extract.RdA 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.
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, ...)
# S3 method for class 'is.Qident'
extract(object, what, ...)
# S3 method for class 'att.hierarchy'
extract(object, what, ...)An object of class CDM, validation,
sim.data, fit, is.Qident,
att.hierarchy.
A character string specifying the name of the component to extract.
Additional arguments (currently ignored).
The requested component. The return type depends on the specified what and the class of the object.
This generic extractor supports three core object classes: CDM, validation,
sim.data, fit, is.Qident, att.hierarchy.
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:
CDMCognitive Diagnosis Model fitting results. Available components:
analysis.objThe internal model fitting object (e.g., GDINA or Baseline Model).
alphaEstimated attribute profiles (EAP estimates) for each respondent.
P.alpha.XiPosterior distribution of latent attribute patterns.
alpha.PMarginal attribute mastery probabilities (estimated).
P.alphaPrior attribute probabilities at convergence.
patternThe attribute mastery pattern matrix containing all possible attribute mastery pattern.
DevianceNegative twice the marginal log-likelihood (model deviance).
nparNumber of free parameters estimated in the model.
AICAkaike Information Criterion.
BICBayesian Information Criterion.
callThe original model-fitting function call.
...validationQ-matrix validation results. Available components:
Q.origThe original Q-matrix submitted for validation.
Q.sugThe suggested (revised) Q-matrix after validation.
time.costTotal computation time for the validation procedure.
processLog of Q-matrix modifications across iterations.
iterNumber of iterations performed during validation.
priorityAttribute priority matrix (available for PAA-based methods only).
Hull.fitData required to plot the Hull method results (for Hull-based validation only).
callThe original function call used for validation.
sim.dataSimulated data and parameters used in cognitive diagnosis simulation studies:
datSimulated dichotomous response matrix (\(N \times I\)).
QQ-matrix used to generate the data.
attributeTrue latent attribute profiles (\(N \times K\)).
catprob.parmItem-category conditional success probabilities (list format).
delta.parmItem-level delta parameters (list format).
higher.order.parmHigher-order model parameters (if used).
mvnorm.parmParameters for the multivariate normal attribute distribution (if used).
LCprob.parmLatent class-based success probability matrix.
callThe original function call that generated the simulated data.
fitRelative fit indices (-2LL, AIC, BIC, CAIC, SABIC) and absolute fit indices (\(M_2\) test, \(RMSEA_2\), SRMSR):
nparThe number of parameters.
-2LLThe Deviance.
AICThe Akaike information criterion.
BICThe Bayesian information criterion.
CAICThe consistent Akaike information criterion.
SABICThe Sample-size Adjusted BIC.
M2A vector consisting of \(M_2\) statistic, degrees of freedom, significance level, and \(RMSEA_2\) (Liu, Tian, & Xin, 2016).
SRMSRThe standardized root mean squared residual (SRMSR; Ravand & Robitzsch, 2018).
is.QidentResults of whether the Q-matrix is identifiable:
completenessTRUE if \(K \times K\) identity submatrix exists.
distinctnessTRUE if remaining columns are distinct.
repetitionTRUE if every attribute appears more than 3 items.
genericCompletenessTRUE if two different generic complete \(K \times K\) submatrices exist.
genericRepetitionTRUE if at least one '1' exists outside those submatrices.
Q1, Q2Identified generic complete submatrices (if found).
Q.starRemaining part after removing rows in Q1 and Q2.
locallyGenericIdentifiabilityTRUE if local generic identifiability holds.
globallyGenericIdentifiabilityTRUE if global generic identifiability holds.
Q.reconstructed.DINAReconstructed Q-matrix with low-frequency attribute moved to first column.
att.hierarchyResults of iterative attribute hierarchy exploration:
noSigTRUE all structural parameters are not greater than 0.
isNonvergeTRUE if convergence was achieved.
statisticA 4-column data.frame results for each structural parameter that is significantly larger than 0.
patternThe attribute pattern matrix under iterative attribute hierarchy.
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
extract(is.Qident): Extract fields from a is.Qident object
extract(att.hierarchy): Extract fields from a att.hierarchy object
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.
library(Qval)
set.seed(123)
# \donttest{
################################################################
# Example 1: sim.data extraction #
################################################################
Q <- sim.Q(3, 10)
data.obj <- sim.data(Q, N = 200)
#> distribute = uniform
#> model = GDINA
#> number of attributes: 3
#> number of items: 10
#> num of examinees: 200
#> average of P0 = 0.174
#> average of P1 = 0.889
extract(data.obj, "dat")
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 1 1 0 1 1 0 0 1 0 0
#> [2,] 0 1 0 0 0 1 1 0 1 1
#> [3,] 1 1 1 0 0 1 0 1 1 0
#> [4,] 0 1 1 0 0 1 1 1 1 1
#> [5,] 0 1 1 0 0 1 0 1 1 0
#> [6,] 1 1 1 1 1 0 1 1 1 1
#> [7,] 1 0 0 0 1 1 1 1 0 1
#> [8,] 0 0 1 0 0 1 0 0 0 0
#> [9,] 0 1 0 0 0 0 1 1 0 1
#> [10,] 0 1 1 0 0 0 1 0 0 1
#> [11,] 0 0 1 0 0 1 1 0 1 1
#> [12,] 1 1 1 1 1 1 1 1 1 1
#> [13,] 1 1 0 0 1 1 0 1 1 0
#> [14,] 0 1 1 1 1 1 1 1 1 0
#> [15,] 0 1 1 0 1 0 1 0 1 1
#> [16,] 0 1 0 0 0 1 1 0 0 1
#> [17,] 0 1 0 0 0 0 0 0 0 0
#> [18,] 0 0 0 0 0 0 1 0 0 1
#> [19,] 1 1 1 0 0 1 0 0 1 0
#> [20,] 0 0 0 0 0 0 0 0 0 0
#> [21,] 1 1 1 1 0 1 1 1 0 1
#> [22,] 1 0 1 0 0 0 1 0 1 1
#> [23,] 1 1 1 1 0 1 0 1 1 0
#> [24,] 0 0 0 0 0 0 0 0 0 0
#> [25,] 1 1 1 1 1 0 0 1 1 0
#> [26,] 0 0 0 0 0 0 1 0 0 1
#> [27,] 0 1 0 1 1 0 0 1 0 0
#> [28,] 1 1 1 1 1 0 0 1 1 0
#> [29,] 1 1 1 0 1 1 1 1 0 0
#> [30,] 1 1 0 0 1 0 0 1 0 1
#> [31,] 1 1 0 1 1 1 1 1 0 1
#> [32,] 1 0 0 0 1 0 1 1 1 1
#> [33,] 0 1 1 0 0 0 0 0 1 0
#> [34,] 1 1 0 1 1 0 1 1 1 1
#> [35,] 1 0 0 0 1 0 0 1 1 1
#> [36,] 1 1 0 0 1 1 1 1 0 1
#> [37,] 1 1 1 1 1 0 0 1 1 0
#> [38,] 1 1 1 1 1 1 0 1 1 0
#> [39,] 1 1 1 1 0 1 1 1 1 1
#> [40,] 0 0 0 0 0 0 0 0 0 0
#> [41,] 1 0 1 0 0 0 1 1 0 1
#> [42,] 0 1 0 0 1 0 1 1 0 1
#> [43,] 0 0 0 0 0 1 0 0 0 1
#> [44,] 0 0 0 0 0 0 1 0 0 1
#> [45,] 1 1 0 0 1 0 0 1 0 0
#> [46,] 0 0 1 0 0 1 1 0 1 1
#> [47,] 0 1 1 1 0 0 0 0 1 0
#> [48,] 1 1 0 0 1 0 1 1 0 1
#> [49,] 1 1 1 1 1 0 0 1 1 0
#> [50,] 0 0 1 0 0 0 0 1 1 1
#> [51,] 0 0 0 0 0 1 0 0 0 0
#> [52,] 1 1 0 0 1 0 0 1 0 0
#> [53,] 1 1 1 0 1 0 1 1 1 1
#> [54,] 0 1 0 0 0 0 1 0 0 0
#> [55,] 1 1 1 1 0 1 1 1 1 1
#> [56,] 1 1 0 0 1 0 1 1 0 1
#> [57,] 0 0 0 0 0 1 0 1 0 1
#> [58,] 1 1 1 0 0 1 0 1 1 1
#> [59,] 0 0 1 0 0 0 0 0 1 0
#> [60,] 1 1 1 1 1 1 1 1 1 1
#> [61,] 1 1 1 1 1 1 1 1 1 1
#> [62,] 1 1 0 1 0 1 1 1 0 1
#> [63,] 1 0 0 0 1 0 0 1 0 1
#> [64,] 1 1 1 1 1 1 1 1 1 0
#> [65,] 0 0 0 0 0 0 0 0 0 0
#> [66,] 1 1 0 0 1 1 1 0 1 1
#> [67,] 1 1 1 0 1 0 1 1 1 1
#> [68,] 1 1 1 1 0 1 1 1 1 1
#> [69,] 0 1 0 0 0 0 0 0 1 0
#> [70,] 1 0 0 1 1 1 1 1 1 1
#> [71,] 0 1 1 0 0 0 1 0 1 1
#> [72,] 1 1 1 1 1 1 1 1 1 1
#> [73,] 1 1 1 1 1 0 0 1 0 1
#> [74,] 0 1 0 0 0 1 0 1 1 1
#> [75,] 1 1 1 1 1 0 0 1 1 0
#> [76,] 1 1 0 0 1 1 1 1 0 1
#> [77,] 0 0 0 0 0 0 1 1 0 1
#> [78,] 1 1 1 1 1 1 0 1 1 0
#> [79,] 0 0 1 0 0 1 1 0 1 1
#> [80,] 0 1 1 0 0 0 1 1 0 1
#> [81,] 1 0 1 0 1 1 1 0 1 1
#> [82,] 0 0 0 0 0 1 0 0 1 0
#> [83,] 0 0 0 0 0 0 1 1 1 1
#> [84,] 0 0 0 0 0 0 1 0 1 1
#> [85,] 0 1 0 0 0 1 1 0 1 1
#> [86,] 1 1 0 1 1 1 1 1 1 0
#> [87,] 0 0 0 0 0 0 1 0 0 1
#> [88,] 0 0 0 0 0 0 1 0 0 1
#> [89,] 1 1 0 0 1 0 1 0 0 0
#> [90,] 0 1 1 1 1 1 0 1 1 0
#> [91,] 1 0 0 0 0 1 1 1 0 0
#> [92,] 1 1 0 0 1 0 0 0 0 1
#> [93,] 1 0 1 0 1 1 1 1 1 1
#> [94,] 0 0 0 0 0 1 0 1 0 1
#> [95,] 1 1 0 1 1 0 1 1 1 1
#> [96,] 1 0 0 1 1 0 1 1 0 1
#> [97,] 0 1 0 0 1 1 1 1 1 1
#> [98,] 0 0 1 0 0 0 0 0 1 0
#> [99,] 1 1 1 1 1 1 1 1 1 1
#> [100,] 1 1 0 1 1 0 1 1 0 1
#> [101,] 0 1 1 0 0 1 0 0 1 0
#> [102,] 0 1 0 1 1 1 1 1 0 0
#> [103,] 1 1 1 0 1 1 0 1 1 0
#> [104,] 1 1 1 1 0 1 1 1 1 1
#> [105,] 0 0 0 0 0 1 0 0 0 0
#> [106,] 0 1 1 0 1 1 0 1 1 0
#> [107,] 0 0 1 0 1 1 0 0 1 0
#> [108,] 1 1 0 1 0 0 0 1 1 0
#> [109,] 0 1 1 0 0 1 1 0 1 1
#> [110,] 1 1 1 1 1 1 0 1 1 0
#> [111,] 1 1 1 1 1 0 0 1 0 0
#> [112,] 0 0 0 0 0 1 1 1 0 1
#> [113,] 0 1 0 0 1 0 1 1 1 1
#> [114,] 1 1 0 1 1 1 1 1 0 1
#> [115,] 1 1 1 0 1 1 0 1 1 0
#> [116,] 1 1 1 1 1 1 1 0 1 0
#> [117,] 1 1 1 1 1 1 1 1 1 1
#> [118,] 1 0 1 1 0 0 0 1 1 0
#> [119,] 0 0 0 0 0 1 1 0 0 1
#> [120,] 0 0 1 0 0 1 1 0 1 1
#> [121,] 1 1 0 0 1 1 1 1 0 1
#> [122,] 1 1 1 1 1 1 1 1 1 1
#> [123,] 1 1 1 1 1 1 1 1 1 1
#> [124,] 1 1 1 1 0 1 1 1 1 1
#> [125,] 0 0 1 0 0 0 1 1 1 1
#> [126,] 0 1 1 1 1 0 0 1 1 1
#> [127,] 0 1 1 0 0 1 1 1 1 0
#> [128,] 1 1 0 1 1 0 0 1 0 0
#> [129,] 1 1 0 1 1 1 1 1 0 1
#> [130,] 0 1 0 0 1 0 1 1 1 1
#> [131,] 1 0 0 0 1 0 0 1 0 0
#> [132,] 0 0 1 0 0 1 1 1 1 1
#> [133,] 0 0 1 0 0 0 1 0 1 1
#> [134,] 0 0 0 0 0 1 1 0 0 0
#> [135,] 1 0 1 0 0 0 0 1 1 0
#> [136,] 0 1 0 0 1 1 1 1 1 1
#> [137,] 0 1 0 0 0 1 1 0 0 1
#> [138,] 0 0 1 0 0 0 0 0 0 0
#> [139,] 1 0 1 1 1 1 0 1 1 1
#> [140,] 0 0 0 0 0 1 1 0 0 1
#> [141,] 0 1 0 1 1 0 1 1 1 1
#> [142,] 0 1 1 0 0 0 1 1 1 1
#> [143,] 0 0 1 0 0 1 0 0 1 0
#> [144,] 1 1 1 1 1 1 1 1 1 1
#> [145,] 1 1 0 0 1 0 0 1 1 0
#> [146,] 1 0 1 1 1 0 1 1 1 0
#> [147,] 1 1 1 1 0 0 0 1 1 0
#> [148,] 1 1 0 0 1 0 1 0 0 0
#> [149,] 0 0 0 0 0 1 0 0 0 0
#> [150,] 0 1 1 1 1 1 1 1 1 1
#> [151,] 0 1 0 1 0 1 1 1 1 1
#> [152,] 1 1 0 0 0 1 1 1 1 1
#> [153,] 0 1 0 1 1 1 1 1 1 0
#> [154,] 0 0 1 0 0 0 0 0 0 1
#> [155,] 1 1 1 1 1 1 1 1 1 1
#> [156,] 1 1 0 1 0 0 1 1 1 1
#> [157,] 1 1 0 0 1 0 1 0 0 1
#> [158,] 0 0 1 0 0 0 1 0 1 0
#> [159,] 1 1 1 1 1 1 1 1 1 1
#> [160,] 1 1 0 0 1 0 0 1 0 1
#> [161,] 1 1 1 1 1 1 1 1 1 1
#> [162,] 0 1 1 0 0 0 0 0 1 0
#> [163,] 0 1 1 0 0 0 1 0 1 1
#> [164,] 1 0 0 0 1 1 0 1 0 1
#> [165,] 1 1 0 1 1 1 0 1 0 0
#> [166,] 1 0 0 0 0 0 0 1 1 1
#> [167,] 0 0 0 0 1 0 1 1 0 1
#> [168,] 0 0 0 0 0 0 0 0 0 0
#> [169,] 0 0 0 0 0 0 1 0 0 1
#> [170,] 1 1 1 0 0 1 1 1 1 0
#> [171,] 1 0 0 0 1 0 1 0 0 1
#> [172,] 0 0 1 0 0 0 1 1 1 0
#> [173,] 0 0 0 0 0 0 1 1 0 0
#> [174,] 1 1 0 1 0 0 0 1 1 1
#> [175,] 1 1 1 1 0 1 1 1 1 0
#> [176,] 1 1 0 1 1 1 1 0 0 1
#> [177,] 0 1 1 0 0 1 1 1 1 1
#> [178,] 0 0 1 0 0 0 1 0 1 1
#> [179,] 0 0 0 0 0 0 1 0 0 1
#> [180,] 0 1 1 0 0 1 0 0 1 1
#> [181,] 0 0 1 0 1 0 1 0 1 1
#> [182,] 1 0 0 0 1 1 1 1 0 1
#> [183,] 1 1 0 0 1 0 1 0 0 1
#> [184,] 0 0 0 0 0 0 0 0 0 0
#> [185,] 1 1 1 1 1 1 1 1 1 1
#> [186,] 1 1 0 0 1 0 0 1 0 0
#> [187,] 1 0 1 1 1 1 1 1 1 0
#> [188,] 0 0 0 0 0 0 1 0 0 1
#> [189,] 1 0 0 0 0 0 1 0 0 1
#> [190,] 0 1 1 0 0 0 1 0 1 0
#> [191,] 0 1 0 0 1 1 1 1 1 1
#> [192,] 1 1 0 0 1 0 1 1 1 1
#> [193,] 0 0 1 0 0 1 1 0 1 1
#> [194,] 1 1 1 1 1 1 1 1 1 0
#> [195,] 1 1 1 1 0 0 0 1 0 0
#> [196,] 0 1 1 1 0 0 1 1 1 1
#> [197,] 0 0 0 0 0 1 0 0 0 0
#> [198,] 1 1 1 1 1 1 1 1 1 1
#> [199,] 1 1 1 1 1 1 1 1 1 1
#> [200,] 0 0 0 0 0 1 1 0 0 1
################################################################
# Example 2: CDM extraction #
################################################################
CDM.obj <- CDM(data.obj$dat, Q)
#>
Iter = 1 Max. abs. change = 0.36945 Deviance = 2399.11
Iter = 2 Max. abs. change = 0.08253 Deviance = 2235.47
Iter = 3 Max. abs. change = 0.04375 Deviance = 2220.28
Iter = 4 Max. abs. change = 0.02282 Deviance = 2216.47
Iter = 5 Max. abs. change = 0.01113 Deviance = 2215.10
Iter = 6 Max. abs. change = 0.01075 Deviance = 2214.35
Iter = 7 Max. abs. change = 0.01064 Deviance = 2213.79
Iter = 8 Max. abs. change = 0.01039 Deviance = 2213.32
Iter = 9 Max. abs. change = 0.01000 Deviance = 2212.90
Iter = 10 Max. abs. change = 0.00948 Deviance = 2212.52
Iter = 11 Max. abs. change = 0.00888 Deviance = 2212.17
Iter = 12 Max. abs. change = 0.00822 Deviance = 2211.86
Iter = 13 Max. abs. change = 0.00754 Deviance = 2211.58
Iter = 14 Max. abs. change = 0.00687 Deviance = 2211.34
Iter = 15 Max. abs. change = 0.00624 Deviance = 2211.12
Iter = 16 Max. abs. change = 0.00565 Deviance = 2210.93
Iter = 17 Max. abs. change = 0.00512 Deviance = 2210.77
Iter = 18 Max. abs. change = 0.00465 Deviance = 2210.62
Iter = 19 Max. abs. change = 0.00423 Deviance = 2210.50
Iter = 20 Max. abs. change = 0.00386 Deviance = 2210.39
Iter = 21 Max. abs. change = 0.00353 Deviance = 2210.29
Iter = 22 Max. abs. change = 0.00324 Deviance = 2210.20
Iter = 23 Max. abs. change = 0.00298 Deviance = 2210.12
Iter = 24 Max. abs. change = 0.00274 Deviance = 2210.05
Iter = 25 Max. abs. change = 0.00253 Deviance = 2209.98
Iter = 26 Max. abs. change = 0.00234 Deviance = 2209.92
Iter = 27 Max. abs. change = 0.00217 Deviance = 2209.87
Iter = 28 Max. abs. change = 0.00201 Deviance = 2209.82
Iter = 29 Max. abs. change = 0.00186 Deviance = 2209.77
Iter = 30 Max. abs. change = 0.00173 Deviance = 2209.73
Iter = 31 Max. abs. change = 0.00161 Deviance = 2209.69
Iter = 32 Max. abs. change = 0.00150 Deviance = 2209.65
Iter = 33 Max. abs. change = 0.00140 Deviance = 2209.62
Iter = 34 Max. abs. change = 0.00130 Deviance = 2209.59
Iter = 35 Max. abs. change = 0.00121 Deviance = 2209.56
Iter = 36 Max. abs. change = 0.00113 Deviance = 2209.53
Iter = 37 Max. abs. change = 0.00106 Deviance = 2209.51
Iter = 38 Max. abs. change = 0.00099 Deviance = 2209.49
Iter = 39 Max. abs. change = 0.00092 Deviance = 2209.47
Iter = 40 Max. abs. change = 0.00086 Deviance = 2209.44
Iter = 41 Max. abs. change = 0.00081 Deviance = 2209.43
Iter = 42 Max. abs. change = 0.00075 Deviance = 2209.41
Iter = 43 Max. abs. change = 0.00070 Deviance = 2209.39
Iter = 44 Max. abs. change = 0.00066 Deviance = 2209.38
Iter = 45 Max. abs. change = 0.00061 Deviance = 2209.36
Iter = 46 Max. abs. change = 0.00057 Deviance = 2209.35
Iter = 47 Max. abs. change = 0.00054 Deviance = 2209.34
Iter = 48 Max. abs. change = 0.00050 Deviance = 2209.32
Iter = 49 Max. abs. change = 0.00047 Deviance = 2209.31
Iter = 50 Max. abs. change = 0.00044 Deviance = 2209.30
Iter = 51 Max. abs. change = 0.00041 Deviance = 2209.29
Iter = 52 Max. abs. change = 0.00039 Deviance = 2209.28
Iter = 53 Max. abs. change = 0.00036 Deviance = 2209.28
Iter = 54 Max. abs. change = 0.00034 Deviance = 2209.27
Iter = 55 Max. abs. change = 0.00032 Deviance = 2209.26
Iter = 56 Max. abs. change = 0.00030 Deviance = 2209.25
Iter = 57 Max. abs. change = 0.00028 Deviance = 2209.25
Iter = 58 Max. abs. change = 0.00026 Deviance = 2209.24
Iter = 59 Max. abs. change = 0.00024 Deviance = 2209.24
Iter = 60 Max. abs. change = 0.00023 Deviance = 2209.23
Iter = 61 Max. abs. change = 0.00021 Deviance = 2209.23
Iter = 62 Max. abs. change = 0.00020 Deviance = 2209.22
Iter = 63 Max. abs. change = 0.00019 Deviance = 2209.22
Iter = 64 Max. abs. change = 0.00017 Deviance = 2209.21
Iter = 65 Max. abs. change = 0.00016 Deviance = 2209.21
Iter = 66 Max. abs. change = 0.00015 Deviance = 2209.21
Iter = 67 Max. abs. change = 0.00014 Deviance = 2209.20
Iter = 68 Max. abs. change = 0.00013 Deviance = 2209.20
Iter = 69 Max. abs. change = 0.00013 Deviance = 2209.20
Iter = 70 Max. abs. change = 0.00012 Deviance = 2209.19
Iter = 71 Max. abs. change = 0.00011 Deviance = 2209.19
Iter = 72 Max. abs. change = 0.00010 Deviance = 2209.19
Iter = 73 Max. abs. change = 0.00010 Deviance = 2209.19
extract(CDM.obj, "alpha")
#> A1 A2 A3
#> [1,] 0 0 1
#> [2,] 1 1 0
#> [3,] 0 1 1
#> [4,] 1 1 0
#> [5,] 0 1 0
#> [6,] 1 1 1
#> [7,] 1 0 1
#> [8,] 0 0 0
#> [9,] 1 0 0
#> [10,] 1 1 0
#> [11,] 1 1 0
#> [12,] 1 1 1
#> [13,] 0 1 1
#> [14,] 1 1 1
#> [15,] 1 1 0
#> [16,] 1 0 0
#> [17,] 0 0 0
#> [18,] 1 0 0
#> [19,] 0 1 0
#> [20,] 0 0 0
#> [21,] 1 1 1
#> [22,] 1 1 0
#> [23,] 0 1 1
#> [24,] 0 0 0
#> [25,] 0 1 1
#> [26,] 1 0 0
#> [27,] 0 0 1
#> [28,] 0 1 1
#> [29,] 1 0 1
#> [30,] 0 0 1
#> [31,] 1 0 1
#> [32,] 1 0 1
#> [33,] 0 1 0
#> [34,] 1 0 1
#> [35,] 0 0 1
#> [36,] 1 0 1
#> [37,] 0 1 1
#> [38,] 0 1 1
#> [39,] 1 1 1
#> [40,] 0 0 0
#> [41,] 1 0 1
#> [42,] 1 0 1
#> [43,] 0 0 0
#> [44,] 1 0 0
#> [45,] 0 0 1
#> [46,] 1 1 0
#> [47,] 0 1 0
#> [48,] 1 0 1
#> [49,] 0 1 1
#> [50,] 0 1 0
#> [51,] 0 0 0
#> [52,] 0 0 1
#> [53,] 1 1 1
#> [54,] 1 0 0
#> [55,] 1 1 1
#> [56,] 1 0 1
#> [57,] 0 0 0
#> [58,] 0 1 1
#> [59,] 0 1 0
#> [60,] 1 1 1
#> [61,] 1 1 1
#> [62,] 1 0 1
#> [63,] 0 0 1
#> [64,] 1 1 1
#> [65,] 0 0 0
#> [66,] 1 0 1
#> [67,] 1 1 1
#> [68,] 1 1 1
#> [69,] 0 1 0
#> [70,] 1 1 1
#> [71,] 1 1 0
#> [72,] 1 1 1
#> [73,] 0 1 1
#> [74,] 0 1 0
#> [75,] 0 1 1
#> [76,] 1 0 1
#> [77,] 1 0 0
#> [78,] 0 1 1
#> [79,] 1 1 0
#> [80,] 1 1 0
#> [81,] 1 1 0
#> [82,] 0 0 0
#> [83,] 1 0 0
#> [84,] 1 0 0
#> [85,] 1 1 0
#> [86,] 1 1 1
#> [87,] 1 0 0
#> [88,] 1 0 0
#> [89,] 1 0 1
#> [90,] 0 1 1
#> [91,] 1 0 1
#> [92,] 0 0 1
#> [93,] 1 1 1
#> [94,] 0 0 0
#> [95,] 1 0 1
#> [96,] 1 0 1
#> [97,] 1 0 1
#> [98,] 0 1 0
#> [99,] 1 1 1
#> [100,] 1 0 1
#> [101,] 0 1 0
#> [102,] 1 0 1
#> [103,] 0 1 1
#> [104,] 1 1 1
#> [105,] 0 0 0
#> [106,] 0 1 1
#> [107,] 0 1 0
#> [108,] 0 1 1
#> [109,] 1 1 0
#> [110,] 0 1 1
#> [111,] 0 1 1
#> [112,] 1 0 0
#> [113,] 1 0 1
#> [114,] 1 0 1
#> [115,] 0 1 1
#> [116,] 1 1 1
#> [117,] 1 1 1
#> [118,] 0 1 1
#> [119,] 1 0 0
#> [120,] 1 1 0
#> [121,] 1 0 1
#> [122,] 1 1 1
#> [123,] 1 1 1
#> [124,] 1 1 1
#> [125,] 1 1 0
#> [126,] 0 1 1
#> [127,] 1 1 0
#> [128,] 0 0 1
#> [129,] 1 0 1
#> [130,] 1 0 1
#> [131,] 0 0 1
#> [132,] 1 1 0
#> [133,] 1 1 0
#> [134,] 1 0 0
#> [135,] 0 1 0
#> [136,] 1 0 1
#> [137,] 1 0 0
#> [138,] 0 0 0
#> [139,] 0 1 1
#> [140,] 1 0 0
#> [141,] 1 0 1
#> [142,] 1 1 0
#> [143,] 0 1 0
#> [144,] 1 1 1
#> [145,] 0 0 1
#> [146,] 1 1 1
#> [147,] 0 1 1
#> [148,] 1 0 1
#> [149,] 0 0 0
#> [150,] 1 1 1
#> [151,] 1 1 1
#> [152,] 1 0 1
#> [153,] 1 1 1
#> [154,] 0 0 0
#> [155,] 1 1 1
#> [156,] 1 0 1
#> [157,] 1 0 1
#> [158,] 1 1 0
#> [159,] 1 1 1
#> [160,] 0 0 1
#> [161,] 1 1 1
#> [162,] 0 1 0
#> [163,] 1 1 0
#> [164,] 0 0 1
#> [165,] 0 0 1
#> [166,] 0 0 1
#> [167,] 1 0 1
#> [168,] 0 0 0
#> [169,] 1 0 0
#> [170,] 1 1 1
#> [171,] 1 0 1
#> [172,] 1 1 0
#> [173,] 1 0 0
#> [174,] 0 1 1
#> [175,] 1 1 1
#> [176,] 1 0 1
#> [177,] 1 1 0
#> [178,] 1 1 0
#> [179,] 1 0 0
#> [180,] 0 1 0
#> [181,] 1 1 0
#> [182,] 1 0 1
#> [183,] 1 0 1
#> [184,] 0 0 0
#> [185,] 1 1 1
#> [186,] 0 0 1
#> [187,] 1 1 1
#> [188,] 1 0 0
#> [189,] 1 0 0
#> [190,] 1 1 0
#> [191,] 1 0 1
#> [192,] 1 0 1
#> [193,] 1 1 0
#> [194,] 1 1 1
#> [195,] 0 1 1
#> [196,] 1 1 1
#> [197,] 0 0 0
#> [198,] 1 1 1
#> [199,] 1 1 1
#> [200,] 1 0 0
extract(CDM.obj, "AIC")
#> AIC
#> 2295.184
################################################################
# Example 3: validation extraction #
################################################################
validation.obj <- validation(data.obj$dat, Q, CDM.obj)
#> GDI method with PAA in test level iteration ...
#> Iter = 1/ 1, 1 items have changed, ΔPVAF=0.02933
Q.sug <- extract(validation.obj, "Q.sug")
print(Q.sug)
#> A1 A2 A3
#> item 1 0 0 1
#> item 2 0 1 1
#> item 3 0 1 0
#> item 4 0 1 1
#> item 5 0 0 1
#> item 6 1 1 1
#> item 7 1 0 0
#> item 8 0 1 1
#> item 9 0 1 0
#> item 10 1 0 0
################################################################
# Example 4: fit extraction #
################################################################
fit.obj <- fit(data.obj$dat, Q.sug, model="GDINA")
#>
Iter = 1 Max. abs. change = 0.32132 Deviance = 2386.78
Iter = 2 Max. abs. change = 0.08058 Deviance = 2235.13
Iter = 3 Max. abs. change = 0.03608 Deviance = 2222.28
Iter = 4 Max. abs. change = 0.01655 Deviance = 2218.94
Iter = 5 Max. abs. change = 0.01094 Deviance = 2217.54
Iter = 6 Max. abs. change = 0.01122 Deviance = 2216.66
Iter = 7 Max. abs. change = 0.01145 Deviance = 2215.96
Iter = 8 Max. abs. change = 0.01151 Deviance = 2215.37
Iter = 9 Max. abs. change = 0.01136 Deviance = 2214.84
Iter = 10 Max. abs. change = 0.01098 Deviance = 2214.37
Iter = 11 Max. abs. change = 0.01042 Deviance = 2213.94
Iter = 12 Max. abs. change = 0.00971 Deviance = 2213.57
Iter = 13 Max. abs. change = 0.00893 Deviance = 2213.25
Iter = 14 Max. abs. change = 0.00812 Deviance = 2212.97
Iter = 15 Max. abs. change = 0.00732 Deviance = 2212.74
Iter = 16 Max. abs. change = 0.00658 Deviance = 2212.53
Iter = 17 Max. abs. change = 0.00589 Deviance = 2212.36
Iter = 18 Max. abs. change = 0.00527 Deviance = 2212.21
Iter = 19 Max. abs. change = 0.00472 Deviance = 2212.09
Iter = 20 Max. abs. change = 0.00423 Deviance = 2211.98
Iter = 21 Max. abs. change = 0.00379 Deviance = 2211.89
Iter = 22 Max. abs. change = 0.00341 Deviance = 2211.80
Iter = 23 Max. abs. change = 0.00307 Deviance = 2211.73
Iter = 24 Max. abs. change = 0.00277 Deviance = 2211.67
Iter = 25 Max. abs. change = 0.00250 Deviance = 2211.61
Iter = 26 Max. abs. change = 0.00226 Deviance = 2211.57
Iter = 27 Max. abs. change = 0.00205 Deviance = 2211.52
Iter = 28 Max. abs. change = 0.00186 Deviance = 2211.48
Iter = 29 Max. abs. change = 0.00169 Deviance = 2211.45
Iter = 30 Max. abs. change = 0.00153 Deviance = 2211.42
Iter = 31 Max. abs. change = 0.00140 Deviance = 2211.39
Iter = 32 Max. abs. change = 0.00127 Deviance = 2211.36
Iter = 33 Max. abs. change = 0.00116 Deviance = 2211.34
Iter = 34 Max. abs. change = 0.00106 Deviance = 2211.32
Iter = 35 Max. abs. change = 0.00097 Deviance = 2211.30
Iter = 36 Max. abs. change = 0.00089 Deviance = 2211.29
Iter = 37 Max. abs. change = 0.00082 Deviance = 2211.27
Iter = 38 Max. abs. change = 0.00075 Deviance = 2211.26
Iter = 39 Max. abs. change = 0.00069 Deviance = 2211.24
Iter = 40 Max. abs. change = 0.00063 Deviance = 2211.23
Iter = 41 Max. abs. change = 0.00058 Deviance = 2211.22
Iter = 42 Max. abs. change = 0.00055 Deviance = 2211.21
Iter = 43 Max. abs. change = 0.00053 Deviance = 2211.20
Iter = 44 Max. abs. change = 0.00051 Deviance = 2211.19
Iter = 45 Max. abs. change = 0.00049 Deviance = 2211.18
Iter = 46 Max. abs. change = 0.00047 Deviance = 2211.18
Iter = 47 Max. abs. change = 0.00045 Deviance = 2211.17
Iter = 48 Max. abs. change = 0.00043 Deviance = 2211.16
Iter = 49 Max. abs. change = 0.00042 Deviance = 2211.16
Iter = 50 Max. abs. change = 0.00040 Deviance = 2211.15
Iter = 51 Max. abs. change = 0.00039 Deviance = 2211.15
Iter = 52 Max. abs. change = 0.00037 Deviance = 2211.14
Iter = 53 Max. abs. change = 0.00036 Deviance = 2211.14
Iter = 54 Max. abs. change = 0.00035 Deviance = 2211.13
Iter = 55 Max. abs. change = 0.00034 Deviance = 2211.13
Iter = 56 Max. abs. change = 0.00032 Deviance = 2211.12
Iter = 57 Max. abs. change = 0.00031 Deviance = 2211.12
Iter = 58 Max. abs. change = 0.00030 Deviance = 2211.12
Iter = 59 Max. abs. change = 0.00029 Deviance = 2211.11
Iter = 60 Max. abs. change = 0.00029 Deviance = 2211.11
Iter = 61 Max. abs. change = 0.00028 Deviance = 2211.11
Iter = 62 Max. abs. change = 0.00027 Deviance = 2211.11
Iter = 63 Max. abs. change = 0.00026 Deviance = 2211.10
Iter = 64 Max. abs. change = 0.00025 Deviance = 2211.10
Iter = 65 Max. abs. change = 0.00025 Deviance = 2211.10
Iter = 66 Max. abs. change = 0.00024 Deviance = 2211.10
Iter = 67 Max. abs. change = 0.00023 Deviance = 2211.10
Iter = 68 Max. abs. change = 0.00022 Deviance = 2211.09
Iter = 69 Max. abs. change = 0.00022 Deviance = 2211.09
Iter = 70 Max. abs. change = 0.00021 Deviance = 2211.09
Iter = 71 Max. abs. change = 0.00021 Deviance = 2211.09
Iter = 72 Max. abs. change = 0.00020 Deviance = 2211.09
Iter = 73 Max. abs. change = 0.00020 Deviance = 2211.09
Iter = 74 Max. abs. change = 0.00019 Deviance = 2211.09
Iter = 75 Max. abs. change = 0.00019 Deviance = 2211.09
Iter = 76 Max. abs. change = 0.00018 Deviance = 2211.08
Iter = 77 Max. abs. change = 0.00018 Deviance = 2211.08
Iter = 78 Max. abs. change = 0.00017 Deviance = 2211.08
Iter = 79 Max. abs. change = 0.00017 Deviance = 2211.08
Iter = 80 Max. abs. change = 0.00016 Deviance = 2211.08
Iter = 81 Max. abs. change = 0.00016 Deviance = 2211.08
Iter = 82 Max. abs. change = 0.00016 Deviance = 2211.08
Iter = 83 Max. abs. change = 0.00015 Deviance = 2211.08
Iter = 84 Max. abs. change = 0.00015 Deviance = 2211.08
Iter = 85 Max. abs. change = 0.00014 Deviance = 2211.08
Iter = 86 Max. abs. change = 0.00014 Deviance = 2211.08
Iter = 87 Max. abs. change = 0.00014 Deviance = 2211.07
Iter = 88 Max. abs. change = 0.00013 Deviance = 2211.07
Iter = 89 Max. abs. change = 0.00013 Deviance = 2211.07
Iter = 90 Max. abs. change = 0.00013 Deviance = 2211.07
Iter = 91 Max. abs. change = 0.00013 Deviance = 2211.07
Iter = 92 Max. abs. change = 0.00012 Deviance = 2211.07
Iter = 93 Max. abs. change = 0.00012 Deviance = 2211.07
Iter = 94 Max. abs. change = 0.00012 Deviance = 2211.07
Iter = 95 Max. abs. change = 0.00011 Deviance = 2211.07
Iter = 96 Max. abs. change = 0.00011 Deviance = 2211.07
Iter = 97 Max. abs. change = 0.00011 Deviance = 2211.07
Iter = 98 Max. abs. change = 0.00011 Deviance = 2211.07
Iter = 99 Max. abs. change = 0.00011 Deviance = 2211.07
Iter = 100 Max. abs. change = 0.00010 Deviance = 2211.07
Iter = 101 Max. abs. change = 0.00010 Deviance = 2211.07
Iter = 102 Max. abs. change = 0.00010 Deviance = 2211.07
extract(fit.obj, "M2")
#> M2 df p.value RMSEA2
#> 12.6310734 16.0000000 0.6995167 0.0000000
# }