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Cognitive diagnosis and Q-matrix
governance
The cognitive-diagnosis layer supplies transparent Q-matrix audits,
attribute-profile enumeration, and deterministic DINA
ideal-response/probability calculations.
Q <- rbind(c(1,0), c(0,1), c(1,1), c(1,0))
aud <- eyeprocess_cdm_qmatrix_audit(Q)
aud
#> eyeprocess CDM Q-matrix audit
#> items : 4
#> attributes: 2
#> empty items: 0 | unmeasured attributes: 0
profiles <- eyeprocess_cdm_attribute_profiles(2)[, c("A1","A2")]
eta <- eyeprocess_cdm_dina_ideal_response(Q, profiles)
eyeprocess_cdm_dina_probability(eta)
#> [,1] [,2] [,3] [,4]
#> [1,] 0.2 0.2 0.2 0.2
#> [2,] 0.9 0.2 0.2 0.9
#> [3,] 0.2 0.9 0.2 0.2
#> [4,] 0.9 0.9 0.9 0.9
These utilities do not replace full cognitive-diagnosis estimation,
Q-matrix validation, or model comparison.
fit_eyeprocess_gdina() delegates exact fitting to GDINA and
gates cleanly when unavailable.
Primary package source: https://cran.r-project.org/package=GDINA.
Visual audit
A compact deterministic Q-matrix makes the structural audit visible.
The display concerns declared item-attribute structure and does not by
itself establish substantive validity.
viz_Q <- rbind(
c(1, 0),
c(0, 1),
c(1, 1),
c(1, 0),
c(0, 1),
c(1, 1)
)
rownames(viz_Q) <- paste0('Item ', seq_len(nrow(viz_Q)))
colnames(viz_Q) <- c('Attribute 1', 'Attribute 2')
viz_qmatrix <- eyeprocess::eyeprocess_cdm_qmatrix_audit(viz_Q)
stopifnot(
inherits(viz_qmatrix, 'eye_cdm_qmatrix_audit')
)
plot(viz_qmatrix)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.
