>
>
>>>> 1. change method to Matrix
>>
>>
>>  System calculation is specific, the condition is =5.14146e-17 (translated
>> from Chinese)
>>
>> Warning messages:
>>
>
> Warning not a problem, but the error suggests scaling problems in the
> covariance matrix of the coefficients. The X variables are most likely
> measured in inappropriate metrics (some coefficients of size 1000, others of
> size 0.0001, for example).
>
> ==>  using standardized regression, the warning disappeared as you advised.
>

  However, another set of warning appears:

  Warning messages:
1: In determinant(x, TRUE) : This version of the Matrix package returns
|determinant(L)| instead of determinant(A), i.e., a
*DIFFERENT* value.
 If still necessary, do change your code, following
http://matrix.r-forge.r-project.org

2: In powerWeights(W = W, rho = lambda, order = con$pWOrder, X = B,  :
  not converged within order iterations
3: In powerWeights(W = t(W), rho = lambda, order = con$pWOrder, X = C,  :
  not converged within order iterations

Please kindly advise how to modify the code below from determinant L to
determinant A and why ?


2. great distance circle

And make sure that you do use Great Circle distances - you are not doing so
now.
With 200 neighbours, you have no viable spatial process, really.

=> longlat = TRUE returns no valid observation ...
     similar error messages have been researched in the archived mail
     but no identical case found...
    Please kindly suggest when longlat=TRUE,
    whether the unit of both longitude and latitude is degree.decimal..
    (confused with the manual explanation "measured in kilometer")

    Also, does "no viable spatial process" mean
   no spatial interaction within the range should be taken into
consideration ?

   Thank you

   Elaine


code
datam <-read.csv("c:/migration/M_R_SummerM20100722.csv",header=T,
row.names=1)

# std method   scale
  datam.sd<-scale(datam)
  datam.std<-as.data.frame(datam.sd)
  summary (datam.std)
  mean(datam.std)

  # obtain standard deviation
  sd(datam.std)

  mig.std
<-lm(datam.std$SummerM_ratio~datam.std$coast+datam.std$topo_mean+datam.std$prec_mean+datam.std$temp_ran+datam.std$evi_ran,data=datam.std)
  summary(mig.std)

  #SAR

  library(ncf)
  library(spdep)

  # Define coordinates, neighbours, and spatial weights
  coords<-cbind(datam$lon,datam$lat)
  coords<-as.matrix(coords)

  #Define neighbourhood (here distance 5)
  nb5<-dnearneigh(coords,0,5,longlat=TRUE)


  #Spatial weights, illustrated with coding style "W" (row standardized)
  nb5.w<-nb2listw(nb5, glist=NULL, style="W", zero.policy=TRUE)


   +++error in nb2listw(nb5, glist = NULL, style = "W", zero.policy = TRUE)
:
  No valid observations  +++
  (then modified as  nb5<-dnearneigh(coords,0,5) and Matrix went on
smoothly )

  # Spatial SAR error model
  sem.nb5.w <-
errorsarlm(datam.std$SummerM_ratio~datam.std$coast+datam.std$topo_mean+datam.std$prec_mean+datam.std$temp_ran+datam.std$evi_ran,data=datam.std,
listw=nb5.w, na.action=na.omit, method="Matrix", zero.policy=TRUE)



>
>
> +++warning
>
>
>> 1: In determinant(x, TRUE) : This version of the Matrix package returns
>>
>> |determinant(L)| instead of determinant(A), i.e., a
>>
>> *DIFFERENT* value.
>>
>> If still necessary, do change your code, following
>> http://matrix.r-forge.r-project.org
>>
>>
>>
>> 2: In powerWeights(W = W, rho = lambda, order = con$pWOrder, X = B,  :
>>
>>  not converged within order iterations
>>
>> 3: In powerWeights(W = t(W), rho = lambda, order = con$pWOrder, X = C,  :
>>
>>  not converged within order iterations  ++++
>>
>>
>>
>>
>>
>>

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