[R-sig-Geo] Base correlation larger than alpha (ME - Spdep package

[Roger Bivand]

On Mon, 10 Jun 2013, Rodrigo Tardin wrote:

> Dear list,
>
> I am new to this list and I am trying to fit a Spatial Eigenvector GLM to
> count data.
>
> I am trying to fit a ME model in spdep package to investigate environmental
> drivers on Bryde's whale distribution. I have 3 environmental variables
> (depth, distance to coast, slope), my response variable is the occurrence
> (counts) of whales in 82 km2 grids.
>
> I used the dnearneigh function to build .nb data using the coordinates data.
>>    nb1  <-  dnearneigh(xy,  0,  10)  ----
> and: lw1  <-  nb2listw(nb1,  style="W")
>
> x - is latitude coordinates
> y - is longitude coordinates
>
>
> Using the glm and Spatial filtering function (as done in spdep manual) I
> had no problems. However, when I used the 'ME' it says: base correlation
> larger than alpha. My alpha is 0.05.
>
> Here it is my code:
> ##This part works well
> glmbase1  <-  glm(OCC  ~  DEPTH  +  DISTCOAST+SLOPE,  offset=RT,
> data=bryde3)
>
> lagcol1  <-  SpatialFiltering(OCC_BIN  ~  1,  ~  DEPTH  +  DISTCOAST+
> SLOPE, data=bryde3,  style="W",  nb=nb1, alpha=0.05,  verbose=TRUE)
>
> glmlag1  <-  glm(OCC ~  DEPTH  +  DISTCOAST+SLOPE +  fitted(lagcol1),
> offset=RT,data=bryde3)

These are not the same models - lagcol1 is as you know the process in the 
response (here OCC - RT??), not in the error. In ME, the process is in the 
error, and it looks as though the covariates mop up much of the spatial 
dependence (it is also a Poisson model with a default log link). 
SpatialFiltering() does not handle an offset (as you have realised, I 
think), so again, this is a possible source of difference. ME is testing 
slightly diffferently too. Do:

errcol1  <-  SpatialFiltering(I(OCC-RT)  ~  DEPTH  +  DISTCOAST +
  SLOPE, data=bryde3,  style="W",  nb=nb1, alpha=0.05,  verbose=TRUE)
glmerr1  <-  glm(OCC ~  DEPTH  +  DISTCOAST+SLOPE +  fitted(errcol1),
  offset=RT, data=bryde3)

and

errcol2  <-  ME(OCC ~  DEPTH  +  DISTCOAST+SLOPE,  offset=RT, 
data=bryde3, family="gaussian", listw=lw1,  alpha=0.05,  verbose=TRUE)
glmerr12  <-  glm(OCC ~  DEPTH  +  DISTCOAST+SLOPE +  fitted(errcol2),
  offset=RT, data=bryde3)

give similar results?

Hope this helps,

Roger



>
> anova(glmlag1)
> anova(glmbase1,  glmlag1)
> set.seed(123)
>
> ##From here on, it does not
> lagcol1  <-  ME(OCC ~  DEPTH  +  DISTCOAST+SLOPE,  offset=RT, data=bryde3,
> family="poisson",
> listw=lw1,  alpha=0.05,  verbose=TRUE)
>
> Error in ME(OCC ~ DEPTH + DISTCOAST + SLOPE, data = bryde3, family =
> "poisson",  :
>  base correlation larger than alpha
>
> When I increase alpha to 0.6 (lower values keep saying the same error),
> this error message does not appear. However, when I try to rerun GLM with
> eigenvectors included as covariates (as done in spdep manual - page 118) I
> have the following error message:
>
> Error in glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  :
>  NA/NaN/Inf in 'x'
> warning message: step size truncated due to divergence
>
> Can anyone please help me?
> Other question, if I may, is there a strong difference between Spatial
> Filtering and ME?
>
> Many thanks in advance,
> Rodrigo
>

-- 
Roger Bivand
Department of Economics, NHH Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no