similar to: computational ontology to research questions, and statistical methods

Are there any R packages to import and use Web Ontology Language (OWL) ontologies (represented in OWL/RDF or other form)? Thanks, -s [[alternative HTML version deleted]]

Dear Jean-Marc: Would you please send Thierry an ontology mapping table specification for speex according to the format he links to at the end of his message below? Thank you. On Mon, Oct 11, 2010 at 9:54 AM, Thierry MICHEL <tmichel at w3.org> wrote: > Dear James, > > In your latest email to the MAWG response to your comment, you seem to > disagree with the MAWG proposal. >

Dear all, I have an ontology which I would like to load in R to then compute the minimum distance between elements. Since this ontology is not only DAG, the problem is non trivial and therefore decided to use R with the powerful e1071 library (allShortestPaths) to do that. That method requires to provide a distance matrix between the various objects. My ontology contains 32768 objects and

Hello, I am writing as an administrator, not as an R user, so forgive me if I am not completely knowledgeable about R. I have a user who is creating an R package for windows from a Linux environment using the crossbuild environment by Jun Yan and A.J. Rossini. The packages she generated worked fine until she tried to install in R 1.9.1 for Windows. Now when she installs with

Hello! I don't know if there is some R-package able to associate ontology to a long list of GeneBank Name (a txt-tab file or an XML file), i.e. I would as output a formatted file with 4 columns (1:GeneBank Name 2,3,4:ontology). I know that I have to perform a mapping of genes, I got a look on AnnBuilder pkg, but I 've not idea from where to start. Some suggestion? Thanks in advance!

Dear All, I need to fetch GO ontologies for Homo sapiens with their mappings to corresponding Uniprot identifiers. I would be using this information to compare result from a clustering algorithm with existing protein complexes. This would be a test to check how the clustering algorithm accurately captures GO terms with respect to the known protein complexes. Can anyone suggest a simple workflow

Dear R-user: After the fitting the Tobit model using zelig, if I use the following command then I can get the regression coefficents: beta=coefficients(il6.out) > beta (Intercept) apache 4.7826 0.9655 How may I extract the "Naive SE" from the following output please? > summary(il6w.out) Call: zelig(formula = il6.data$il6 ~ il6.data$apache, model =

Could you help me with a problem? I should put non-linear variables into zelig-model, how can that be done? I'm dealing with air pollution data, trying to find out daily associations between mortality and air pollutants. Weather variables used as confounders are in some cases non-linear. Since smoothing is not an option I don't know how to proceed. Thanks, Jaana

Greetings all, please consider the following data: #Build Data frame Slope<-c(1.291370, 12.208500, 2.110930, 0.578990, 5.019520, 0.807444, 0.554079 , 1.257080, 0.241504 , 0.184337 , 0.383044 , 0.342021) Exposure<-c(790.54, 1167.79 , 845.58 , 1082.47 , 1189.61 , 677.17 , 2058.56 , 469.09 , 112.02 , 803.31 , 254.14 ,1336.16) FwyDist<-c(11809.4222 ,10623.0458, 12279.6271,

Hi: I have clustered microarray gene expression data and trying to map between microarray probe, gene, pathway, gene ontology, and homology for a set of (affy) microarray probes. Is there any package in R which facilitates this? I am looking at bioconductor, but till now could not find a solution. A link to some worked example would be appreciated. Thanks and regards. John [[alternative HTML

Hi, y is nominal (3 categories), x1 to 3 is scale. What I want is a regression, showing the probability to fall in one of the three categories of y according to the x. How can I perform such a regression in R? Thanks for your help S?ren

Pls advise how I can use R in conjoint analysis?? regds Faisal Afzal Siddiqui Karachi, Pakistan ____________________________________________________________________________________ Looking for last minute shopping deals?

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I have found the "simulate" method (incorporated in some packages) very handy. As far as I can tell the only class for which simulate is actually implemented in base R is lm ... this is actually a little dangerous for a naive user who might be tempted to try simulate(X) where X is a glm fit instead, because it defaults to simulate.lm (since glm inherits from the lm class), and the

I use mnp to run a multinominal probit regression model, but the summary doesn't contain the model statistics, such as the log-likelihood and degree of freedom, for the assessment of the goodness-of-fit of the fitted model. Is there any way that I can generate these statistics for the fitted model in R? Many thanks in advance! SC

Hello all, if I have multiple imputed data sets, is there a command or function in R in any package you know of to combine those, I know one common MI approach is rubins rules, is there a way to do this using his rules or others? I know theres ways, like using Amelia from Gary King's website to create the imputed data sets, but how to make them into one or combine them for analysis.

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Dear R-help, I was bitten by the behavior of all() when given logical(0): It is TRUE! (And any(logical(0)) is FALSE.) Wouldn't it be better to return logical(0) in both cases? The problem surfaced because some un-named individual called randomForest(x, y, xtest, ytest,...), and gave y as a two-level factor, but ytest as just numeric vector. I thought I check for that in my code by testing

https://gist.github.com/rpietro/6430771 stemDocument function doesn't seem to be working. Tried to look up and a few people have reported the problem, but no solution that I could find. would appreciate any help

Hello R-users, I have observed that is.na() behaves strange on some lists. Here is a simple example: > a = list(list('asd')) > a [[1]] [[1]][[1]] [1] "asd" > for(i in 1:5) + print(is.na(a)) [1] TRUE [1] FALSE [1] TRUE [1] TRUE [1] TRUE > > for(i in 1:10) + print(as.integer(is.na(a))) [1] 0 [1] 10 [1] 1 [1] 0 [1] 140897024 [1] 134567568 [1] 1 [1] 1953720684