dBeta.pBeta: An implementation of a Beta-density Compound Cumulative-Beta...

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

An implementation of a Beta-density Compound Cumulative-Beta Distribution.

Description

The Beta Compound Beta distribution: The product of the four-parameter Beta probability density function and the Beta cumulative probability function. Used in the Livingston and Lewis approach to classification accuracy and consistency, the output can be interpreted as the population density of passing scores produced at "x" (a value of true-score).

Usage

dBeta.pBeta(x, l, u, alpha, beta, n, c, lower.tail = FALSE)

Arguments

x	x-axis input for which p (proportion or probability) is to be computed.
l	The lower-bound of the four-parameter Beta distribution.
u	The upper-bound of the four-parameter Beta distribution.
alpha	The alpha shape-parameter of the Beta density distribution.
beta	The beta shape-parameter of the Beta density distribution.
n	The number of trials for the Beta cumulative probability distribution.
c	The "true-cut" (proportion) of on the Beta cumulative probability distribution.
lower.tail	Logical. Whether to compute the lower or upper tail of the Beta cumulative probability distribution. Default is FALSE (i.e., upper tail).

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 Research Report Series.

Livingston, Samuel A. and Lewis, Charles. (1995). Estimating the Consistency and Accuracy of Classifications Based on Test Scores. Journal of Educational Measurement, 32(2).

Lord, Frederic M. (1965). A Strong True-Score Theory, With Applications. Psychometrika, 30(3).

Examples

# Given a four-parameter Beta distribution with parameters l = 0.25, u = 0.75,
# alpha = 5, and beta = 3, and a Beta error distribution with number of
# trials (n) = 10 and a cutoff-point (c) at 50% correct (i.e., proportion correct
# of 0.5), the population density of passing scores produced at true-score
# (x) = 0.5 can be calculated as:
dBeta.pBeta(x = 0.5, l = 0.25, u = 0.75, a = 5, b = 3, n = 10, c = 0.5)

# Conversely, the density of failing scores produced at x can be calculated
# by passing the additional argument "lower.tail = TRUE" to the function.
# That is:
dBeta.pBeta(x = 0.5, l = 0.25, u = 0.75, a = 5, b = 3, n = 10, c = 0.5,
lower.tail = TRUE)

# By integration, the population proportion of (e.g.) passing scores in some
# region of the true-score distribution (e.g. between 0.25 and 0.5) can be
# calculated as:
integrate(function(x) { dBeta.pBeta(x, 0.25, 0.75, 5, 3, 10, 0.5) },
lower = 0.25, upper = 0.5)

betafunctions documentation built on May 29, 2024, 1:13 a.m.