Thank you very much for wonderful reply! Its exactly what i was looking for
last couple of hours.
Its incredible smart tool!
Kind regards,
Robert.






2010/6/24 Roger Bivand <Roger.Bivand@nhh.no>

> On Thu, 24 Jun 2010, Robert Pazur wrote:
>
>  Dear all,
>>
>> I would like to perform Moran'I correlogram (sp.correlogram method in
>> spdep
>> package) based on euclidian fixed distances  but I have following problem:
>> I created an artificial table, containing long and lati of regular points
>>
>>> points <-read.table("http://www.scandinavia.sk/data/moran5.csv",
>>> sep=",",
>>>
>> header=T)
>> following the manual I also identified neighbours of region
>>
>>> dnb <- dnearneigh(as.matrix(points$long, points$lati), 0, 20, longlat=T)
>>>
>>
> No, from your helpful link to the data, you have projected coordinates, not
> geographical. In addition, your use of as.matrix() instead of cbind() has
> bad consequences:
>
> str(as.matrix(points$long, points$lati))
> str(cbind(points$long, points$lati))
>
> dnearneigh() will be revised to trap this.
>
> Had you said:
>
> coordinates(points) <- c("long", "lati")
>
> then:
>
> proj4string(points) <- CRS("+proj=longlat")
>
> you would have seen the problem, because the sp classes check for the
> bounds on objects.
>
> So after doing:
>
>
> points <-read.table("http://www.scandinavia.sk/data/moran5.csv", sep=",",
>  header=T)
> coordinates(points) <- c("long", "lati")
> dnb <- dnearneigh(points, 0, 20)
>
> you are good to go. Next step - how to replicate the ArcGIS Moran's I - is
> easy with the correct dnb:
>
> moran.test(points$GRID_CODE, listw=nb2listw(dnb, style="B"))
>
> You might use correlog() in pgirmess for distance bins, but you'll have
> more control over the bin boundaries by makin new sets of neighbours for
> your chosen bin thresholds.
>
> Hope this helps (and thank you for reverting to the list after writing to
> me directly 70 minutes earlier. List is always best).
>
> Roger
>
>
>  neighbours list
>>
>>> ME200.listw <- nb2listw(dnb, style="W", zero.policy=T)
>>>
>> but if I perform sp.correlogram function:
>>
>>> correl<-sp.correlogram(dnb, points$GRID_CODE, order = 2, method = "I",
>>>
>> style = "W", randomisation = TRUE, zero.policy = TRUE, spChk=NULL)
>> my results are :
>> Spatial correlogram for points$GRID_CODE
>> method: Moran's I
>>   estimate expectation   variance standard deviate Pr(I) two sided
>> 1 -0.0029855  -0.0344828  0.0019674           0.7101          0.4776
>> 2 -0.0044436  -0.0344828  0.0022585           0.6321          0.5273
>>
>> and if i perform this part of this task in Arcgis for the same point
>> shapefile Moran Calculation for Fixed distance band, Euclidian distance a
>> and 20m threshold, result of Moran coefficient is
>> (SpatialAutocorrelation moran GRID_CODE false "Fixed Distance Band"
>> "Euclidean Distance" None 20 # 0 0 0) results are:
>> Global Moran's I Summary
>> Moran's Index:   0.746511
>> Expected Index:  -0.003521
>> Variance:        0.001827
>> Z Score:         17.545122
>> p-value:         0.000000
>>
>> I would like to perform the same task like in Arcgis but for multiple
>> distances. However Arcgis cannot deal with large data with multiple
>> points,
>> thatswhy I
>> would like to use R. Its seems to me much better software, but
>> unfortunatelly I never use it (but I really want)
>> If you could give me some advice i will be very happy.
>>
>> Robert.
>>
>>
>> -------------------------------------------------------
>> Robert Pazur
>> PhD student
>> Institute of Geography
>> Slovak Academy Of Sciences
>>
>> Mobile : +421 948 001 705
>> Skype  : ruegdeg
>>
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>>
>>
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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.Bivand@nhh.no
>
>

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