you can try memory.limit(size=4000)
only if you have 4GB of memory on the system This is not guaranteed to solve your problem though With big datasets like lidar, you are much better off getting access to a 64bit system with a ton of RAM (>64GB). Cheers Matt On Tue, Aug 5, 2008 at 1:47 PM, Alessandro <[EMAIL PROTECTED]>wrote: > > > Hi All, > > > > I am a PhD student in forestry science and I am working with LiDAR data > set (huge data set). I am a brand-new in R and geostatistic (SORRY, my > background it's in forestry) but I wish improve my skill for improve > myself. I wish to develop a methodology to processing a large data-set of > points (typical in LiDAR) but there is a problem with memory. I had created > a subsample data-base but the semivariogram is periodic shape and I am not > to able to try a fit the model. This is a maximum of two weeks of work (day > bay day) SORRY. Is there a geostatistical user I am very happy to listen his > suggests. Data format is X, Y and Z (height to create the DEM) in txt format > > I have this questions: > > > > 1. After the random selection (10000 points) and fit.semivariogram > model is it possible to use all LiDAR points? Because the new LiDAR power is > to use huge number of points (X;Y; Z value) to create a very high resolution > map of DEM and VEGETATION. The problem is the memory, but we can use a > cluster-linux network to improve the capacity of R > > > > 2. Is it possible to improve the memory capacity? > > > > 3. The data has a trend and the qqplot shows a Gaussian trend. Is it > possible to normalize the data (i.e. with log)? > > > > 4. When I use this R code "subground.uk = krige(log(Z)~X+Y, > subground, new.grid, v.fit, nmax=40)" to appear an Error massage: Error in > eval(expr, envir, enclos) : oggetto "X" non trovato > > > > I send you a report and attach the image to explain better. > > > > all procedure is write in R-software and to improve in gstat . The number > of points of GROUND data-set (4x2 km) is 5,459,916.00. The random sub- set > from original data-set is 10000 (R code is: > samplerows > <-sample(1:nrow(testground),size=10000,replace=FALSE) > subground > <-testground[samplerows,]) > > > > 1. Original data-set Histogram: show two populations; > > 2. original data-set density plot: show again two populations of > data; > > 3. Original data-set Boxplot: show there aren't outlayers un the > data-set (the classification with terrascan is good and therefore there > isn't a problem with original data) > > 4. ordinary kriging: show a trend in the space (hypothesis: the > points are very close in the space) > > 5. de-trend dataset with: v <- variogram (log(Z)~X+Y, subground, > cutoff=1800, width=100)) > > 6. map of semi-variogram: show an anisotropy in the space (0° is > Nord= 135° major radius 45° minus radius) > > 7. semi-variogram with anisotropy (0°, 45°, 90°, 135°), shows a best > shape is from 135° > > 8. semi-variogram fit with Gaussian Model. R code is (see the fig): > > > v = variogram(Z~X+Y, subground, cutoff=1800, width=200, alpha=c(135)) > > > v.fit = fit.variogram(v, vgm(psill = 1, model="Gau", range=1800, nugget= > 0, anis=c(135, 0.5))) > > > > R code: > > > > testground2 <- > read.table(file="c:/work_LIDAR_USA/R_kriging_new_set/ground_26841492694149_xyz.txt", > header=T) > > class (testground2) > > coordinates (testground2)=~X+Y # this makes testground a > SpatialPointsDataFrame > > class (testground2) > > str(as.data.frame(testground)) > > > > hist(testground$Z,nclass=20) #this makes a histogram > > plot(density(testground$Z)) #this makes a plot density > > boxplot(testground$Z)#this makes a boxplot > > > > samplerows<-sample(1:nrow(testground),size=10000,replace=FALSE) #select n. > points from all data-base > > subground <-testground[samplerows,] > > hist(subground$Z,nclass=20) #this makes a histogram > > plot(density(subground$Z)) #this makes a plot density > > boxplot(subground$Z)#this makes a boxplot > > spplot(subground["Z"], col.regions=bpy.colors(), at = seq(850,1170,10)) > > > > library(maptools) > > library(gstat) > > plot(variogram(Z~1, subground)) #Ordinary Kriging (without detrend) > > # if there is a trend we must use a detrend fuction Z~X+Y > > x11(); plot(variogram(log(Z)~X+Y, subground, cutoff=1800, width=80)) > #Universal Kriging (with detrend) > > x11(); plot(variogram(log(Z)~X+Y, subground, cutoff=1800, width=80, map=T)) > > x11(); plot(variogram(log(Z)~X+Y, subground, cutoff=1800, width=80, > alpha=c(0, 45, 90, 135))) > > v = variogram(log(Z)~X+Y, subground, cutoff=1800, width=80, alpha=c(135, > 45)) > > v.fit = fit.variogram(v, vgm(psill = 1, model="Gau", range=1800, nugget= 0, > anis=c(135, 0.5))) > > plot(v, v.fit, plot.nu=F, pch="+") > > # create the new grid > > new.grid <- spsample(subground, type="regular", cellsize=c(1,1)) > > gridded(new.grid) <- TRUE > > fullgrid(new.grid) <- TRUE > > [EMAIL PROTECTED] > > #using Universal Kriging > > subground.uk = krige(log(Z)~X+Y, subground, new.grid, v.fit, nmax=40) > #ERROR > > > > > > > > > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > > -- Matthew J. Oliver Assistant Professor College of Marine and Earth Studies University of Delaware 700 Pilottown Rd. Lewes, DE, 19958 302-645-4079 http://www.ocean.udel.edu/people/profile.aspx?moliver [[alternative HTML version deleted]]
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