Thanks to all that replied. everything works well now. THank you all for such an elegant way to model!!! Much more fun to play with GRASS and a stats package at the same time.
I have just received my copy of Open Source GIS A GRASS GIS Approach", 3rd edition and at a first glance much of the issues are covered in Chapter 10. Once again thanks. Andy On 12/20/07, Roger Bivand <[EMAIL PROTECTED]> wrote: > > On Wed, 19 Dec 2007, Dylan Beaudette wrote: > > > On Wednesday 19 December 2007, Roger Bivand wrote: > >> On Wed, 19 Dec 2007, Daniel McInerney wrote: > >> > >> The original questioner should have written to the grass-stats list in > the > >> > >> first place - thanks for CC-ing. See below for inline comments: > >>> Hi Andy, > >>> > >>>> I am unsure how to then move the 'outmap' back across to grass. > >>>> How can i convert the df to a spatial grid object. > >>> > >>> AFAIK, 'predict' won't create a dataframe object. > >>> R should return FALSE for is.data.frame(outmap) and > >>> TRUE for is.numeric(outmap) > >>> > >>> You can slot the model output to the AttributeList of > >> > >> It hasn't been an AttributeList for a long time - the data slot *is* a > >> data frame. We *always* need the output of sessionInfo() to help - > >> update.packages() is most often very helpful too. > >> > >>> one of the SpatialGridDataFrames that you created when > >>> you read in a GRASS raster and then use writeRAST6 to > >>> write it back to GRASS. > >>> > >>> e.g. using 'anmax' from your example > >>> > >>> anmax$anmax <- outmap > >>> #if that doesn't work, you might try > >>> anmax$anmax <- as.numeric(outmap) > >>> writeRAST6(anmax, "NameOfNewGRASSRaster", "anmax") > >>> > >>> Regards, > >>> Daniel. > >>> > >>> cc: [EMAIL PROTECTED] > >>> > >>> andrew haywood wrote: > >>>> Dear List, > >>>> > >>>> I am having some problems analysing some ecoligical models in grass > >>>> using the spgrass package through R. > >>>> > >>>> I have 130 plot locations where i have observed presence/absence of > a > >>>> species. I have followed a similar framework to the BUGSITE > modelling > >>>> example from Markus's 2003 grass gis handouts (Grass 5) I have no > >>>> problems constructing the model based on the 130 plots and the > >>>> environmental layers from grass. > >> > >> See the OSGeo tutorial September 2006: > >> > >> > http://www.foss4g2006.org/contributionDisplay.py?contribId=46&sessionId=59&; > >> confId=1 > >> > >> and the OSGeo Journal note: > >> > >> http://www.osgeo.org/files/journal/final_pdfs/OSGeo_vol1_GRASS-R.pdf > >> > >> for more up-to-date information. > >> > >>>> However, I am having problems bringing all the maps through into R > so I > >>>> can make a prediction map. > >>>> The region isnt too large 1600 by 800 cells at 10m resolution > >>>> I can bring all the environmental layers through to R using > readRAST6() > >>>> which doesnt take too much time at all. > >>>> > >>>> However i assume I must convert the spatial grid objects into > >>>> dataframes to apply the predicted model function. > >> > >> No, usually not at all, since the objects have a data.frame in the data > >> slot, and have the standard access methods. > >> > >>>> So I then coerce them into dataframes using as.dataframe (this takes > >>>> ages) I then merge all the dataframes into a single dataframe. (this > >>>> takes ages) > >>>> > >>>> I then apply the model predict to the new data frame. > >>>> > >>>> I am unsure how to then move the 'outmap' back across to grass. > >>>> How can i convert the df to a spatial grid object. > >>>> > >>>> Im thinking i must be doing something wrong. As it quite quick to > pull > >>>> through the layers . But seems to take quite a lot of processing to > get > >>>> the layers into a datframe appropppriate for applying the > predictions. > >>>> > >>>> Any help would be greatly appreciated. > >>>> > >>>> Andy > >>>> > >>>> > >>>> # pull through environmental layers > >>>> # FAST > >>>> anmax <- readRAST6("anmax", ignore.stderr=TRUE) > >>>> anmin <- readRAST6("anmin", ignore.stderr=TRUE) > >>>> aspect <- readRAST6("aspect", ignore.stderr=TRUE) > >>>> dem10_lidar <- readRAST6("dem10_lidar", ignore.stderr=TRUE) > >> > >> Wrong, do: > >> > >> mydata <- readRAST6(c("anmax", "anmin", "aspect", "dem10_lidar"), > >> ignore.stderr=TRUE) > >> > >> then the data slot of the object is a data frame. Look at > >> > >> summary(mydata) > >> > >> for a sanity check. > >> > >>>> # coerce to dataframe > >>>> # SLOW > >>>> mypred_anmaxDF<-as.data.frame(anmax) > >>>> mypred_anminDF<-as.data.frame(anmin) > >>>> mypred_aspectDF<-as.data.frame(aspect) > >>>> mypred_dem10_lidarDF<-as.data.frame(dem10_lidar) > >>>> > >>>> # merge into single dataframe > >>>> # VERY SLOW > >>>> merge_tmp<-merge(mypred_anmaxDF,mypred_anminDF) > >>>> rm(mypred_anmaxDF,mypred_anminDF) > >>>> merge_tmp1<-merge(merge_tmp,mypred_aspectDF) > >>>> rm(merge_tmp,mypred_aspectDF) > >>>> mypredDF<-merge(merge_tmp1,mypred_dem10_lidarDF) > >>>> > >>>> #apply model > >> > >> What is tree? You may need to do extra steps depending on what > class(tree) > >> says - if you have used rpart() or some such, you may find that > >> > >> outmap <- predict(tree,newdata=mydata, type="class") > >> > >> works, > >> > >> or > >> > >> outmap <- predict(tree,newdata=as(mydata, "data.frame"), > type="class") > >> > >> Maybe just assign into mydata straight away: > >> > >> mydata$outmap <- predict(tree,newdata=as(mydata, "data.frame"), > >> type="class") > >> > >> given ?predict.rpart saying: > >> > >> "If 'type="class"': (for a classification tree) a factor of > >> classifications based on the responses." > >> > >> which looks like a vector for a vector response in the formula to > rpart(). > >> But do check what happens if there are NA in the newdata, because the > >> default predict() behaviour may be to drop those observations. Look at > >> summary(mydata). > >> > >> Some formula-using model fitting functions just work, like lm() and > >> the predict() method for lm objects. > >> > >>> From there, as Daniel wrote: > >> > >> writeRAST6(mydata, "rpartpred", "outmap") > >> > >> Hope this helps, > >> > >> Roger > >> > >>>> outmap <- predict(tree,newdata=mypredDF, type="class") > >>>> > >>>> > > > > > > An article on this in the OSGeo newsletter might be a nice way to > document > > simple modeling examples with GRASS and R > > Perhaps, and then there is the section in chapter 10 in "Open Source GIS > A GRASS GIS Approach", 3rd edition (my copy is still on its way, but from > the ToC, it looks as though pages 353-363 should be very helpful). > > In fact, your site is a convenient collection of resources, I ought to > have mentioned it in my reply! > > Roger > > > > > D > > > > > > > > -- > 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: [EMAIL PROTECTED] >
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