I am using a shape file .shp and then converting it to a boundary file .bnd using BayesX to use it as a random effect in my model. But it throws back an error of ERROR: map is disconnected, spatial effect cannot be estimated from theBayesX log file. What could be the problem
Here is the code
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)
And here is the data
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"))
