Hi Marco, I saw this post and was wondering if you would be able to help me.
I have a gene expression data file that i would like to build a bayesian n/w on. I input a file with samples as rows and columns as features into the bnlearn package. I read through the pdf file that talks about the bnlearn package. I understood the many different approaches for discrete data, but did not really understand what to do with continuous data. The example for continuous data on your web site, built an empty network with only nodes when I implemented it. (code shown below: data(gaussian.test) res = empty.graph(names(gaussian.test)) modelstring(res) = "[A][B][C][D][C|A:B][D|B][F|A:D:E:G]" plot(res) Do you have your own example which describes what functions to use step and step and how to plot the n/w ? Any help appreciated. thanks -- View this message in context: http://r.789695.n4.nabble.com/Bayesian-Belief-Networks-tp3162133p4536033.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
