similar to: Bland-Altman method to measure agreement with repeated measures

Dear R people Could you please help with following Trying to compare accuracy of tumor size evaluation by different methods. So data looks like id true metod1 method2 ... 1 2 2 2.5 2 1.5 2 2 3 2 2 2 etc. Could you please give a hint how to deal with that. Seems like {merror} does not suite to me because I am trying to compare accuracy of measurements with their true known values not just

> De : Bill Venables <venables at acland.qld.cmis.csiro.au> > Objet : Re: [R] paste ? > Date?: mardi 25 avril 2000 08:45 > (...) > Secondly, I'm curious about the history of this kind of plot. > I've only heard it called a "Tukey mean difference" plot, (and > Trellis graphics has a function, tmd(), that does it, but no one > knows about it...).

Hello, I have the following data.frame structure(list(Study = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,

Dear all; A very basic question. I have the following data: ************************************************************************************ A <- 1/1000*c(347,328,129,122,18,57,105,188,57,257,53,108,336,163, 62,112,334,249,45,244,211,175,174,26,375,346,153,32, 89,32,358,202,123,131,88,36,30,67,96,135,219,122, 89,117,86,169,179,54,48,40,54,568,664,277,91,290,

Hi This is probably more of a statistics question than a specific R question, although I will be using R and need to know how to solve the problem in R. I have several sets of data (ejection fraction measurements) taken in various ways from the same set of (~400) patients (so it is paired data). For each individual measurement I can make an estimate of the percentage uncertainty in the

Dear all, sorry for this perhaps stupid question, but it concerns my recent problem as well as the current thread on B-A plot. I would like to plot something like x+y vs. x-y or mean(x,y) vs difference (x,y), but my "variables" x,y are actually pairs of replicates (two observed values of a growth rate by 60 clones). So the order x,y does not matter and each x,y pair can be arranged

Dear friends. I've made a very simple procedure to make Bland-Altman plots and it works OK except for the inability to take a variate as argument in annotation. I'm sure it is a very simple error, so if you have 5 seconds please tell me. Best wishes Troels bland <- function (x) #accepts two columns { mn <- 0.5*(x[,1]+x[,2]) diff <- x[,1]-x[,2]

Hi. I have data which i would want to assess the degree of agreement between two assays, e.g., to evaluate reproducibility or for inter-rater reliability. I have used the Pearson product-moment correlation coefficient. It looks good ranginging between 0.90 to 0.998. Though this looks good. I am told the Concordance correlation coefficient will give a better picture of how reproducible the assay

Hi I have a long data set on which I want to do Bland-Altman style plots for each rhythm type Using ggplot2, when I use geom_hline with facet_grid I get an extra set of empty panels. I can't get it to do it with the "Diamonds" data supplied with the package so here is a (much abbreviated) example: > lvexs cvd_basestudy ecd_rhythm fixed_time variable_time 1 CBP05J02

Dear R-helpers, This may sound simple to you, but I'm a beginner in this, so please be forgiving. I have a following problem: two analytes were measured in patient's blood on 4 occasions: ProteinA and ProteinB. How to correctly evaluate correlation between ProteinA and ProteinB? I tried: x <- data.frame(Patient.ID=rep(1:10, each=4), Visit=rep(c(1:4),10), ProteinA=rnorm(m=10,

There is now an updated version of "Using R for Data Analysis & Graphics" on CRAN, under Documentation | Contributed. Data sets and scripts are available from my web page, to which there is a link on the relevant CRAN page. John Maindonald email: john.maindonald at anu.edu.au phone : (6125)3473 fax : (6125)5549 Statistical Consulting Unit, Room 1194,

Hi, I would like to compare two models in R with the same dependant variable but different predictors (two different types of frequency and reaction times as RT). An editor told me to have a look at Lorch and Myers 1990. Lorch and Myers use the following technique: 1) they perform regressions on individual subjects' data 2) they extract the beta weights 3) they run t-test on these beta

Hello all. I'm having a problem debugging an IVR I'm building. I can't see any reason this shouldn't be working. Firstly the asterisk version is: Asterisk SVN-trunk-r7230 built by root @ localhost.localdomain on a i686 running Linux on 2006-02-17 22:44:48 UTC Basically the problem is this. While the playbacks are happening you can push any one of the options and to happily

I have a new device that takes measurements anywhere from every second, to every 15 minutes (depending on changes). The matrix has a date, time and Y column (Y is the measurement). For three days it is 25,000 rows. How do I average the measurements by every 30 minutes so my matrix is 48 rows per day? I have been working on this and cannot figure out a simple method. Any ideas? Thank you. ----- In

I'm working on an IVR that gives the users the option (number 5 in the main menu) to dial by extension: exten => 5,1,Set(TIMEOUT(digit)=5) ; Dial Extension exten => 5,2,Set(TIMEOUT(response)=10) exten => 5,3,Background(LCL/prompt-60) exten => 5,4,WaitExten(15) When going option 5 you can dial some extensions such as 2802, it goes to the extension (all extens start with 28 on the

Is length(unique()) what you are looking for? Andy > From: Christoph Lehmann > > Hi I have a question concerning aggregation > > (simple demo code S. below) > > I have the data.frame > > id meas date > 1 a 0.637513747 1 > 2 a 0.187710063 2 > 3 a 0.247098459 2 > 4 a 0.306447690 3 > 5 b 0.407573577 2 > 6 b

I have the code below and it works fine if I print the xyplot but if I take the print out, then I just get a blank pdf. The same holds if I just send the plot to the console without the print ( I get nothing ). My question is whether this is always the case with xyplot or is there something wrong with my settings ? I am on linux ( redhat ) and using R.2.5.0. Thanks. load("stocks.dat")

Dear listers, I am trying to fit a model using nlsList() using alternately a SSfol() selfstart function or its developped equivalent formulae. This preliminary trial works well mydata<-groupedData(Conc~Tps|Organ,data=mydata) mymod1<-nls(Conc~SSfol(Dose,Tps,lKe,lKa,lCl),data=mydata) as well as a developped form: mymod2<-nls(Conc~Dose * exp(lKe+lKa-lCl) *

Hi, I wonder if anybody could help me in converting this easy SAS program into R. (I'm still trying to do that!) PROC IMPORT OUT= WORK.CHLA_italian DATAFILE= "C:\Documents and Settings\carleal\My Documents\REBECCA\stat\sas\All&nutrients.xls" DBMS=EXCEL2000 REPLACE; GETNAMES=YES; RUN; data chla_italian; set chla_italian;

I have a combined date and time. I would like to separate them out into two columns so I can do things such as take the mean by time across all dates. meas<-runif(435) nTime<-seq(1303975800, 1304757000, 1800) nDateT<-as.POSIXct(nTime, origin="1970-01-01") mat1<-cbind(nDateT,meas) means1<- aggregate(mat1$meas, list(nDateT), mean) This doesn't do anything as each day