similar to: xyplot

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

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")

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 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 useRs, I have a longitudinal experiment with several treatment groups, ~20 subjects per group, ~6 timepoints and a continuous dependent variable. I have been successfully been using lattice::xyplot with this data. However, I have been stumped with a particular application of it. I would like to use xyplot on my data, broken into treatment groups with the groups argument, using

I'm trying to understand how to plot individual growth curve trajectories, with the overall mean trajectory superimposed (preferably in a slightly thicker line, maybe in black) over the individual trajectories. Using the sleepstudy data in lme4, here is the code I have so far: library(lme4) library(lattice) xyplot(Reaction ~ Days, data = sleepstudy, group = Subject, type = 'l')

Hello. I have 5 measurement points, my dependent variable is ordinal (0 - 3), and I want to visualize my data. I'm pretty new to R. What I want is to find out whether people with different baseline covariates have different trajectories, so I want a plot with the means trajectory of my dependent variable (the individual points do not make a lot of sense in ordinal data) on each measurement

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 <-

Hi all, I am just learning R and I am trying to work through the book "Applied Longitudinal Data Analysis" by Singer & Willett. I have some code for this book that supposedly works in S-Plus (I don't have S-Plus so I can't verify that) and I am trying to run the examples in R. Most of the examples run, but I have one plot that gives me an error message. I have

Hello, I have a longitudinal data with about 30 subjects. I used xyplot() to plot the longitudinal data. One problem is that xyplot() recycles the color of auto.key so that every 7th subject has the same color (symbol if setps() was used). Is there a way so that every subject will have a unique color or symbol? Thanks Osman -- Osman O. Al-Radi, MD, MSc, FRCSC Fellow, Cardiovascular Surgery

Hi, I am a beginner of xyplot() (or lattice package). On one hand, I immediately realized it's a very powerful utility. On the other hand, there are too many things for me to learn. Still haven't figure out a generalization of the syntax and usage under many different circumstances. Let me give an example dataset:

Dear List: I have a data set with over 7000 students with about 4 observations over time per student. I want to examine the within-group fits of a random sample of this group as it takes forever to compute and draw all 7000 regressions. Here is the code I have used so far. >group<-groupedData(math~year|childid, data=scores) >group.list<-lmList(group)

Hi: In lattice, how does one handle separate graphical behavior for two different factors? In the xyplot below, the objective is to use the levels of one factor to distinguish corresponding shapes and colors, and the levels of the other factor to perform level-wise loess smooths. # Illustrative data: d <- data.frame(time = rep(1:8, each = 6), val = rnorm(48), gp1 =

Hi: Yesterday, I posted a question regarding how to handle different graphical behavior between two factors in xyplot() [package lattice]. After a public and private reply from Deepayan Sarkar, the problem has been resolved nicely, including the addition of a stacked legend for the two factors in question. The latter requires package latticeExtra. library(lattice) library(latticeExtra) # Test

I have a number of x, y observations (Time, Conc) for a number of Subjects (with subject number Subj) and Doses. I can plot the individual points with xyplot fine: xyplot(Conc ~ Time | Subj, Groups=Dose, data=myData, panel = function(x,y) { panel.xyplot(x, y) panel.superpose(???) # Needs more here } ) I also like to plot on

?kml? is an implementation of k-means for longitudinal data (or trajectories). This algorithm is able to deal with missing value and provides an easy way to re roll the algorithm several times, varying the starting conditions and/or the number of clusters looked for. KmL 1.1.1 addition: - 7 imputations methods for longitudinal data - Calculus of three qualities criterion (Calinski&Harabatz,

?kml? is an implementation of k-means for longitudinal data (or trajectories). This algorithm is able to deal with missing value and provides an easy way to re roll the algorithm several times, varying the starting conditions and/or the number of clusters looked for. KmL 1.1.1 addition: - 7 imputations methods for longitudinal data - Calculus of three qualities criterion (Calinski&Harabatz,

Dear R gurus.. Is it possible to control span settings for different values of a grouping variable, when using xyplot? an example code shown below d=data.frame(x=rep(sample(1:5,rep=F),10),y=rnorm(50),z=rep(sample(LETTERS[1:2],rep=F),25)) xyplot(y~x,data=d,groups=z,panel=panel.superpose,panel.groups=panel.loess(span=c(2/3, 3/4,1/2)) or something like..

Fellow R-users, I have a longitudinal data set with missing values in it. I would like to produce a residual plot for each time using panel.xyplot function but I get an error message. Here's a simple example, library(nlme) set.seed(1544) longdata <- data.frame(ID=gl(10,1,50), y=rnorm(50), time = as.numeric(gl(5,10,50)), x = rnorm(50)) longdata$y[5] <- NA longdata$x[35] <- NA

Hi, for the data below: time<-c(rep(1:10,5)) y<-time+rnorm(50,5,2) subject<-c(rep('a',10),rep('b',10),rep('c',10),rep('d',10),rep('e',10)) group<-c(rep('A',30),rep('B',20)) df<-data.frame(subject,group,time,y) I'd like to produce a plot with a single pannel with two loess curves one for each group. the code below does