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, ...)An object of class CDM, validation, sim.data, or fit.
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 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:
CDMCognitive Diagnosis Model fitting results. Available components:
The internal model fitting object (e.g., GDINA or Baseline Model).
Estimated attribute profiles (EAP estimates) for each respondent.
Posterior distribution of latent attribute patterns.
Marginal attribute mastery probabilities (estimated).
Prior attribute probabilities at convergence.
Negative twice the marginal log-likelihood (model deviance).
Number of free parameters estimated in the model.
Akaike Information Criterion.
Bayesian Information Criterion.
The original model-fitting function call.
validationQ-matrix validation results. Available components:
The original Q-matrix submitted for validation.
The suggested (revised) Q-matrix after validation.
Total computation time for the validation procedure.
Log of Q-matrix modifications across iterations.
Number of iterations performed during validation.
Attribute priority matrix (available for PAA-based methods only).
Data required to plot the Hull method results (for Hull-based validation only).
The original function call used for validation.
sim.dataSimulated data and parameters used in cognitive diagnosis simulation studies:
Simulated dichotomous response matrix (\(N \times I\)).
Q-matrix used to generate the data.
True latent attribute profiles (\(N \times K\)).
Item-category conditional success probabilities (list format).
Item-level delta parameters (list format).
Higher-order model parameters (if used).
Parameters for the multivariate normal attribute distribution (if used).
Latent class-based success probability matrix.
The 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):
The number of parameters.
The Deviance.
The Akaike information criterion.
The Bayesian information criterion.
The consistent Akaike information criterion.
The Sample-size Adjusted BIC.
A vector consisting of \(M_2\) statistic, degrees of freedom, significance level, and \(RMSEA_2\) (Liu, Tian, & Xin, 2016).
The standardized root mean squared residual (SRMSR; Ravand & Robitzsch, 2018).
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
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 = 0.33678 Deviance = 2399.11
Iter = 2 Max. abs. change = 0.08307 Deviance = 2237.93
Iter = 3 Max. abs. change = 0.03743 Deviance = 2224.60
Iter = 4 Max. abs. change = 0.01680 Deviance = 2221.36
Iter = 5 Max. abs. change = 0.00878 Deviance = 2220.16
Iter = 6 Max. abs. change = 0.00888 Deviance = 2219.50
Iter = 7 Max. abs. change = 0.00903 Deviance = 2219.02
Iter = 8 Max. abs. change = 0.00913 Deviance = 2218.64
Iter = 9 Max. abs. change = 0.00914 Deviance = 2218.30
Iter = 10 Max. abs. change = 0.00903 Deviance = 2218.00
Iter = 11 Max. abs. change = 0.00881 Deviance = 2217.73
Iter = 12 Max. abs. change = 0.00847 Deviance = 2217.48
Iter = 13 Max. abs. change = 0.00800 Deviance = 2217.25
Iter = 14 Max. abs. change = 0.00749 Deviance = 2217.04
Iter = 15 Max. abs. change = 0.00695 Deviance = 2216.85
Iter = 16 Max. abs. change = 0.00636 Deviance = 2216.68
Iter = 17 Max. abs. change = 0.00580 Deviance = 2216.53
Iter = 18 Max. abs. change = 0.00530 Deviance = 2216.39
Iter = 19 Max. abs. change = 0.00485 Deviance = 2216.27
Iter = 20 Max. abs. change = 0.00445 Deviance = 2216.15
Iter = 21 Max. abs. change = 0.00409 Deviance = 2216.05
Iter = 22 Max. abs. change = 0.00377 Deviance = 2215.95
Iter = 23 Max. abs. change = 0.00348 Deviance = 2215.86
Iter = 24 Max. abs. change = 0.00322 Deviance = 2215.78
Iter = 25 Max. abs. change = 0.00298 Deviance = 2215.70
Iter = 26 Max. abs. change = 0.00277 Deviance = 2215.62
Iter = 27 Max. abs. change = 0.00257 Deviance = 2215.56
Iter = 28 Max. abs. change = 0.00238 Deviance = 2215.49
Iter = 29 Max. abs. change = 0.00222 Deviance = 2215.43
Iter = 30 Max. abs. change = 0.00207 Deviance = 2215.37
Iter = 31 Max. abs. change = 0.00192 Deviance = 2215.31
Iter = 32 Max. abs. change = 0.00179 Deviance = 2215.26
Iter = 33 Max. abs. change = 0.00166 Deviance = 2215.21
Iter = 34 Max. abs. change = 0.00154 Deviance = 2215.16
Iter = 35 Max. abs. change = 0.00143 Deviance = 2215.12
Iter = 36 Max. abs. change = 0.00135 Deviance = 2215.07
Iter = 37 Max. abs. change = 0.00127 Deviance = 2215.03
Iter = 38 Max. abs. change = 0.00119 Deviance = 2214.99
Iter = 39 Max. abs. change = 0.00112 Deviance = 2214.96
Iter = 40 Max. abs. change = 0.00105 Deviance = 2214.92
Iter = 41 Max. abs. change = 0.00136 Deviance = 2214.89
Iter = 42 Max. abs. change = 0.00125 Deviance = 2214.85
Iter = 43 Max. abs. change = 0.00115 Deviance = 2214.81
Iter = 44 Max. abs. change = 0.00104 Deviance = 2214.78
Iter = 45 Max. abs. change = 0.00093 Deviance = 2214.75
Iter = 46 Max. abs. change = 0.00084 Deviance = 2214.72
Iter = 47 Max. abs. change = 0.00076 Deviance = 2214.70
Iter = 48 Max. abs. change = 0.00069 Deviance = 2214.68
Iter = 49 Max. abs. change = 0.00061 Deviance = 2214.66
Iter = 50 Max. abs. change = 0.00058 Deviance = 2214.64
Iter = 51 Max. abs. change = 0.00050 Deviance = 2214.62
Iter = 52 Max. abs. change = 0.00045 Deviance = 2214.61
Iter = 53 Max. abs. change = 0.00049 Deviance = 2214.60
Iter = 54 Max. abs. change = 0.00036 Deviance = 2214.58
Iter = 55 Max. abs. change = 0.00033 Deviance = 2214.57
Iter = 56 Max. abs. change = 0.00044 Deviance = 2214.56
Iter = 57 Max. abs. change = 0.00026 Deviance = 2214.56
Iter = 58 Max. abs. change = 0.00029 Deviance = 2214.55
Iter = 59 Max. abs. change = 0.00021 Deviance = 2214.54
Iter = 60 Max. abs. change = 0.00042 Deviance = 2214.53
Iter = 61 Max. abs. change = 0.00017 Deviance = 2214.53
Iter = 62 Max. abs. change = 0.00015 Deviance = 2214.52
Iter = 63 Max. abs. change = 0.00014 Deviance = 2214.52
Iter = 64 Max. abs. change = 0.00013 Deviance = 2214.52
Iter = 65 Max. abs. change = 0.00013 Deviance = 2214.51
Iter = 66 Max. abs. change = 0.00012 Deviance = 2214.51
Iter = 67 Max. abs. change = 0.00010 Deviance = 2214.51
Iter = 68 Max. abs. change = 0.00021 Deviance = 2214.50
Iter = 69 Max. abs. change = 0.00009 Deviance = 2214.50
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 0 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
#> 2300.499
################################################################
# 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=0.01864
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
# }