similar to: select observations from longitudinal data

I have the following longitudinal data: id time y 1 1 10 1 2 12 1 3 15 1 6 18 2 1 8 2 3 9 2 4 11 2 5 12 3 1 8 3 4 16 4 1 9 4 5 13 5 1 7 5 2 9 5 6 11 .... I want to select the observations at time 4. if the observation at time 4 is missing, then i want to slect the observation at time 3. if the observation at time 3 is also missing, then i want to select observation at time 5. otherwise i will

i have a large longitudinal data set. The number of observations for each subject is not the same across the sample. The largest number of a subject is 5 and the smallest number is 1. now i want to make each subject to have the same number of observations by filling zero, e.g., my original sample is id x 001 10 001 30 001 20 002 10 002 20 002 40 002 80 002 70 003 20 003 40 004 ...... now i wish

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]]

i have a data set with repeated measures on same people, structure like below: id x1 x2 ... 001 10 20 ... 001 8 45 ... 001 4 2 ... 002 .... 002 ... 002 .... 002 .... 003 .... ....... what is the easist way to show how many observations for each subject id? [[alternative HTML version deleted]]

I have a longitudinal competing risk data of the form: ID COMPL SEX HEREDITY 1 0 1 2 1 0 1 2 1 3 1 2 2 0 0 1 2 1 0 1 2 2 0 1 2 2 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 2 0 1 4 0 1 2 4 0 1

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)

Dear r-helpers, I have a very simple question. Suppose my data is like id=c(rep(1,2),rep(2,2)) b=c(2,3,4,5) m=cbind(id,b) > m id b [1,] 1 2 [2,] 1 3 [3,] 2 4 [4,] 2 5 I wish to select the first observation for each id. That is, I want to quickly select two rows: id b 1 2 2 4 only. how should i do this? [[alternative HTML version deleted]]

Hello, Hope that someone could help me plotting longitudinal data below: 7213 3333330001 0.8300 13.05.09 1 1 3333330001 0.8700 09.02.05 NULL 4797 3333330001 0.7700 21.03.07 NULL 2399 3333330001 0.7800 12.04.06 NULL 2400 3333330002 NULL 27.03.06 NULL 7230 3333330002 0.8200 14.05.09 0 2 3333330002 0.8400 09.02.05 NULL 4798 3333330002 0.8700 20.03.07 0 4799 3333330003 0.9000 20.03.07 13 2401

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 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 R-help mailing list,   I have this problem, I have a joint  longitudinal and survival data of the form say   ID Time Failuretime Censoringind longitudinalmeasure 1  0     35                  0                123 1  10    35                 0                 120  1   25   35                 1                   123 2   0    23                  0                   100 2   10  23                 

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 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

I have the complete data like id time censor 1 10 0 1 20 0 1 30 0 2 10 0 2 20 1 2 30 0 2 40 0 3 10 0 3 20 0 3 30 1 .... for id 1, i want to select the last row since all censor indicator is 0; for id 2, i want to select the row where censor ==1; for id 3, i also want to select the row where censor==1. So if there is a 1 for censor, then I want to select such a row, otherwise I want to select the

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 List: I am working to construct a matrix of a particular form. For the most part, developing the matrix is simple and is built as follows: vl.mat<-matrix(c(0,0,0,0,0,64,0,0,0,0,64,0,0,0,0,64),nc=4) Now to expand this matrix to be block-diagonal, I do the following: sample.size <- 100 # number of individual students I<- diag(sample.size) bd.mat<-kronecker(I,vl.mat) This

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

Dear all, I want to draw a barplot with the following data: [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 110.0675 118.4167 117.16 109.735416 101.6312 104.0312 101.8263 99.38541670 114.2613 [2,] 0.0000 0.0000 0.00 1.658333 0.0000 0.6250 0.0000 0.05208333 0.0000 [,10] [,11] [,12] [,13] [,14] [,15] [,16]

Hi again! Following up my previous posting below (to which no response as yet), I have located a report which situates this type of question in a longitudinal modelling context. http://www4.stat.ncsu.edu/~dzhang2/paper/glm.ps Generalized Linear Models with Longitudinal Covariates Daowen Zhang & Xihong Lin (This work seems to originally date from around 1999). They consider an outcome Y,

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