[R] Help with ICC
Richards, Tom
richards at upci.pitt.edu
Wed Nov 28 08:37:41 CET 2001
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 2 2 0.09919
5 3 1 0.16000
6 3 2 0.12000
7 4 1 0.00000
8 4 2 0.00000
9 5 1 0.44023
10 5 2 0.32977
11 6 1 2.67081
12 6 2 2.63919
13 7 1 0.09050
14 7 2 0.03950
15 8 1 0.44019
16 8 2 0.30981
17 9 1 0.59000
18 9 2 0.57000
19 10 1 4.03000
20 10 2 3.77000
Variable i is for patient, j is for occasion, and x is measured on
each occasion. These data have the same mean and variance listed on page 9
of Fleiss's book, Design and Analysis of Clinical Experiments, given below:
> lapply(split(iccdata$x,iccdata$i),function(x){c(mean(x),var(x))})
$"1"
[1] 0.23500000 0.02650062
$"2"
[1] 0.1150000000 0.0004999122
$"3"
[1] 0.1400 0.0008
$"4"
[1] 0 0
$"5"
[1] 0.385000000 0.006100706
$"6"
[1] 2.6550000000 0.0004999122
$"7"
[1] 0.0650000 0.0013005
$"8"
[1] 0.375000000 0.008499472
$"9"
[1] 0.5800 0.0002
$"10"
[1] 3.9000 0.0338
Now, how do you use (lme in) R to compute the Intraclass correlation for
these data? I have written brute-force code to get the "right" answer, but
I cannot get the proper mean squares from any regression model I have tried.
It seems like it should be trivial, but I cannot do it! I need this to work
in order to apply the technique to a much larger data set. I could use my
silly code, in SAS, but I want to see and understand the variance
components. After studying Pinheiro and Bates for a while, I naively tried
the following, which is not what I want.
> ICC <- groupedData(x~j|i,data=iccdata)
> ICC.lme <- lme(x~j,data=ICC)
Can you help me? Thanks in advance!
Tom
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