search for: visualisers

Hi all, Version 0.2.2 of the googVis package has been released on CRAN and will be available from your local CRAN mirror in due course. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google. We presented our initial ideas on

Hi all, Version 0.2.2 of the googVis package has been released on CRAN and will be available from your local CRAN mirror in due course. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google. We presented our initial ideas on

Hi all, Version 0.2.4 of the googVis package has been released on CRAN and will be available from your local CRAN mirror soon. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google Since the last version a lot of work has been

Hi all, Version 0.2.4 of the googVis package has been released on CRAN and will be available from your local CRAN mirror soon. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google Since the last version a lot of work has been

Hello my friends, I have put together some visualisations of the LLVM source code because I think it is great to see how much work has gone into LLVM to date. Code Visualisation - Orion Transfer Ltd http://www.oriontransfer.co.nz/research/code-visualisation These are based of the 2.8 source code up until a few days ago. I hope this is of enjoyment for some of you. Kind regards, Samuel

Dear list, I wish to visualise some 4D data as a kind of colour / translucent cloud in 3D. I haven't seen such plots in R (but perhaps I missed a feature of rgl). The easiest option I found would be to export the data in povray's df3 (density file) format and visualise it with povray. The format specification baffles me a little, http://www.povray.org/documentation/view/3.6.1/374/ ;

Dear R users, I have used the following function (in blue) aiming to find the linear regression between MOE and XLA and nesting my data by Species. I have obtained the following results (in green). model4<-lme(MOE~XLA, random = ~ XLA|Species, method="ML")summary(model4) Linear mixed-effects model fit by maximum likelihood Data: NULL         AIC     BIC   logLik  -1.040187 8.78533

Hi useRs, I had posted about Adobe FLEX talking to R for rich visualisation. Reply from Jeffery Horner contained links to the revolution-computing.com webpage which had information pertaining to the Bay Users R group Meetup on Web Dashboards with R. I have a very specific project that I need to implement. I wish to use the graphics capabilities provided by Adobe FLEX to visualise outputs from R.

Hi, this may sound like a very stupid question and perhaps it is, so I apologise in advance if anyone feels bored by it. I have done some principal component analysis with prcomp and what I would like to do is to visualise the effect of a principal component, i.e. show a graph of my data and then show how that graph would change if I go, let's say, one standard deviation, in either

Hi all R-helpers!!! I have only one small question: Which visualisation method would you suggest for results of function cmeans {e1071}? Thanks! Branimir K. Hackenberger

Having a look at clang's source, you can find in "lib/CodeGen/BackendUtil.cpp" the functions where clang builds the passes to emit code. The optimization passes used are there and you can simulate them via the "opt" utility, by running each pass one at a time. LLVM also declares standard module/function passes on include/llvm/Support/StandardPasses.h. Have a look and see

Is someone working on a 3d vizualisation set of functions ? I read dream dream dream in PROJECTS about 3D GRAPHICS. When I want to visualize 3d data, I use Geomview but with a dataset greater than 50x50 that's too hard for my poor computer. I know a little R but nothing with S+, are the plan to emulate S+ or to have R functions to visualize 3d data ? -- Hervé Dréau Interne en Médecine hdreau

I am wanting to plot a series of wind vectors onto a contoured area map for a series of weather stations (eg arrows showing wind speed/direction for a particular time snapshot), can someone please advise me how best to approach this? My desired end point is to be able to link a time series of such data together so that I will in effect have a "movie" displaying the evolution of these

I sent a mail last week asking for some advise in relation to displaying wind vectors on a contour map of a region. Whilst I have had some useful feedback relating to the second part of this question (namely how to animate a time series of still frames), I haven't recieved any advise on how I might create the still images of the spatially distributed wind vector data at any given time point.

Hello R users, I'm using SOM() to cluster a gene expression data set the syntax i used was dataGrid <- c(somgrid(xdim = 3, ydim = 3, topo = c("rectangular","hexagonal"))) dataClusters <- SOM(dataMatrix, grid = dataGrid) plot(dataClusters) it seems that this works just fine but the thing i can't figure out is how to determine where each data point has been

Dear List, A student in the Department where I work would like to produce a graphic similar to this one: http://image.guardian.co.uk/sys-files/Guardian/documents/2009/09/16/Public_spending_160909.pdf Does anyone know if the figure in the pdf can be generated in a specific software application for example? Any suggestions would be most gratefully received by the student concerned. Many thanks,

When I'm trying to understand how or why the back end does something I've found the -view-* options to llc to be useful. It's not ideal, because "clang" doesn't take those options, so I have to invoke first "clang" and then "llc", and sometimes that combination gives different final code from invoking just "clang", but usually I can construct

> Having a look at clang's source, you can find in > "lib/CodeGen/BackendUtil.cpp" the functions where clang builds the > passes to emit code. The optimization passes used are there and you can > simulate them via the "opt" utility, by running each pass one at a time. > LLVM also declares standard module/function passes on >

Hello all, I would like to get parameter estimates for different models. For one of them I give the code in example. I am estimating the parameters (i,j and k) with the nls function, which sees the error distribution as normal, I would however like to do the same as nls with the assumption that the errors are poisson distributed. Is there a way to do this with R? Are there packages designed

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one. So I have two trend lines. One linear trend line