similar to: lme - Random Effects Struture

Dear R users, I need help with translating these SAS codes into R with lme()? I have a longitudinal data with repeated measures (measurements are equally spaced in time, subjects are measured several times a year). I need to allow slope and intercept vary. SAS codes are: proc mixed data = survey method=reml; class subject var1 var3 var2 time; model score = var2 score_base var4 var5 var3

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,

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

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,

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-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

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'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 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 <-

Dear R People: In S Plus, if you have a function which calls the plot function several times, you get several "pages" of graphics output. Is there an eqivalent in R, please? R version 2.2.1 windows Thanks in advance! Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: hodgess at gator.uhd.edu

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

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