Error is spdep spautolm()

On Tue, 12 Apr 2016, Samar Deen wrote:

 I was running a CAR with different inputs into the function, but have a
 few
 errors that I am unable to resolve.

 CAR1 = spautolm(s2008 ~ 1, data = df4, listw = gab.nhbr.listw,
 + weights = 1, family = "CAR")
 Error in model.frame.default(formula = s2008 ~ 1, data = df4, weights = 1,
 :
  variable lengths differ (found for '(weights)')

This is trivial, input weights must be of the same length as the number of 
observations.

 weights <- 1
 length(weights)

[1] 1

... more below

 CAR1 = spautolm(s2008 ~ 1, data = df4, listw = gab.nhbr.listw,
 + weights = tempmean, family = "CAR")
 Error in validObject(.Object) :
  invalid class ?dgRMatrix? object: slot j is not increasing inside a
  column

 Here in both cases I used a Delaunay Triangulation to determine neighbors
 and also made the neighbors symmetric using the command:

 nc_nb.tri <- make.sym.nb(nc_nb.tri)

 In my code I also have other methods for determining the neighbors. I seem
 to be getting different errors for all.

 I would really appreciate any guidance on how I may resolve this error.

(I have access to the data from an earlier offline exchange involving issues 
with missing data)

The points are (most likely) in geographical coordinates, so almost all of 
your attempts to create neighbour objects are wrong - all graph methods are 
only on the plane. Distances can be used when longlat=TRUE only.

Your use of scaled inverted spatial weights is contorted and the declared 
listw object style wrong, this ought to be sufficient:

gab.dist.sinv <- lapply(gab.dist, function(x) min(unlist(gab.dist))/x)

Much seems to have been copied from a script detailing analysis of the NC 
SIDS data set (the 30 mile threshold was used there).

It isn't obvious what you are trying to do.

Roger

 Any help will be really appreciated.

 Thank you & Regards,

 My code is pasted below:

 library(Matrix)
 library(spdep)
 library(RColorBrewer)

 #Get cents3
 setwd("~/Desktop/Flounder")
 cents3 <- read.csv("cents3.csv")

 #Subset the relevant data
 df=data.frame(cents3)
 df2 <- subset(df, select = c(8,9,41,49,50))
 df3 <- df2[complete.cases(df2), ]
 df4 <- na.omit(df3)
 coordinates(df4) = c("x","y")

 ##SET the neighbours
 coords = coordinates(df4)
 IDs = row.names(data.frame(df4))

 # Delaunay Triangulation

 nc_nb.tri = tri2nb(coords,row.names=IDs)
 nc_nb.tri <- make.sym.nb(nc_nb.tri)
 #
 plot(df4, border = "grey")
 plot(nc_nb.tri, coordinates(df4), add = TRUE, col = "blue")

 # Sphere of Influence

 nc_nb.soi = graph2nb(soi.graph(nc_nb.tri,coords),row.names=IDs)
 nc_nb.soi <- make.sym.nb(nc_nb.soi)

 #
 plot(df4, border = "grey")
 plot(nc_nb.soi, coordinates(df4), add = TRUE, col = "blue")

#  Relative Graph
#
 nc_nb.relative = graph2nb(relativeneigh(coords),row.names=IDs)
 #
 plot(df4, border = "grey")
 plot(nc_nb.relative, coordinates(df4), add = TRUE, col = "blue")
# 
#  Neighbors based on distance (Here all neighbors withing 30 miles
#  This one is a bit messy
 nc_nb.dnn = dnearneigh(cbind(df4$x,df4$y),0,2,row.names=IDs)
 #
 plot(df4, border = "grey")
 plot(nc_nb.dnn, coordinates(df4), add = TRUE, col = "blue")
# 
#  K nearest neighbors
#
 nc_nb.knn = knn2nb(knearneigh(coordinates(df4),k=5),row.names=IDs)
 nc_nb.knn <- make.sym.nb(nc_nb.knn)
 #
 plot(df4, border = "grey")
 plot(nc_nb.knn, coordinates(df4), add = TRUE, col = "blue")
 #

 #7- Making Neighborhood Wieghts
# 
# Compute distances between neighbors

 gab.dist = nbdists(nc_nb.tri, cbind(df4$x, df4$y))
# 
#  Create a spatial neighbor object from weights object
#  using inverse distance weighting, style B gives equal weights to the
 nrihbors
 gab.nhbr = listw2sn(nb2listw(nc_nb.tri, glist = gab.dist,
                             style = "U", zero.policy = TRUE))
# 
# The distance between neighbor i and j
 dij = gab.nhbr[, 3]
# 
# SSTobserved
 n = df4$tempmean
# 
# Scaled inverse distance
 el1 = min(dij)/dij
# 
# Square root of ratio of central fish to neighbor fish quantities
# not needed
 el2 = sqrt(n[gab.nhbr$to]/n[gab.nhbr$from])
# 
# Combine distance and birth weightings
 gab.nhbr$weights = el1
# 
# Create spatial weights object from spatial neighbor object
 gab.nhbr.listw = sn2listw(gab.nhbr)

 gab.nhbr.listw$style <- "W"

 #8- CAR with no predictor
# when wieght = 1 it will be propotional to the variance-
# since it is a standardized survey across the region we can assume it is 1

 CAR1 = spautolm(s2008 ~ tempmean, data = df4, listw = gab.nhbr.listw,
               verbose = TRUE, family = "CAR", method = "Matrix_J")

 CAR1 = spautolm(s2008 ~ 1, data = df4, listw = gab.nhbr.listw,
                weights = 1, family = "CAR")

 summary(CAR1)

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