Importing raster data from Arcview to R for fitting a ppm

Hi,

I don't remeber if this is corrected in the current version of
spatstat (please, actualize) but, anyway, I think you can troubleshot
the problem with this:


io <- !is.na(slope.subset[xxx04, drop=F])

ppm(xxx04[io], ~ x + y + slope, Poison(), covariates = 
list(slope=slope.subset)



I hope this helps,

Marcelino



At 20:49 15/04/2008, Honey Giroday wrote:

Hi All!

I am a relatively new UseR and am currently using R 2.5.1 on a linux
operating system (I am connected to the linux system via remote
desktop connection).  I am trying to import a raster dataset (in
ASCII format) to fit as a covariate in a ppm.  However, I keep
getting this error message every time I got to fit the model.

"Warning message:
Values of covariate "slope" were NA or undefined at 1% (1 out of
80812) of the quadrature points.  Occurred while executing:
ppm(xxx04, ~ x + y + slope, Poison(), covariates = list(slope=slope.subset)"

At first I thought the problem sprung from the alignment of my image
(slope) with the object window of my point process
(xxx04).  However, I have the datasets are
in the same projection (UTM zone 10), and I have set the owin for
the points and subset the slope covariate to the same extent.  I
have also viewed the slope and image datasets in a plot and they
appear to line up correctly (i.e. there are no points that are
outside the extent of the slope image).  Can anyone assist me with
understanding the cause of this error message and suggest a method
to rectify/troubleshoot this problem?  Please see the code below to
see the steps I went through to fit the ppm.  Your help would be
much appreciated and thank you for your time in considering this issue.





Sincerely,





Honey-Marie Giroday, M.Sc. Candidate, A.Ag., B.I.T.

University of Northern British Columbia





#Read in shapefile data (2004, 2005, and 2006)
mpb04 <-
readShapePoints("N:/data/original/mpb/licensees/final/2004mpb_licensees")

#Extract X and Y coordinates from ShapePoints
X <- mpb04$Easting
Y <- mpb04$Northing

#Read in site characteristic covariates (ASCII)
slope <-
readAsciiGrid("N:/data/original/base_data/dem/bc/utm_tpi/slope20utm.txt",
as.image=TRUE)

#Read in susceptibility covariates (ASCII)
sus0 <-
readAsciiGrid("N:/data/original/susceptibilitydata/climate_factor/u 

tm_tpi/sus0.txt",

as.image=TRUE)

#Convert covariates from list class to image class
image.slope <- as.im(slope)
sus0 <- as.im(sus0)

#Create object window for analysis from one of the susceptibility datasets
sus0.win <- as.owin(sus0)

#Subset site characteristic and landform datasets susceptibility boundary
slope.subset <- image.slope[sus0.win, drop=FALSE]

#Use cut command to make landform and susceptibility a factor!
sus0.cut <- cut(sus0, 2)

#Convert ShapePoints to point pattern manually
xxx04 <- ppp(x=mpb04$Easting, y=mpb04$Northing, window=sus0.win)

#Test if model fitting works
fm1 <- ppm(xxx04, ~ x + y + slope, Poisson(), covariates=list(slope=slope))

***************************************
Marcelino de la Cruz Rot
Depto. Biologia Vegetal
EUIT Agricola
Universidad Politecnica de Madrid
28040-Madrid
SPAIN
***************************************
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