[GRASS-stats] Re: grass-stats Digest, Vol 49, Issue 1

[Duccio Rocchini]

Dear Nikos
many compliments for this elegant solution.

At a first glance, I would suggest using ANOSIM which is implemented
into vegan too but it seems that MRPP worked in a great way and that
you have seen the sunny part of the road in few time!

Duccio

-- 
Duccio Rocchini, PhD

Edmund Mach Foundation
IASMA Research and Innovation Centre
Department of Biodiversity and Molecular Ecology
GIS and Remote Sensing Unit
Via Mach 1, 38010 San Michele all'Adige (TN) - Italy
Phone +39 0461 615 570
ducciorocchini at gmail.com
duccio.rocchini at iasma.it
skype: duccio.rocchini
web page: http://gis.fem-environment.eu/rocchini/



2011/1/6  <grass-stats-request at lists.osgeo.org>:
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> Today's Topics:
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>   1. Re: Spatial autocorrelation of multi-spectral,    uni- and
>      mutli-temporal data sets (Nikos Alexandris)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Thu, 6 Jan 2011 16:56:36 +0200
> From: Nikos Alexandris <nik at nikosalexandris.net>
> Subject: Re: [GRASS-stats] Spatial autocorrelation of multi-spectral,
>        uni- and mutli-temporal data sets
> To: grass-stats at lists.osgeo.org
> Message-ID: <201101061656.38683.nik at nikosalexandris.net>
> Content-Type: Text/Plain;  charset="utf-8"
>
> On Sunday 26 of December 2010 19:00:32 Nikos Alexandris wrote:
>> Greets to the statists,
>>
>> I want to "describe" my multispectral (Landsat5_TM) composite datasets with
>> respect to their between vs. within heterogeneity.
>
> (Replying to myself and for the potential interest of anybody reading the
> list... )
>
> Finally I went for MRPP test(s) implemented in the R-package "vegan"[1][2]. I
> did a sampling of major land cover classes after all (such as Urban areas,
> vegetation, bare ground, water bodies, etc)., put the data in data.frames and
> ran the tests.
>
> The data.frames look like:
>
> str ( samples_postfire_modis )
> 'data.frame':   1040 obs. of  6 variables:
>  $ Band 1: int  1354 1458 1458 1458 1550 1145 1428 1573 1573 1657 ...
>  $ Band 2: int  3088 2971 2971 2971 2902 2990 2942 2824 2824 2917 ...
>  $ Band 5: int  3533 3506 3506 3506 3323 3535 3337 3239 3239 3552 ...
>  $ Band 6: int  2778 2803 2803 2803 2974 2646 2674 2883 2883 3071 ...
>  $ Band 7: int  2019 2146 2146 2146 2042 1719 2045 2114 2114 2373 ...
>  $ Class : Factor w/ 5 levels "Urban","Vegetation",..: 1 1 1 1 1 1 1 1 1 1 ...
>> str ( samples_bite )
> samples_bitemporal_modis.colnames  samples_bitemporal_modis
>> str ( samples_bitemporal_modis )
> 'data.frame':   1040 obs. of  7 variables:
>  $ Prefire B2 : int  3377 3425 3304 3179 3247 3247 3235 3043 3043 3197 ...
>  $ Prefire B6 : int  2726 2683 2737 2991 2934 2934 2928 2984 2984 3199 ...
>  $ Prefire B7 : int  1864 1932 2005 2185 2068 2068 2223 2331 2331 2314 ...
>  $ Postfire B2: int  3088 2971 2971 2971 2902 2990 2942 2824 2824 2917 ...
>  $ Postfire B6: int  2778 2803 2803 2803 2974 2646 2674 2883 2883 3071 ...
>  $ Postfire B7: int  2019 2146 2146 2146 2042 1719 2045 2114 2114 2373 ...
>  $ Class      : Factor w/ 5 levels "Urban","Vegetation",..: 1 1 1 1 1 1 1 1 1
> 1 ...
>
> Some of the results look like this:
>
> --%<---
> Call:
> mrpp(dat = samples_postfire_modis.smallsample.300[, 1:5], grouping =
> samples_postfire_modis.smallsample.300[["Class"]])
>
> Dissimilarity index: euclidean
> Weights for groups:  n
>
> Class means and counts:
>
>      Urban Vegetation Bare ground Burned Water
> delta  1241  1029       1550        1228  855.2
> n     97    81         63          53     6
>
> Chance corrected within-group agreement A: 0.3956
> Based on observed delta 1239 and expected delta 2050
>
> Significance of delta: 0.001
> Based on  999  permutations
> -->%---
>
> and
>
> --%<---
> + samples_postfire_modis.smallsample.300.vegdist <- vegdist (
> samples_postfire_modis.smallsample.300[,1:5] )
>
> + samples_postfire_modis.smallsample.300.md <- meandist (
> samples_postfire_modis.smallsample.300.vegdist ,
> samples_postfire_modis.smallsample.300[["Class"]] )
>
> + summary(samples_postfire_modis.smallsample.300.md)
>
> Mean distances:
>                 Average
> within groups  0.0950253
> between groups 0.2023877
> overall        0.1754765
>
> Summary statistics:
>                        Statistic
> MRPP A weights n        0.4224480
> MRPP A weights n-1      0.4263591
> MRPP A weights n(n-1)   0.4584728
> Classification strength 0.1010409
> --%<---
>
>
> Running the above test on samples with observations > 3000 is a high load for
> a home-machine (working here on Core2 Duo @2.53GHz and 6GB RAM). In fact,
> running the process for 18K observations times 6 variables (the number of
> permutations increases like crazy...) took 2+1/2 days (double that for another
> data.frame of the same size).
>
> Wish I had access to some OSGeo super-computer for 30 mins to get this job
> done.
>
> Greets, Nikos
>
>
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> End of grass-stats Digest, Vol 49, Issue 1
> ******************************************
>