[GRASS-stats] Re: [GRASS-user] grass/stats mapping/prediction question

[andrew haywood]

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 <Roger.Bivand at nhh.no> 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: grass-stats at lists.osgeo.org
> >>>
> >>> 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: Roger.Bivand at nhh.no
>
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