search for: nsubj

...dmit that nesting is nowhere indicated. I know how to handle the case is Seq if assumed a random effect, but I don't see why it should be random, AND I want an estimate of the effect. So, if the question is stupid, please help me out at least an tell my WHY. # cross-over design library(nlme) NSubj<-20 # Placebo Phase # Seq: 1 for "Placebo first phase", 2 for "Verum first phase" glup<-data.frame(Patient=as.factor(1:NSubj), Seq=as.factor(rep(c(1,2),NSubj/2)), Treat=rep("Pla",NSubj), Glu=rnorm(NSubj,30,4) ) #...

Hallo! I have data of the following design: NSubj were measured at Baseline (visit 1) and at 3 following time points (visit 2, visit 3, visit 4). There is or is not a treatment. Most interesting is the question if there is a difference in treatment between the results of visit 4 and baseline. (The other time points are also of interest.) The leve...

I have been asked to analyse the results of (what is to me) a very complicated experiment. The dependent measure is the estimated distance, which is measured as a function of the actual distance. There are also several other IVs. The plot of log estimated distance as a function of log distance is linear. So in the rest of the analysis I will use logestimate and logdistance. My plan is to see

...but in an unbalanced design there are differences depending on fitting y~visit*treat*gender or y~gender*visit*treat - at least with anova (see example). Does this make sense? Which ordering might be the correct one? Here the example script: library(nlme) set.seed(123) # Random generation of data: NSubj<-40 # No. of subjects set.seed(1234) id<-factor(rep(c(1:NSubj),4)) # ID of subjects treat<-factor(rep(rep(1:4,each=5),4)) # Treatment 4 Levels gender<-factor(rep(rep(1:2, each=20),4)) visit<-factor(rep(1:4, each=NSubj)) y<-runif(4*NSubj) # Results # Add effects y<-y+0.01*as.int...

...read a lattice conditioned plot over multiple pages, keeping the same layout as if I had only one page as shown in the code below. My workaround is to divide the dataframe into subset that fit on one page, but the code is ugly. Is there a build-in way to achieve this? Dieter library(lattice) nsubj = 13 # This number is variable dt = expand.grid(time=1:20,comp=LETTERS[1:3],subj=letters[1:nsubj]) dt$val = rnorm(nrow(dt)) #pdf(file="multpageOk.pdf") # How it should look: xyplot(val~time|subj+comp, data=dt,type="l",layout=c(10,3), subset=as.integer(subj) <= 10) #dev.off...

...my problem while I m trying to run a program .I m attaching both the program and the data to which I have to obtain my estimation results. "Motives.dat" is the data file, and "OBTfile4.3" is the complete code of program. by Running this // rawdata<-matrix(scan(inputFile, n = nsubj*ncomp), nsubj, ncomp, byrow = TRUE) \\ The error appears to be // Error in file(file, "r") : unable to open connection In addition: Warning message: cannot open file 'motives_pc.dat', reason 'No such file or directory' in: file(file, "r") \\ where # name of pref...

...ction. Tore Wentzel-Larsen statistician Centre for Clinical Research Haukeland University Hospital N-5021 Bergen, Norway email: tore.wentzel-larsen at helse-bergen.no the function: --------------------------------------------------------------------------------- relInterIntra<-function(Results,nsubj=40,nrater=3,nmeas=2,raterLabels=c('a','b','c'),rho0inter=0.6,rho0intra=.8,conf.level=.95) { # gives the reliability coefficients in the article by Eliasziw et. al. 1994; Phys. Therapy 74.8; 777-788. # all references in this function are to this article. # input: Results, dat...

...t from SPSS. the design is 1 repeated measure (session) and 1 between measure (cond). my dependent measure is rl. here is the data I'm using (in a data.frame): mig <- data.frame(subj=factor(rep(subj,3)), cond=factor(rep(cond,3)), session=factor(c(rep(1,nsubj),rep(2,nsubj),rep(3,nsubj))), rl) > mig subj cond session rl 1 401.1 NW 1 6.4081 2 402.1 NW 1 5.8861 3 500.1 NWC 1 5.3492 4 502.1 NWC 1 8.5302 5 601.1 NWR 1 2.7519 6 602.1 NWR 1 4.5404 7 603.1 NWR 1 4.344...

First of all, I really like R! Still being a newbie, I find things (the difficult ones) to be very simple. Alas, some 'simple' things still escape me. (Maybe the tutorials are often too much focused on the 'difficult' items??) Here comes my 'problem', over which I have sweated for the last 2 hours: My data are of a matrix 10x31, Likert Scale (1-5). 10 questions, 31

When finally got some time to do some coding, I started and stopped right after. The stationary test is a good starting point because it demonstrates how we should be able to move the very basic R matrices. I have a real- world small N data set with rows: id(n=1)---t1---variable1 ... id=(N=20)---T=21---variable1 Thus, a good test case. For first id I was considering something like this: lag

Hello, I have a multi-year dataset (see below) with date, a data value and a flag for the data value. I want to find the monthly median for each month in this dataset and then plot it. If anyone has suggestions they would be greatly apperciated. It should be noted that there are some dates with no values and they should be removed. Thanks Emily > print ( str(data$flow$daily) )

...g on fitting y~visit*treat*gender or y~gender*visit*treat - at least with anova (see example). Does this make sense? Which ordering might be the correct one? > > > > Here the example script: > > library(nlme) > > set.seed(123) > > # Random generation of data: > > NSubj<-40 # No. of subjects > > set.seed(1234) > > id<-factor(rep(c(1:NSubj),4)) # ID of subjects > > treat<-factor(rep(rep(1:4,each=5),4)) # Treatment 4 Levels > > gender<-factor(rep(rep(1:2, each=20),4)) > > visit<-factor(rep(1:4, each=NSubj)) > > y&lt...

...intervals > reported > # for the reliability coefficients > # output reformatted as an "irrlist" stucture - Jim Lemon 2009-05-27 > > relInterIntra<-function(x,nrater=1,raterLabels=NULL, >  rho0inter=0.6,rho0intra=0.8,conf.level=.95) { > >  xdim<-dim(x) >  nsubj<-xdim[1] >  nmeas<-xdim[2]/nrater >  if(is.null(raterLabels)) raterLabels<-letters[1:nrater] >  Frame1<-data.frame(cbind(rep(1:nsubj,nrater*nmeas), >  rep(1:nrater,rep(nsubj*nmeas,nrater)), >  rep(rep(1:nmeas,rep(nsubj,nmeas)),nrater), >  matrix(as.matrix(x),ncol=1)))...