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 list elements (notation follows Barnhart et al. 2002):
occc
: the value of the overall concordance correlation coefficient (\rho_{o}^{c}
),
oprec
: overall precision (\rho
),
oaccu
: overall accuracy (\chi^{a}
),
pairs
: a list with following elements (only if pairs = TRUE
, otherwise NULL
;
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 (\rho_{jk}^{c}
),
prec
: pairwise precision values (\rho_{jk}
),
accu
: pairwise accuracy values (\chi_{jk}^{a}
),
ksi
: pairwise weights (\xi_{jk}
),
scale
: pairwise scale values (v_{jk}
),
location
: pairwise location values (u_{jk}
),
data.name
: name of the input data dat
.
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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