similar to: jointprior in deal package

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

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

Dear R-users I am looking for a help in using the "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 <-

Dear R-users, Is there a way to get p-values for a one-sided hypothesis test about a poisson mean? Thanks, Jan Wijffels University Center for Statistics W. de Croylaan 54 3001 Heverlee Belgium tel: +32 (0)16 322784 fax: +32 (0)16 322831 <http://www.kuleuven.be/ucs> http://www.kuleuven.be/ucs Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm [[alternative HTML version

Dear R-users, We'd like to announce the release of our new package "grouped" (available from CRAN), for fitting models for grouped or coarse data, under the Coarsened At Random assumption. This is useful in cases where the true response variable is known only up to an interval in which it lies. Features of the package include: power calculations for two-group comparisons,

Dear R-users, We'd like to announce the release of our new package "grouped" (available from CRAN), for fitting models for grouped or coarse data, under the Coarsened At Random assumption. This is useful in cases where the true response variable is known only up to an interval in which it lies. Features of the package include: power calculations for two-group comparisons,

Hi all, How can the label of the x-axes in the plot() of a stl.object be adapted? e.g., When plotting: plot(stl(nottem, "per")) In the labels of the x-axes is “time”. How can this be changed to e.g., “Time (dekade) “? It does not work with xlab or others anymore… Thanks, Jan _______________________________________________________________________ Ir. Jan Verbesselt Research

Thanks Dimitris!!! That's much clearer now. Still have a lot of work to do this weekend to understand every bit but your code will prove very useful. Cheers, Aziz -----Original Message----- From: Dimitrios Rizopoulos [mailto:Dimitris.Rizopoulos at med.kuleuven.be] Sent: May 12, 2006 4:35 PM To: Chaouch, Aziz Subject: RE: [R] Maximum likelihood estimate of bivariate

Dear R-users, I have noticed small discrepencies in the reported estimate of the variance of the frailty by the print method for survreg() and the 'theta' component included in the object fit: # Examples in R-2.6.2 for Windows library(survival) # version 2.34-1 (2008-03-31) # discrepancy fit1 <- survreg(Surv(time, status) ~ rx + frailty(litter), rats) fit1 fit1$history[[1]]$theta

Dear all, I would like to Boostrap the stepAIC() procedure from package MASS for variety of model objects, i.e., fn <- function(object, data, B = 2){ n <- nrow(data) res <- vector(mode = "list", length = B) index <- sample(n, n * B, replace = TRUE) dim(index) <- c(n, B) for (i in 1:B) { up.obj <- update(object, data = data[index[, i], ])

hello I'm having the following problem: On a share I have a user with read-only access to word files. Another user has read-write access to these files. When the user with read-only access opens a word file and then the user with read-write access to these files opens the file, the read-write user has only read-only access. If the read-write user opens the word file first, then he has

Dear R-devels, I'd like to create a plot method for a class of objects that passes the '...' argument to both plot() and legend(), e.g., x <- list(data = rnorm(1000)) class(x) <- "foo" plot.foo <- function(x, legend = FALSE, cx = "topright", cy = NULL, ...){ dx <- sort(x$data) plot(dx, dnorm(dx), type = "l", ...) if (legend)

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either

Dear useRs, I very much like the effect display of the proportional odds model on page 29 (Figure 8) of the following paper by John Fox: http://socserv.mcmaster.ca/jfox/Papers/logit-effect-displays.pdf It really gives a very concise overview of the model. I would like to use it to illustrate the proportional odds mixed models we fit here for a project on Diabetes but I can't seem to reproduce

Dear list-members, a model is fit with lmer, but I want to force the variance parameter values to be as defined by me I thought, use 'start' to specify initial values and only allow for one iteration ? my question is how to do that ? to specify the 2x2 matrix of variance parameter values: start=list(groups=array(2,-0.5,1),dim=c(2,2)) now I need to make sure the mean structure is

Dear R-users, I'd like to announce the new version of package 'ltm' for Item Response Theory analysis. The function grm() (along with supporting methods, i.e., anova, margins, factor.scores, etc.) has been added for fitting the Graded Response Model for ordinal polytomous manifest variables. An extra feature of the plot method for classes 'grm', 'ltm' and

Dear R-users, I'd like to announce the new version of package 'ltm' for Item Response Theory analysis. The function grm() (along with supporting methods, i.e., anova, margins, factor.scores, etc.) has been added for fitting the Graded Response Model for ordinal polytomous manifest variables. An extra feature of the plot method for classes 'grm', 'ltm' and

Dear R-users, I tried to install the R(D)COM server version 1.35, to connect MS Excel and R. I am using R-version 2.1.0 on a Windows environment. And I am using MS Excel 2000 in a dutch version. I get the following error when I open Excel: "Compile error, can not find the project or the library" (translated from dutch). And it indicates LEFT in blue bold. Does anyone of you know what

Dear R users, I'd like to announce the new version of the package "ltm" (available from CRAN), for fitting Latent Trait Models (including the Rasch and two-parameter logistic models) under the Item Response Theory approach. Three main extra features have been added: (i) now both ltm() and rasch() permit general fixed-value constraints (e.g., useful for scaling purposes), (ii)