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.
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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