similar to: Question of "Quantile Regression for Longitudinal Data"

Dear All, What is the preferred way to get Bayesian analysis results (such as those from MCMCpacki, MCMCglmm, and DPpackage) into LaTeX table automatically? I have been using the "apsrtable" package and similar functions in "memisc" package, but neither seems to handle MCMC output directly. Many thanks. Shige

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R This code run perfectly. Then I want to know what the result means.The result show $ierr,$it,and $time. What these estimators

Hi - I am doing a monte carlo experiment that requires to do a linear regression of a matrix of vectors of dependent variables on a fixed set of covariates (one regression per vector). I am wondering if anyone has any idea of how to speed up the computations in R. The code follows: #regression function #Linear regression code qreg <- function(y,x) { X=cbind(1,x) m<-lm.fit(y=y,x=X)

Hi R users: I want to know if any of you had used Stata or Statgraphics. What are the advantages and disadvantages with respect to R on the following aspects? 1. Statistical functions or options for advanced experimental design (fractional, mixed models, greco-latin squares, split-plot, etc). 2. Bayesian approach to experimental design. 3. Experimental design planing options. 4. Manuals

I am trying to get a measure of how R compares in usage as a statistical platform compared to other software. I would guess it is the most widely used among statisticians at least by virtue of it being open source. But is there any study to which I can refer? By asking this list I am not exactly adopting a rigorous approach! Best wishes John John Logsdon

Good day R-listers, I'm currently working on a panel data analysis (N=17, T=5), in order to check for the spurious regression problem, i have to ?test for stationarity but i've read somewhere ?that i needn't to test for it as ?my T<10 , what do you think? if yes ?is there any other test ?i have to ?perform in such case (a kind of cointegration test for small T?) Any hint would be

Dear all, There have been a variety of discussions on the R list regarding the use of R in clinical trials. The following post from the STATA list provides an interesting opinion regarding why SAS remains so popular in this arena: http://www.stata.com/statalist/archive/2008-01/msg00098.html Regards, -Cody Hamilton

Dear R-list, I am currently working on a dataset with a colleague who uses stata. We fit a random intercept model to the data (decisions clustered in participants) and get closely the same results in stata (using xtreg re) and R (using the lme4 or multilevel package). Now in stata, there is an additional option for the regression to control for clustering; the vce(cluster clustvar) option,

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R While this code run perfectly, it does not work for my data providing a warning message: In rq.fit.sfn(D, y, rhs = a) : tiny

Quantile Regression for Longitudinal Data. Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R I am trying to change the number quantiles being estimated. I change the codes about

Dear R users I am runing into a problem in reading data in R This is the error information a<-read.table("tt_mb_200409.txt",as.is=T) Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 1653997 did not have 5 elements It seams that some lines don't have equal variables. I want to try >

I am just starting to use R, converting over from SAS. I want to estimate the upper quartile in a scatterplot of demographic rates against rainfall in a small mammal population. The data looks like this: | |* * <- point in question |* |* * |* * |* * * |* * * * * * |_______________________________________ Based on a paper by Scharf et al. in Ecology (vol 79,

I used gam for data analysis a lot. Is it possible to use gam to analyze longitudinal data? I mean, besides the working independence assumption, can i specify other more reasonable covariance structure in gam? [[alternative HTML version deleted]]

Hi all, does anybody know about R implementations for quantile regression for longitudinal data? I am just aware of a very basic version of R. Koenker's approach using fixed effects. Thanks in advance Armin

Dear Dimitris, I have exactly the same problem than you, Do you get some solution? Thanks, Lola Lola Gadea Profesora titular de Economía Aplicada/Lecturer in Applied Economics Universidad de Zaragoza/University of Zaragoza (Spain) lgadea@unizar.es <http://estructuraehistoria.unizar.es/personal/lgadea/index.html>http://estructuraehistoria.unizar.es/personal/lgadea/index.html Grupo de

Hi R-masters! I trying model Heart disease mortality in my country with a lme model like this: m1.lme<-lme(log(rdeath)~age*year,random=~age|year,data=dados) where: rdeath is rate of mortality per 100000 person per age and year age: age of death (22 27 32 37 42 47 52 57 62 67 72 77 82) year: year of death (1980:2002) I don?t have problem to fit the model, but in

 I would like to use GLM to analysis  a longitudinal data  any one know which library in R do so thanks alot [[alternative HTML version deleted]]

Hi there, I have a dataset from a longitudinal study with a lot of drop-out. I want to implement the inverse probability weighting method by Robins 1995 JASA paper "Analysis of semiparametric regression models for repeated outcomes in the presence of missing data". Does anyone know if there is a package to do it in R (or other software)? Thanks a lot! Lei

Hi, I am trying to model a score over time. This score shows a ceiling effect. I was willing to use a longitudinal tobit model, such as the one described by Twisk et al. (Twisk_Longitudinal tobit regression: A new approach to analyze outcome variables with floor or ceiling effects_JCE_2009) but it is programmed for STATA. Has anyone used such models in R? Any other idea? David Biau.

Hi, Can anyone recommend one or more good sample datasets for multivariate longitudinal data? (eg. a dataset containing a mixture of continuous, categorical and date/time variables) I'm looking for something which I can use for testing and examples for my ggplot graphics package. A selection of datasets which use different date time classes would also be useful, although I could probably