CA: Calculate classification accuracy

In GDINA: The Generalized DINA Model Framework

Calculate classification accuracy

Description

This function calculate test-, pattern- and attribute-level classification accuracy indices based on GDINA estimates from the GDINA function using approaches in Iaconangelo (2017) and Wang, Song, Chen, Meng, and Ding (2015). It is only applicable for dichotomous attributes.

Usage

CA(GDINA.obj, what = "MAP")

Arguments

GDINA.obj	estimated GDINA object returned from GDINA
what	what attribute estimates are used? Default is "MAP".

Value

a list with elements

tau

estimated test-level classification accuracy, see Iaconangelo (2017, Eq 2.2)

tau_l

estimated pattern-level classification accuracy, see Iaconangelo (2017, p. 13)

tau_k

estimated attribute-level classification accuracy, see Wang, et al (2015, p. 461 Eq 6)

CCM

Conditional classification matrix, see Iaconangelo (2017, p. 13)

Author(s)

Wenchao Ma, The University of Alabama, [e-mail]

References

Iaconangelo, C.(2017). Uses of Classification Error Probabilities in the Three-Step Approach to Estimating Cognitive Diagnosis Models. (Unpublished doctoral dissertation). New Brunswick, NJ: Rutgers University.

Ma, W., & de la Torre, J. (2020). GDINA: An R Package for Cognitive Diagnosis Modeling. Journal of Statistical Software, 93(14), 1-26.

Wang, W., Song, L., Chen, P., Meng, Y., & Ding, S. (2015). Attribute-Level and Pattern-Level Classification Consistency and Accuracy Indices for Cognitive Diagnostic Assessment. Journal of Educational Measurement, 52 , 457-476.

Examples

## Not run: 
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
fit <- GDINA(dat = dat, Q = Q, model = "GDINA")
fit
CA(fit)

## End(Not run)

GDINA documentation built on July 9, 2023, 6:16 p.m.