If you don't have access to a 64-bit system that works with R and/or more than 4 gb ram you might try using a 3 gb switch in your boot.ini file on windows. This is because windows will often conveniently limit you to 2 gb memory because of its reserved overhead.
http://www.microsoft.com/whdc/system/platform/server/PAE/PAEmem.mspx I don't know if that switch works on Vista though since they want you to just upgrade to 64-bit Vista and buy more RAM. On my XP Pro machine this switch has worked very well with Matlab and R in allowing me more access to physcial ram. Maybe you have a copy of XP Pro laying around and you can ditch Vista? Adam On Tue, Aug 5, 2008 03:40 PM, "Alessandro" <[EMAIL PROTECTED]> wrote: > My notebook is: > > > >Hp Pavilion dv6700 notebook PC > >Intel() Core �2 Duo CPU T9300 �2.50gHz 2,50GHz > >RAM: 4.00 GB > >OS: 32bit > >Windows (TERRIBLE!!!!!!!!!) VISTA > > > > > >Da: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Per conto di >Matt Oliver >Inviato: marted� 5 agosto 2008 12.02 >A: Alessandro >Cc: [EMAIL PROTECTED]; r-sig-geo@stat.math.ethz.ch >Oggetto: Re: [R-sig-Geo] LIDAR Problem in R (THANKS for HELP) > > > >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_x >yz.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]] > >_______________________________________________ >R-sig-Geo mailing list >R-sig-Geo@stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/r-sig-geo > [[alternative HTML version deleted]]
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