Curtail: Simulate deterministic curtailment on item scores of a test
In marjoleinF/curtail: A package for test curtailment

Description Usage Arguments Details Value References Examples

Simulates deterministic curtailment on an existing dataset of item scores, based on a user-specified cut-off value.

1	Curtail(dataset.test, Xstar, highest = NULL, lowest = NULL, plot = TRUE)

`dataset.test`	A dataframe containing item scores only.
`Xstar`	Cut-off value to be used for classifying observations as 'at risk' (test-score values greater than or equal to the cut-off value) or 'not at risk' (test-score values less than cut-off value)
`highest`	Highest possible item score. If not provided, function will take the max of all observed item scores.
`lowest`	Lowest possible item score. If not provided, function will take the min of all observed item scores.
`plot`	Should a histogram of test lengths be plotted?

Note that currently, only items with the same number of response categories, and the same ordering of the response categories, are supported.

The code is still under development and might change in future versions.

The function prints accuracy estimates to the command line, and plots the curtailed test length distribution. In addition, the function invisibly returns a list with the following elements:

`test.results`	data.frame with columns full.lenght.decision (classification decsion according to full-length test); curtailed.decision (classification decision according to curtailed test administration); current.item (item at which testing was halted); current.score (cumulative testscore at item at which testing was halted).
`curtailed.test.length.distribution`	Descriptive statistics of number of items administered and number of tests curtailed.
`confusion.martrix`	Confusion matrix of full-length and curtailed test classification decisions.
`accuracy`	Correct classification rate (accuracy), sensitivity and specificity.

Fokkema, M., Smits, N., Finkelman, M. D., Kelderman, H., & Cuijpers, P. (2014). Curtailment: A method to reduce the length of self-report questionnaires while maintaining diagnostic accuracy. Psychiatry Research 215, 477-482.

Fokkema, M., Smits, N., Kelderman, H., Carlier, I.V. & Van Hemert, A.M. (2014). Combining decision trees and stochastic curtailment for assessment length reduction of test batteries used for classification. Applied Psychological Measurement, 38(1), 3-17.

Finkelman, M.D., Smits, N., Kim, W. & Riley, B. (2012). Curtailment and stochastic curtailment to shorten the CES-D. Applied Psychological Measurement, 36(8), 632-658.