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 D -- Dylan Beaudette Soil Resource Laboratory http://casoilresource.lawr.ucdavis.edu/ University of California at Davis 530.754.7341 _______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
