similar to: bayesian belief networks to determine causality

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Can somebody save-me? Thanks in advance! #R script: #trying to find out how solve a discrete Bayesian Belief Network. #option: using 'catnet' package #BEGIN library(catnet) cnet <- cnNew(nodes = c("a", "b", "c"), cats = list(c("1", "2"), c("1", "2"), c("1", "2")), parents = list(NULL, c(1), c(1,

Okay, been working with tripack, seems the most mature package for this. Got it to work well with their test data set - data(tritest). When i tried random numbers to explore further, i am getting some results that don't reconcile. example run this: library(tripack) y <- runif(100) x <- runif(100) vm <- voronoi.mosaic(x,y) plot(vm) par(new=T) plot(x,y,col='blue') when

Hello, trying to locate all the string commands in the base version of R, can't seem to find an area that describes them. I am in need to do some serious parsing of text data to create my dataset. Is there a summary link to all the character operators? string manipulations that would help in parsing text.

Hi, I need a help to know whether I can perform the following in R: I have a set of observations (Ns) and each observation is drawn from a poisson distribution with an unkown mean, lambda. The set of lambdas in their turn are drawn from a common prior distribution which is supposed to be a a mixture of two gamma distributions. Is there a way to determine the poisson means in R, given the Ns and

Hello, for a frequency modelling problem I want to combine expert knowledge with incoming real-life data (which is not available up to now). The frequency has to be modelled with a poisson distribution. The parameter lambda has to be normal distributed (for certain reasons we did not NOT choose gamma althoug it would make everything easier). I've started with the subsequent two functions to

h?, I am preparing undergraduate thesis If you help me this would make me feel good. First I need to analyze effect of Dow Jones Industrial average(DJIA)'s return on Istanbul Stock Exchange(ISE). I want to use Markov-Switching Bayesian Vector Autoregression Models (MSBVAR) that is used to examine the effect of a large economy?s stock exchange movement on a small economy?s stock exchange

the R developer site has been down for some time, what gives? http://developer.r-project.org/

Hello, writing some R code to cleanse a data set, if the following set of symbols are identified then perform some actions. trying to write the minimum code to do this. tname = "VIX" checkticker = c("VIX", "TYX", "TNX", "IRX") if (tname == checkticker) { //perform some operations } result i get is > tname == checkticker

Hello, we are needing to generate optimal (Fractional) designs for discrete choice applications, where we will be using logistic regression or multinomial logit as the modeling technique. It looks like optFederov, in the AlgDesign package may work, but not sure if this algorithm works when the variable of interest is binary or nominal? Anyone who are experts in this area, anyone interested

Hello, not understanding the output of prcomp, I reduce the number of components and the output continues to show cumulative 100% of the variance explained, which can't be the case dropping from 8 components to 3. How do i get the output in terms of the cumulative % of the total variance, so when i go from total solution of 8 (8 variables in the data set), to a reduced number of

Hello, we are writing rich internet user interfaces and like to call R for some of the computational needs on the data, as well as some creation of image files. Our objects communicate via the SOAP interface. We have been researching the various packages to expose R as a SOAP service. No current CRAN SOAP packages however. Found 3 to date: RSOAP (http://sourceforge.net/projects/rsoap/)

hello, i just got introduced to R - WOW its beautiful.. I am presently a SAS user and wanted to configure R to work in a multi-user enteprise environment. Client - Server. Where we have a strong LINUX server supporting about 10 statisticians with R. Anyone have any backround or information they can share to help me get jump-started on setting up R in this environment? Does each user have

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Deal R Users, I'm trying to find out how can I compute probabilities on a Bayesian Network using R. The Bayesian Network I modelled is shown at http://www.dsr.inpe.br/~mello/1727/BNgrapmodel.png and I'd like to know how can I proceed (commands on R) to answer questions like: (1) what is the probability of S=T given C=T, L=T, R=F, H=F and D=F; or (2) what is the probability of S=T

Looking for help with a project for the US Navy, requires knowledge of Bayesian Statistics, Bayesian Networks and Survival Analysis. Please respond with CV. Thanks. -- David Katz www.davidkatzconsulting.com [[alternative HTML version deleted]]

Hello, I am trying to count IR instructions in a function for static analysis using llvm pass. In contrast with existing examples, I am trying to include instruction counts of all the callees of the function. Counting the instructions of a function is easy using passes, but iterating through the module's CallGraph is proving to be confusing. I believe I have to use CallGraphWrapperPass to

Thanks so much John! That works :) Previously I wrote a pass that inherited from CallGraphWrapperPass and used its getCallGraph() function within runOnModule() which resulted in segmentation faults for me. Not sure why that happened, perhaps callgraph wasn't setup by the time runOnModule() was called internally. If you know why, kindly enlighten me and perhaps document the behavior for the

Dear Achim, R Users, What am I doing wrong in this example ? a<-zoo(rnorm(100),order.by=1:100) b<-lag(a) regr<-na.exclude(merge(a,b)) plot(regr) grangertest(regr[,1],regr[,2],3) > a<-zoo(rnorm(100),order.by=1:100) > b<-lag(a) > regr<-na.exclude(merge(a,b)) > plot(regr) > grangertest(regr[,1],regr[,2],3) Error in solve(vc[ovar, ovar]) : subscript out of bounds

Hello, does anyone know of any R routines capable of whats called Hierarchical Forecasting, reconciling the different hierarchies. Example: A top down forecast where the corporate forecast is created and then all the regions within the corporate entity are also forecasted, with the constraint they sum to the corporate forecast.