| blimit {pks} | R Documentation |
Basic Local Independence Model Identification Analysis
Description
Tests the local identifiability of a basic local independence model (BLIM).
Usage
blimit(K, beta = NULL, eta = NULL, pi = NULL, file_name = NULL)
Arguments
K |
a state-by-problem indicator matrix representing the knowledge structure. An element is one if the problem is contained in the state, and else zero. |
beta, eta, pi |
vectors of parameter values for probabilities of careless errors, lucky guesses, and knowledge states, respectively. |
file_name |
name of an output file. |
Details
See Stefanutti et al. (2012) for details.
The blimit function has been adapted from code provided by Andrea
Brancaccio, Debora de Chiusole, and Luca Stefanutti. It contains a function
to compute the reduced row echelon form based on an implementation in the
pracma package.
Value
A list having the following components:
NItems |
the number of items. |
NStates |
the number of knowledge states. |
NPar |
the number of parameters. |
Rank |
the rank of the Jacobian matrix. |
NSD |
the null space dimension. |
RankBeta, RankEta, RankPi, RankBetaEta, RankBetaPi, RankEtaPi |
the rank of submatrices of the Jacobian. |
DiagBetaEta, DiagBetaPi, DiagEtaPi, DiagBetaEtaPi |
diagnostic information about specific parameter trade-offs. |
Jacobian |
the Jacobian matrix. |
beta, eta, pi |
the parameter values used in the analysis. |
References
Stefanutti, L., Heller, J., Anselmi, P., & Robusto, E. (2012). Assessing the local identifiability of probabilistic knowledge structures. Behavior Research Methods, 44(4), 1197–1211. doi:10.3758/s13428-012-0187-z
See Also
Examples
K <- as.binmat(c("0000", "1000", "0100", "1110", "1101", "1111"))
set.seed(1234)
info <- blimit(K)