View source: R/classification.R
MC.out.tabular | R Documentation |
LL.CA.MC()
function.Function that takes the output from the LL.CA.MC()
function and organizes it in a table with accuracy and consistency indices represented by columns and categories as rows.
MC.out.tabular(x)
x |
The list-output from the |
# Generate some fictional data. Say, 1000 individuals take a test with a
# maximum score of 100 and a minimum score of 0.
set.seed(1234)
p.success <- rBeta.4P(1000, 0.1, 0.95, 5, 3)
for (i in 1:100) {
if (i == 1) {
rawdata <- matrix(nrow = 1000, ncol = 100)
}
rawdata[, i] <- rbinom(1000, 1, p.success)
}
# Estimate accuracy and consistency where the lowest category are scores
# below 50, second lowest 60, then 70, 80, and 90. Using the cba() function
# to estimate the reliability of this test, to use the LL.CA.MC() function
# or estimating diagnostic performance and consistency indices of
# classifications when using several cut-points:
output <- LL.CA.MC(rowSums(rawdata), cba(rawdata), seq(50, 90, 10), 0, 100)
# As this output can get quite verbose as the number of categories increase,
# the MC.out.tabular() function can be used to organize the output more
# concisely in a tabular format.
MC.out.tabular(output)
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