On Thu, 12 Aug 2010, elaine kuo wrote:
Dear List,
I wanna know if lag or error model is better to examine a spatial regression
model.
However, the results of their p-values are the same, which shows no
difference between the models in the aspect.
Please kindly help and thanks.
Given the possible errors in your construction of spatial weights in your
other thread on this list, and the very high levels of residual
correlation found here, I would not even start trying to consider this
question. Unless you fully understand what the lag model does, and are an
econometrician, avoid it. It makes little sense in other fields than
spatial econometrics, and its interpretation is non-standard. So stay with
errorsarlm() for error SAR models, or spautolm() for a wider selection of
error models.
Roger
Elaine
code
rm(list=ls())
datam <-read.csv("c:/migration/Mig_ratio_20100808.csv",header=T,
row.names=1)
library(ncf)
library(spdep)
# get the upper bound
up <- knearneigh(cbind(datam$lat,datam$lon))
upknn <- knn2nb(up)
updist1 <- nbdists(upknn,cbind(datam$lat,datam$lon))
updist1
updistvec <- unlist(updist1)
updistvec
upmaxd <- max(updistvec)
upmaxd
# Define coordinates, neighbours, and spatial weights
coords<-cbind(datam$lat,datam$lon)
coords<-as.matrix(coords)
# Define neighbourhood (here distance 8)
nb8<-dnearneigh(coords,0,8.12)
summary(nb8)
#length(nb8)
#sum(card(nb8))
# Spatial weights, illustrated with coding style "W" (row standardized)
nb8.w<-nb2listw(nb8, glist=NULL, style="W")
# std model
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$S ~ datam.std$coast + datam.std$topo_var
+datam.std$prec_ran, data = datam.std)
summary(mig.std)
mig.lagrange
<-lm.LMtests(mig.std,nb8.w,test=c("LMerr","RLMerr","LMlag","RLMlag","SARMA"))
print(mig.lagrange)
Lagrange multiplier diagnostics for spatial dependence
data:
model: lm(formula = datam.std$S ~ datam.std$coast + datam.std$topo_var
+datam.std$prec_ran, data = datam.std)
weights: nb8.w
LMerr = 79589.91, df = 1, p-value < 2.2e-16
Lagrange multiplier diagnostics for spatial dependence
data:
model: lm(formula = datam.std$S ~ datam.std$coast + datam.std$topo_var
+datam.std$prec_ran, data = datam.std)
weights: nb8.w
RLMerr = 68943.02, df = 1, p-value < 2.2e-16
Lagrange multiplier diagnostics for spatial dependence
data:
model: lm(formula = datam.std$S ~ datam.std$coast + datam.std$topo_var
+datam.std$prec_ran, data = datam.std)
weights: nb8.w
LMlag = 13000.91, df = 1, p-value < 2.2e-16
Lagrange multiplier diagnostics for spatial dependence
data:
model: lm(formula = datam.std$S ~ datam.std$coast + datam.std$topo_var
+datam.std$prec_ran, data = datam.std)
weights: nb8.w
RLMlag = 2354.020, df = 1, p-value < 2.2e-16
Lagrange multiplier diagnostics for spatial dependence
data:
model: lm(formula = datam.std$S ~ datam.std$coast + datam.std$topo_var
+datam.std$prec_ran, data = datam.std)
weights: nb8.w
SARMA = 81943.93, df = 2, p-value < 2.2e-16
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--
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Helleveien 30, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: roger.biv...@nhh.no
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