similar to: New package for grouped data models

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 Xavier, the reason you observe this feature is that in the 'constraint' argument you should specify the values under the additive parameterization, i.e., when in the second column of the matrix supplied in 'constraint' you specify 2, then you need to provide the easiness parameters (not the difficulty parameters) in the third column. Check the Details section of ?tpm() and

Thanks for the help Dimitris, However I still have a question, this time I'll be more specific, the following is my SAS code proc mixed data=Reg; class ID; model y=Time Time*x1 Time*x2 Time*x3 /S; random intercept Time /S type=UN subject=ID G GCORR V; repeated /subject = ID R RCORR; run; ** (Type =UN for random effects) The eqivalent lme statement I

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'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)

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)

Dear R-users, I'd like to announce the release of the new version of package 'ltm' for analyzing multivariate dichotomous and polytomous data under the Item Response Theory approach. New features: * function tpm() (along with supporting methods, i.e., anova, plot, margins, factor.scores, etc.) has been added for fitting Birnbaum's Three Parameter Model. * grm() can now

Dear R-users, I'd like to announce the release of the new version of package 'ltm' for analyzing multivariate dichotomous and polytomous data under the Item Response Theory approach. New features: * function tpm() (along with supporting methods, i.e., anova, plot, margins, factor.scores, etc.) has been added for fitting Birnbaum's Three Parameter Model. * grm() can now

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], ])

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)

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

Hi Dimitris: Thanks for your help, I will try. BR Yiyao -----ԭʼÓʼþ----- ·¢¼þÈË: Dimitris Rizopoulos [mailto:dimitris.rizopoulos at med.kuleuven.ac. be] ·¢ËÍʱ¼ä: 2004Äê10ÔÂ25ÈÕ 15:39 ÊÕ¼þÈË: YiYao_Jiang ³­ËÍ: r-help at stat.math.ethz.ch Ö÷Ìâ: Re: [R] Multiple formula in one block Hi YiYao, you need the `?panel.abline()' function, somehing like: panel=function(x, breaks,

Dear R-users, I'd like to announce the release of my new package "ltm" (available from CRAN), for fitting Latent Trait Models (including the Rasch model) under the Item Response Theory approach. The latent trait model is the analogous of the factor analysis model for Bernoulli response data. "ltm" fits the linear one- and two-factor models but also allows for

Dear R-users, I'd like to announce the release of my new package "ltm" (available from CRAN), for fitting Latent Trait Models (including the Rasch model) under the Item Response Theory approach. The latent trait model is the analogous of the factor analysis model for Bernoulli response data. "ltm" fits the linear one- and two-factor models but also allows for

Hi to all, > dlogis(-2000) [1] NaN Warning message: NaNs produced in: dlogis(x, location, scale, log) > dnorm(-2000) [1] 0 Is this an expected behaviour of `dlogis()'? Thanks in advance for any comments, Dimitris platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 9.1

I have a somewhat related question. A while back I was doing some simulations using for() loops, and I wanted to keep track of the iterations using a line of code quite similar to what Dimitris presented below. Instead of printing the iteration message at the end of each iteration (actually, at the end of every 100th), nothing was printed until the for() loop was complete, and *then* all

Dear R helpers: Thanks for the previous reply. I am using Friedman racing test. According the the book "Pratical Nonprametric Statistic" by WJ Conover, after computing the statistics, he suggested to use chi-squared or F distribution to accept or reject null hypothesis. After looking into the source code, I found that R uses chi-sqaured distribution as below: PVAL <-