我正在使用形状文件.shp，然后使用.bnd将其转换为边界文件BayesX，以将其用作模型中的随机效果。但它会从ERROR: map is disconnected, spatial effect cannot be estimated日志文件中返回BayesX的错误。可能是什么问题
这是代码
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)
这是数据
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"))