# ccStats

0th

Percentile

##### Classification Consistency Statistics.

Provides a set of statistics often used for conveying information regarding the consistency of classifications based on tests.

##### Usage
ccStats(ii, ij, ji, jj)
##### Arguments
ii

The frequency or rate of consistent classifications into category "i".

ij

The frequency or rate of inconsistent classifications into categories "i" and "j".

ji

The frequency or rate of inconsistent classifications into categories "j" and "i".

jj

The frequency or rate of consistent classifications into category "j".

##### Value

A list of classification consistency statistics. Specifically, the coefficient of consistent classification (p), the coefficient of consistent classification by chance (p_c), and Cohen's Kappa coefficient.

##### References

Hanson, Bradley A. (1991). Method of Moments Estimates for the Four-Parameter Beta Compound Binomial Model and the Calculation of Classification Consistency Indexes. American College Testing.

• ccStats
##### Examples
# NOT RUN {
# Generate some fictional data. Say, 100 individuals take a test with a
# maximum score of 100 and a minimum score of 0.
set.seed(1234)
testdata <- rbinom(100, 100, rBeta.4P(100, .25, .75, 5, 3))
hist(testdata, xlim = c(0, 100))

# Suppose the cutoff value for attaining a pass is 50 items correct, and
# that the reliability of this test was estimated to 0.7. First, compute the
# estimated consistency matrix using LL.CA():
cmat <- LL.CA(x = testdata, reliability = .7, cut = 50, min = 0,
max = 100)\$consistencymatrix

# To estimate and retrieve consistency statistics using ccStats(),
# feed it the appropriate entries of the consistency matrix.
ccStats(ii = cmat["i", "i"], ij = cmat["i", "j"],
ji = cmat["j", "i"], jj = cmat["j", "j"])
# }

Documentation reproduced from package betafunctions, version 1.4.0, License: CC0

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