC -1.65427 -1.62266 -1.60883 -1.58070 -1.52803 -1.5973
## HOVAL -0.30704 -0.29166 -0.27768 -0.26118 -0.23198 -0.2739Using Different Bandwidth
colg2 <- gwr(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y),bandwidth=3)
colg2## Call:
## gwr(formula = CRIME ~ INC + HOVAL, data = columbus, coords = cbind(X,
## Y), bandwidth = 3)
## Kernel function: gwr.Gauss
## Fixed bandwidth: 3
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max. Global
## X.Intercept. 24.823915 58.337575 66.888807 70.734368 77.517040 68.6190
## INC -2.910848 -2.055262 -1.370336 -0.489550 0.605579 -1.5973
## HOVAL -0.938862 -0.370668 -0.072330 -0.016077 0.417796 -0.2739colg3 <- gwr(CRIME ~ INC + HOVAL, data=columbus,
coords=cbind(X,Y),bandwidth=2)
colg3## Call:
## gwr(formula = CRIME ~ INC + HOVAL, data = columbus, coords = cbind(X,
## Y), bandwidth = 2)
## Kernel function: gwr.Gauss
## Fixed bandwidth: 2
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max. Global
## X.Intercept. 22.991392 51.964036 62.876904 69.090317 81.631781 68.6190
## INC -3.386156 -1.956824 -0.702189 -0.325575 1.339862 -1.5973
## HOVAL -1.209784 -0.372961 -0.115808 0.038842 0.879194 -0.2739Membandingkan antar Model Lokal
hovg3 <- colg2$SDF$HOVAL
hovg20 <- colg1$SDF$HOVAL
hovg2 <- colg3$SDF$HOVAL
boxplot(bihoval,hovg20,hovg3,hovg2,
names=c("Expansion","bw=20","bw=3","bw=2"))
Perhatikan bahwa sebaran pada bandwidth 20 terkonsentrasi di sekitar median, sedangkan pada bandwidth 2 menunjukkan beberapa pencilan. Sebaran pada model linear expansion mirip dengan bandwidth 3, namun median-nya lebih rendah.
Menentukan Bandwidth Optimal
bw1 <- gwr.sel(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y))## Bandwidth: 12.65221 CV score: 7432.209
## Bandwidth: 20.45127 CV score: 7462.704
## Bandwidth: 7.83213 CV score: 7323.545
## Bandwidth: 4.853154 CV score: 7307.57
## Bandwidth: 5.125504 CV score: 7322.796
## Bandwidth: 3.012046 CV score: 6461.764
## Bandwidth: 1.874179 CV score: 6473.378
## Bandwidth: 2.475485 CV score: 6109.995
## Bandwidth: 2.447721 CV score: 6098.372
## Bandwidth: 2.228647 CV score: 6064.1
## Bandwidth: 2.264538 CV score: 6060.774
## Bandwidth: 2.280666 CV score: 6060.649
## Bandwidth: 2.274969 CV score: 6060.601
## Bandwidth: 2.2751 CV score: 6060.601
## Bandwidth: 2.27506 CV score: 6060.601
## Bandwidth: 2.275019 CV score: 6060.601
## Bandwidth: 2.27506 CV score: 6060.601colg4 <- gwr(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y),bandwidth=bw1)
bw2 <- gwr.sel(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y),method="aic")## Bandwidth: 12.65221 AIC: 383.2507
## Bandwidth: 20.45127 AIC: 383.5182
## Bandwidth: 7.83213 AIC: 382.7555
## Bandwidth: 4.853154 AIC: 381.4751
## Bandwidth: 3.012046 AIC: 384.5411
## Bandwidth: 5.991021 AIC: 382.3503
## Bandwidth: 4.149913 AIC: 380.7132
## Bandwidth: 3.715287 AIC: 380.7565
## Bandwidth: 3.980563 AIC: 380.6324
## Bandwidth: 3.955538 AIC: 380.6289
## Bandwidth: 3.927578 AIC: 380.6281
## Bandwidth: 3.934794 AIC: 380.628
## Bandwidth: 3.935053 AIC: 380.628
## Bandwidth: 3.934987 AIC: 380.628
## Bandwidth: 3.934946 AIC: 380.628
## Bandwidth: 3.934987 AIC: 380.628colg5 <- gwr(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y),bandwidth=bw2)
bwbs1 <- gwr.sel(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y),gweight=gwr.bisquare)## Bandwidth: 12.65221 CV score: 8180.619
## Bandwidth: 20.45127 CV score: 7552.85
## Bandwidth: 25.27136 CV score: 7508.227
## Bandwidth: 23.68132 CV score: 7519.864
## Bandwidth: 28.25033 CV score: 7491.85
## Bandwidth: 30.09144 CV score: 7486.673
## Bandwidth: 31.69353 CV score: 7483.663
## Bandwidth: 31.08159 CV score: 7484.706
## Bandwidth: 32.21945 CV score: 7482.846
## Bandwidth: 32.54449 CV score: 7482.371
## Bandwidth: 32.74538 CV score: 7482.088
## Bandwidth: 32.86953 CV score: 7481.916
## Bandwidth: 32.94626 CV score: 7481.812
## Bandwidth: 32.99368 CV score: 7481.748
## Bandwidth: 33.02299 CV score: 7481.708
## Bandwidth: 33.04111 CV score: 7481.684
## Bandwidth: 33.0523 CV score: 7481.669
## Bandwidth: 33.05922 CV score: 7481.659
## Bandwidth: 33.0635 CV score: 7481.654
## Bandwidth: 33.06614 CV score: 7481.65
## Bandwidth: 33.06777 CV score: 7481.648
## Bandwidth: 33.06878 CV score: 7481.647
## Bandwidth: 33.06941 CV score: 7481.646
## Bandwidth: 33.06979 CV score: 7481.645
## Bandwidth: 33.07003 CV score: 7481.645
## Bandwidth: 33.07018 CV score: 7481.645
## Bandwidth: 33.07027 CV score: 7481.645
## Bandwidth: 33.07032 CV score: 7481.645
## Bandwidth: 33.07037 CV score: 7481.645
## Bandwidth: 33.07037 CV score: 7481.645## Warning in gwr.sel(CRIME ~ INC + HOVAL, data = columbus, coords = cbind(X, :
## Bandwidth converged to upper bound:33.0704149683672colg6 <- gwr(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y),gweight=gwr.bisquare,
bandwidth=bwbs1)
bwbs2 <- gwr.sel(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y),gweight=gwr.bisquare,method="aic")## Bandwidth: 12.65221 AIC: 382.7242
## Bandwidth: 20.45127 AIC: 384.3786
## Bandwidth: 7.83213 AIC: 386.7498
## Bandwidth: 15.27372 AIC: 384.0654
## Bandwidth: 10.81111 AIC: 381.6533
## Bandwidth: 9.673238 AIC: 383.0491
## Bandwidth: 11.25258 AIC: 381.6384
## Bandwidth: 11.07023 AIC: 381.6049
## Bandwidth: 11.05193 AIC: 381.6044
## Bandwidth: 11.04548 AIC: 381.6044
## Bandwidth: 11.04647 AIC: 381.6044
## Bandwidth: 11.04651 AIC: 381.6044
## Bandwidth: 11.04655 AIC: 381.6044
## Bandwidth: 11.04651 AIC: 381.6044colg7 <- gwr(CRIME ~ INC + HOVAL,data=columbus,
coords=cbind(X,Y),gweight=gwr.bisquare,
bandwidth=bwbs2)Selanjutnya, kita dapat memperoleh penduga koefisien dan prediksi dengan syntax berikut.
bihoval<-colg7$SDF$HOVAL
prediction<-colg7$SDF$predcol.shp@data$bi7<-bihoval
spplot(col.shp, zcol="bi7")
Practice
To practice, plot or map the coefficient vector for the other coefficients in the model. Alternatively, check for continuous spatial heterogeneity in the BOSTON or BALTIMORE data sets. Compare the insights provided by the expansion method to those from GWR, and carry out sensitivity analysis for the choice of bandwidth and kernel function.
Jawab:
Practice 1 plot or map the coefficient vector for the other coefficients in the model
Penduga koefisien INC:
inc.<-colg7$SDF$INCcol.shp@data$binc<-inc.
spplot(col.shp, zcol="binc")
Interpretasi terhadap penduga parameter untuk peubah penjelas INC pada mode RTG:
Warna biru pada peta sebaran penduga parameter untuk peubah penjelas INC di atas menunjukkan nilai yang rendah, warna merah muda untuk nilai sedang, hingga warna kuning menunjukkan nilai tinggi.
Tidak ada penduga parameter untuk peubah penjelas INC yang memiliki nilai positif atau nol. Peubah penjelas INC berkontribusi negatif terhadap peubah respon CRIME di semua lokasi amatan.
Peta sebaran penduga parameter untuk peubah penjelas INC menunjukkan kemiripan antarwilayah yang berdekatan. Kemiripan ini ditunjukkan dengan pola kecenderungan warna-warna yang sama untuk mengelompok dengan wilayah tetangganya.
Practice 2 Check for continuous spatial heterogeneity in the BOSTON or BALTIMORE data sets.
Exercise kali ini dengan boston.c dataset
library(spdep)
library (MASS)
library(spData)head(boston.c)## TOWN TOWNNO TRACT LON LAT MEDV CMEDV CRIM ZN INDUS CHAS
## 1 Nahant 0 2011 -70.9550 42.2550 24.0 24.0 0.00632 18 2.31 0
## 2 Swampscott 1 2021 -70.9500 42.2875 21.6 21.6 0.02731 0 7.07 0
## 3 Swampscott 1 2022 -70.9360 42.2830 34.7 34.7 0.02729 0 7.07 0
## 4 Marblehead 2 2031 -70.9280 42.2930 33.4 33.4 0.03237 0 2.18 0
## 5 Marblehead 2 2032 -70.9220 42.2980 36.2 36.2 0.06905 0 2.18 0
## 6 Marblehead 2 2033 -70.9165 42.3040 28.7 28.7 0.02985 0 2.18 0
## NOX RM AGE DIS RAD TAX PTRATIO B LSTAT
## 1 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
## 2 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
## 3 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
## 4 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
## 5 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
## 6 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21attach(boston.c)Corrected Boston Housing Data
Description
The boston.c data frame has 506 rows and 20 columns. It contains the Harrison and Rubinfeld (1978) data corrected for a few minor errors and augmented with the latitude and longitude of the observations. Gilley and Pace also point out that MEDV is censored, in that median values at or over USD 50,000 are set to USD 50,000. The original data set without the corrections is also included in package mlbench as BostonHousing. In addition, a matrix of tract point coordinates projected to UTM zone 19 is included as boston.utm, and a sphere of influence neighbours list as boston.soi.
Format
This data frame contains the following columns:
TOWN a factor with levels given by town names
TOWNNO a numeric vector corresponding to TOWN
TRACT a numeric vector of tract ID numbers
LON a numeric vector of tract point longitudes in decimal degrees
LAT a numeric vector of tract point latitudes in decimal degrees
MEDV a numeric vector of median values of owner-occupied housing in USD 1000
CMEDV a numeric vector of corrected median values of owner-occupied housing in USD 1000
CRIM a numeric vector of per capita crime
ZN a numeric vector of proportions of residential land zoned for lots over 25000 sq. ft per town (constant for all Boston tracts)
INDUS a numeric vector of proportions of non-retail business acres per town (constant for all Boston tracts)
CHAS a factor with levels 1 if tract borders Charles River; 0 otherwise
NOX a numeric vector of nitric oxides concentration (parts per 10 million) per town
RM a numeric vector of average numbers of rooms per dwelling
AGE a numeric vector of proportions of owner-occupied units built prior to 1940
DIS a numeric vector of weighted distances to five Boston employment centres
RAD a numeric vector of an index of accessibility to radial highways per town (constant for all Boston tracts)
TAX a numeric vector full-value property-tax rate per USD 10,000 per town (constant for all Boston tracts)
PTRATIO a numeric vector of pupil-teacher ratios per town (constant for all Boston tracts)
B a numeric vector of 1000*(Bk - 0.63)^2 where Bk is the proportion of blacks
LSTAT a numeric vector of percentage values of lower status population
Note Details of the creation of the tract shapefile given in final don’t run block; tract boundaries for 1990: http://www.census.gov/geo/cob/bdy/tr/tr90shp/tr25_d90_shp.zip, counties in the BOSTON SMSA http://www.census.gov/population/metro/files/lists/historical/63mfips.txt; tract conversion table 1980/1970: https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/7913?q=07913&permit[0]=AVAILABLE, http://www.icpsr.umich.edu/cgi-bin/bob/zipcart2?path=ICPSR&study=7913&bundle=all&ds=1&dups=yes. The shapefile contains corrections and extra variables (tract 3592 is corrected to 3593; the extra columns are:
unitsnumber of single family houses
cu5kcount of units under USD 5,000
c5_7_5counts USD 5,000 to 7,500
C*_*interval counts
co50kcount of units over USD 50,000
medianrecomputed median values
BBrecomputed black population proportion
censoredwhether censored or not
NOXIDNOX model zone ID
POPtract population
Pada latihan kali ini akan digunakan peubah tak bebas nya adalah CRIM a numeric vector of per capita crime.
Sedangkan peubah penjelasnya adalah INDUS a numeric vector of proportions of non-retail business acres per town (constant for all Boston tracts) dan LSTAT a numeric vector of percentage values of lower status population.
Standard Regression
boston.ols <- lm(CRIM ~ (INDUS+ LSTAT))
summary(boston.ols)##
## Call:
## lm(formula = CRIM ~ (INDUS + LSTAT))
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.496 -2.652 -0.544 1.370 81.741
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.31565 0.72109 -5.985 4.12e-09 ***
## INDUS 0.25943 0.06135 4.229 2.79e-05 ***
## LSTAT 0.39832 0.05894 6.758 3.87e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.539 on 503 degrees of freedom
## Multiple R-squared: 0.2348, Adjusted R-squared: 0.2318
## F-statistic: 77.17 on 2 and 503 DF, p-value: < 2.2e-16vif(boston.ols)## INDUS LSTAT
## 1.573748 1.573748Diagnostik Model
Berikut ini apabila pemeriksaan sisaan dilakukan secara visual.
residb<-residuals(boston.ols)
par(mfrow=c(2,2))
qqnorm(residb); qqline(residb, col="red");
plot(residb~fitted(boston.ols),xlab = "Predicted Values",ylab = "Residuals")
abline(h=0, col="red")
hist(residb) #histogram utk residual
plot(1:nrow(boston.c), residb, pch=20,type="b")
abline(h=0, col="red")
Berikut ini salah satu contoh untuk memeriksa asumsi pada sisaan menggunakan uji formal.
shapiro.test(residb)##
## Shapiro-Wilk normality test
##
## data: residb
## W = 0.55776, p-value < 2.2e-16Hasil uji asumsi normal menunjukkan bahwa sisaan tidak menyebar normal (tolak Ho karena p_value < alpha=5%)
coordsb<-boston.c[c("LAT","LON")]
jarakb<-as.matrix(1/dist(coordsb))
lm.morantest(boston.ols,listw=mat2listw(jarakb), alternative="two.sided")##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = CRIM ~ (INDUS + LSTAT))
## weights: mat2listw(jarakb)
##
## Moran I statistic standard deviate = 28.291, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## Observed Moran I Expectation Variance
## 9.926142e-02 -2.881082e-03 1.303558e-05Terlihat pada output bahwa hasil tes menunjukkan kesimpulan tolak H0 yang menyatakan bahwa terdapat autokorelasi pada sisaan model regresi klasik pada taraf nyata 5%.
lmtest::bptest(boston.ols)##
## studentized Breusch-Pagan test
##
## data: boston.ols
## BP = 12.852, df = 2, p-value = 0.001619Hasil uji asumsi kehomogenan ragam dengan Breusch-Pagan test menunjukkan bahwa sisaan memiliki ragam yang tidak homogen (tolak Ho karena p_value < alpha=5%)
Karena sisaan tidak homogen, maka selanjutnya digunakan GWR dan juga dibandingkan dengan Spatially Dissagregated Model .
Practice 3 Compare the insights provided by the expansion method to those from GWR, and carry out sensitivity analysis for the choice of bandwidth and kernel function.
Spatially Dissagregated Model
boston.sd <- lm(CRIM ~ (INDUS+ LSTAT)*(LAT + LON))
summary(boston.sd)##
## Call:
## lm(formula = CRIM ~ (INDUS + LSTAT) * (LAT + LON))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.317 -2.330 -0.384 0.659 83.243
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4094.5296 915.6054 -4.472 9.61e-06 ***
## INDUS 134.0545 128.9281 1.040 0.298956
## LSTAT 490.5703 130.1910 3.768 0.000184 ***
## LAT 55.5023 12.5358 4.427 1.17e-05 ***
## LON -24.5940 9.3749 -2.623 0.008974 **
## INDUS:LAT -1.9502 1.3983 -1.395 0.163741
## INDUS:LON 0.7252 1.4583 0.497 0.619212
## LSTAT:LAT -6.4855 1.5158 -4.279 2.26e-05 ***
## LSTAT:LON 3.0451 1.1891 2.561 0.010736 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.242 on 497 degrees of freedom
## Multiple R-squared: 0.3025, Adjusted R-squared: 0.2912
## F-statistic: 26.94 on 8 and 497 DF, p-value: < 2.2e-16Perhatikan bahwa R-square pada model ini sudah lebih baik dibandingkan dengan model regresi klasik, namun perhatikan pula bahwa peubah yang signifikan hanyalah sedikit.
Selanjutnya, misalkan kita ingin mengekstrak koefisien pada model tersebut.
b <- boston.sd$coefficients
b[2]## INDUS
## 134.0545b[3]## LSTAT
## 490.5703b[8]## LSTAT:LAT
## -6.485513b[9]## LSTAT:LON
## 3.045127Peubah INDUS:
Koefisien pada setiap titik lokasi.
INDUS <- b[2] + b[8] * LAT + b[9] * LON
INDUS## [1] -356.0579 -356.2535 -356.1816 -356.2221 -356.2363 -356.2585 -356.2724
## [8] -356.3613 -356.3606 -356.3743 -356.3926 -356.4265 -356.3765 -356.3268
## [15] -356.3172 -356.3763 -356.4375 -356.3225 -356.3504 -356.3050 -356.2874
## [22] -356.3087 -356.2821 -356.2684 -356.2740 -356.2734 -356.2798 -356.2506
## [29] -356.2261 -356.1949 -356.2501 -356.2248 -356.2491 -356.2687 -356.2367
## [36] -356.2922 -356.3458 -356.4114 -356.4666 -356.6107 -356.7338 -356.7260
## [43] -356.6483 -356.5720 -356.4981 -356.4517 -356.4288 -356.4339 -356.4568
## [50] -356.4770 -356.5581 -356.6219 -356.6572 -356.6954 -357.0038 -356.8979
## [57] -356.7536 -356.7358 -356.5978 -356.5116 -356.5136 -356.4520 -356.4553
## [64] -356.4580 -356.3523 -356.9157 -357.0374 -356.9956 -357.0751 -356.9999
## [71] -356.9590 -356.8254 -356.9151 -356.8095 -356.7519 -356.7102 -356.6538
## [78] -356.6501 -356.7299 -356.7963 -356.7473 -356.7942 -356.8610 -356.7721
## [85] -356.6664 -356.6390 -356.6197 -356.5310 -356.4824 -356.4524 -356.3965
## [92] -356.4314 -356.5379 -356.6496 -356.5703 -356.5723 -356.5997 -356.5730
## [99] -356.5815 -356.5229 -356.3951 -356.3767 -356.4195 -356.3905 -356.3597
## [106] -356.3155 -356.3072 -356.3082 -356.3332 -356.3712 -356.4011 -356.3785
## [113] -356.3312 -356.3447 -356.2909 -356.2998 -356.3422 -356.3271 -356.3012
## [120] -356.2885 -356.2255 -356.2608 -356.2579 -356.2330 -356.2239 -356.1975
## [127] -356.1958 -356.2299 -356.2418 -356.2712 -356.3019 -356.3260 -356.3278
## [134] -356.3706 -356.3323 -356.3014 -356.2617 -356.2409 -356.2054 -356.2058
## [141] -356.1983 -356.1754 -356.1284 -356.1454 -356.1265 -356.1259 -356.1541
## [148] -356.1512 -356.1665 -356.1640 -356.1801 -356.1606 -356.1281 -356.1319
## [155] -356.1449 -356.1426 -356.1711 -356.2332 -356.2012 -356.1808 -356.1902
## [162] -356.2449 -356.2348 -356.2485 -356.2896 -356.2793 -356.2705 -356.3257
## [169] -356.2782 -356.3019 -356.3236 -356.3323 -356.3730 -356.3904 -356.4000
## [176] -356.4974 -356.5375 -356.4931 -356.4369 -356.4074 -356.3650 -356.3153
## [183] -356.3304 -356.3571 -356.3739 -356.4230 -356.4644 -356.5762 -356.6237
## [190] -356.7020 -356.6625 -356.7866 -356.7554 -356.9512 -357.0035 -356.7543
## [197] -356.9719 -357.0393 -357.0915 -356.8569 -356.7850 -356.4744 -356.6066
## [204] -356.6517 -356.4818 -356.5760 -356.4948 -356.4658 -356.4386 -356.4144
## [211] -356.3900 -356.3985 -356.4069 -356.4872 -356.4520 -356.3970 -356.3425
## [218] -356.3104 -356.2650 -356.2810 -356.2620 -356.3144 -356.3058 -356.3248
## [225] -356.2121 -356.1302 -356.2244 -356.1571 -356.0759 -356.0754 -356.1563
## [232] -356.1841 -356.2295 -356.2722 -356.3323 -356.3499 -356.3618 -356.3117
## [239] -356.3829 -356.3736 -356.2034 -356.2590 -356.3668 -356.3664 -356.3398
## [246] -356.3746 -356.4350 -356.3978 -356.4636 -356.4784 -356.5046 -356.5680
## [253] -356.6137 -356.5159 -356.4442 -356.3214 -356.1606 -356.0923 -356.1096
## [260] -356.1198 -356.1078 -356.0918 -356.0925 -356.0536 -356.0739 -356.0514
## [267] -356.0363 -356.0484 -356.1685 -355.8383 -355.7205 -355.7062 -355.7773
## [274] -355.8767 -356.0248 -356.0853 -356.0274 -356.1184 -356.1417 -356.2111
## [281] -356.2594 -356.3030 -356.2069 -356.0246 -355.8059 -355.8398 -355.5727
## [288] -355.4944 -355.5542 -355.6487 -355.7638 -355.8593 -355.7146 -355.5865
## [295] -355.6897 -355.6959 -355.6495 -355.5744 -355.3767 -355.2283 -355.3330
## [302] -355.4237 -355.3930 -355.5828 -355.6231 -355.7037 -355.7094 -355.6414
## [309] -355.5917 -355.6400 -355.7313 -355.7184 -355.6699 -355.6101 -355.5334
## [316] -355.5034 -355.4677 -355.4775 -355.5444 -355.5813 -355.4036 -355.4026
## [323] -355.3972 -355.3823 -355.3296 -355.2325 -355.3017 -355.3596 -355.3577
## [330] -355.4133 -355.2528 -355.1371 -355.1094 -355.1240 -355.0896 -355.1956
## [337] -355.2982 -355.2436 -355.3205 -355.3761 -355.4093 -355.1487 -355.3200
## [344] -355.3059 -355.1333 -354.8499 -354.9242 -354.8360 -354.9564 -354.9427
## [351] -354.7784 -354.5261 -354.3452 -354.3380 -354.4307 -354.2865 -356.2025
## [358] -356.2053 -356.2097 -356.1668 -356.1352 -356.1665 -356.1368 -356.1989
## [365] -356.0815 -356.0681 -356.0364 -356.0383 -356.0525 -356.0706 -356.0601
## [372] -356.0690 -356.0772 -356.0757 -356.0649 -356.1056 -356.1104 -356.1258
## [379] -356.1209 -356.1384 -356.1729 -356.1512 -356.0944 -356.0788 -356.0652
## [386] -356.0479 -356.0575 -356.0645 -356.0630 -356.0716 -356.0660 -356.0629
## [393] -356.0321 -355.8956 -355.8946 -355.9026 -355.9129 -355.9299 -355.9666
## [400] -355.9595 -355.9446 -355.9393 -355.9218 -355.9264 -355.9439 -355.9726
## [407] -356.0854 -356.0266 -356.0037 -356.0157 -356.0276 -356.0177 -356.0008
## [414] -355.9853 -355.9564 -355.9463 -355.9608 -355.9944 -355.9910 -356.0160
## [421] -356.0199 -356.0478 -356.0213 -355.9930 -355.9660 -355.9925 -355.9673
## [428] -355.9780 -355.9580 -355.9340 -355.9335 -355.9142 -355.9019 -355.8690
## [435] -355.8804 -355.8921 -355.9074 -355.9199 -355.9335 -355.9120 -355.8873
## [442] -355.8460 -355.8704 -355.8833 -355.9097 -355.8963 -355.8714 -355.8471
## [449] -355.8433 -355.8680 -355.8551 -355.8301 -355.8056 -355.8109 -355.8263
## [456] -355.8509 -355.8238 -355.8005 -355.7818 -355.7681 -355.7928 -355.7794
## [463] -355.7348 -355.7365 -355.7418 -355.7787 -355.8014 -355.8965 -355.8439
## [470] -355.8700 -355.8560 -355.8936 -355.9298 -355.9574 -355.8955 -355.9154
## [477] -355.9325 -355.9471 -355.9517 -356.0066 -355.9766 -355.9524 -355.8950
## [484] -355.8809 -355.7847 -355.7132 -355.7641 -355.7832 -356.1189 -356.1249
## [491] -356.1515 -356.1347 -356.2186 -356.1759 -356.2006 -356.2250 -356.1818
## [498] -356.1438 -356.1485 -356.1125 -356.0603 -355.9979 -355.9892 -355.9910
## [505] -355.9558 -355.9211summary(INDUS)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -357.1 -356.4 -356.2 -356.1 -355.9 -354.3Berikut ini kita akan coba memvisualisasikan koefisien tersebut.
library(rgdal)boston.tr <- readOGR(system.file("shapes/boston_tracts.shp", package="spData")[1])## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\User\Documents\R\win-library\4.1\spData\shapes\boston_tracts.shp", layer: "boston_tracts"
## with 506 features
## It has 36 fieldsboston_nb <- poly2nb(boston.tr)boston.tr@data$INDUS<-INDUS
spplot(boston.tr, zcol="INDUS",main="Peta Sebaran Penduga Parameter INDUS")
Peubah LSTAT:
Koefisien pada setiap titik lokasi.
LSTAT. <- b[3] + b[8] * LAT + b[9] * LON
LSTAT.## [1] 0.4578918323 0.2623382898 0.3341548804 0.2936607664 0.2795039639
## [6] 0.2573390845 0.2433576958 0.1544783334 0.1552103796 0.1414488358
## [11] 0.1231780724 0.0892864141 0.1393111685 0.1889991355 0.1985882472
## [16] 0.1394865823 0.0783279369 0.1932885101 0.1653479482 0.2108300354
## [21] 0.2284244159 0.2070500539 0.2337241529 0.2474272255 0.2418318619
## [26] 0.2424058046 0.2360433122 0.2652281215 0.2896637480 0.3209055378
## [31] 0.2656979889 0.2910471536 0.2666861357 0.2470904380 0.2791227452
## [36] 0.2235784088 0.1700325815 0.1043869322 0.0491793832 -0.0948853125
## [41] -0.2179931206 -0.2101604575 -0.1325319286 -0.0561782867 0.0177259240
## [46] 0.0641126631 0.0869511174 0.0819296966 0.0590327711 0.0387578824
## [51] -0.0422832008 -0.1060638831 -0.1414252498 -0.1796284983 -0.4879909067
## [56] -0.3821212840 -0.2377560565 -0.2200197099 -0.0820452687 0.0041842251
## [61] 0.0022079316 0.0638203069 0.0605190790 0.0577522680 0.1634827326
## [66] -0.3998658061 -0.5215902087 -0.4798240157 -0.5592812560 -0.4841049663
## [71] -0.4432155935 -0.3096168298 -0.3992810936 -0.2936535314 -0.2361329014
## [76] -0.1944307956 -0.1380374705 -0.1343187684 -0.2141434358 -0.2805211314
## [81] -0.2314623081 -0.2783834641 -0.3451564183 -0.2563355272 -0.1505688070
## [86] -0.1232433487 -0.1039037517 -0.0151635474 0.0334221382 0.0633946570
## [91] 0.1192535653 0.0844322315 -0.0221250062 -0.1338428228 -0.0544941009
## [96] -0.0564926101 -0.0838987552 -0.0571661850 -0.0657248160 -0.0070607437
## [101] 0.1206893639 0.1390609324 0.0962960712 0.1252993886 0.1561459197
## [106] 0.2003425985 0.2086317995 0.2075924326 0.1825639180 0.1446389857
## [111] 0.1146636589 0.1373044838 0.1846208985 0.1710731214 0.2249190182
## [116] 0.2160018464 0.1736205496 0.1886763881 0.2146184409 0.2273333666
## [121] 0.2903174532 0.2550379462 0.2579439154 0.2827748006 0.2919263197
## [126] 0.3182563800 0.3199838241 0.2859368704 0.2739539908 0.2446183291
## [131] 0.2139065130 0.1897592618 0.1880390688 0.1451511869 0.1834713780
## [136] 0.2144042119 0.2541445264 0.2749421376 0.3104148308 0.3100357094
## [141] 0.3174787298 0.3403478237 0.3874198320 0.3703554252 0.3893098227
## [146] 0.3899027108 0.3617060482 0.3645813777 0.3492533013 0.3517801491
## [151] 0.3357083322 0.3552043976 0.3876481010 0.3838709275 0.3709115954
## [156] 0.3732129822 0.3447440817 0.2826134269 0.3146457341 0.3350041176
## [161] 0.3256154433 0.2708560083 0.2809517052 0.2672486326 0.2261555523
## [166] 0.2365412597 0.2452813826 0.1901061084 0.2375544301 0.2139254583
## [171] 0.1922421406 0.1834552406 0.1428409139 0.1253578599 0.1158433568
## [176] 0.0184053635 -0.0217157074 0.0227225698 0.0788904339 0.1084426703
## [181] 0.1508239671 0.2005135692 0.1853948163 0.1586928858 0.1419446860
## [186] 0.0927689202 0.0513729623 -0.0603726859 -0.1078928082 -0.1861860258
## [191] -0.1467090631 -0.2707788215 -0.2395955030 -0.4353688904 -0.4877128041
## [196] -0.2384541580 -0.4560778529 -0.5234659108 -0.5756645882 -0.3410810241
## [201] -0.2692281777 0.0413888404 -0.0908321338 -0.1359524099 0.0339680356
## [206] -0.0601972722 0.0210414405 0.0500286204 0.0772004184 0.1013755011
## [211] 0.1257949903 0.1172496886 0.1089020164 0.0286236189 0.0638402115
## [216] 0.1187972759 0.1733356930 0.2054164124 0.2507726708 0.2347988511
## [221] 0.2537488055 0.2014444176 0.2100175509 0.1910280706 0.3036872570
## [226] 0.3856112388 0.2913675900 0.3587027927 0.4398830340 0.4403645956
## [231] 0.3595239497 0.3316640746 0.2862682903 0.2435752297 0.1835132497
## [236] 0.1658910376 0.1540222925 0.2040793217 0.1328596000 0.1421926111
## [241] 0.3123707924 0.2567518473 0.1490205277 0.1494464261 0.1760398382
## [246] 0.1411906727 0.0807919052 0.1180376466 0.0521693443 0.0373918220
## [251] 0.0111936687 -0.0522502261 -0.0978964950 -0.0001483729 0.0716343079
## [256] 0.1943746889 0.3552041840 0.4234559465 0.4062451305 0.3959707496
## [261] 0.4080121005 0.4240282541 0.4232962079 0.4621508158 0.4419037588
## [266] 0.4644054256 0.4794919037 0.4673698660 0.3472487139 0.6774754698
## [271] 0.7952835408 0.8096379727 0.7384788196 0.6390745918 0.4909544065
## [276] 0.4304999757 0.4883908409 0.3974266665 0.3740481794 0.3047116595
## [281] 0.2563848824 0.2127955940 0.3089203473 0.4912467628 0.7098948602
## [286] 0.6759809863 0.9430950188 1.0213608670 0.9615493715 0.8670919560
## [291] 0.7519559690 0.6565047906 0.8011817820 0.9293416505 0.8260550011
## [296] 0.8198489770 0.8662958225 0.9414414726 1.1390519956 1.2875089684
## [301] 1.1828110818 1.0920945840 1.1228241725 0.9330018815 0.8926691411
## [306] 0.8120539562 0.8064396473 0.8744205933 0.9241469133 0.8757778023
## [311] 0.7844946129 0.7974071681 0.8459093589 0.9057152383 0.9823834521
## [316] 1.0123809945 1.0481097883 1.0383008316 0.9713887193 0.9344519337
## [321] 1.1121521872 1.1132210208 1.1185485895 1.1334580187 1.1862233876
## [326] 1.2833112641 1.2141417340 1.1562258452 1.1580629807 1.1024601730
## [331] 1.2629584967 1.3786511160 1.4063940486 1.3917835161 1.4262095913
## [336] 1.3201867704 1.2176043355 1.2722028590 1.1953136274 1.1397197060
## [341] 1.1064737910 1.3671161019 1.1958206748 1.2098967905 1.3824949362
## [346] 1.6658590074 1.5915639811 1.6798403962 1.5593490090 1.5730937047
## [351] 1.7373692018 1.9897113281 2.1705958669 2.1777912106 2.0850539880
## [356] 2.2292578418 0.3132679447 0.3104510864 0.3060648876 0.3489689068
## [361] 0.3805781237 0.3493056943 0.3790249205 0.3168781284 0.4343437959
## [366] 0.4477379127 0.4794027928 0.4774732762 0.4633003363 0.4451525936
## [371] 0.4556685728 0.4468143153 0.4386364408 0.4400843958 0.4509238331
## [376] 0.4101558460 0.4054066631 0.3899833976 0.3949185156 0.3774076299
## [381] 0.3429230836 0.3646342329 0.4213921771 0.4369852404 0.4505491549
## [386] 0.4678624111 0.4583200764 0.4512533694 0.4528037647 0.4441632739
## [391] 0.4498070495 0.4528844515 0.4837087670 0.6202251941 0.6212016466
## [396] 0.6131657382 0.6029308832 0.5858709196 0.5491567870 0.5563214911
## [401] 0.5711591197 0.5764822453 0.5939931309 0.5894371343 0.5718588912
## [406] 0.5431528354 0.4303912088 0.4891677804 0.5120647060 0.5000979637
## [411] 0.4882381049 0.4981361724 0.5149868125 0.5305008240 0.5593972580
## [416] 0.5694929549 0.5550110591 0.5213771365 0.5248292167 0.4997684274
## [421] 0.4958609821 0.4679571380 0.4945199105 0.5227956248 0.5497859307
## [426] 0.5232626841 0.5485049655 0.5378119374 0.5578262823 0.5817597667
## [431] 0.5823365174 0.6016115650 0.6139062084 0.6468125280 0.6353918968
## [436] 0.6237458047 0.6084060340 0.5958859296 0.5822663519 0.6038077035
## [441] 0.6284570968 0.6697901403 0.6453706512 0.6325020651 0.6061207846
## [446] 0.6194870697 0.6444131440 0.6686628354 0.6724984801 0.6477583409
## [451] 0.6607132299 0.6856671359 0.7102257831 0.7049260461 0.6894749489
## [456] 0.6649441333 0.6919973536 0.7153056752 0.7339716973 0.7476747699
## [461] 0.7230298198 0.7363654652 0.7809735401 0.7793279558 0.7740120814
## [466] 0.7370752958 0.7143759996 0.6192672247 0.6719418477 0.6458021656
## [471] 0.6597613388 0.6222038335 0.5860430631 0.5584292295 0.6203360584
## [476] 0.6004330400 0.5833408016 0.5686657196 0.5640701971 0.5092111299
## [481] 0.5392388497 0.5633861008 0.6207675728 0.6348965437 0.7310697091
## [486] 0.8026402583 0.7516858283 0.7325660762 0.3969097736 0.3908707393
## [491] 0.3642918294 0.3810956925 0.2972216136 0.3398695323 0.3152245822
## [496] 0.2908329246 0.3340297624 0.3720220522 0.3672478458 0.4032549558
## [501] 0.4554758488 0.5178481019 0.5266188645 0.5247756508 0.5599761060
## [506] 0.5946582818summary(LSTAT.)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.5757 0.1448 0.3491 0.3992 0.6013 2.2293Berikut ini kita akan coba memvisualisasikan koefisien tersebut.
library(rgdal)boston.tr@data$LSTAT<-LSTAT.
spplot(boston.tr, zcol="LSTAT",main="Peta Sebaran Penduga Parameter LSTAT")
Basic GWR
library(spgwr)Menggunakan Bandwidth=20:
gwr.boston <- gwr(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON),bandwidth=20)
gwr.boston## Call:
## gwr(formula = CRIM ~ (INDUS + LSTAT), data = boston.c, coords = cbind(LAT,
## LON), bandwidth = 20)
## Kernel function: gwr.Gauss
## Fixed bandwidth: 20
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max. Global
## X.Intercept. -4.31571 -4.31567 -4.31567 -4.31566 -4.31564 -4.3156
## INDUS 0.25943 0.25943 0.25943 0.25943 0.25943 0.2594
## LSTAT 0.39832 0.39832 0.39832 0.39832 0.39832 0.3983Menentukan Bandwidth Optimal
Berdasarkan cv dan Model GWR Kernel function: gwr.Gauss:
bw1.boston <- gwr.sel(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON))## Bandwidth: 0.2273468 CV score: 28928.76
## Bandwidth: 0.3674875 CV score: 28987.32
## Bandwidth: 0.140735 CV score: 28755.41
## Bandwidth: 0.087206 CV score: 28255.7
## Bandwidth: 0.05412325 CV score: 27196.09
## Bandwidth: 0.03367699 CV score: 25607.39
## Bandwidth: 0.0210405 CV score: 23385.73
## Bandwidth: 0.01323073 CV score: 21034.9
## Bandwidth: 0.008404017 CV score: 19649.11
## Bandwidth: 0.005420948 CV score: NA## Warning in optimize(gwr.cv.f, lower = beta1, upper = beta2, maximum = FALSE, :
## NA/Inf replaced by maximum positive value## Bandwidth: 0.01024766 CV score: 20125.49
## Bandwidth: 0.007264586 CV score: 19401.45
## Bandwidth: 0.006560379 CV score: NA## Warning in optimize(gwr.cv.f, lower = beta1, upper = beta2, maximum = FALSE, :
## NA/Inf replaced by maximum positive value## Bandwidth: 0.00769981 CV score: 19489.93
## Bandwidth: 0.006995603 CV score: NA## Warning in optimize(gwr.cv.f, lower = beta1, upper = beta2, maximum = FALSE, :
## NA/Inf replaced by maximum positive value## Bandwidth: 0.007430827 CV score: 19434.02
## Bandwidth: 0.007161844 CV score: 19382.27
## Bandwidth: 0.007098345 CV score: NA## Warning in optimize(gwr.cv.f, lower = beta1, upper = beta2, maximum = FALSE, :
## NA/Inf replaced by maximum positive value## Bandwidth: 0.007202534 CV score: 19389.77
## Bandwidth: 0.007161844 CV score: 19382.27gwr.bostoncv <- gwr(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON),bandwidth=bw1.boston)
gwr.bostoncv## Call:
## gwr(formula = CRIM ~ (INDUS + LSTAT), data = boston.c, coords = cbind(LAT,
## LON), bandwidth = bw1.boston)
## Kernel function: gwr.Gauss
## Fixed bandwidth: 0.007161844
## Summary of GWR coefficient estimates at data points:## Warning in print.gwr(x): NAs in coefficients dropped## Min. 1st Qu. Median 3rd Qu. Max.
## X.Intercept. -1.4497e+01 -1.5917e+00 -3.4157e-02 3.0317e-01 1.7458e+02
## INDUS -9.3138e+00 -5.7613e-03 2.8933e-02 1.9571e-01 9.0828e-01
## LSTAT -7.6555e-01 -8.4898e-04 1.2285e-02 1.7122e-01 1.6518e+00
## Global
## X.Intercept. -4.3156
## INDUS 0.2594
## LSTAT 0.3983Berdasarkan AIC dan Model GWR Kernel function: gwr.Gauss:
bw2.boston <- gwr.sel(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON),method="aic")## Bandwidth: 0.2273468 AIC: 3484.438
## Bandwidth: 0.3674875 AIC: 3485.055
## Bandwidth: 0.140735 AIC: 3482.655
## Bandwidth: 0.087206 AIC: 3478.053
## Bandwidth: 0.05412325 AIC: 3474.048
## Bandwidth: 0.03367699 AIC: 3484.634
## Bandwidth: 0.06675974 AIC: 3474.929
## Bandwidth: 0.04657227 AIC: 3474.999
## Bandwidth: 0.05684603 AIC: 3474.058
## Bandwidth: 0.05520268 AIC: 3474.035
## Bandwidth: 0.05528758 AIC: 3474.035
## Bandwidth: 0.05532827 AIC: 3474.035
## Bandwidth: 0.05524689 AIC: 3474.035
## Bandwidth: 0.05524689 AIC: 3474.035gwr.bostonaic <- gwr(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON),bandwidth=bw2.boston)
gwr.bostonaic## Call:
## gwr(formula = CRIM ~ (INDUS + LSTAT), data = boston.c, coords = cbind(LAT,
## LON), bandwidth = bw2.boston)
## Kernel function: gwr.Gauss
## Fixed bandwidth: 0.05524689
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max. Global
## X.Intercept. -6.5141132 -4.7142701 -4.3013542 -3.6572734 -0.0950198 -4.3156
## INDUS 0.0472121 0.1682818 0.1903810 0.2434683 0.3780145 0.2594
## LSTAT -0.0054539 0.3357467 0.4877656 0.5435015 0.6057270 0.3983Berdasarkan CV dan Model GWR Kernel function: gwr.bisquare :
bwbs1.boston <- gwr.sel(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON),gweight=gwr.bisquare)## Bandwidth: 0.2273468 CV score: 28520.97
## Bandwidth: 0.3674875 CV score: 28887.86
## Bandwidth: 0.140735 CV score: 27464.94
## Bandwidth: 0.087206 CV score: 25969.22
## Bandwidth: 0.05412325 CV score: NA## Warning in optimize(gwr.cv.f, lower = beta1, upper = beta2, maximum = FALSE, :
## NA/Inf replaced by maximum positive value## Bandwidth: 0.1076523 CV score: 26611.11
## Bandwidth: 0.07456951 CV score: 25318.48
## Bandwidth: 0.06675974 CV score: NA## Warning in optimize(gwr.cv.f, lower = beta1, upper = beta2, maximum = FALSE, :
## NA/Inf replaced by maximum positive value## Bandwidth: 0.07939622 CV score: 25605.29
## Bandwidth: 0.07158644 CV score: 25121.47
## Bandwidth: 0.06974281 CV score: NA## Warning in optimize(gwr.cv.f, lower = beta1, upper = beta2, maximum = FALSE, :
## NA/Inf replaced by maximum positive value## Bandwidth: 0.07272587 CV score: 25198.3
## Bandwidth: 0.07088224 CV score: 25072.97
## Bandwidth: 0.07044701 CV score: 25042.6
## Bandwidth: 0.07017803 CV score: 25023.68
## Bandwidth: 0.07001179 CV score: 25011.93
## Bandwidth: 0.06990905 CV score: 25004.65
## Bandwidth: 0.06984555 CV score: 25000.14
## Bandwidth: 0.06980486 CV score: NA## Warning in optimize(gwr.cv.f, lower = beta1, upper = beta2, maximum = FALSE, :
## NA/Inf replaced by maximum positive value## Bandwidth: 0.06984555 CV score: 25000.14gwr.bostonbs <- gwr(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON),gweight=gwr.bisquare, bandwidth=bwbs1.boston)
gwr.bostonbs## Call:
## gwr(formula = CRIM ~ (INDUS + LSTAT), data = boston.c, coords = cbind(LAT,
## LON), bandwidth = bwbs1.boston, gweight = gwr.bisquare)
## Kernel function: gwr.bisquare
## Fixed bandwidth: 0.06984555
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max. Global
## X.Intercept. -9.3795067 -2.7094549 -0.8827906 -0.1073159 2.3209521 -4.3156
## INDUS -0.2085938 0.0100675 0.0707898 0.1643640 0.4491845 0.2594
## LSTAT -0.0663816 0.0029311 0.2616100 0.5586709 0.8006971 0.3983Berdasarkan AIC dan Model GWR Kernel function: gwr.bisquare :
bwbs2.boston <- gwr.sel(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON),gweight=gwr.bisquare,method="aic")## Bandwidth: 0.2273468 AIC: 3482.375
## Bandwidth: 0.3674875 AIC: 3484.461
## Bandwidth: 0.140735 AIC: 3479.518
## Bandwidth: 0.087206 AIC: 3492.311
## Bandwidth: 0.1738178 AIC: 3480.475
## Bandwidth: 0.1202887 AIC: 3479.989
## Bandwidth: 0.1423683 AIC: 3479.545
## Bandwidth: 0.1369631 AIC: 3479.47
## Bandwidth: 0.130594 AIC: 3479.476
## Bandwidth: 0.1341197 AIC: 3479.456
## Bandwidth: 0.1340545 AIC: 3479.456
## Bandwidth: 0.1337516 AIC: 3479.455
## Bandwidth: 0.1325455 AIC: 3479.458
## Bandwidth: 0.1337982 AIC: 3479.455
## Bandwidth: 0.1338389 AIC: 3479.455
## Bandwidth: 0.1337982 AIC: 3479.455gwr.bostonbsaic <- gwr(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON),gweight=gwr.bisquare, bandwidth=bwbs2.boston)
gwr.bostonbsaic## Call:
## gwr(formula = CRIM ~ (INDUS + LSTAT), data = boston.c, coords = cbind(LAT,
## LON), bandwidth = bwbs2.boston, gweight = gwr.bisquare)
## Kernel function: gwr.bisquare
## Fixed bandwidth: 0.1337982
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max. Global
## X.Intercept. -7.390289 -4.650110 -4.294599 -3.185295 0.068982 -4.3156
## INDUS -0.015499 0.158148 0.171396 0.212683 0.364991 0.2594
## LSTAT -0.036006 0.279449 0.496623 0.546361 0.651621 0.3983Membandingkan antar Model Lokal
Peubah INDUS:
hb20 <- gwr.boston$SDF$INDUS
hbGaussAIC <- gwr.bostonaic$SDF$INDUS
hbBisquareCV <- gwr.bostonbs$SDF$INDUS
hbBisquareAIC<- gwr.bostonbsaic$SDF$INDUS
hbGaussCV<- gwr.bostoncv$SDF$INDUS
boxplot(LSTAT.,hb20,hbGaussAIC,hbBisquareCV,hbBisquareAIC,hbGaussCV,
names=c("Expansion","bw=20","GaussAIC","BiSCV","BiSAIC","hbGaussCV"))
Perhatikan bahwa sebaran pada bandwidth “bw=20”,“GaussAIC”,“BisquareCV”,“BisquareAIC” terkonsentrasi di sekitar median dengan sedikit pencilan, pada bandwidth “hExpansion” menunjukkan beberapa pencilan yang bernilai positif, sedangkan pada bandwidth “hbGaussCV” menunjukkan beberapa pencilan yang bernilai negatif. Selanjutnya akan dibandingkan bandwidth yang memberikan sedikit outlier.
par(mfrow = c(2, 2))
boxplot(hb20,main="bw=20")
boxplot(hbGaussAIC,main="GaussAIC")
boxplot(hbBisquareCV, main="BisquareCV")
boxplot(hbBisquareAIC,main="BisquareAIC")
Perhatikan bahwa sebaran pada bandwidth “GaussAIC” dan “BisquareCV” terkonsentrasi di sekitar median dengan sedikit pencilan dibandingkan “bw=20” dan BisquareAIC. Pada bandwidth “GaussAIC” menunjukkan sedikit pencilan dengan rentang nilai positif, sedangkan pada bandwidth “BisquareCV” menunjukkan sedikit pencilan dengan rentang nilai penduganya antara negatif s.d. positif. Dari sebaran data ini, alternatif yang dipilih bandwidth “GaussAIC” atau “BisquareCV”
par(mfrow = c(1, 2))
boxplot(hbGaussAIC,main="GaussAIC")
boxplot(hbBisquareCV, main="BisquareCV")
Perhatikan bahwa sebaran pada bandwidth bandwidth “GaussAIC” menunjukkan sedikit pencilan dengan rentang nilai positif, sedangkan pada bandwidth “BisquareCV” menunjukkan sedikit pencilan dengan rentang nilai penduganya antara negatif s.d. positif. Dari sebaran data ini, alternatif yang dipilih bandwidth “GaussAIC” karena semua penduga parameternya positif dan tidak ada yang nol.
Peubah LSTAT:
hb20L <- gwr.boston$SDF$LSTAT
hbGaussAICL <- gwr.bostonaic$SDF$LSTAT
hbBisquareCVL <- gwr.bostonbs$SDF$LSTAT
hbBisquareAICL<- gwr.bostonbsaic$SDF$LSTAT
hbGaussCVL<- gwr.bostoncv$SDF$LSTAT
boxplot(LSTAT.,hb20L,hbGaussAICL,hbBisquareCVL,hbBisquareAICL,hbGaussCVL,
names=c("Expansion","bw=20","GaussAIC","BiSCV","BiSAIC","hbGaussCV"))
Perhatikan bahwa sebaran pada bandwidth “bw=20”,“GaussAIC”,“BisquareCV”,“BisquareAIC” terkonsentrasi di sekitar median dengan sedikit pencilan, pada bandwidth “hExpansion” menunjukkan beberapa pencilan yang bernilai positif, sedangkan pada bandwidth “hbGaussCV” menunjukkan beberapa pencilan yang bernilai negatif. Selanjutnya akan dibandingkan bandwidth yang memberikan sedikit outlier.
par(mfrow = c(2, 2))
boxplot(hb20L,main="bw=20")
boxplot(hbGaussAICL,main="GaussAIC")
boxplot(hbBisquareCVL, main="BisquareCV")
boxplot(hbBisquareAICL,main="BisquareAIC")
Perhatikan bahwa sebaran pada bandwidth “GaussAIC” dan “BisquareCV” terkonsentrasi di sekitar median dengan sedikit pencilan dibandingkan “bw=20” dan BisquareAIC. Pada bandwidth “GaussAIC” menunjukkan sedikit pencilan dengan rentang nilai positif, sedangkan pada bandwidth “BisquareCV” menunjukkan sedikit pencilan dengan rentang nilai penduganya antara negatif s.d. positif. Dari sebaran data ini, alternatif yang dipilih bandwidth “GaussAIC” atau “BisquareCV”. Karena pada penduga parameter sebelumnya dipilih “GaussAIC”, maka selanjutnya akan digunakan bandwith “GaussAIC”.
boxplot(hbGaussAICL,main="GaussAIC")
Pemodelan RTG/GWR menggunakan matriks pembobot fungsi pembobot Kernel function: gwr.Gauss dengan lebar jendela optimum menggunakan AIC.
gwr.bostonaic## Call:
## gwr(formula = CRIM ~ (INDUS + LSTAT), data = boston.c, coords = cbind(LAT,
## LON), bandwidth = bw2.boston)
## Kernel function: gwr.Gauss
## Fixed bandwidth: 0.05524689
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max. Global
## X.Intercept. -6.5141132 -4.7142701 -4.3013542 -3.6572734 -0.0950198 -4.3156
## INDUS 0.0472121 0.1682818 0.1903810 0.2434683 0.3780145 0.2594
## LSTAT -0.0054539 0.3357467 0.4877656 0.5435015 0.6057270 0.3983Nilai penduga parameter peubah penjelas INDUS bernilai positif menunjukkan bahwa peubah penjelas berkontribusi positif terhadap peubah respon CRIM. Sementara nilai penduga parameter peubah penjelas LSTAT ada yang bernilai positif dan negatif menunjukkan bahwa peubah penjelas berkontribusi negatif, positif,dan ada yang tidak berkontribusi terhadap peubah respon CRIM.
Plot Sebaran Penduga Parameter Model RTG Terpilih
Penduga parameter untuk peubah penjelas INDUS
b.INDUS<- gwr.bostonaic$SDF$INDUS
boston.tr@data$b.INDUS<- b.INDUS
spplot(boston.tr,zcol="b.INDUS",main="Peta Sebaran Penduga Parameter INDUS Model RTG")
Interpretasi terhadap penduga parameter untuk peubah penjelas INDUS pada mode RTG:
Warna biru pada peta sebaran penduga parameter untuk peubah penjelas INDUS di atas menunjukkan nilai yang rendah, warna merah muda untuk nilai sedang, hingga warna kuning menunjukkan nilai tinggi.
Tidak ada penduga parameter untuk peubah penjelas INDUS yang memiliki nilai NEGATIF atau nol. Peubah penjelas INDUS berkontribusi positif terhadap peubah respon CRIM di semua lokasi amatan.
Peta sebaran penduga parameter untuk peubah penjelas INDUS menunjukkan kemiripan antarwilayah yang berdekatan. Kemiripan ini ditunjukkan dengan pola kecenderungan warna-warna yang sama untuk mengelompok dengan wilayah tetangganya.
Penduga parameter untuk peubah penjelas LSTAT
b.LSTAT<- gwr.bostonaic$SDF$LSTAT
boston.tr@data$b.LSTAT<- b.LSTAT
spplot(boston.tr,zcol="b.LSTAT",main="Peta Sebaran Penduga Parameter LSTAT Model RTG")
Interpretasi terhadap penduga parameter untuk peubah penjelas LSTAT pada mode RTG:
Warna biru pada peta sebaran penduga parameter untuk peubah penjelas LSTAT di atas menunjukkan nilai yang rendah, warna merah muda untuk nilai sedang, hingga warna kuning menunjukkan nilai tinggi.
Ada penduga parameter untuk peubah penjelas LSTAT yang memiliki nilai Positif atau NEGATIF atau nol. Peubah penjelas LSTAT berkontribusi positif atau negatif dan ada juga yang tidak berkontribusi terhadap peubah respon CRIM di beberapa lokasi amatan.
Peta sebaran penduga parameter untuk peubah penjelas LSTAT menunjukkan kemiripan antarwilayah yang berdekatan. Kemiripan ini ditunjukkan dengan pola kecenderungan warna-warna yang sama untuk mengelompok dengan wilayah tetangganya.
Penduga parameter untuk peubah respon CRIM:
pred.CRIM<- gwr.bostonaic$SDF$pred
boston.tr@data$pred.CRIM<- pred.CRIM
boston.tr@data$CRIM.awal <- boston.c$CRIM
spplot(boston.tr,c("CRIM.awal","pred.CRIM"),names.attr=c("Peta Sebaran Peubah CRIM pada Data","Peta Sebaran Dugaan Peubah Respon CRIM Model RTG"),as.table = TRUE, main = "Nilai Peubah CRIM VS Dugaan CRIM RTG")
#Statistik Amatan Peubah CRIM dan dugaan model RTG
summary(boston.tr@data[,c("CRIM.awal","pred.CRIM")])## CRIM.awal pred.CRIM
## Min. : 0.00632 Min. :-2.9883
## 1st Qu.: 0.08205 1st Qu.: 0.1265
## Median : 0.25651 Median : 2.2659
## Mean : 3.61352 Mean : 3.8607
## 3rd Qu.: 3.67708 3rd Qu.: 7.0257
## Max. :88.97620 Max. :19.3390Dengan membandingkan peta sebaran peubah respon CRIM dengan dugaan CRIM berdasarkan model RTG, terlihat bahwa model RTG memberikan nilai dugaan yang tidak jauh berbeda dengan nilai amatan peubah respon CRIM. Dari pola warna yang ada,perbedaan yang terjadi adalah nilai dugaan menjadi sedikit lebih RENDAH untuk amatan yang sebelumnya tinggi.
Berdasarkan ringkasan statistik dari data dugaan CRIM, rataan dari nilai dugaan CRIM tidak jauh berbeda dengan rataan amatan CRIM.
Plot Sisaan Model RTG
gwr.sisaan <- boston.c$CRIM - gwr.bostonaic$SDF$pred
boston.tr@data$gwr.sisaan<- gwr.sisaan
spplot(boston.tr,zcol="gwr.sisaan",main="Peta Sebaran Sisaan Model RTG")
Peta sebaran sisaan model RTG/GWR menunjukkan kecenderungan nilai sisaan yang relatif kecil.
Peta Sebaran R-Square
Assessing the global fit of the model, marginal improvements are observed. The AIC and Residual sum of squares experienced marginal reductions, while the R2 increased compared to the GRW based on a fixed kernel. To gain a better understanding of these changes, as above, we map the R2 values for the estimated local regressions (sumber: https://gdsl-ul.github.io/san/geographically-weighted-regression.html#interpretation-1)
localR2<- gwr.bostonaic$SDF$localR2
boston.tr@data$localR2 <- localR2
spplot(boston.tr,zcol="localR2",main="Peta Sebaran localR2 Model RTG")
Peta sebaran R-Square model RTG/GWR menunjukkan kecenderungan nilai R-Square yang relatif tinggi.
RTG vs Regresi Linier
Pengujian secara global dengan menggunakan ANOVA untuk mengidentifikasi kebaikan model RTG dibanding model Regresi Linier Berganda dalam menjelaskan hubungan peubah respon dengan peubah penjelas.
gwr.bostonaicc <- gwr(CRIM ~ (INDUS+ LSTAT),data=boston.c,
coords=cbind(LAT,LON),bandwidth=bw2.boston,hatmatrix=TRUE)
BFC99.gwr.test(gwr.bostonaicc)##
## Brunsdon, Fotheringham & Charlton (1999) ANOVA
##
## data: gwr.bostonaicc
## F = 2.0222, df1 = 108.38, df2 = 492.84, p-value = 2.071e-07
## alternative hypothesis: greater
## sample estimates:
## SS GWR improvement SS GWR residuals
## 1944.971 26645.516BFC02.gwr.test(gwr.bostonaicc)##
## Brunsdon, Fotheringham & Charlton (2002, pp. 91-2) ANOVA
##
## data: gwr.bostonaicc
## F = 1.073, df1 = 503.00, df2 = 485.48, p-value = 0.2172
## alternative hypothesis: greater
## sample estimates:
## SS OLS residuals SS GWR residuals
## 28590.49 26645.52BFC02.gwr.test(gwr.bostonaicc,approx = T)##
## Brunsdon, Fotheringham & Charlton (2002, pp. 91-2) ANOVA (approximate
## degrees of freedom - only tr(S))
##
## data: gwr.bostonaicc
## F = 1.073, df1 = 503.00, df2 = 491.35, p-value = 0.2164
## alternative hypothesis: greater
## sample estimates:
## SS OLS residuals SS GWR residuals
## 28590.49 26645.52anova(gwr.bostonaicc)## Analysis of Variance Table
## Df Sum Sq Mean Sq F value
## OLS Residuals 3.000 28591
## GWR Improvement 17.524 1945 110.988
## GWR Residuals 485.476 26646 54.885 2.0222anova(gwr.bostonaicc,approx=T)## Analysis of Variance Table
## approximate degrees of freedom (only tr(S))
## Df Sum Sq Mean Sq F value
## OLS Residuals 3.000 28591
## GWR Improvement 11.651 1945 166.939
## GWR Residuals 491.349 26646 54.229 3.0784Berdasarkan hasil ANOVA, jika menggunakan Brunsdon, Fotheringham & Charlton (1999) ANOVA didapatkan nilai-p < tarafnyata 5% sehingga diputuskan untuk menolak hipotesis nol (H0: model RTG dan model RLB sama baik). Dapat disimpulkan bahwa model RTG lebih efektif dalam menghubungkan peubah penjelas dengan peubah respon dibandingkan model regresi linier klasik.
Namun, jika berdasarkan Brunsdon, Fotheringham & Charlton (2002, pp. 91-2) ANOVA dan Brunsdon, Fotheringham & Charlton (2002, pp. 91-2) ANOVA (approximate degrees of freedom - only tr(S)), didapatkan nilai-p >tarafnyata 5% sehingga diputuskan untuk tidak menolak hipotesis nol (H0: model RTG dan model RLB sama baik). Dapat disimpulkan bahwa model RTG dan Regresi Linier Klasik sama baik dalam menghubungkan peubah penjelas dengan peubah respon.
Kebaikan model dapat dilihat dari nilai AIC, yaitu model terbaik adalah model dengan nilai AIC lebih kecil.
#AIC GWR Model
gwr.bostonaicc[["results"]][["AICh"]]## [1] 3456.319#AIC Regresi Linier Berganda
AIC(boston.ols)## [1] 3485.318Model RTG memiliki AIC yang lebih kecil dari model RLB, sehingga model RTG lebih baik.
Perbandingan antara model RTG dan RLB:
reg.klasik.pred <- Predict(boston.ols,data=boston.c)
boston.tr@data$rlbpred <- reg.klasik.pred
gwr.bostonaicc.pred <- gwr.bostonaicc$SDF$pred
boston.tr@data$gwr.bostonaicc.pred <- gwr.bostonaicc.predspplot(boston.tr,c("gwr.bostonaicc.pred","rlbpred","CRIM.awal"),names.attr=c("Dugaan CRIM RTG","Dugaan CRIM RLB","Peubah CRIM pada Data"),as.table = TRUE, main = "Nilai CRIM VS Dugaan CRIM Model RTG VS RLB")
#Statistik Amatan Peubah CRIM, dugaan CRIM model RTG,dan dugaan CRIM model RLB
summary(boston.tr@data[,c("gwr.bostonaicc.pred","rlbpred","CRIM.awal")])## gwr.bostonaicc.pred rlbpred CRIM.awal
## Min. :-2.9883 Min. :-3.01330 Min. : 0.00632
## 1st Qu.: 0.1265 1st Qu.: 0.08031 1st Qu.: 0.08205
## Median : 2.2659 Median : 2.80881 Median : 0.25651
## Mean : 3.8607 Mean : 3.61352 Mean : 3.61352
## 3rd Qu.: 7.0257 3rd Qu.: 6.79523 3rd Qu.: 3.67708
## Max. :19.3390 Max. :15.50419 Max. :88.97620gwr.sisaanc <- boston.c$CRIM - gwr.bostonaicc$SDF$pred
boston.tr@data$gwr.sisaanc<- gwr.sisaanc
reg.klasik.sisaan <- boston.c$CRIM - reg.klasik.pred
boston.tr@data$reg.klasik.sisaan<- reg.klasik.sisaan
#Statistik Sisaan model RTG dan model RLB
summary(boston.tr@data[,c("reg.klasik.sisaan","gwr.sisaanc")])## reg.klasik.sisaan gwr.sisaanc
## Min. :-14.496 Min. :-15.6405
## 1st Qu.: -2.652 1st Qu.: -2.6506
## Median : -0.544 Median : -0.3778
## Mean : 0.000 Mean : -0.2472
## 3rd Qu.: 1.370 3rd Qu.: 0.7246
## Max. : 81.741 Max. : 81.3699Dari nilai statistik di atas, Dugaan dengan model RTG memberikan sisaan yang lebih kecil dibandingkan model RLB.
TERIMA KASIH
Referensi
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https://gdsl-ul.github.io/san/geographically-weighted-regression.html#interpretation-1
Mahasiswa Pascasarjana Statistika dan Sains Data, IPB University, reniamelia@apps.ipb.ac.id↩︎