search for: jointprior

Dear all, I recently started using the deal package for learning Bayesian networks. When using the jointprior function on a particular dataset, I get the following message: >tor.prior<-jointprior(tor.nw) Error in array(1, Dim) : 'dim' specifies too large an array What is the problem? How can I resolve it? Thanks, Tim Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

Greetings all, I am trying to use Deal to learn a Bayesian Network for discrete data. I came across the following problem with jointprior function. >library(deal) > a <- read.csv("prepared.cluster1.csv") > fit <- network(a) > fit.prior <- jointprior(fit) Error in array(1, Dim) : 'dim' specifies too large an array I have tried debugging array and still can't fully understand what the proble...

Greetings all, I am trying to use Deal to learn a Bayesian Network for discrete data. I came across the following problem with jointprior function. >library(deal) > a <- read.csv("prepared.cluster1.csv") > fit <- network(a) > fit.prior <- jointprior(fit) Error in array(1, Dim) : 'dim' specifies too large an array I have tried debugging array and still can't fully understand what the proble...

...ot;deal" package. I followed the manual and paper from the author's web site to learn it, as shown below, but I could not figure out how to access the local and posterior probability of the nodes in the constructed network. library(deal) data(ksl) ksl.nw <- network(ksl) ksl.prior <- jointprior(ksl.nw) banlist <- matrix(c(5,5,6,6,7,7,9, 8,9,8,9,8,9,8), ncol = 2) ksl.nw$banlist <- banlist ksl.nw <- learn(ksl.nw, ksl, ksl.prior)$nw According to the manual, the local prior and posterior can be accessed for each node using localprior() and localposterior()...

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",