extract {Qval} | R Documentation |
Extract Components from Qval Package Objects
Description
A unified extractor function for retrieving internal components from objects produced by the Qval package. This method allows users to access key elements such as model results, validation logs, and simulation settings in a structured and object-oriented manner.
Usage
extract(object, what, ...)
## S3 method for class 'CDM'
extract(object, what, ...)
## S3 method for class 'validation'
extract(object, what, ...)
## S3 method for class 'sim.data'
extract(object, what, ...)
## S3 method for class 'fit'
extract(object, what, ...)
Arguments
object |
An object of class |
what |
A character string specifying the name of the component to extract. |
... |
Additional arguments (currently ignored). |
Details
This generic extractor supports three core object classes: CDM
, validation
,
sim.data
and fit
.
It is intended to streamline access to commonly used internal components without manually referencing object slots.
The available components for each class are listed below:
CDM
Cognitive Diagnosis Model fitting results. Available components:
- analysis.obj
The internal model fitting object (e.g., GDINA or Baseline Model).
- alpha
Estimated attribute profiles (EAP estimates) for each respondent.
- P.alpha.Xi
Posterior distribution of latent attribute patterns.
- alpha.P
Marginal attribute mastery probabilities (estimated).
- P.alpha
Prior attribute probabilities at convergence.
- Deviance
Negative twice the marginal log-likelihood (model deviance).
- npar
Number of free parameters estimated in the model.
- AIC
Akaike Information Criterion.
- BIC
Bayesian Information Criterion.
- call
The original model-fitting function call.
- ...
validation
Q-matrix validation results. Available components:
- Q.orig
The original Q-matrix submitted for validation.
- Q.sug
The suggested (revised) Q-matrix after validation.
- time.cost
Total computation time for the validation procedure.
- process
Log of Q-matrix modifications across iterations.
- iter
Number of iterations performed during validation.
- priority
Attribute priority matrix (available for PAA-based methods only).
- Hull.fit
Data required to plot the Hull method results (for Hull-based validation only).
- call
The original function call used for validation.
sim.data
Simulated data and parameters used in cognitive diagnosis simulation studies:
- dat
Simulated dichotomous response matrix (
N \times I
).- Q
Q-matrix used to generate the data.
- attribute
True latent attribute profiles (
N \times K
).- catprob.parm
Item-category conditional success probabilities (list format).
- delta.parm
Item-level delta parameters (list format).
- higher.order.parm
Higher-order model parameters (if used).
- mvnorm.parm
Parameters for the multivariate normal attribute distribution (if used).
- LCprob.parm
Latent class-based success probability matrix.
- call
The original function call that generated the simulated data.
fit
Relative fit indices (-2LL, AIC, BIC, CAIC, SABIC) and absolute fit indices (
M_2
test,RMSEA_2
, SRMSR):- npar
The number of parameters.
- -2LL
The Deviance.
- AIC
The Akaike information criterion.
- BIC
The Bayesian information criterion.
- CAIC
The consistent Akaike information criterion.
- SABIC
The Sample-size Adjusted BIC.
- M2
A vector consisting of
M_2
statistic, degrees of freedom, significance level, andRMSEA_2
(Liu, Tian, & Xin, 2016).- SRMSR
The standardized root mean squared residual (SRMSR; Ravand & Robitzsch, 2018).
Value
The requested component. The return type depends on the specified what
and the class of the object
.
Methods (by class)
-
extract(CDM)
: Extract fields from a CDM object -
extract(validation)
: Extract fields from a validation object -
extract(sim.data)
: Extract fields from a sim.data object -
extract(fit)
: Extract fields from a fit object
References
Khaldi, R., Chiheb, R., & Afa, A.E. (2018). Feed-forward and Recurrent Neural Networks for Time Series Forecasting: Comparative Study. In: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications (LOPAL 18). Association for Computing Machinery, New York, NY, USA, Article 18, 1–6. DOI: 10.1145/3230905.3230946.
Liu, Y., Tian, W., & Xin, T. (2016). An application of M2 statistic to evaluate the fit of cognitive diagnostic models. Journal of Educational and Behavioral Statistics, 41, 3–26. DOI: 10.3102/1076998615621293.
Ravand, H., & Robitzsch, A. (2018). Cognitive diagnostic model of best choice: a study of reading comprehension. Educational Psychology, 38, 1255–1277. DOI: 10.1080/01443410.2018.1489524.
Examples
library(Qval)
set.seed(123)
################################################################
# Example 1: sim.data extraction #
################################################################
Q <- sim.Q(3, 10)
data.obj <- sim.data(Q, N = 200)
extract(data.obj, "dat")
################################################################
# Example 2: CDM extraction #
################################################################
CDM.obj <- CDM(data.obj$dat, Q)
extract(CDM.obj, "alpha")
extract(CDM.obj, "AIC")
################################################################
# Example 3: validation extraction #
################################################################
validation.obj <- validation(data.obj$dat, Q, CDM.obj)
Q.sug <- extract(validation.obj, "Q.sug")
print(Q.sug)
################################################################
# Example 4: fit extraction #
################################################################
fit.obj <- fit(data.obj$dat, Q.sug, model="GDINA")
extract(fit.obj, "M2")