Calculate Misclassification Matrices

This function computes profile-level and attribute-level misclassification matrices from a fitted GDINA model. This function is only applicable to models with binary attributes.

Usage

CM(object, classification = "MAP", matrixtype = "profile")

Arguments

object: An estimated GDINA object returned from GDINA.
classification: A character string specifying the classification rule. Supported values are "MAP", "MLE", and "EAP". Alternatively, a matrix of user-supplied classifications can be provided, with one row per respondent and one column per attribute.
matrixtype: A character string specifying which matrix to return. Supported values are "profile", "attribute", and "both".

Value

Depending on matrixtype, the function returns one of the following:

"profile": A class-by-class matrix of estimated \(P(\hat{\alpha} = c' \mid \alpha = c)\).
"attribute": A list of \(2 \times 2\) matrices, one for each attribute, with estimated \(P(\hat{\alpha}_k = a' \mid \alpha_k = a)\).
"both": A list with elements profile_classification and att_classification.

For both pattern-level and attribute-level matrices, rows correspond to true classes and columns correspond to classified classes.

Details

The profile-level matrix is computed from posterior latent class probabilities. The attribute-level matrices are computed from marginal attribute mastery probabilities and the chosen classifications.

Author

Wenchao Ma, The University of Minnesota, wma@umn.edu

Examples

if (FALSE) { # \dontrun{
dat <- realdata_ECPE$dat
Q <- realdata_ECPE$Q
fit <- GDINA(dat = dat, Q = Q, model = "GDINA")
CM(fit)
CM(fit, matrixtype = "both")
} # }