similar to: Response to query re: calculating intraclass correlations

It sounds as thought you are interested in Hoyt's Anova which is a form of generalizability theory. This is usually estimated using by getting the variance components from ANOVA. > -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Karl Knoblick > Sent: Tuesday, May 16, 2006 6:10 AM > To: r-help at

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

The R code to reproduce the solutions to the example problems in Design of Experiments with MINITAB by Paul Mathews (ASQ Quality Press, 2005) is now posted at http://www.mmbstatistical.com/DOEwithMINITAB.html. Enjoy! Paul Mathews -- Paul Mathews Mathews Malnar and Bailey, Inc. 217 Third Street, Fairport Harbor, OH 44077 Phone: 440-350-0911 Fax: 440-350-7210 E-mail: Paul: pmathews at

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 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

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

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

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

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the

Hello R users: I am fairly new to R and am trying to figure out how to compute an intraclass correlation (ICC) and/or design effect for binary data? More specifically, I am trying to determine the amount of clustering in a data set - that is, whether certain treatment programs tend to work with more or less severe clients. The outcome variable is dichotomous (low severity / high severity)

Hi, I have been working with a mixed effects model in R where I have a lot of fixed effects with a lot of variables. When I use the summary command I can only view the end of the output with the intraclass correlations and distribution of residuals. I need to be able to see the summary table at the top of the output. Is there a way to display an entire output? Thanks for the

I am fitting quantile regression models using data collected from a sample of 124 patients. When modeling cross-sectional associations, I have noticed that nonparametric bootstrap estimates of the variances of parameter estimates are much greater in magnitude than the empirical Huber estimates derived using summary.rq's "nid" option. The outcome variable is severely skewed, and I am

Hello, R-folks: Here is a statement I use to make a data frame: iccdata <- data.frame(i=rep(1:10,rep(2,10)),j=rep(1:2,10), x=c(0.35011,0.11989,0.13081,0.09919,0.16000,0.12000,0.00000,0.00000, 0.44023,0.32977,2.67081,2.63919,0.09050,0.03950,0.44019,0.30981,0.59000, 0.57000,4.03000,3.77000)) Then here are the data: > iccdata i j x 1 1 1 0.35011 2 1 2 0.11989 3 2 1 0.13081 4

Hi R users: Is there a function to obtain the correlation within groups from this very simple lme model? > modeloMx1 Linear mixed-effects model fit by REML Data: barrag Log-restricted-likelihood: -70.92739 Fixed: fza_tension ~ 1 (Intercept) 90.86667 Random effects: Formula: ~1 | molde (Intercept) Residual StdDev: 2.610052 2.412176 Number of Observations: 30 Number

Hi all, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a

Given a square matrix of variables X, is there any way to compute a multivariable correlation among all the variables in the array? It is possible to calculate the correlation of all pairs of variables in the array, but I want to know the correlation of all the variables taken together, i.e. for the matrix X=x1 x2 x3 x4 x5 x6 x7 x8 x9 I don't want the pair-wise correlations