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Title: Sparse Latent Class Model for Cognitive Diagnosis
Version: 0.1.0
Description: Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) <doi:10.1007/s11336-019-09693-2>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
RoxygenNote: 7.2.3
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp
URL: https://github.com/tmsalab/slcm, https://tmsalab.github.io/slcm/
BugReports: https://github.com/tmsalab/slcm/issues
Suggests: edmdata
NeedsCompilation: yes
Packaged: 2023-08-11 06:44:17 UTC; ronin
Author: James Joseph Balamuta ORCID iD [aut, cre, cph], Steven Andrew Culpepper ORCID iD [aut, cph]
Maintainer: James Joseph Balamuta <balamut2@illinois.edu>
Repository: CRAN
Date/Publication: 2023-08-12 09:10:02 UTC

slcm: Sparse Latent Class Model for Cognitive Diagnosis

Description

Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) doi:10.1007/s11336-019-09693-2.

Author(s)

Maintainer: James Joseph Balamuta balamut2@illinois.edu (ORCID) [copyright holder]

Authors:

See Also

Useful links:


Print the SLCM object

Description

Custom printing class to reveal features of the fitted SLCM.

Usage

## S3 method for class 'slcm'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

x

the slcm object.

digits

the number of significant digits

...

further arguments passed to or from other methods.

Value

Print details and estimates found within the fitted SLCM. Return the model invisibly (via invisible())


Sparse Latent Class Model for Cognitive Diagnosis (SLCM)

Description

Performs the Gibbs sampling routine for a sparse latent class model as described in Chen et al. (2020) <doi: 10.1007/s11336-019-09693-2>

Usage

slcm(
  y,
  k,
  burnin = 1000L,
  chain_length = 10000L,
  psi_invj = c(1, rep(2, 2^k - 1)),
  m0 = 0,
  bq = 1
)

Arguments

y

Item Matrix

k

Dimension to estimate for Q matrix

burnin

Amount of Draws to Burn

chain_length

Number of Iterations for chain.

psi_invj, m0, bq

Additional tuning parameters.

Details

The estimates list contains the mean information from the sampling procedure. Meanwhile, the chain list contains full MCMC values. Lastly, the details list provides information regarding the estimation call.

Value

An slcm object containing three named lists:

Examples

# Use a demo data set from the paper
if(requireNamespace("edmdata", quietly = TRUE)) {
  data("items_matrix_reasoning", package = "edmdata")
  
  burnin = 50        # Set for demonstration purposes, increase to at least 1,000 in practice.
  chain_length = 100 # Set for demonstration purposes, increase to at least 10,000 in practice.  
  
  model_reasoning = slcm(items_matrix_reasoning, k = 4, 
                         burnin = burnin, chain_length = chain_length)
}

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.
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