similar to: nlme cross-over and fixed nested

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 level of significance is alpha=0.0179 (because of an

Dear R-Community! For example I have a study with 4 treatment groups (10 subjects per group) and 4 visits. Additionally, the gender is taken into account. I think - and hope this is a goog idea (!) - this data can be analysed using lme as below. In a balanced design everything is fine, but in an unbalanced design there are differences depending on fitting y~visit*treat*gender or

Dear latticists, I would like to spread 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 =

Dear helpers please provide me some helpful answer to 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) \\

Hi, all. I have a simple design I'm comparing to output 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))),

Hallo! I have two groups (placebo/verum), every subject is measured at 5 times, the first time t0 is the baseline measurement, t1 to t4 are the measurements after applying the medication (placebo or verum). The question is, if there is a significant difference in the two groups and how large the differnce is (95% confidence intervals). Let me give sample data # Data

I'm using rsync-2.5.7-2 on an updated Fedora Core 1 athlon system. The -vn option lists all files to be copied or deleted, including symlinks, but it does not appear to list empty directories to be copied, even tough it copies them. It does list empty directories to be deleted. The -v option does list and copy empty directories. -v should behave the same in both instances, and the desired

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

Dear R users The following function is R code for the main compuations in the article: M. Eliasziw, S Lorraine Young, M Gail Woodbury and Karen Fryday-Field (1994): Statistical Methodology for the Concurrent Assessment of Intrarater and Intrarater Reliability: Using Goniometric Measurements as an Example. Physical Therapy 74 (8); 777-788 The function gives the estimated inter- and intrarater

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) )

is there a way to use the actual index value for plotting zoo objects this is the way that the index is set up and a sample range of what I would like to plot 01/01/06 00:00:00 - 01/01/06 23:45:00 { library(zoo) # chron library(chron) fmt.chron <- function(x) { chron(sub(" .*", "", x), gsub(".* (.*)", "\\1:00", x)) }} x <- structure(c(15.57, 15.5,

Hello, I have a time-series that has some missing samples. I was thinking on completing them using either zero-order hold or linear interpolation. I am looking for an efiicient way (other than a loop...) of identifiying the missing time slots and filling them. Can you think of any methods that might help here? (obviously which(diff(time)>min(diff(time))) will give the locations, but what

The aggregate function for some reason will now work for me. The error I'm getting is: "Error in sort.list(y) : 'x' must be atomic for 'sort.list' Have you called 'sort' on a list?" agPriceList=aggregate(PriceList$Size, list(PriceList$bandNum),sum) *Price list dataframe:* dput(PriceList) structure(list(Price = c(0, 8.18, 8.27, 10.42, 10.5, 10.6, 11.13,

Hi all! I would like to import a matlab or gauss code to R. Could you help me? Bye, Sebasti?n. 2007/6/23, r-help-request en stat.math.ethz.ch <r-help-request en stat.math.ethz.ch>: > Send R-help mailing list submissions to > r-help en stat.math.ethz.ch > > To subscribe or unsubscribe via the World Wide Web, visit >

I'm trying to write a function to automate doing a variance analysis, part of which involves doing some further calculations. The method I've been using isn't very robust, if variable names change then it stops working. For this dummy data > dput(assayvar,"") structure(list(Run = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L), .rk.invalid.fields =

Hi there, Does anyone know how to extract elements from the table returned by Manova()? Using the univariate equivalent, Anova(), it's easy: a.an<-Anova(lm(y~x1*x2)) a.an$F This will return a vector of the F-values in order of the terms of the model. However, a similar application using Manova(): m.an<-Manova(lm(Y~x1~x2)) m.an$F Returns NULL. So does any attempt at calling the

Hello. I'm trying to find a way in R to calculate between and within subject variances and confidence intervals for some analytical method development data. I've found a reference to a method in Burdick, R. K. & Graybill, F. A. 1992, Confidence Intervals on variance components, CRC Press. This example is for Balanced Data confidence interval calculation from Pg 62. The data are

If you are interesting to play around X32, you may refer to http://sourceware.org/glibc/wiki/x32 to bootstrap a local environment on Linux. Yours - Michael -----Original Message----- From: cfe-commits-bounces at cs.uiuc.edu [mailto:cfe-commits-bounces at cs.uiuc.edu] On Behalf Of Liao, Michael Sent: Monday, June 04, 2012 5:09 PM To: llvm-commits at cs.uiuc.edu; cfe-commits at cs.uiuc.edu