Thanks for your suggestions. It looks like the R Borg is continuing to assimilate procedures that once required specialty software. Time to learn some new packages!
Tyler On March 11, 2014 1:08:10 PM EDT, Alex Mandel <[email protected]> wrote: >You are right, I didn't read it that closely 1st time around. My point >was that all of it can be done in R, and there are geospatial specific >packages that have all the tests one might want. The bare minimum >interaction is via rgdal or spgrass to pull data over from existing >GRASS data sets. If the data isn't already in GRASS then rgdal one can >read the original files directly. No need to pass csv around. Of course >if it is in GRASS then you should have it all in the same projection >already anyways if you put it all into the same mapset/location. > >The other book likely to have exactly what you want (field sampling >design) is Ch 5. >http://www.amazon.com/Spatial-Analysis-Ecology-Agriculture-Using/dp/1439819130/ref=la_B001K6MGR8_1_1?s=books&ie=UTF8&qid=1394557436&sr=1-1 > >Enjoy, >Alex > >On 03/11/2014 09:58 AM, Thomas Adams wrote: >> Alex, >> >> I believe Tyler does plan on using R for the statistical analyses, >but >> using GRASS GIS in combination with R is the easiest path, I think. >> >> Tom >> >> On Tuesday, March 11, 2014, Alex Mandel <[email protected]> >wrote: >> >>> Use R. It includes Moran's I and Geary's C tests for >>> spatial-autocorrelation. Look like it has mantel too. >>> >>> You'll probably need the sp, spdep and rgdal packages. You might >also >>> want to use the Raster package to extract the sampling data, or you >can >>> use spGRASS to tie the R and Grass together. >>> >>> See chapter 9 (1st ed) of Applied Spatial Data Analysis with R. >>> http://www.asdar-book.org/ >>> >>> Enjoy, >>> Alex >>> >>> On 03/11/2014 09:18 AM, Tyler Smith wrote: >>>> Hello, >>>> >>>> We're preparing a field sampling program, and would like to >determine >>>> a minimum distance between samples to reduce/eliminate spatial >>>> autocorrelation. I think a good approach would be to calculate a >>>> mantel correlogram, and use the range of the correlogram as our >>>> minimum sampling distance. >>>> >>>> * Questions >>>> >>>> 1) is this a reasonable approach >>>> 2) if so, how best to do this? >>>> >>>> * Details >>>> We have a vector map with the point coordinates of several hundred >>>> potential sampling sites, and ~ 10 raster layers with appropriate >data >>>> to test for spatial autocorrelation (WORLDCLIM, soils). I could do >>>> something like the following, but I'm not sure if there's a simpler >or >>>> more appropriate approach: >>>> >>>> 1) extract the raster data for each point >>>> 2) save the data to csv; import into R >>>> 3) calculate the spatial distances between points, after projecting >>>> the lat-long data into an appropriate scale (?) >>>> 4) calculate the climate distance using the WORLDCLIM data >>>> 5) use the 'mgram' function in the 'ecodist' package to calculate >the >>>> actual correlogram between the spatial distance and climate >distance >>>> >>>> Any suggestions on the approach or the methods would be welcome! >>>> >>>> Thanks, >>>> >>>> Tyler > >> > >_______________________________________________ >grass-user mailing list >[email protected] >http://lists.osgeo.org/mailman/listinfo/grass-user _______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
