similar to: bnlearn and cpquery

Dear Ross, This usually happen because you have parameters with a value of NaN in your network, because the data you estimate the network from are sparse and you are using maximum likelihood estimates. You should either 1) use simpler networks for which you can estimate all conditional distributions from the data or 2) use posterior estimates for the parameters. Cheers, Marco On 13 July

Hi everyone, I'm attempting to use the bnlearn package to calculate conditional probabilities, and I'm running into a problem when the "cpquery" function is used within a loop. I've created an example, shown below, using data included with the package. When using the cpquery function in a loop, a variable created in the loop ("evi" in the example) is not

I'm trying to teach myself about Bayesian Networks and am working with the following data and the bnlearn package. I understand the conceptual aspects of BNs, but I'm not sure how to specify the response variables in R when constructing a dag plot. I've cecked ?hc and done numerous google searches without luck. Can anyone help? library("bnlearn")

Hi all. I've recently uploaded a package (bnlearn) to CRAN. It builds fine on both Linux (32 and 64 bit) and Windows, but fails on MacOSX ix86 because of C90 vs C99 issues: http://www.r-project.org/nosvn/R.check/r-patched-macosx-ix86/bnlearn-00install.html Since I've no MacOSX machine at hand, I would like to ask you: why is C99 not the default for gcc on MacOSX ix86? Is it safe to

Dear R-help list: I have two related questions regarding the functions boot.strength and custom.strength in bnlearn. 1) I am using the following commands (on a set of continuous data, the example here run on fake data): > myblacklist<-data.frame(from=c("x1", "x1", "x1", "x2", "x2", "x2")) , to=c("x2",

Hi, I am interested in using a bayesian network as a predictor (machine learning); however, I can't get any of the implementations (deal, nblearn) to learn & predict stuff. Shouldn't there also be probabilites for each node after the learning phase, how can I access these? Cheers, Stephan -- View this message in context:

Hi Anyone have experience with very large datasets and the Bayesian Network package, bnlearn? In my experience R doesn't react well to very large datasets. Is there a way to divide up the dataset into pieces and incrementally learn the network with the pieces? This would also be helpful incase R crashes, because I could save the network after learning each piece. Thank you.

Hi, I need to classify, using Naive Bayes and Bayes Networks, and estimate their performance using cross validation. How can I do this? I tried the bnlearn package for Bayes Networks, althought I need to get more indexes, not only the error rate (precision, sensitivity, ...). I also tried the *e1071* package, but I could not find a way to do cross-validation. Thanks for everyone. Guilherme.

Hello: I have looked at the bnlearn and deal packages for infering bayesian network. Can anyone suggest any other suitable package for constructing bayesian gene regulatory network using gene expression data? Thanks! John [[alternative HTML version deleted]]

Hi every one I am working on shiny app using bnlearn for Bayesian networks and using r studio I get a fatal error and when I use R GUI I get this error ** caught segfault *** address 0xfffffffc0fcd6248, cause 'memory not mapped' Traceback: 1: .Call("mappred", node = node, fitted = fitted, data = data, n = as.integer(n), from = from, prob = prob, debug = debug) 2:

Hi R-users Is there any functions in R that can implement "expert systems"? The aim of an expert system is to produce a probable diagnosis for a patient with certain symptoms. In the classical expert system a mumber of "experts" are asked to make "statements" on the probabilities for different diseases when a combination of systems would appear. One typical

Since 2008, Microsoft (formerly Revolution Analytics) staff and guests have written about R every weekday at the Revolutions blog (http://blog.revolutionanalytics.com) and every month I post a summary of articles from the previous month of particular interest to readers of r-help. In case you missed them, here are some articles related to R from the month of May: Many interesting presentations

CRAN (and crantastic) updates this week New packages ------------ * DCGL (1.0) Bao-Hong Liu http://crantastic.org/packages/DCGL Functions for basic differential coexpression analyses: gene filtering, link filtering, DCG (Differentially-Coexpressed Gene) identification and DCL (Differentially-Coexpressed Links) identification.Two algorithms,named DCP and DCe, are provided for

CRAN (and crantastic) updates this week New packages ------------ * Bergm (1.0) Alberto Caimo http://crantastic.org/packages/Bergm Functions implementing Bayesian estimation for exponential random graph models via exchange algorithm Updated packages ---------------- lmtest (0.9-26), logcondens (1.3.5), MTSKNN (0.0-4), pmml (1.2.21), r2lUniv (0.9.4), rattle (2.5.11), rgdal (0.6-23),

Dear R developers, I've run into a very cryptic error message from R CMD check while working on a new package. This is the relevant output: [fizban@~/Rmap]:R CMD check Rmap * checking for working pdflatex ... OK * using log directory '/home/fizban/Rmap/Rmap.Rcheck' * using R version 2.9.0 (2009-04-17) * using session charset: UTF-8 * checking for file 'Rmap/DESCRIPTION' ...

CRAN (and crantastic) updates this week New packages ------------ * EffectStars (1.0) Maintainer: Unknown Author(s): Gunther Schauberger License: GPL-2 http://crantastic.org/packages/EffectStars The package provides functions to visualize regression models with categorical response. The effects of the covariates are plotted with star plots in order to allow for an optical

The Wine development release 3.8 is now available. What's new in this release (see below for details): - Infrastructure for writing kernel driver tests. - Support for showing file properties in the Shell browser. - MP3 decoder as a DirectX Media Object. - More support for the Task Scheduler. - Support for COM aggregation in UrlMon. - Various bug fixes. The source is available

CRAN (and crantastic) updates this week New packages ------------ * allan (1.0) Alan Lee http://crantastic.org/packages/allan Automates Large Linear Analysis Model Fitting * andrews (1.0) Jaroslav Myslivec http://crantastic.org/packages/andrews Andrews curves for visualization of multidimensional data * anesrake (0.3) Josh Pasek http://crantastic.org/packages/anesrake This

I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",