similar to: intraclass correlation

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

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! 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

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

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

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, 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:

I am trying to model a dependent variable as a threshold function of my independent variable. What I mean is that I want to fit different intercepts to y following 2 breakpoints, but with fixed slopes. I am trying to do this with using ifelse statements in the nls function. Perhaps, this is not an appropriate approach. I have created a very simple example to illustrate what I am trying to do.

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

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

Hi R-users, I just started learning R. I have a project on lot quality assurance sampling (LQAS). In this project I have to develop LQAS plans to make decision on stopping / continuing a programme. The LQAS plans is based on cluster sampling: selection of k clusters (villages) of m children each for a total sample of n. In this connection I would like to estimate the classification errors ( alpha

Hi R-users, I just started learning R. I have a project on lot quality assurance sampling (LQAS). In this project I have to develop LQAS plans to make decision on stopping / continuing a programme. The LQAS plans is based on cluster sampling: selection of k clusters (villages) of m children each for a total sample of n. In this connection I would like to estimate the classification errors ( alpha

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

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

Dear all, I have a question on the use of GAM with repeated measures. My dataset is as follows: - a number of study areas where bird abundance has been determined. Counts have been performed in 3 consecutive years and there were 2 counts per year (i.e. in total 6 counts). - a number of environmental predictors that do not change over year Xi). When using a GLM, a repeated measures design would

Greetings- Running R 2.4.0 under Debian Linux, I am getting a memory error trying to read a very large file: > library(foreign) > oldgrades.df <- read.spss('Individual grades with AI (Nov 7 2006).sav',to.data.frame=TRUE) Error: cannot allocate vector of size 10826 Kb This file is, granted, quite large: aperrin at perrin:/data0/grading$ ls -l total 630304 -r-xr-xr-x 1 aperrin

Dear all, I have been searching far and wide for a solution to the problem of negative intracluster correlation in the case of a binomial response variable (also known as under-dispersion). Prentice (1986) (http://www.jstor.org/stable/2289219) developed an extension of the beta-binomial model which allowed for this negative correlation. Palmquist