similar to: New Package 'JMbayes' for the Joint Modeling of Longitudinal and Survival Data under a Bayesian approach

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

Hi R experts I am just wondering if something is already available (or easily adaptable) to do the following. I am planning to build linear models for all possible combinations of terms, so for example if the terms are sent into a function as this string " X1 + X2 + X3 + X4 + X1:X2" I would want to build models for all possible combinations of these 5 terms, e.g. m1 <- lm( y ~

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Position: Bayesian Modeling Statistician at the level of Research Scientist, Senior Scientist or Principal Scientist, depending on experience. MetrumRG is selectively seeking enthusiastic and energetic individuals to join a team of scientists in a unique working environment. As a member of the MetrumRG team, you will participate in the research, development, and application of

Hi R, What package of R can I use for Bayesian States Space Modeling? And any other supporting packages? Thanks in advance, Shubha [[alternative HTML version deleted]]

Dear all, I'm modeling growth curve of some ecosystems with respect to their rainfall-productivity relationship using a simple linear regression (ANPP(t)=a+b*Rain(t)) and a modified version of the Brody Model ANPP(t)=a*(1-exp(-b*rain(t))) I would like to know why the "best model" is function of the criteria that I use (maximizing the fit using R2 or testing the Null hypothesis with

Hi Friends, I'm trying to model the consumer decisions (Click-Through Rate and Conversion) in Search Engine Advertising using a hierarchical Bayesian binary logit. The input data is the weekly CTRs and Avg. Position for each search keyword. CTR is modeled as (for each keyword i and week j): Pij = exp(C + Bi x Positionij + A1 x Lengthi + A2 x Brandi + A3 x ProductSpecifici) / [1 + exp(C +

I am trying to model survival data with a Weibull distribution using survreg. Units are clustered two apiece, sometimes receiving the same treatment and sometimes opposing treatment.

Here's a more general question following up on the specific question I asked earlier: Can anybody recommend an R command other than mle.aic() (from the wle package) that will give back a ranked list of submodels? It seems like a pretty basic piece of functionality, but the closest I've been able to find is stepAIC(), which as far as I can tell only gives back the best submodel, not a

Hello, I am using sm.density() to find the bivariate joint PDFof events: For eg, x<-cbind(rnorm(30),rnorm(30)) den<-sm.density(x) Then I get the joint pdf from den$estimate in order to constructthe joint cumulative PDF. However, summing up all the values from den$estimateisnot equal to 1(have multipliedby the grid size). Anyone could help? Thanks. mc [[alternative HTML version

Folks, I'm using stepAIC(MASS) to do some automated, exploratory, model selection for binomial and Poisson glm models in R 1.3. Because I wanted to experiment with the small-sample correction AICc, I dug around in the code for the functions glm.fit stepAIC dropterm.glm addterm.glm extractAIC.glm and came across something I just don't understand. stepAIC() passes dropterm.glm() a

Hello List: Please find uploaded to CRAN a new package, PwrGSD The package is intended for the design and analysis of group sequential trials There are two main functions, (1) GrpSeqBnds: computes group sequential stopping boundaries for interim analysis of a sequential trial based upon a normally distributed test statistic. This can be done via the Lan-Demets procedure with

Hello List: Please find uploaded to CRAN a new package, PwrGSD The package is intended for the design and analysis of group sequential trials There are two main functions, (1) GrpSeqBnds: computes group sequential stopping boundaries for interim analysis of a sequential trial based upon a normally distributed test statistic. This can be done via the Lan-Demets procedure with

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 list. I need help (e.g., a reference, code, package, etc.) in calculating the joint entropy of many variables (some sure highly mutually-informative and some not). Is there anyone here who knows a computationally-efficient solution (such as an R package)? I appreciate you help ... Best, Reza [[alternative HTML version deleted]]

Strange. Both systems are Linux. I compiled the LDAP slave Samba with CFLAGS="-O2 -march=i686" and SuSE's GCC 3.3.1. The fine working master got the same CFLAGS but SuSE's GCC 2.95.3. Without CFLAGS the GCC 3.3.1 works fine too. Didn't know that optimization (and GCC>3) is that dangerous.... Thanks anyway Daniel ------ Hi! We have a replicated LDAP Samba network. I

Dear R-helpers, I have dist class objects for 10 individuals rating the dissimilarities (on a 100-point scale) of the same 10 faces (analogous to the bread data). I would like to get an individual differences scaling jointly for the individual judges and the faces, and plot them on the same axes. This is the example: library(smacof) data(breakfast) res.rect<-smacofRect(breakfast,

Hi I'm trying to maximize a joint likelihood of 2 likelihoods (Likelihood 1 and Likelihood 2) in mle2, where the parameters I estimate in Likelihood 2 go into the likelihood 1. In Likelihood 1 I estimate the vector logN with length 37, and for the Likelihood 2 I measure a vector s of length 8. The values of s in Lieklihood 2 are used in the Likelihood 1. I have 2 questions: ##1 I manage