Dear Thiago, Sorry for the late response.
It would seem that the problem is here: *> r<-raster(cmorph$datancfile)* * * I assume that *cmorph$datancfile *is a matrix. You can make a RasterLayer this way, but its geo-reference is unlikely to be correct. perhaps something like this will work (you need to adjust the values for xmin, xmax, ymin, and ymax): r <- raster(ncol = 126, nrow=98, xmin=0, xmax=10, ymin=0, ymax=10) r <- setValues(r, as.vector(*cmorph$datancfile))* # or perhaps # r <- setValues(r, as.vector(t (*cmorph$datancfile)))* * * *Best, Robert* * * On Mon, Jul 5, 2010 at 7:13 AM, Thiago Veloso <thi_vel...@yahoo.com.br>wrote: > Dear Dr. Hijmans, > > Thank you very much for the advice. According to the poor results I > received from interpolating gauge data, your approach really seems to be > more promising. > > I'm trying to perform the sattelite data interpolation as you suggested. > However, I'm facing an error. My satellite data consists of thousands of raw > binary hourly files which I converted to netcdf monthly values and loaded > into a variable: > > *>summary(cmorph)* > * Length Class Mode * > *lonncfile 126 -none- numeric* > *latncfile 98 -none- numeric* > *datancfile 12348 -none- numeric* > * > * > The 'cmorph' variable contains rainfall estimates to the southern most > Brazilian state for january 2009. Everything goes fine when I create the > raster object and extract the coordinates. The 'gauge' variable contains > lon, lat and rainfall station values for the same january 2009: > > *> r<-raster(cmorph$datancfile)* > *> res<-xyValues(r,gauge[,1:2],method='bilinear')* > > However, when trying to plot: > > *> plot(res,gauge[,3])* > *Error in plot.window(...) : need finite 'xlim' values* > *In addition: Warning messages:* > *1: In min(x) : no non-missing arguments to min; returning Inf* > *2: In max(x) : no non-missing arguments to max; returning -Inf* > > This error is certainly caused by the contents of 'res' variable: > > > res > [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA > NA NA > [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA > NA NA > [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA > NA NA > [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA > NA NA > [101] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA > NA NA > [126] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA > > Could you figure out what I'm doing wrong?? > > Best regards, > > Thiago Veloso. > > --- On *Tue, 29/6/10, Robert J. Hijmans <r.hijm...@gmail.com>* wrote: > > > From: Robert J. Hijmans <r.hijm...@gmail.com> > > Subject: Re: [R-sig-Geo] Spatial data interpolation on R > To: "Thiago Veloso" <thi_vel...@yahoo.com.br> > Cc: r-sig-geo@stat.math.ethz.ch > Date: Tuesday, 29 June, 2010, 15:15 > > > Thiago, > > I think a better approach would be to estimate the value for the sat > images at the stations' locations; and not the other way around, as > your are proposing. > > A simple way to do that is bilinear interpolation of the sat image > values for the weather station locations. > > If 'xyv' is your lon/lat/prec matrix and 'f' is a filename for one of > your sat images. > > library(raster) > r <- raster(f) > res <- xyValues(r, xyv[,1:2], method='bilinear') > plot(res, xyv[,3]) > > > ..... and now analyze the difference between res and xyv[,3] data > taking spatial autocorrelation into account > > > > Nevertheless, if you insist on interpolating the weather station data, > you could do that with a spline > > library(fields) > tps <- Tps(xyv[,1:2], xyv[,3]) > p <- raster(r) > p <- interpolate(p, tps) > plot(p, r) > > > In the end, the best approach to obtaining a raster of the rainfall > data, if that is what you are after, would probably be a hybrid where > the sat image is use as an independent variable (covariate) in the > spline (or other interpolation method). > > Robert > > > On Tue, Jun 29, 2010 at 8:59 AM, Don MacQueen > <m...@llnl.gov<http://mc/compose?to=m...@llnl.gov>> > wrote: > > If you are willing to do simple interpolation, i.e., ignoring any spatial > > correlation, you could look at the interp() function, which is in the > akima > > package. Even if you need to incorporate spatial correlation, starting > with > > the interp() function would probably serve as a good way to get started > > learning R. The help page for interp() has some examples. > > > > Here's an excerpt from the help page for the interp() function: > > > > interp package:akima R Documentation > > > > Gridded Bivariate Interpolation for Irregular Data > > > > Description: > > > > These functions implement bivariate interpolation onto a grid for > > irregularly spaced input data. Bilinear or bicubic spline > > interpolation is applied using different versions of algorithms > > from Akima. > > > > > > Install the akima package using the R console GUI (Mac or Windows) or the > > install.packages() function (linux). > > > > Then there's the question of coordinate systems. interp() assumes > cartesian > > coordinates, but lat/long is not cartesian. If your site is too large, > you > > shouldn't ignore this, so you will have to learn how to project from > > lat/long to UTM or other appropriate local coordinate system. For this, > I > > use the spTransform() function in the rgdal package. Looking on the CRAN > > website, it appears there is a Windows binary for rgdal; for the other > > platforms (I use Mac), it can be more challenging. Converting your data > into > > a "spatial" class object, so that it can be projected, will be a > challenge > > at first. > > > > Gettng the book that Mark Connolly mentioned would help a lot. > > > > -Don > > > > > > At 10:20 AM -0700 6/2/10, Thiago Veloso wrote: > >> > >> Content-Type: text/plain > >> Content-Disposition: inline > >> Content-length: 937 > >> > >> Dear R colleagues! > >> I´d like to start my participation in this list by describing my > current > >> problem: inside my area of study I need to compare precipitation data > from > >> two different sources: both station (total of 86) and a grid (at 8km) of > >> satellite estimates. > >> My specific objective is to interpolate the station data into a regular > >> grid in the same resolution of the satellite estimates, preferentially > >> having control of the spatial domain (lat/lon coordinates). As far as I > know > >> this is the correct way of making such comparison. > >> Could anybody please point directions to perform this task using R? I´m > >> such a beginner that I don´t even know if > >> there´s a package designed to create regular grids from "random" data > >> (interpolating by kriging or other technique). Usage examples would be > >> welcomed as well! > >> Thanks in advance, > >> Thiago. > >> > >> > >> > >> [[alternative HTML version deleted]] > >> > >> > >> _______________________________________________ > >> R-sig-Geo mailing list > >> R-sig-Geo@stat.math.ethz.ch<http://mc/compose?to=r-sig-...@stat.math.ethz.ch> > >> https://*stat.ethz.ch/mailman/listinfo/r-sig-geo > > > > > > -- > > -------------------------------------- > > Don MacQueen > > Environmental Protection Department > > Lawrence Livermore National Laboratory > > Livermore, CA, USA > > 925-423-1062 > > > > _______________________________________________ > > R-sig-Geo mailing list > > R-sig-Geo@stat.math.ethz.ch<http://mc/compose?to=r-sig-...@stat.math.ethz.ch> > > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > > > > > [[alternative HTML version deleted]]
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