epi.occc | R Documentation |
Overall concordance correlation coefficient (OCCC) for agreement on a continuous measure based on Lin (1989, 2000) and Barnhart et al. (2002).
epi.occc(dat, na.rm = FALSE, pairs = FALSE)
## S3 method for class 'epi.occc'
print(x, ...)
## S3 method for class 'epi.occc'
summary(object, ...)
dat |
a matrix, or a matrix like object. Rows correspond to cases/observations, columns corresponds to raters/variables. |
na.rm |
logical. Should missing values (including |
pairs |
logical. Should the return object contain pairwise statistics? See Details. |
x , object |
an object of class |
... |
further arguments passed to |
The index proposed by Barnhart et al. (2002) is the same as the index suggested by Lin (1989) in the section of future studies with a correction of a typographical error in Lin (2000).
An object of class epi.occc
with the following elements (notation follows Barnhart et al. 2002):
occc : |
the value of the overall concordance correlation coefficient, |
oprec : |
overall precision, |
oaccu : |
overall accuracy, |
data.name : |
name of the input data, |
If pairs = TRUE
a list with the following elements. Column indices for the pairs (j,k) follow lower-triangle column-major rule based on a ncol(x)
times ncol(x)
matrix):
ccc : |
pairwise CCC values, |
prec : |
pairwise precision values, |
accu : |
pairwise accuracy values, |
ksi : |
pairwise weights, |
scale : |
pairwise scale values, |
location : |
pairwise location values, |
Peter Solymos, solymos@ualberta.ca.
Barnhart H X, Haber M, Song J (2002). Overall concordance correlation coefficient for evaluating agreement among multiple observers. Biometrics 58: 1020 - 1027.
Lin L (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics 45: 255 - 268.
Lin L (2000). A note on the concordance correlation coefficient. Biometrics 56: 324 - 325.
epi.ccc
## EXAMPLE 1:
## Generate some rating data:
## Not run:
set.seed(1234)
p <- runif(n = 10, min = 0, max = 1)
x <- replicate(n = 5, expr = rbinom(n = 10, size = 4, prob = p) + 1)
rval.occc01 <- epi.occc(dat = x, pairs = TRUE)
print(rval.occc01); summary(rval.occc01)
## End(Not run)
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