[R-sig-Geo] Moran's I with Objects of Different Length

[Chanda Chiseni]

I am trying to perform moran's I test on residuals from by probit model
using k-nearest neighbour weights, however i run into an error which i
cant seem to find the solution anywhere online the error is

> moran.test(residuals.glm(svyprobitest),knear2weight)
Error in moran.test(residuals.glm(svyprobitest), knear2weight) :
  objects of different length


The codes I use to come up to this are below

> library(foreign)
> dhsanalysis2= read.dta("DHSSpatialreg11.dta")
##removing NAs in PSU
> dhsanalysis3=subset(dhsanalysis2, !is.na(psu))
##generating a survey design
> mydesighdhs= svydesign(ids =~psu, data =dhsanalysis3, weight = ~wgt,
strata = ~v023,nest=TRUE)
##Defining coordinate colums
> coordinates(dhsanalysis3)= c("longnum","latnum")
#Defining Projection
> proj4string(dhsanalysis3) <- CRS("+init=epsg:4326")
##binding longiude and latitude
> lon2<- dhsanalysis3$longnum
> lat2<- dhsanalysis3$latnum
> coords<- cbind(lon2,lat2)
##Creating spatial weights based on the nearest neighbour
> knear2= knearneigh(coords,k=2,longlat=T)
Warning message:
In knearneigh(coords, k = 2, longlat = T) :
  knearneigh: identical points found ### I get a warning message on
identical points found, is this a problem and how would i deal with this
> knear2.nb= knn2nb(knear2)
> knear2weight= nb2listw(knear2.nb, style="W",zero.policy=T)

##declaring categorical variables as factor variables
> dhsanalysis3$hivpositive.f <- factor(dhsanalysis3$hivpositive)
> dhsanalysis3$protestant.f <- factor(dhsanalysis3$protestant2)
> dhsanalysis3$Married.f <- factor(dhsanalysis3$Married)
> dhsanalysis3$female.f <- factor(dhsanalysis3$female)
> dhsanalysis3$urban1.f <- factor(dhsanalysis3$urban1)
> dhsanalysis3$river10kmdum.f <- factor(dhsanalysis3$river10kmdum)
> dhsanalysis3$Explorer50kmdum.f <- factor(dhsanalysis3$Explorer50kmdum)
> dhsanalysis3$Rail50kmdum.f <- factor(dhsanalysis3$Rail50kmdum)
> dhsanalysis3$Province1.f <- factor(dhsanalysis3$Province1)
> dhsanalysis3$WealthIndex.f <- factor(dhsanalysis3$WealthIndex)
> dhsanalysis3$occupation2.f <- factor(dhsanalysis3$occupation2)
> dhsanalysis3$highested.f <- factor(dhsanalysis3$highested)
> protestant.f= dhsanalysis3$protestant.f
> hivpositive.f=dhsanalysi3$hivpositive.f
> Married.f=dhsanalysis3$Married.f
> female.f=dhsanalysis3$female.f
> urban1.f=dhsanalysis3$urban1.f
> river10kmdum.f=dhsanalysis3$river10kmdum.f
> Explorer50kmdum.f=dhsanalysis2$Explorer50kmdum.f
> Province1.f=dhsanalysis3$Province1.f
> WealthIndex.f=dhsanalysis3$WealthIndex.f
> occupation2.f=dhsanalysis3$occupation2.f
> highested.f=dhsanalysis3$highested.f
> Age=dhsanalysis3$Age
> Age2=dhsanalysis3$Age2
> HIVKnowledge=dhsanalysis3$HIVKnowledge
> churchkm=dhsanalysis3$churchkm
> lnhospitalkm=dhsanalysis3$lnhospitalkm
> lnElevationMean=dhsanalysis3$lnElevationMean

> ###VARIABLES IN SURVEY DESIGN
> mydesighdhs$hivpositive.f <- factor(dhsanalysis3$hivpositive)
> mydesighdhs$protestant.f <- factor(dhsanalysis3$protestant2)
> mydesighdhs$Married.f <- factor(dhsanalysis3$Married)
> mydesighdhs$female.f <- factor(dhsanalysis3$female)
> mydesighdhs$urban1.f <- factor(dhsanalysis3$urban1)
> mydesighdhs$river10kmdum.f <- factor(dhsanalysis3$river10kmdum)
> mydesighdhs$Explorer50kmdum.f <- factor(dhsanalysis3$Explorer50kmdum)
> mydesighdhs$Rail50kmdum.f <- factor(dhsanalysis3$Rail50kmdum)
> mydesighdhs$Province1.f <- factor(dhsanalysis3$Province1)
> mydesighdhs$WealthIndex.f <- factor(dhsanalysis3$WealthIndex)
> mydesighdhs$occupation2.f <- factor(dhsanalysis3$occupation2)
> mydesighdhs$highested.f <- factor(dhsanalysis3$highested)
> mydesighdhs$Age=Age
> mydesighdhs$Age2=Age2
> mydesighdhs$HIVKnowledge=HIVKnowledge
> mydesighdhs$churchkm=churchkm
> mydesighdhs$lnhospitalkm=lnhospitalkm
> mydesighdhs$lnElevationMean=lnElevationMean

> svyprobitest= svyglm(hivpositive.f~ churchkm +lnhospitalkm +protestant.f+
Age+
Age2+Married.f+female.f+urban1.f+river10kmdum.f+Explorer50kmdum.f+Rail50kmdum.f+lnElevationMean+Province1.f
+ WealthIndex.f + HIVKnowledge+ occupation2.f+
highested.f,design=mydesighdhs,family = quasibinomial(link =
"probit"),data=dhsanalysis3)
Error in .subset2(x, i, exact = exact) : subscript out of bounds

## Iran my probit model without implementing the survey design in the model
just to see whether Moran test is working
> svyprobitest2= glm(hivpositive.f~ churchkm +lnhospitalkm +protestant.f+
Age+
Age2+Married.f+female.f+urban1.f+river10kmdum.f+Explorer50kmdum.f+Rail50kmdum.f+lnElevationMean+Province1.f
+ WealthIndex.f + HIVKnowledge+ occupation2.f+ highested.f,family =
quasibinomial(link = "probit"),data=dhsanalysis3)
#Moran test
> moran.test(residuals.glm(svyprobitest),knear2weight)
Error in moran.test(residuals.glm(svyprobitest), knear2weight) :
  objects of different length


Kind Regards,

Michael Chanda Chiseni

Phd Candidate

Department of Economic History

Lund University

Visiting address: Alfa 1, Scheelevägen 15 B, 22363 Lund



*Africa is not poor, it is poorly managed (Ellen Johnson-Sirleaf ). *

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