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Error is spdep spautolm()

4 messages · Roger Bivand, Samar Deen

Samar Deen
# 
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)')

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.

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)
Roger Bivand
#
On Tue, 12 Apr 2016, Samar Deen wrote:

            

      
      
      
    

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

            

      
      
      
    

        
[1] 1

... more below

            

      
      
      
    

        
(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

            

      
      
      
    

        

      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Roger Bivand
    

    

      
      #
    

  


  

      
On Tue, 12 Apr 2016, Roger Bivand wrote:

        

            

      
      
      
    

        
This was a long-standing and previously unknown bug in sn2listw() used 
inside tri2nb(), and a new release of spdep has been submitted to CRAN.

Roger

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Samar Deen
    

    

      
      #
    

  


  

      
Thank you. I made the recommended changes and the CAR function is now
working.
Regards,

        
On Tue, Apr 12, 2016 at 6:40 AM, Roger Bivand <Roger.Bivand at nhh.no> wrote: