私は形状ファイル.shpを使用しており、それを境界ファイルに変換して、モデルでランダム効果として使用しています.bnd。しかし、ログファイルからのBayesXエラーが返されます。何が問題になる可能性がありますかERROR: map is disconnected, spatial effect cannot be estimatedBayesX
ここにコードがあります
fileUrl <- "http://biogeo.ucdavis.edu/data/gadm2.8/shp/ZAF_adm_shp.zip"
download.file(fileUrl, destfile = "ZAF_adm_shp.zip")
unzip(zipfile = "ZAF_adm_shp.zip",exdir = "ZAF_adm_shp")
library(BayesX)
library("R2BayesX")
shpname <- file.path(getwd(), "ZAF_adm_shp" , "ZAF_adm2")
SA_bnd <- BayesX::shp2bnd(shpname = shpname, regionnames = "ID_2", check.is.in = F)
za <- bayesx(dp2011 ~ code + sx(ID_2, bs = "mrf", map = SA_bnd) +
sx(ID_2, bs = "re"), iter = 1200, step = 10, data = data)
で、データはこちら
data<-dput(structure(list(ID_2 = 1:52, NAME_2B = structure(c(1L, 3L, 5L,
10L, 6L, 24L, 30L, 33L, 20L, 26L, 27L, 51L, 40L, 18L, 12L, 36L,
13L, 50L, 2L, 19L, 23L, 41L, 42L, 43L, 44L, 45L, 46L, 52L, 38L,
8L, 28L, 47L, 48L, 37L, 17L, 22L, 32L, 21L, 25L, 29L, 35L, 39L,
4L, 14L, 31L, 15L, 11L, 7L, 9L, 16L, 34L, 49L), .Label = c("Alfred Nzo",
"Amajuba", "Amathole", "Bojanala", "Buffalo City", "Cacadu",
"Cape Winelands", "Capricorn", "Central Karoo", "Chris Hani",
"City of Cape Town", "City of Johannesburg", "City of Tshwane",
"Dr Kenneth Kaunda", "Dr Ruth Segomotsi Mompati", "Eden", "Ehlanzeni",
"Ekurhuleni", "eThekwini", "Fezile Dabi", "Frances Baard", "Gert Sibande",
"iLembe", "Joe Gqabi", "John Taolo Gaetsewe", "Lejweleputswa",
"Mangaung", "Mopani", "Namakwa", "Nelson Mandela Bay", "Ngaka Modiri Molema",
"Nkangala", "O.R.Tambo", "Overberg", "Pixley ka Seme", "Sedibeng",
"Sekhukhune", "Sisonke", "Siyanda", "Thabo Mofutsanyane", "Ugu",
"Umgungundlovu", "Umkhanyakude", "Umzinyathi", "Uthukela", "Uthungulu",
"Vhembe", "Waterberg", "West Coast", "West Rand", "Xhariep",
"Zululand"), class = "factor"), code = structure(c(38L, 5L, 1L,
6L, 4L, 7L, 51L, 8L, 13L, 10L, 50L, 9L, 11L, 47L, 49L, 36L, 52L,
41L, 18L, 48L, 22L, 14L, 15L, 20L, 17L, 16L, 21L, 19L, 37L, 29L,
27L, 28L, 30L, 40L, 26L, 24L, 25L, 46L, 39L, 43L, 44L, 45L, 31L,
35L, 32L, 33L, 2L, 12L, 42L, 34L, 23L, 3L), .Label = c(" BUF",
" CPT", " DC1", " DC10", " DC12", " DC13", " DC14", " DC15",
" DC16", " DC18", " DC19", " DC2", " DC20", " DC21", " DC22",
" DC23", " DC24", " DC25", " DC26", " DC27", " DC28", " DC29",
" DC3", " DC30", " DC31", " DC32", " DC33", " DC34", " DC35",
" DC36", " DC37", " DC38", " DC39", " DC4", " DC40", " DC42",
" DC43", " DC44", " DC45", " DC47", " DC48", " DC5", " DC6",
" DC7", " DC8", " DC9", " EKU", " ETH", " JHB", " MAN", " NMA",
" TSH"), class = "factor"), dp2011 = c(9.7, 7.1, 11.5, 11, 7.2,
10.7, 13.8, 12.6, 5.8, 8.2, 7.9, 12.3, 11.7, 10.8, 15.5, 7.6,
8.1, 17.6, 11.3, 33.5, 18.4, 12.5, 14.7, 15.8, 8.9, 8.8, 20.3,
17.6, 10.5, 10.5, 14.6, 16.4, 8.5, 10.7, 7.1, 6.6, 7.5, 9.9,
7.5, 4.8, 11, 7.9, 5.4, 11.6, 12.5, 8.3, 14.9, 29.2, 30.7, 8.5,
16.5, 15.8)), .Names = c("ID_2", "NAME_2B", "code", "dp2011"), row.names = c(NA,
-52L), class = "data.frame"))