[GRASSLIST:5254] Re: LSD program

[Michael Barton]

Roger,

Thanks for the response. Craig sent me a chapter from the ADE-4 manual that
goes over the application of various versions of a Ripley's module. It does
indeed look like it is closely similar (identical?) to Johnson's LDA
routine. 

It would be nice indeed to be able to access R routines from within GRASS
and have them act on GRASS data directly. If people could then start writing
a few scripts that incorporated R commands, they could give them a
reasonably nice GUI face using g_parser calls.

For testing my LDA script, I just used the bugsite and archsite points in
the Spearfish for 5.7 demo set.

Craig's email got me to thinking about ADE again as an interesting package
to re-look at. It has such a klunky and antiquated data structure though. I
don't know how it could read GRASS data.

Cheers,
Michael


On 1/4/05 1:21 PM, "Roger Bivand" <Roger.Bivand at nhh.no> wrote:

> On Tue, 4 Jan 2005, Michael Barton wrote:
> 
>> Thanks Craig.
>> 
>> I realize that R has the potential to be a powerful statistical companion to
>> GRASS. The reasons I didn't use R for this LDA routine are:
>> 
>> 1. I don't know R command syntax. This seems to be a fairly steep learning
>> curve, though I am sure the results are worth the effort. While I keep
>> thinking it would be useful to learn it, I have so much on my plate that I
>> simply haven't had time. I should learn C too. It would encourage me to
>> tackle R if it had a menu-driven interface to augment the command line, as I
>> am familiar with a variety of other stat packages (SPSS, Systat, Statistica,
>> and JMP--the latter being my package of choice in a large part because of
>> the UI). So far, I haven't found one that works on my Mac (a menu that
>> primarily lets me cut and paste commands, and open help files is nice but
>> not sufficient).
> 
> This is fair - although there are some GUIs being done (also using tcltk),
> this isn't a priority for developers (or users) - see the R GUI link on
> the side bar of the R home page http://www.r-project.org.
> 
>> 
>> 2. Most GRASS users don't know R command syntax. I suspect that many of them
>> will echo my comments in #1. The LDA routine I did is pretty simple to use.
>> Hopefully, this will make it more accessible to GRASS users.
>> 
> 
> Maybe - the upside of writing a small R batch program is that the R
> internals are heavily used, and most likely less exposed to coding error
> than less used code. The downside is writing the code, though, and for
> regular work done very often, an optimized GRASS module may execute
> faster.
> 
>> 3. I'm not clear about this, but it sounds from recent comments to the GRASS
>> developer's list like R will not currently read GRASS 5.7/6 vector data. The
>> commands you list below, suggest that you are working with GRASS 5.4 (or
>> earlier) vector data and an associated PostgreSQL database. I hope I am
>> wrong about this.
> 
> You aren't right (nor wrong either as of now), 5.7/6 is certainly going to
> be there, but as of now there have only been two specific requests for 5.7
> vector support (in fact for points), and these can be handled through
> reading and writing files.  There is a lot of ongoing work on designing
> robust spatial data classes for R, and they will make exchange though OGR
> at least, possibly also with vector topology, much easier to code. Classes
> first (we hope this month), then interface. 5.0.* and 5.4 raster and sites
> continue to be supported, though the new compression style for integer
> rasters is not yet there.
> 
>> 
>> Finally, perhaps you could offer some advice given your knowledge of R. LDA
>> is not a clustering technique, but a way to quantitatively measure the to
>> what extent geospatial data are clumped or dispersed (along the lines of
>> nearest neighbor analysis but without some of the drawbacks of NN). Is there
>> such a routine in R? And are there modules that measure in other ways the
>> degree to which geospatial data are clumped or dispersed (evenly or
>> randomly)? 
> 
> I haven't been able to get hold of the references you posted for LDA.
> There are several packages including functions for testing cluster,
> random, or dispersed. These cover the NN methods (Ghat, Fhat), but also
> Khat, which seems like your method. Khat compares the relative intensity
> of points in distance bands around each point (boundary adjusted) - I
> would guess Johnson's method is based on the same use of the data. Brian
> Ripley's "spatial" package permits rectangular window boundary adjustment,
> splancs permits boundary adjustment in an arbitrary polygon, while
> spatstat allows more options. One nice feature is that they all allow the
> testing of results by simulation from completely spatially random and
> other spatial processes.
> 
> The web references to LDA I have seen make it look as though the Khat
> components (for each point) may be of interest - these are accessible in
> the khat() function in splancs. I've seen mention of comparison of i and j
> artefacts too - this looks like a marked point process, aka k12hat. If you
> can feed me a specification and a test data set, I'll see if I can figure
> out how this could be done, if you like.
> 
> Best wishes,
> 
> Roger
> 
>> 
>> Thanks for the suggestion.
>> 
>> Michael Barton
>> 
>> On 1/4/05 10:12 AM, "Craig Aumann" <caumann at ualberta.ca> wrote:
>> 
>>> In glancing at your script, I wondered why you didn't just import the
>>> stuff into a statistical package like R and then use all the clustering
>>> routines it has.    See www.r-project.org
>>> 
>>> 
>>> 
>>> 
>>> ## Key commands in R are:
>>> 
>>> ## Load the packages to access the database, in particular the PgSQL ##
>>> database
>>> require(Rdbi)
>>> require(RdbiPgSQL)
>>> 
>>> ## Load some clustering routines
>>> 
>>> require(cluster)
>>> require(mclust)
>>> 
>>> 
>>> ### Connect to the database and read in a points data set "jogis"
>>> ## and read in the dataset "wells_att".
>>> 
>>> 
>>> conn <- dbConnect(PgSQL(), dbname="jogis", user="caumann")
>>> wells <- dbReadTable(conn, "wells_att")
>>> dbDisconnect(conn)
>>> 
>>> ## Now you can apply any clustering or kernel smoothing technique you
>>> want to the dataset.
>>> 
>>> If this is of any help, let me know and I can provide more details.
>>> Cheers!
>>> Craig
>>> 
>> 
>> ______________________________
>> Michael Barton, Professor of Anthropology
>> School of Human, Evolution and Social Change
>> Arizona State University
>> Tempe, AZ  85287-2402
>> USA
>> 
>> voice: 480-965-6262; fax: 480-965-7671
>> www: http://www.public.asu.edu/~cmbarton
>> 
>> 

____________________
C. Michael Barton, Professor of Anthropology
School of Human Evolution and Social Change
PO Box 872402
Arizona State University
Tempe, AZ  85287-2402
USA

Phone: 480-965-6262
Fax: 480-965-7671
www: <www.public.asu.edu/~cmbarton>