SpatialGridDataFrame to data.frame

Strange. You could try
      predrast <- setValues(predrast, as.vector(pred), r)

But it would be good to know what pred is.

Can you do this:

class(pred)
dim(pred)
v <- as.vector(pred)
class(v)
length(v) == ncol(spot)


Robert




On Wed, Feb 11, 2009 at 11:16 PM, Ned Horning <horning at amnh.org> wrote:
  

Robert and Roger,

Thanks for the information and pointers. The raster package looks quite
interesting and I'll try to get up to speed on some of its capabilities. Are
the man pages the best way to do that or is that a single document
available?

I made some progress but still have some questions. I followed the steps
laid out by Robert and everything went fine except I ran into an error with
"predrast <- setValues(predrast, pred, r)" in the for loop when I tried
processing one line at a time and "r <- setValues(r, pred)" when I ran the
full image in one go. The error was: "values must be a vector." Any idea
what I'm doing wrong?

I tried to read the GRASS files directly but got a message saying it is not
a supported file format. Can you confirm that is the case or am I doing
something wrong? I was able to read a tiff version of the image. I am able
to run gdalinfo on GRASS files just fine from a terminal window.

Thanks again for the help.

Ned


Robert Hijmans wrote:
    

Ned,

This is an example of running a RandomForest prediction with the
raster package (for the simple case that there are no NA values in the
raster data; if there are, you have to into account that "predict'
does not return any values (not even NA) for those cells).

Robert

#install.packages("raster", repos="http://R-Forge.R-project.org")
require(raster)
require(randomForest)

# for single band files
spot <- stack('b1.tif', 'b2.tif', 'b3.tif')
# for multiple band files
# spot <- stackFromFiles(c('bands.tif', 'bands.tif', 'bands.tif'),
c(1,2,3) )

# simulate random points and values to model with
xy <- xyFromCell(spot, round(runif(100) * ncell(spot)))
response <- runif(100) * 100
# read values of raster layers at points, and bind to respinse
trainvals <- cbind(response, xyValues(spot, xy))

# run RandomForest
randfor <- randomForest(response ~ b1 + b2 + b3, data=trainvals)

# apply the prediction, row by row
predrast <- setRaster(spot)
filename(predrast) <- 'RF_pred.grd'
for (r in 1:nrow(spot)) {
       spot <- readRow(spot, r)
       rowvals <- values(spot, names=TRUE)
# this next line should not be necessary, but it is
# I'll fix that
       colnames(rowvals) <- c('b1', 'b2', 'b3')
       pred <- predict(randfor, rowvals)
       predrast <- setValues(predrast, pred, r)
       predrast <- writeRaster(predrast, overwrite=TRUE)
}

plot(predrast)




On Wed, Feb 11, 2009 at 5:09 PM, Roger Bivand <Roger.Bivand at nhh.no> wrote:

      

Ned:


The three bands are most likely treated as 4-byte integers, so the object
will be 2732 by 3058 by 3 by 4 plus a little bit. That's 100MB. They may
get copied too. There are no single byte user-level containers for you
(there is a raw data type, but you can't calculate with it). Possibly
saying spot_frame <- slot(spot, "data") will save one copying operation,
but its hard to tell - your choice of method first adds inn all the
coordinates, which are 8-byte numbers, so more than doubles its size and
makes more copies (to 233MB for each copy). Running gc() several times
manually between steps often helps by making the garbage collector more
aggressive.

I would watch the developments in the R-Forge package "raster", which
builds on some of these things, and try to see how that works. If you
have
the GDAL-GRASS plugin for rasters, you can use readGDAL to read from
GRASS
- which would work with raster package functions now. Look at the code of
recent readRAST6 to see which incantations are needed. If you are going
to
use randomForest for prediction, you can use smaller tiles until you find
an alternative solution. Note that feeding a data frame of integers to a
model fitting or prediction function will result in coercion to a
matrix of doubles, so your subsequent workflow should take that into
account.
 Getting more memory is another option, and may be very cost and
especially
time effective - at the moment your machine is swapping. Buying memory
may
save you time programming around too little memory.

Hope this helps,

Roger


---
Roger Bivand, NHH, Helleveien 30, N-5045 Bergen,
Roger.Bivand at nhh.no



-----Original Message-----
From: r-sig-geo-bounces at stat.math.ethz.ch on behalf of Ned Horning
Sent: Wed 11.02.2009 07:40
To: r-sig-geo at stat.math.ethz.ch
Subject: [R-sig-Geo] SpatialGridDataFrame to data.frame

Greetings,

I am trying to read an image from GRASS using the spgrass6 command
readRAST6 and then convert it into a data.frame object so I can use it
with randomForest. The byte image I'm reading is 2732 rows x 3058
columns x 3 bands. It's a small subset of a larger image I would like to
use eventually. I have no problem reading the image using readRAST6 but
when I try to convert it to a data.frame object my linux system
resources (1BG RAM, 3GB swap) nearly get maxed out and it runs for a
couple hours before I kill the process. The image is less than 25MB so
I'm surprised it requires this level of memory. Can someone let me know
why this is. Should I use something other than the GRASS interface for
this? These are the commands I'm using:

spot <- readRAST6(c("subset.red", "subset.green", "subset.blue"))
spot_frame <- as(spot, "data.frame")

Any help would be appreciated.

All the best,

Ned