similar to: Interrater and intrarater variability (intraclass correlationcoefficients)

Hello! I want to calculate the intra- and interrater reliability of my study. The design is very simple, 5 raters rated a diagnostic score 3 times for 19 patients. Are there methods/funtions in R? I only found packages to calculate interrater variability and intraclass correlation coefficients for matrices of n*m (n subjects, m raters) - I have n subjects, m raters and r repetitions. Can

Karl, If you use one of the specialized packages to calculate your ICC, make sure that you know what you're getting. (I haven't checked the packages out myself, so I don't know either.) You might want to read David Futrell's article in the May 1995 issue of Quality Progress where he describes six different ways to calculate ICCs from the same data set, all with different

Hello dear R help group. I encountered this old thread (http://tinyurl.com/dklgsk) containing the a similar question to the one I have, but left without an answer. I am and hoping one of you might help. A simplified situation: I have a factorial design (with 2^3 experiment combinations), for 167 subjects, each one has answered the same question twice (out of a bunch of "types" of

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

Hi All: Could anyone point me to a package that can calculate stratified kappa? My design is like this, 4 raters, 30 types of diagnosis scores, 20 patients. Each rater will rate each patient for each type of diagnosis score. The rater's value is nominal. I know I can measure the agreement between raters for each type of diagnosis score, e.g., calculate out 30 kappa values. My problem is I

Hi, all There are 62 samples in my data and I tested 3 times for each one, then I want to use ICC(intraclass correlation) from irr package to test the consistency among the tests. *combatexpdata_p[1:62] is the first text results and combatexpdata_p[63:124] * is the second one and *combatexpdata_p[125:186]* is the third. Here is the result:

Hi, Can R calculate an intraclass correlation coefficient for clustered data, when the outcome variable is dichotomous? By now I calculate it by hand, estimating between- and intracluster variance by one-way ANOVA - however I don't feel very comfortable about this, since the distributional assumptions are not really met.... Maybe anyone can help me? Best regards and many many thanks,

Hi, I'm trying to run a ICC calculation on a data frame. I get the following error message: Error in data.frame(x.s, subs = rep(paste("S", 1:n.obs, sep = ""), nj)) : arguments imply differing number of rows: 1700, 1750 I've looked at the data in the file and it seems to be okay. Any thoughts would be much appreciated. I'm getting used to R so if possible, I

I have a quick statistical question and hoped somebody has a tip for me without me having to go to the local statistician on Monday. I assess 4 statements from 90 subjects. Each of the 4 statements receives one of three responses (say -1, 0, or 1). I can use Cramer's V or Spearman correlations to assess the correlation between each pair of statements, but I am looking for a measure of

Hello! I have the problem that in my function the passed variable is not used, but the variable name of the dataframe itself?- difficult to explain, but an easy example: TestFunc<-function(df, group) { ??? print(names(subset(df, select=group))) } df1<-data.frame(group="G1", visit="V1", value=0.9) TestFunc(df1, c("group", "visit")) Result: [1]

Hallo! GENERAL QUESTION: I'm trying to change the tick marks of the x-axis in a grouped data plot (nlme). CONCRETE EXAMPLE: In the example (see below) I want the x-axis to have tick marks at 0, 6, 12, 18, 24. How can I do this? WHAT I TRIED I tried "normal" methods like axis(...) but this does not work with this plot. And I also tried xlim=c(0,24) but the ticks are unchanged and

Hallo! I want to understand / recalculate what is done to get the CI of the logistic regression evaluated with lrm. As far as I came back, my problem is the variance-covariance matrix fit$var of the fit (fit<-lrm(...), fit$var). Here what I found and where I stucked: ----------------- library(Design) # data D<-c(rep("a", 20), rep("b", 20)) V<-0.25*(1:40) V[1]<-25

Hallo! I have a problem with fitting data with nls. The first example with y1 (data frame df1) shows an error, the second works fine. Is there a possibility to get a fit (e.g. JMP can fit also data I can not manage to fit with R). Sometimes I also got an error singularity with starting parameters. # x-values x<-c(-1,5,8,11,13,15,16,17,18,19,21,22) # y1-values (first data set)

Dear all, i need help about comparing 2 alpha cronbach. How to derive W statistic. Are alpha cronbach and Hoyt's method using ANOVA identical? Thank you for your help. Regards --------------------------------- Luggage? GPS? Comic books? [[alternative HTML version deleted]]

Dear R-users, consider a 2-level linear mixed effects model (LME) with random intercept AND random slope for level 1 AND 2. Does anybody know how to calculate Intraclass-coefficient (ICC) for highest (innermost) level 2 ??? In the literature, I did not find an example for these kind of komplex models. For 1-level Random-Intercept models it would be easy: ICC = variance due to the clustering

Hi! Is there a possibilty in R to carry out LOCF (Last Observation Carried Forward) analysis or to create a new data frame (array, matrix) with LOCF? Or some helpful functions, packages? Karl --------------------------------- Gesendet von http://mail.yahoo.de Schneller als Mail - der neue Yahoo! Messenger. [[alternative HTML version deleted]]

I have a spectrophotometric dataset with repeated measures of a value at 200 wavelengths for each of 150 individuals. I would like to use the repeated samples to at each wavelength to look at measurement/observer error, compared to difference between individuals error I have looked at doing this with icc{irr} or using an anova approach, but I am unclear how to acheive this given that there

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

Hallo! I read SPSS data in the following way: library(Hmisc) library(foreign) dat<-spss.get("surv_abb.sav") In R1.9.1 I got the message: "Error in all(arg == choices) : Object "typeDate" not found" In R1.8.0 the same script works fine. Does anybody know a possibilty to read a SPSS file under R1.9.1? Thanks! Karl

Hallo! I want to use the package bayesmix. Trying the examples I had no success. The reason is, I think: > haveJAGS() [1] FALSE Have read of JAGS executable (I could not find JAGS.exe in my R directory). What is JAGS? Where can I find or download it? Karl