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
