Test-specific and Item-specific Entropy for Latent Class Models
Computes test-specific and item-specific entropy as test-diagnostic criteria of cognitive diagnostic models (Asparouhov & Muthen, 2014).
entropy.lca(object) ## S3 method for class 'entropy.lca' summary(object, digits=2, ...)
A list with the data frame entropy
as an entry.
Asparouhov, T. & Muthen, B. (2014). Variable-specific entropy contribution. Technical Appendix. http://www.statmodel.com/7_3_papers.shtml
See cdi.kli
for test diagnostic indices based on the
Kullback-Leibler information and cdm.est.class.accuracy
for calculating the classification accuracy.
############################################################################# # EXAMPLE 1: Entropy for DINA model ############################################################################# data(sim.dina, package="CDM") data(sim.qmatrix, package="CDM") # fit DINA Model mod1 <- CDM::din( sim.dina, q.matrix=sim.qmatrix, rule="DINA") summary(mod1) # compute entropy for test and items emod1 <- CDM::entropy.lca( mod1 ) summary(emod1) ## Not run: ############################################################################# # EXAMPLE 2: Entropy for polytomous GDINA model ############################################################################# data(data.pgdina, package="CDM") dat <- data.pgdina$dat q.matrix <- data.pgdina$q.matrix # pGDINA model with "DINA rule" mod1 <- CDM::gdina( dat, q.matrix=q.matrix, rule="DINA") summary(mod1) # compute entropy emod1 <- CDM::entropy.lca( mod1 ) summary(emod1) ############################################################################# # EXAMPLE 3: Entropy for MCDINA model ############################################################################# data(data.cdm02, package="CDM") dat <- data.cdm02$data q.matrix <- data.cdm02$q.matrix # estimate model with polytomous atribute mod1 <- CDM::mcdina( dat, q.matrix=q.matrix ) summary(mod1) # computre entropy emod1 <- CDM::entropy.lca( mod1 ) summary(emod1) ## End(Not run)
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