similar to: Longitudinal categorical response data

Dear R List, I want to download kinship_1.2_S.tar.gz in http://mayoresearch.mayo.edu/mayo/research/biostat/splusfunctions.cfm to R. Once save this file to C:\, how I could load into R? I am working in Windows XP. Usually what I do is, I go to "packages" and then "install packages from local zip files". This procedure fails for .tar.gz files. Can someone help here please....

Dear list   I have 5 markers that can be used to detect an infection in combination. Could you please advise me how to use functions in ROCR/ other package to produce the ROC curve for a combination of markers?   I have used the following to get ROC statistics for each marker. pred <- prediction(y$marker1, y$infectn) perf <-performance(pred,"tpr","fpr")

Hi   I am using following code to produce a xyplot for some longitudinal data. There are 2 panels. It produced all longitudinal trajectories with mean profile. But since the dataset it very large plot looks very messy. I want to show, say 10 randomly selected individual longitudinal trajectories together with mean profile for entire dataset. Could any help me to alter the following code to do

Hello, I am working in a longitudinal study, with a categorical variable as a dependent variable (alcohol consumption: no use, use, abuse and dependence) with repeated measures (baseline, 1 year, 2 years). Besides I have another variable with two groups: control and experimental. I would like to analyze the evolution in each group, I have though in lme, but I am not sure if I can do an lme with

Dear List,   I am new to ROC analysis and the package ROCR. I want to compute the confidence intervals of sensitivity and specificity for a given cutoff value. I have used the following to calculate sensitivity and specificity:   data(ROCR.simple) pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)   se.sp <- function (cutoff, performance) {     sens <-

Dear list members, I am estimating an ordinal generalized estimating equations (GEE) regression model on repeated measurements data. Is there any way (preferably in R) to test the proportional odds assumption of this model? Thanks in advance. Andreas [[alternative HTML version deleted]]

Background: Our research group collected data from students via the web about their drinking habits (alcohol) over the last 90 days. As you might guess, some students seem to have lost interest and completed some information but not all. Unfortunately, the survey was programmed to "pre-populate" the fields with zeroes (to make it easier for students to complete). Obviously, when

Dear list, I'd like to create a visual plot of a clmm() I've fitted using the 'ordinal' package in R. It's possible to do this with a glm() by using the 'effects' package. For example: library(effects) data(BEPS) mod <- lm(political.knowledge ~ age + gender + vote, data=BEPS) eff <- effect("age", mod, default.levels=100) plot(eff,

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 all, I am using "coxme" function in Kinship library to fit random treatment effect nested within centre. I got 3 treatments (0,1,2) and 3 centres. I used following commands, but got an error. > ugroup=paste(rep(1:3,each=3),rep(0:2,3),sep='/') > mat1=bdsmatrix(rep(c(1,1,1,1,1,1,1,1,1),3),blocksize=rep(3,3),dimnames=list(ugroup,ugroup)) >

Hi all, I am trying to fit a random effect  model to categorical response variable using package "ordinal" /"clmm". How can I find the correlation between random effects (random intercept and random slope)   Thanks in advance  Ana [[alternative HTML version deleted]]

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

Dear List, I am using both the clm() and clmm() functions from the R package 'ordinal'. I am fitting an ordinal dependent variable with 5 categories to 9 continuous predictors, all of which have been normalised (mean subtracted then divided by standard deviation), using a probit link function. From this global model I am generating a confidence set of 200 models using clm() and 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 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 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,

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

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.