similar to: cartograms in R?

I have a question which is not necessarily related to R. I want to use a distorted political map of the world with certain countries increased in size according to a certain variable (an idea similar to the homunculi usually found in psychology textbooks) to demonstrate the coverage of particular countries in a news service. Does anyone have any idea of how to approach this? I don't think

I am trying to install two R packages to produce cartograms like in the work of Gastner and Newman: http://www.pnas.org/content/101/20/7499.full.pdf but I have a problem installing Rcartogram and rdyncall packages. Both are not available in CRAN and have to be installed from archivea and produce errors: > install.packages("C:/Users/Milena/Downloads/*Rcartogram*_0.2-2.tar.gz", >

Hello, I apologize that this is off-topic. I am seeking information on perception of graphical data, in an effort to improve the plots I produce. Would anyone point me to literature reviews in this area? (Or keywords to try on google?) Is this located somewhere near cognitive science, psychology, human factors research? For example, some specific questions I have are: I recall as a child

Hi all, In Tibshirani's PNAS paper about nearest shrunken centroid analysis of microarrays (PNAS vol 99:6567), they used cross validation to choose the amount of shrinkage used in the model, and then test the performance of the model with the cross-validated shrinkage in separate independent testing set. If I don't have the luxury of having independent testing set, can I just use the

Dear List, We are planning a genotyping study to be analyzed using false discovery rates (FDRs) (See Storey and Tibshirani PNAS 2003; 100:9440-5). I am interested in learning if there is any consensus as to how many features (ie. how many P values) need to be studied before reasonably reliable FDRs can be derived. Does anyone know of a citation where this is discussed? Bill Dupont William D.

Dear friends - a couple of papers in PNAS (lastly:framework for making better predictions by directly estimating variables' predictivity, Lo et al PNAS 2016; 113:14277-14282) have focused interest on mapping complex traits to multiple loci spread all over the genome. I have been around on the relevant taskview(s) I hope but fail to see that the backward haplotype transmission association

I realize that matrix indexing has been addressed in various flavors, but I''m stumped and didn''t find anything in the archives. It''s not clear if it is an igraph issue or a more general problem. Thanks in advance for your patience. I am using igraph to read a gml file (http://www-personal.umich.edu/~mejn/netdata/football.zip ). The gml file contains vertex attributes

Hello to everyone! I have built CRAN task view for genetics. For now I have not submit it to CRAN yet and it can be accessible from: http://www.bfro.uni-lj.si/MR/ggorjan/software/R/Genetics.html http://www.bfro.uni-lj.si/MR/ggorjan/software/R/Genetics.ctv I have not submitted it to CRAN, since I would like first some opinion about it. Genetics is really so broad field that I belive one person

Hi I have a matrix with 30 observations and roughly 30000 variables, each obs belongs to one of two groups. With svm and slda I get into memory troubles ('cannot allocate vector of size' roughly 2G). PCA LDA runs fine. Are there any way to use the memory issue withe SVM's? Or can you recommend any other classification method for such huge datasets? P.S. I run suse 9.1 on a 2G RAM

I''m a newbie attempting to get a custom fact distributed to clients. I''ve followed the instructions at http://reductivelabs.com/trac/puppet/wiki/AddingFacts and based my first fact on a simple recipe. I''m having at least one problem and possibly more. 1) Puppet/facter fails to load the custom fact, complaining that "non- sh interpreters are not currently

Hello developers folks! First, congratulations for the wonderful work with R. For science, barplots with error bars are very important. We were wondering that is so easy to use the boxplot function: boxplot(Spores~treatment, col=treatment_colors) But there is no such function for barplots with standard deviation or standard error. It becomes a "journey" to plot a simple graph (e.g.

In my recent security email, I got the following errors: cantona.dnswatchdog.com login failures: Aug 20 02:37:19 cantona sshd[9444]: fatal: Write failed: Operation not permitted Aug 20 04:30:42 cantona sshd[16142]: fatal: Write failed: Operation not permitted Aug 20 21:21:51 cantona sshd[45716]: fatal: Write failed: Operation not permitted So three questions: What is it? Should I be worried?

Hi everyone, first of all, I would like to say that I am a newbie in R, so I apologize in advance if my questions seem to be too easy for you. Well, I'm looking for periodicity in histograms. I have histograms of certain phenomenons and I'm asking whether a periodicity exists in these data. So, I make a periodogram with the function spec.pgram. For instance, if I have a histogram h, I