Hi,

     I'm a French PhD student in Economics and I would like to tests the
existence of spatial autocorrelation in a linear model with the functions
lm.morantest and lm.LMtest of the package spded (I'm not a specialist in
R!).

     Its seems to be ok with lm.morantest() but with lm.LMtest() I have the
error message:

   “NOTE:  lm.LMtests(LM1, LISTW, zero.policy = NULL, test = "LMlag", spChk
= NULL,  :   Spatial weights matrix not row standardized”


     So, I tried to row-standardized the matrix and I nomore have this
message when processing lm.LMtest().


     However the results seems odd. Indeed with the initial matrix,
converted into a listw object with “LISTW<-mat2listw (W, row.names=NULL)”,
I rejected the null hypothesis both with the Moran's tests and the LMtest
(LMerr).  To the contrary, with the row-standardized matrix, I cannot
reject the null hypothesis according the LMerr test and the Morantest gives
me a Moran's I statistic standard deviate equal to 0 and a p-value eual to
0.5 (which seems odd…).


      So I don’t know which matrix is adequate for which test and if the
results with the row-standardized matrix are ok...


      Maybe it comes from the characteristics of my W matrix  in which wij=
population j * exp (-dij/h)² , so because of the population terms, its not
a symmetric matrix?


      Maybe it comes from the way I row-standardized it, with
“LISTWW<-nb2listw(LISTW$neighbours, glist=NULL, style="W",
zero.policy=FALSE)” ?



I thank you in advance for your help.


Best regards,



Laurence

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