I found this link to resolve: '3 GB Switch Windows Vista'
http://usa.autodesk.com/adsk/servlet/ps/item?siteID=123112 <http://usa.autodesk.com/adsk/servlet/ps/item?siteID=123112&id=9583842&linkID=9242018> &id=9583842&linkID=9242018 Da: Adam Terando [mailto:[EMAIL PROTECTED] Inviato: martedì 5 agosto 2008 13.24 A: Alessandro Oggetto: Re: R: [R-sig-Geo] R: LIDAR Problem in R (THANKS for HELP) If an extra GB of memory will solve the problem, you might try doing a google search on '3 GB Switch Windows Vista' and see if it's possible to do such a thing (it probably is possible). Adam On Tue, Aug 5, 2008 04:17 PM, "Alessandro" <[EMAIL PROTECTED]> wrote: Thanks But No this is the problem, I donât ditch VISTA (the nightmare) in this moment Da: Adam Terando [mailto:[EMAIL PROTECTED] Inviato: martedì 5 agosto 2008 13.00 A: Alessandro Cc: 'Matt Oliver'; [EMAIL PROTECTED] Oggetto: Re: [R-sig-Geo] R: LIDAR Problem in R (THANKS for HELP) 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: r-help@r-project.org; [EMAIL PROTECTED] 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 [EMAIL PROTECTED] 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 [EMAIL PROTECTED] https://stat.ethz.ch/mailman/listinfo/r-sig-geo [[alternative HTML version deleted]]
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