MC.out.tabular: Tabular organization of accuracy and consistency output from...

In hthaa/betafunctions: Functions for Working with Two- And Four-Parameter Beta Probability Distributions and Psychometric Analysis of Classifications

Tabular organization of accuracy and consistency output from the LL.CA.MC() function.

Description

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.

Usage

MC.out.tabular(x)

Arguments

x	The list-output from the LL.CA.MC() function.

Examples

# 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)

hthaa/betafunctions documentation built on March 10, 2024, 7:20 p.m.