similar to: [follow-up] "Longitudinal" with binary covariates and outcome

Hi Folks, I'd be grateful for suggestions about approaching the following kind of data. I'm not sure what general class of models it is best situated in (that's just my ignorance), and in particular if anyone could point me to case studies associated with an R approach that would be most useful. Suppose I have data of the following kind. Each "subject" is observed at say 4

Dear R-help subscribers, I use xyplot to plot longitudinal data as follows: score<-runif(100,-4,5) group<-sample(1:4,100,rep=T) subject<-rep(1:25,4) age<-rep(runif(4,1,40),25) df<-data.frame(score,group,age,subject) xyplot(score~age|group, group=subject, panel=function(...){ panel.loess(...,lwd=4) panel.superpose(...)} ,data=df) this produced a plot with four panels one for each

Hi all, I a semi-beginner with R and I am working with the plm package to examine a longitudinal dataset. Each individual in this dataset has a longitudinal weight for the probability that he or she remains in the sample. Unfortunately, I have not found an argument to use weights in the plm function? I tried ?weights=? like in standard lm or in nlme or lm4 but it does not work. I asked the

Dear List,   I have some longitudinal data, each patient was followed at times 0, 12, 16, 24 weeks and measure severity of a illness (0-worse, 1-same, 2-better). So, longitudinal response is categorical.  I was wondering whether lmer in R can fit a model for this type of data. If so, how we code? Or any other function in R that can fit this type of longitudinal data? Any suggestion would be

Hi all: I have a question about the sample size calculation. It's a pilot study,which includes 2 groups(low,high),3 time point(3,6,9 monthes).Each person has 3 results which according to the 3 time points.So it's a longitudinal study. I want to calculate the minimum sample size from the pilot study, but can't find the solution since the data is highly skewed and it's a

Dear R-Users! Is there a possibility in R to do analyze longitudinal survey data (repeated measures in a survey)? I know that for longitudinal data I can use lme() to incorporate the correlation structure within individual and I know that there is the package survey for analyzing survey data. How can I combine both? I am trying to calculate design-based estimates. However, if I use svyglm() from

i am studying Longitudinal Data Analysis and want to carry it with R.anyone knows any materials about Longitudinal Data Analysis with R in the internet which i can download? thank you.

hi, I'm a total noob who is having to ramp up to full speed very quickly due to an unfortunate abrupt staffing change at my job :) I have longitudinal data that looks like this: PID OBSDATE DaysAgo CleanValue NAME 1 1410164934000610 8/17/2004 13:03:38 1345 6.2 HGBA1C 2 1410164934000610 11/16/2004 10:39:51 1254 7.1

Hi All, I am doing econometric modeling of panel data (fixed effects). We currently use Eviews to do this, but I have discovered a bug in Eviews 7 and am exploring the use of R to build panel data models / longitudinal data models. I looked at the plm package but do not see how I can incorporate AR terms in the model using the plm package. I have an Eviews model with two AR terms, AR(1) and

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

Hello R user, I have a data set from a longitudinal study ( sample below) where subjects are followed over time. Second column (status) contains info about if subject is dead or still in the study and third column is time measured in the week. Here is what I need: if status is not dead or unknown take the last week, if status is dead or unknown I need to have corresponding week. Desired resulst:

Dear R Help, I am attempting to make a plot of longitudinal data, a sample data frame of which is shown below. I'd like to show all of the subjects in the same plot, with a set of connecting line segments for each subject. 'age' would be the x-axis and 'score' would be the y-axis. subject age score 1 10123 12 51.06 2 10123 14 50.00 3 10123 15 62.22 4

Hi, does anyone out there have a recommendation for multilevel / random effects and longitudinal analysis? My dream book would be something that's both accessible to a non-statistician but rigorous (because I seem to be slowly turning into a statistician) and ideally would use R. Peter

Dear R people, I have a matrix like this: var1 var2 var3 var4 a1 7.1 7.2 8.1 8.2 a2 10.5 10.6 ... ... a3 b1 b2 b3 b4 c1 c2 ... The matrix row names are "a1", "a2", ...... and the matrix column names are "var1", "var2", "var3" and "var4". Now I want to reshape this data into a

Hi, Can one tell me which procedure will fit an ordinal logistic regression model for longitudinal data set. To be precise, I have both dichotomous and polytomous items. Also, I would like to specify different covariance structures (unstructured, ar1 etc) for trial runs. Thanks -- View this message in context:

Hi all, I asked this before the holiday, didn't get any response. So would like to resend the message, hope to get any fresh attention. Since this is not purely lme technical question, so I also cc-ed R general mailing list, hope to get some suggestions from there as well. I asked some questions on how to analyze longitudinal study with only 2 time points (baseline and a follow-up)

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 How to estimate the panel data quantile regression if the regression contains no constant term? I tried to change the code of

Hi All, I have a dataset, longitudinal in nature, each row is a 'visit' to a clinic, which has numerous data fields and a count variable for the number of 'events' that occurred since the previous visit. ~50k rows, ~2k unique subjects so ~25 rows/visits per subject, some have 50 some have 3 or 4. In STATA there is an adjustment for the fact that you have multiple rows per

Hi everyone I have the following situation: In a longitudinal study, subjects fill out a questionnaire every year (repeated measurements over time). Also, the subjects are nested within departments. There is an intervention going on over time. The outcome variable is continuous. Now I'd like to analyse two things: 1. Is there a significant change over time? I think this is done by a

Dear list, I have been struggling with this for some time now, and for the last hour I have been struggling to make a working example for the list. I hope someone out there have some experience with plotting longitudinal data that they will share. My data is some patient data with three different time stamps. First the patients are identified at different times (first time stamp). Second, they