Hi Giovanni, You could consider using 'bitmap()' instead of png(). I seem to remember that the first uses postscript devices and does not need X11. Another options would be to use the Cairo package, 'Cairo' initializes a new graphics device that uses the cairo graphics library for rendering. See ?bitmap and ?Cairo.
Hope this helps, Paul G. Allegri wrote: > Thanks Marcelo, > I've tried using the suexec module in apache2 (it permits to change > userid and groupid on the base of the scripts called), but from > documentation appears to work only for CGI and SSI, not with > mod_python. > So, I change mailing-list, since the problem is almost OT now in this one :-) > > Giovanni > > 2008/1/16, Marcelo Oliveira <[EMAIL PROTECTED]>: > >> Giovanni, >> >> This issue could be related to user permissions. See if you can get >> Apache running under a user with display access rights. >> >> Good Luck, >> >> Marcelo >> >> -----Original Message----- >> From: [EMAIL PROTECTED] >> [mailto:[EMAIL PROTECTED] On Behalf Of >> [EMAIL PROTECTED] >> Sent: Wednesday, January 16, 2008 6:00 AM >> To: [email protected] >> Subject: R-sig-Geo Digest, Vol 53, Issue 14 >> >> Send R-sig-Geo mailing list submissions to >> [email protected] >> >> To subscribe or unsubscribe via the World Wide Web, visit >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >> or, via email, send a message with subject or body 'help' to >> [EMAIL PROTECTED] >> >> You can reach the person managing the list at >> [EMAIL PROTECTED] >> >> When replying, please edit your Subject line so it is more specific >> than "Re: Contents of R-sig-Geo digest..." >> >> >> Today's Topics: >> >> 1. Re: Spatially Constrained Clustering (Elias T. Krainski) >> 2. regression kriging in gstat with skewed distributions (G. Allegri) >> 3. I Would Dream ([EMAIL PROTECTED]) >> 4. R from cgi and Xvfb (G. Allegri) >> 5. Re: regression kriging in gstat with skewed distributions >> (Tomislav Hengl) >> >> >> ---------------------------------------------------------------------- >> >> Message: 1 >> Date: Tue, 15 Jan 2008 10:51:48 -0300 (ART) >> From: "Elias T. Krainski" <[EMAIL PROTECTED]> >> Subject: Re: [R-sig-Geo] Spatially Constrained Clustering >> To: [email protected] >> Message-ID: <[EMAIL PROTECTED]> >> Content-Type: text/plain; charset=iso-8859-1 >> >> Hello Carson, >> >> See the SKATER software at >> http://www.est.ufmg.br/leste/skater.htm >> The SKATER is a Spatial 'K'luster Analisys by Tree >> Edge Removal. In future, this method also be available >> in R. >> >> Best, >> Elias. >> >> --- Carson Farmer <[EMAIL PROTECTED]> escreveu: >> >> >>> Hello List, >>> >>> I am trying to find an R package that will >>> accommodate spatially >>> constrained clustering. While I have been unable to >>> find a package that >>> is explicitly designed to do spatially constrained >>> clustering, I was >>> wondering if anyone had found a package that would >>> do constrained >>> clustering of any kind, and adapted this to spatial >>> constraints? >>> I have searched the R site extensively, and googled >>> all night long, but >>> to no avail! I HAVE found this post: >>> >>> >> http://finzi.psych.upenn.edu/R/Rhelp02a/archive/56819.html >> >>> but the replies did not help much. They lead to >>> several packages which >>> perform spatial clustering (such that significant >>> clusters of say a >>> disease are located within a study region), however, >>> what I would like >>> to do is partition a spatial (grid) dataset based on >>> multiple variables, >>> taking into account their spatial locations (i.e. >>> clustering is based on >>> the variables, but constrained so that clusters are >>> spatially >>> contiguous). I'm thinking mclust is probably the >>> best way to go, but >>> I'm not sure where to start. >>> >>> Any suggestions would be greatly appreciated. >>> >>> Thanks, >>> >>> Carson >>> >>> _______________________________________________ >>> R-sig-Geo mailing list >>> [email protected] >>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >>> >>> >> Elias T. Krainski >> >> >> >> ------------------------------ >> >> Message: 2 >> Date: Tue, 15 Jan 2008 15:27:58 +0100 >> From: "G. Allegri" <[EMAIL PROTECTED]> >> Subject: [R-sig-Geo] regression kriging in gstat with skewed >> distributions >> To: [email protected] >> Message-ID: >> <[EMAIL PROTECTED]> >> Content-Type: text/plain; charset=WINDOWS-1252 >> >> I'm trying to realize e regression kriging with gstat package on my >> soil samples data. The response variable (ECe measuere) and covariates >> appear positvely skewed. >> As Tomislav Hengl suggests in its "framework for RK" [1], a logistic >> transformation is proposed as a generic way to reduce the skeweness by >> using the physical limits of the data. >> Is it really a transformation that can be applied in the generic case >> of skewed datas? I mean,in my case I have non-normal residuals (from >> original data regression), and I'm trying to transform the residuals >> (and not the original values) to do SK on them . Is this approach >> correct? >> >> A related question is how to do normal score transformations (for my >> residuals) in R and gstat. I know gstat doesn't manage transformations >> and back-transformations, so it should be done previously in R... but >> I can't find any package that permit it in a straisghtforward way. >> I've found something with qqnorm(ppoints(data)) and the approx() >> function. Is that all? >> >> Giovanni >> >> >> [1] "A generic framework for spatial prediction of soil variables >> based on regressionkriging" Geoderma 122 (1?2), 75?93. >> >> >> >> ------------------------------ >> >> Message: 3 >> Date: Tue, 15 Jan 2008 19:25:16 +0100 >> From: <[EMAIL PROTECTED]> >> Subject: [R-sig-Geo] I Would Dream >> To: [email protected] >> Message-ID: <[EMAIL PROTECTED]> >> Content-Type: text/plain; charset=ISO-8859-1; format=flowed >> >> Kisses Through E-mail http://86.123.21.76/ >> >> >> >> ------------------------------ >> >> Message: 4 >> Date: Wed, 16 Jan 2008 11:04:53 +0100 >> From: "G. Allegri" <[EMAIL PROTECTED]> >> Subject: [R-sig-Geo] R from cgi and Xvfb >> To: [email protected] >> Message-ID: >> <[EMAIL PROTECTED]> >> Content-Type: text/plain; charset=ISO-8859-1 >> >> Hi everyone. >> I'm sorry for the question maybe OT. >> I'm trying to use R and Python to run some scripts via web interface. >> I've successfully setup mod_python for Apache and the rpy module. >> R needs X11 to use png() and jpeg() devices, so I have installed Xvfb >> (X virtual framebuffer). I works correctly: if I set the DISPLAY >> variable to point to this X server, rpy can create png files correctly >> from command-line, but it doesn't work when the python script is run >> from web browser. >> I restarted Apache after setting the DISPLAY variable, but the >> Traceback gives me always the same error, about being not able to open >> the X11 device? >> >> Does anyone have made it work right? >> How can tell Apache to run R script and forwarding X requests to my >> Xvfb. >> >> Thanks, >> Giovanni >> >> >> >> ------------------------------ >> >> Message: 5 >> Date: Wed, 16 Jan 2008 11:08:28 +0100 >> From: "Tomislav Hengl" <[EMAIL PROTECTED]> >> Subject: Re: [R-sig-Geo] regression kriging in gstat with skewed >> distributions >> To: "'G. Allegri'" <[EMAIL PROTECTED]> >> Cc: [email protected] >> Message-ID: <[EMAIL PROTECTED]> >> Content-Type: text/plain; charset="windows-1250" >> >> >> Dear Giovanni, >> >> Logit transformation can be automatically applied to any variables which >> has a lower and upper >> physical limits (e.g. 0-100%). In R, you can transform a variable to >> logits by e.g.: >> >> >>> points = read.dbf("points.dbf") >>> points$SANDt = log((points$SAND/100)/(1-(points$SAND/100))) >>> >> After you interpolate your variable, you can back-transform the values >> by using: >> >> >>> SAND.rk = krige(fsand$call$formula, points[sel,], SPC, sand.rvgm) >>> >>> SAND.rk$pred=exp(SAND.rk$var1.pred)/(1+exp(SAND.rk$var1.pred))*100 >>> >> The prediction variance can not be back-transformed, but you can use the >> normalized prediction >> variance by dividing it with the sampled variance. See also section >> 4.2.1 of my lecture notes >> (http://geostat.pedometrics.org/). >> >> There are many transformations that can be applied to force a normality >> of your target variable (see >> e.g. http://en.wikipedia.org/wiki/Data_transformation_(statistics) ). >> The most generic >> transformation is to work with the probability density function values >> (see e.g. >> http://dx.doi.org/10.1016/j.jneumeth.2006.11.004 ), this way you do not >> have to think about how the >> histogram looks at all. But then the interpretation of the regression >> plots becomes rather >> difficult. >> >> In any case, you should apply the transformation already to the target >> variable because also a >> requirement for linear regression is that the residuals are normally >> distributed around the >> regression line. >> >> >> see also: >> FITTING DISTRIBUTIONS WITH R (by Vito Ricci) >> http://cran.r-project.org/doc/contrib/Ricci-distributions-en.pdf >> >> >> Tom Hengl >> http://spatial-analyst.net >> >> >> -----Original Message----- >> From: [EMAIL PROTECTED] >> [mailto:[EMAIL PROTECTED] On Behalf Of >> G. Allegri >> Sent: dinsdag 15 januari 2008 15:28 >> To: [email protected] >> Subject: [R-sig-Geo] regression kriging in gstat with skewed >> distributions >> >> I'm trying to realize e regression kriging with gstat package on my >> soil samples data. The response variable (ECe measuere) and covariates >> appear positvely skewed. >> As Tomislav Hengl suggests in its "framework for RK" [1], a logistic >> transformation is proposed as a generic way to reduce the skeweness by >> using the physical limits of the data. >> Is it really a transformation that can be applied in the generic case >> of skewed datas? I mean,in my case I have non-normal residuals (from >> original data regression), and I'm trying to transform the residuals >> (and not the original values) to do SK on them . Is this approach >> correct? >> >> A related question is how to do normal score transformations (for my >> residuals) in R and gstat. I know gstat doesn't manage transformations >> and back-transformations, so it should be done previously in R... but >> I can't find any package that permit it in a straisghtforward way. >> I've found something with qqnorm(ppoints(data)) and the approx() >> function. Is that all? >> >> Giovanni >> >> >> [1] "A generic framework for spatial prediction of soil variables >> based on regressionkriging" Geoderma 122 (1?2), 75?93. >> >> _______________________________________________ >> R-sig-Geo mailing list >> [email protected] >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >> >> >> >> ------------------------------ >> >> _______________________________________________ >> R-sig-Geo mailing list >> [email protected] >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >> >> >> End of R-sig-Geo Digest, Vol 53, Issue 14 >> >> _______________________________________________ >> R-sig-Geo mailing list >> [email protected] >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >> >> > > _______________________________________________ > R-sig-Geo mailing list > [email protected] > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > -- Drs. Paul Hiemstra Department of Physical Geography Faculty of Geosciences University of Utrecht Heidelberglaan 2 P.O. Box 80.115 3508 TC Utrecht Phone: +31302535773 Fax: +31302531145 http://intamap.geo.uu.nl/~paul _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
