get.beta.RdThe function is able to calculate the \(\beta\) index for all items after fitting CDM or directly.
get.beta(
Y = NULL,
Q = NULL,
att.str = NULL,
CDM.obj = NULL,
mono.constraint = FALSE,
model = "GDINA"
)A required \(N\) × \(I\) matrix or data.frame consisting of the responses of N individuals
to \(N\) × \(I\) items. Missing values need to be coded as NA.
A required binary \(I\) × \(K\) matrix containing the attributes not required or required
master the items. The ith row of the matrix is a binary indicator vector indicating which
attributes are not required (coded by 0) and which attributes are required (coded by 1) to master
item \(i\).
Specify attribute structures. NULL, by default, means there is no structure. Attribute structure
needs be specified as a list - which will be internally handled by att.structure function.
See examples. It can also be a matrix giving all permissible attribute profiles.
An object of class CDM.obj. Can be NULL, but when it is not NULL, it enables
rapid validation of the Q-matrix without the need for parameter estimation.
@seealso CDM.
Logical indicating whether monotonicity constraints should be fulfilled in estimation.
Default = FALSE.
Type of model to be fitted; can be "GDINA", "LCDM", "DINA", "DINO",
"ACDM", "LLM", or "rRUM". Default = "GDINA".
An object of class matrix, which consisted of \(\beta\) index for each item and each possible attribute mastery pattern.
For item \(i\) with the q-vector of the \(c\)-th (\(c = 1, 2, ..., 2^{K}\)) type, the \(\beta\) index is computed as follows:
$$ \beta_{ic} = \sum_{l=1}^{2^K} \left| \frac{r_{li}}{n_l} P_{ic}(\boldsymbol{\alpha}_{l}) - \left(1 - \frac{r_{li}}{n_l}\right) \left[1 - P_{ic}(\boldsymbol{\alpha}_{l})\right] \right| = \sum_{l=1}^{2^K} \left| \frac{r_{li}}{n_l} - \left[1 - P_{ic}(\boldsymbol{\alpha}_{l}) \right] \right| $$
In the formula, \(r_{li}\) represents the number of examinees in attribute mastery pattern \(\boldsymbol{\alpha}_{l}\) who correctly answered item \(i\), while \(n_l\) is the total number of examinees in attribute mastery pattern \(\boldsymbol{\alpha}_{l}\). \(P_{ic}(\boldsymbol{\alpha}_{l})\) denotes the probability that an examinee in attribute mastery pattern \(\boldsymbol{\alpha}_{l}\) answers item \(i\) correctly when the q-vector for item \(i\) is of the \(c\)-th type. In fact, \(\frac{r_{li}}{n_l}\) is the observed probability that an examinee in attribute mastery pattern \(\boldsymbol{\alpha}_{l}\) answers item \(i\) correctly, and \(\beta_{jc}\) represents the difference between the actual proportion of correct answers for item \(i\) in each attribute mastery pattern and the expected probability of answering the item incorrectly in that state. Therefore, to some extent, \(\beta_{jc}\) can be considered as a measure of discriminability.
Li, J., & Chen, P. (2024). A new Q-matrix validation method based on signal detection theory. British Journal of Mathematical and Statistical Psychology, 00, 1–33. DOI: 10.1111/bmsp.12371
# \donttest{
library(Qval)
set.seed(123)
## generate Q-matrix and data
K <- 4
I <- 20
Q <- sim.Q(K, I)
IQ <- list(
P0 = runif(I, 0.0, 0.3),
P1 = runif(I, 0.7, 0.9)
)
model <- "GDINA"
data <- sim.data(Q = Q, N = 500, IQ = IQ, model = model, distribute = "horder")
#> distribute = horder
#> model = GDINA
#> number of attributes: 4
#> number of items: 20
#> num of examinees: 500
#> average of P0 = 0.128
#> average of P1 = 0.798
#> theta_mean = -0.06 , theta_sd = 0.996
#> a = 1.5 1.5 1.5 1.5
#> b = 0.5 1.5 -1.5 -0.5
## calculate beta directly
beta <-get.beta(Y = data$dat, Q = Q, model = model)
#>
Iter = 1 Max. abs. change = 0.38660 Deviance = 12248.06
Iter = 2 Max. abs. change = 0.10531 Deviance = 11181.61
Iter = 3 Max. abs. change = 0.06712 Deviance = 11126.61
Iter = 4 Max. abs. change = 0.03723 Deviance = 11109.01
Iter = 5 Max. abs. change = 0.02670 Deviance = 11101.45
Iter = 6 Max. abs. change = 0.02183 Deviance = 11097.63
Iter = 7 Max. abs. change = 0.01829 Deviance = 11095.36
Iter = 8 Max. abs. change = 0.01557 Deviance = 11093.82
Iter = 9 Max. abs. change = 0.01336 Deviance = 11092.67
Iter = 10 Max. abs. change = 0.01150 Deviance = 11091.75
Iter = 11 Max. abs. change = 0.01016 Deviance = 11090.99
Iter = 12 Max. abs. change = 0.01009 Deviance = 11090.35
Iter = 13 Max. abs. change = 0.00997 Deviance = 11089.80
Iter = 14 Max. abs. change = 0.00976 Deviance = 11089.32
Iter = 15 Max. abs. change = 0.00944 Deviance = 11088.90
Iter = 16 Max. abs. change = 0.00899 Deviance = 11088.54
Iter = 17 Max. abs. change = 0.00844 Deviance = 11088.23
Iter = 18 Max. abs. change = 0.00777 Deviance = 11087.95
Iter = 19 Max. abs. change = 0.00703 Deviance = 11087.72
Iter = 20 Max. abs. change = 0.00624 Deviance = 11087.52
Iter = 21 Max. abs. change = 0.00543 Deviance = 11087.35
Iter = 22 Max. abs. change = 0.00480 Deviance = 11087.21
Iter = 23 Max. abs. change = 0.00428 Deviance = 11087.09
Iter = 24 Max. abs. change = 0.00379 Deviance = 11086.99
Iter = 25 Max. abs. change = 0.00332 Deviance = 11086.91
Iter = 26 Max. abs. change = 0.00288 Deviance = 11086.84
Iter = 27 Max. abs. change = 0.00275 Deviance = 11086.79
Iter = 28 Max. abs. change = 0.00272 Deviance = 11086.75
Iter = 29 Max. abs. change = 0.00267 Deviance = 11086.72
Iter = 30 Max. abs. change = 0.00262 Deviance = 11086.69
Iter = 31 Max. abs. change = 0.00257 Deviance = 11086.67
Iter = 32 Max. abs. change = 0.00251 Deviance = 11086.65
Iter = 33 Max. abs. change = 0.00245 Deviance = 11086.64
Iter = 34 Max. abs. change = 0.00238 Deviance = 11086.63
Iter = 35 Max. abs. change = 0.00232 Deviance = 11086.62
Iter = 36 Max. abs. change = 0.00225 Deviance = 11086.61
Iter = 37 Max. abs. change = 0.00219 Deviance = 11086.60
Iter = 38 Max. abs. change = 0.00213 Deviance = 11086.60
Iter = 39 Max. abs. change = 0.00206 Deviance = 11086.59
Iter = 40 Max. abs. change = 0.00200 Deviance = 11086.59
Iter = 41 Max. abs. change = 0.00194 Deviance = 11086.59
Iter = 42 Max. abs. change = 0.00188 Deviance = 11086.58
Iter = 43 Max. abs. change = 0.00183 Deviance = 11086.58
Iter = 44 Max. abs. change = 0.00177 Deviance = 11086.58
Iter = 45 Max. abs. change = 0.00172 Deviance = 11086.58
Iter = 46 Max. abs. change = 0.00167 Deviance = 11086.57
Iter = 47 Max. abs. change = 0.00162 Deviance = 11086.57
Iter = 48 Max. abs. change = 0.00157 Deviance = 11086.57
Iter = 49 Max. abs. change = 0.00153 Deviance = 11086.57
Iter = 50 Max. abs. change = 0.00148 Deviance = 11086.57
Iter = 51 Max. abs. change = 0.00144 Deviance = 11086.57
Iter = 52 Max. abs. change = 0.00140 Deviance = 11086.57
Iter = 53 Max. abs. change = 0.00136 Deviance = 11086.56
Iter = 54 Max. abs. change = 0.00132 Deviance = 11086.56
Iter = 55 Max. abs. change = 0.00128 Deviance = 11086.56
Iter = 56 Max. abs. change = 0.00124 Deviance = 11086.56
Iter = 57 Max. abs. change = 0.00121 Deviance = 11086.56
Iter = 58 Max. abs. change = 0.00117 Deviance = 11086.56
Iter = 59 Max. abs. change = 0.00114 Deviance = 11086.56
Iter = 60 Max. abs. change = 0.00110 Deviance = 11086.56
Iter = 61 Max. abs. change = 0.00107 Deviance = 11086.56
Iter = 62 Max. abs. change = 0.00104 Deviance = 11086.55
Iter = 63 Max. abs. change = 0.00101 Deviance = 11086.55
Iter = 64 Max. abs. change = 0.00098 Deviance = 11086.55
Iter = 65 Max. abs. change = 0.00095 Deviance = 11086.55
Iter = 66 Max. abs. change = 0.00092 Deviance = 11086.55
Iter = 67 Max. abs. change = 0.00089 Deviance = 11086.55
Iter = 68 Max. abs. change = 0.00086 Deviance = 11086.55
Iter = 69 Max. abs. change = 0.00083 Deviance = 11086.55
Iter = 70 Max. abs. change = 0.00081 Deviance = 11086.55
Iter = 71 Max. abs. change = 0.00078 Deviance = 11086.55
Iter = 72 Max. abs. change = 0.00075 Deviance = 11086.55
Iter = 73 Max. abs. change = 0.00073 Deviance = 11086.55
Iter = 74 Max. abs. change = 0.00070 Deviance = 11086.55
Iter = 75 Max. abs. change = 0.00068 Deviance = 11086.55
Iter = 76 Max. abs. change = 0.00065 Deviance = 11086.55
Iter = 77 Max. abs. change = 0.00063 Deviance = 11086.55
Iter = 78 Max. abs. change = 0.00061 Deviance = 11086.54
Iter = 79 Max. abs. change = 0.00058 Deviance = 11086.54
Iter = 80 Max. abs. change = 0.00056 Deviance = 11086.54
Iter = 81 Max. abs. change = 0.00054 Deviance = 11086.54
Iter = 82 Max. abs. change = 0.00052 Deviance = 11086.54
Iter = 83 Max. abs. change = 0.00050 Deviance = 11086.54
Iter = 84 Max. abs. change = 0.00048 Deviance = 11086.54
Iter = 85 Max. abs. change = 0.00046 Deviance = 11086.54
Iter = 86 Max. abs. change = 0.00044 Deviance = 11086.54
Iter = 87 Max. abs. change = 0.00042 Deviance = 11086.54
Iter = 88 Max. abs. change = 0.00041 Deviance = 11086.54
Iter = 89 Max. abs. change = 0.00039 Deviance = 11086.54
Iter = 90 Max. abs. change = 0.00037 Deviance = 11086.54
Iter = 91 Max. abs. change = 0.00036 Deviance = 11086.54
Iter = 92 Max. abs. change = 0.00034 Deviance = 11086.54
Iter = 93 Max. abs. change = 0.00033 Deviance = 11086.54
Iter = 94 Max. abs. change = 0.00031 Deviance = 11086.54
Iter = 95 Max. abs. change = 0.00030 Deviance = 11086.54
Iter = 96 Max. abs. change = 0.00028 Deviance = 11086.54
Iter = 97 Max. abs. change = 0.00027 Deviance = 11086.54
Iter = 98 Max. abs. change = 0.00026 Deviance = 11086.54
Iter = 99 Max. abs. change = 0.00025 Deviance = 11086.54
Iter = 100 Max. abs. change = 0.00024 Deviance = 11086.54
Iter = 101 Max. abs. change = 0.00022 Deviance = 11086.54
Iter = 102 Max. abs. change = 0.00021 Deviance = 11086.54
Iter = 103 Max. abs. change = 0.00020 Deviance = 11086.54
Iter = 104 Max. abs. change = 0.00019 Deviance = 11086.54
Iter = 105 Max. abs. change = 0.00018 Deviance = 11086.54
Iter = 106 Max. abs. change = 0.00018 Deviance = 11086.54
Iter = 107 Max. abs. change = 0.00017 Deviance = 11086.54
Iter = 108 Max. abs. change = 0.00016 Deviance = 11086.54
Iter = 109 Max. abs. change = 0.00015 Deviance = 11086.54
Iter = 110 Max. abs. change = 0.00014 Deviance = 11086.54
Iter = 111 Max. abs. change = 0.00014 Deviance = 11086.54
Iter = 112 Max. abs. change = 0.00013 Deviance = 11086.54
Iter = 113 Max. abs. change = 0.00012 Deviance = 11086.54
Iter = 114 Max. abs. change = 0.00012 Deviance = 11086.54
Iter = 115 Max. abs. change = 0.00011 Deviance = 11086.54
Iter = 116 Max. abs. change = 0.00011 Deviance = 11086.54
Iter = 117 Max. abs. change = 0.00010 Deviance = 11086.54
Iter = 118 Max. abs. change = 0.00010 Deviance = 11086.54
print(beta)
#> 0000 1000 0100 0010 0001 1100 1010
#> item 1 0 12.527727 6.358972 6.670214 6.552147 12.649119 12.548779
#> item 2 0 4.707686 4.805865 7.887206 4.827853 4.877648 7.790552
#> item 3 0 6.007082 5.962630 11.083919 5.817212 6.023786 11.054038
#> item 4 0 6.616149 6.429311 12.031596 6.331844 6.612520 11.961236
#> item 5 0 4.409362 4.455982 4.430382 8.285693 4.479183 4.411032
#> item 6 0 7.272588 7.201482 13.368557 7.049923 7.339736 13.470615
#> item 7 0 6.071023 5.986996 10.713982 6.043961 6.074383 10.747575
#> item 8 0 6.356890 4.405264 6.587027 4.600520 6.170474 8.374300
#> item 9 0 5.970399 4.737318 4.840204 3.881811 6.228024 5.874771
#> item 10 0 5.965935 5.106570 6.591726 5.945526 5.936155 7.836351
#> item 11 0 6.455260 6.542014 6.289457 11.504399 6.579281 6.348872
#> item 12 0 11.565140 6.553855 6.234117 6.559488 11.550940 11.557522
#> item 13 0 6.202124 3.555491 3.858179 3.722392 6.182562 5.857571
#> item 14 0 5.141324 3.080738 3.658573 2.836921 5.107840 4.794495
#> item 15 0 4.597051 4.280770 3.213487 4.655373 5.447809 4.344889
#> item 16 0 6.478585 3.868452 3.791174 4.155395 6.426057 6.508694
#> item 17 0 5.314695 7.443550 5.009076 5.533609 7.257750 5.355726
#> item 18 0 5.174471 9.204049 5.042170 5.315518 9.166368 5.075680
#> item 19 0 6.400072 6.279604 9.616281 6.660542 6.489507 9.543705
#> item 20 0 4.739066 8.683251 4.965112 4.879851 8.715230 4.773643
#> 1001 0110 0101 0011 1110 1101 1011
#> item 1 12.529287 6.569089 6.481237 6.687773 12.673875 12.662218 12.574348
#> item 2 4.954292 8.818432 4.990899 7.471134 8.686694 5.303359 7.492397
#> item 3 6.007267 11.148447 5.883571 11.115449 11.243742 6.024432 11.071739
#> item 4 6.616414 12.199613 6.246872 12.027932 12.179587 6.769262 11.966216
#> item 5 8.276556 4.444049 8.161379 8.269638 4.390555 8.176133 8.131217
#> item 6 7.272939 13.424433 7.094848 13.394183 13.535782 7.390438 13.484083
#> item 7 6.170483 10.750257 6.039117 10.614278 10.828102 6.151650 10.687193
#> item 8 6.368025 6.464822 4.432688 6.592052 8.209456 6.220541 8.427957
#> item 9 5.858782 5.713194 4.654576 4.792043 7.386009 6.344011 5.740506
#> item 10 6.359140 6.519868 6.226253 7.093199 7.933930 6.713593 8.054612
#> item 11 11.512609 6.376044 11.502417 11.634465 6.749914 11.461080 11.671445
#> item 12 11.538174 6.354960 6.741973 6.418011 11.530548 11.622188 11.603220
#> item 13 6.330339 3.839737 3.822262 3.990550 5.748591 6.402797 6.004828
#> item 14 5.132207 3.633435 3.086874 3.658142 4.818689 5.225482 4.818454
#> item 15 5.380013 4.426031 4.766321 4.387786 5.127774 5.353639 5.074417
#> item 16 6.573114 3.850395 4.208478 4.167015 6.503793 6.595944 6.663296
#> item 17 6.204606 7.424443 7.650946 5.579625 7.542292 8.544501 6.438509
#> item 18 5.488626 9.223433 9.295840 5.186586 9.238719 9.324354 5.997775
#> item 19 7.041727 9.575667 6.637693 9.504269 10.626007 7.675357 9.623505
#> item 20 4.883998 8.532811 8.614262 4.909266 8.450907 8.663136 4.883410
#> 0111 1111
#> item 1 6.859112 12.771521
#> item 2 8.749377 8.630208
#> item 3 11.071170 11.310109
#> item 4 12.163319 12.172053
#> item 5 8.016842 8.136382
#> item 6 13.453244 13.612043
#> item 7 10.663114 10.715242
#> item 8 6.491115 8.285429
#> item 9 6.074754 7.418073
#> item 10 7.313084 8.708345
#> item 11 11.670328 11.660831
#> item 12 7.407881 11.786779
#> item 13 4.676216 6.058610
#> item 14 3.718746 5.131611
#> item 15 4.823958 5.308987
#> item 16 4.081800 6.855066
#> item 17 8.398387 8.398041
#> item 18 9.264553 9.629526
#> item 19 9.630452 10.106430
#> item 20 8.343755 8.322236
## calculate beta after fitting CDM
CDM.obj <- CDM(data$dat, Q, model=model)
#>
Iter = 1 Max. abs. change = 0.38660 Deviance = 12248.06
Iter = 2 Max. abs. change = 0.10531 Deviance = 11181.61
Iter = 3 Max. abs. change = 0.06712 Deviance = 11126.61
Iter = 4 Max. abs. change = 0.03723 Deviance = 11109.01
Iter = 5 Max. abs. change = 0.02670 Deviance = 11101.45
Iter = 6 Max. abs. change = 0.02183 Deviance = 11097.63
Iter = 7 Max. abs. change = 0.01829 Deviance = 11095.36
Iter = 8 Max. abs. change = 0.01557 Deviance = 11093.82
Iter = 9 Max. abs. change = 0.01336 Deviance = 11092.67
Iter = 10 Max. abs. change = 0.01150 Deviance = 11091.75
Iter = 11 Max. abs. change = 0.01016 Deviance = 11090.99
Iter = 12 Max. abs. change = 0.01009 Deviance = 11090.35
Iter = 13 Max. abs. change = 0.00997 Deviance = 11089.80
Iter = 14 Max. abs. change = 0.00976 Deviance = 11089.32
Iter = 15 Max. abs. change = 0.00944 Deviance = 11088.90
Iter = 16 Max. abs. change = 0.00899 Deviance = 11088.54
Iter = 17 Max. abs. change = 0.00844 Deviance = 11088.23
Iter = 18 Max. abs. change = 0.00777 Deviance = 11087.95
Iter = 19 Max. abs. change = 0.00703 Deviance = 11087.72
Iter = 20 Max. abs. change = 0.00624 Deviance = 11087.52
Iter = 21 Max. abs. change = 0.00543 Deviance = 11087.35
Iter = 22 Max. abs. change = 0.00480 Deviance = 11087.21
Iter = 23 Max. abs. change = 0.00428 Deviance = 11087.09
Iter = 24 Max. abs. change = 0.00379 Deviance = 11086.99
Iter = 25 Max. abs. change = 0.00332 Deviance = 11086.91
Iter = 26 Max. abs. change = 0.00288 Deviance = 11086.84
Iter = 27 Max. abs. change = 0.00275 Deviance = 11086.79
Iter = 28 Max. abs. change = 0.00272 Deviance = 11086.75
Iter = 29 Max. abs. change = 0.00267 Deviance = 11086.72
Iter = 30 Max. abs. change = 0.00262 Deviance = 11086.69
Iter = 31 Max. abs. change = 0.00257 Deviance = 11086.67
Iter = 32 Max. abs. change = 0.00251 Deviance = 11086.65
Iter = 33 Max. abs. change = 0.00245 Deviance = 11086.64
Iter = 34 Max. abs. change = 0.00238 Deviance = 11086.63
Iter = 35 Max. abs. change = 0.00232 Deviance = 11086.62
Iter = 36 Max. abs. change = 0.00225 Deviance = 11086.61
Iter = 37 Max. abs. change = 0.00219 Deviance = 11086.60
Iter = 38 Max. abs. change = 0.00213 Deviance = 11086.60
Iter = 39 Max. abs. change = 0.00206 Deviance = 11086.59
Iter = 40 Max. abs. change = 0.00200 Deviance = 11086.59
Iter = 41 Max. abs. change = 0.00194 Deviance = 11086.59
Iter = 42 Max. abs. change = 0.00188 Deviance = 11086.58
Iter = 43 Max. abs. change = 0.00183 Deviance = 11086.58
Iter = 44 Max. abs. change = 0.00177 Deviance = 11086.58
Iter = 45 Max. abs. change = 0.00172 Deviance = 11086.58
Iter = 46 Max. abs. change = 0.00167 Deviance = 11086.57
Iter = 47 Max. abs. change = 0.00162 Deviance = 11086.57
Iter = 48 Max. abs. change = 0.00157 Deviance = 11086.57
Iter = 49 Max. abs. change = 0.00153 Deviance = 11086.57
Iter = 50 Max. abs. change = 0.00148 Deviance = 11086.57
Iter = 51 Max. abs. change = 0.00144 Deviance = 11086.57
Iter = 52 Max. abs. change = 0.00140 Deviance = 11086.57
Iter = 53 Max. abs. change = 0.00136 Deviance = 11086.56
Iter = 54 Max. abs. change = 0.00132 Deviance = 11086.56
Iter = 55 Max. abs. change = 0.00128 Deviance = 11086.56
Iter = 56 Max. abs. change = 0.00124 Deviance = 11086.56
Iter = 57 Max. abs. change = 0.00121 Deviance = 11086.56
Iter = 58 Max. abs. change = 0.00117 Deviance = 11086.56
Iter = 59 Max. abs. change = 0.00114 Deviance = 11086.56
Iter = 60 Max. abs. change = 0.00110 Deviance = 11086.56
Iter = 61 Max. abs. change = 0.00107 Deviance = 11086.56
Iter = 62 Max. abs. change = 0.00104 Deviance = 11086.55
Iter = 63 Max. abs. change = 0.00101 Deviance = 11086.55
Iter = 64 Max. abs. change = 0.00098 Deviance = 11086.55
Iter = 65 Max. abs. change = 0.00095 Deviance = 11086.55
Iter = 66 Max. abs. change = 0.00092 Deviance = 11086.55
Iter = 67 Max. abs. change = 0.00089 Deviance = 11086.55
Iter = 68 Max. abs. change = 0.00086 Deviance = 11086.55
Iter = 69 Max. abs. change = 0.00083 Deviance = 11086.55
Iter = 70 Max. abs. change = 0.00081 Deviance = 11086.55
Iter = 71 Max. abs. change = 0.00078 Deviance = 11086.55
Iter = 72 Max. abs. change = 0.00075 Deviance = 11086.55
Iter = 73 Max. abs. change = 0.00073 Deviance = 11086.55
Iter = 74 Max. abs. change = 0.00070 Deviance = 11086.55
Iter = 75 Max. abs. change = 0.00068 Deviance = 11086.55
Iter = 76 Max. abs. change = 0.00065 Deviance = 11086.55
Iter = 77 Max. abs. change = 0.00063 Deviance = 11086.55
Iter = 78 Max. abs. change = 0.00061 Deviance = 11086.54
Iter = 79 Max. abs. change = 0.00058 Deviance = 11086.54
Iter = 80 Max. abs. change = 0.00056 Deviance = 11086.54
Iter = 81 Max. abs. change = 0.00054 Deviance = 11086.54
Iter = 82 Max. abs. change = 0.00052 Deviance = 11086.54
Iter = 83 Max. abs. change = 0.00050 Deviance = 11086.54
Iter = 84 Max. abs. change = 0.00048 Deviance = 11086.54
Iter = 85 Max. abs. change = 0.00046 Deviance = 11086.54
Iter = 86 Max. abs. change = 0.00044 Deviance = 11086.54
Iter = 87 Max. abs. change = 0.00042 Deviance = 11086.54
Iter = 88 Max. abs. change = 0.00041 Deviance = 11086.54
Iter = 89 Max. abs. change = 0.00039 Deviance = 11086.54
Iter = 90 Max. abs. change = 0.00037 Deviance = 11086.54
Iter = 91 Max. abs. change = 0.00036 Deviance = 11086.54
Iter = 92 Max. abs. change = 0.00034 Deviance = 11086.54
Iter = 93 Max. abs. change = 0.00033 Deviance = 11086.54
Iter = 94 Max. abs. change = 0.00031 Deviance = 11086.54
Iter = 95 Max. abs. change = 0.00030 Deviance = 11086.54
Iter = 96 Max. abs. change = 0.00028 Deviance = 11086.54
Iter = 97 Max. abs. change = 0.00027 Deviance = 11086.54
Iter = 98 Max. abs. change = 0.00026 Deviance = 11086.54
Iter = 99 Max. abs. change = 0.00025 Deviance = 11086.54
Iter = 100 Max. abs. change = 0.00024 Deviance = 11086.54
Iter = 101 Max. abs. change = 0.00022 Deviance = 11086.54
Iter = 102 Max. abs. change = 0.00021 Deviance = 11086.54
Iter = 103 Max. abs. change = 0.00020 Deviance = 11086.54
Iter = 104 Max. abs. change = 0.00019 Deviance = 11086.54
Iter = 105 Max. abs. change = 0.00018 Deviance = 11086.54
Iter = 106 Max. abs. change = 0.00018 Deviance = 11086.54
Iter = 107 Max. abs. change = 0.00017 Deviance = 11086.54
Iter = 108 Max. abs. change = 0.00016 Deviance = 11086.54
Iter = 109 Max. abs. change = 0.00015 Deviance = 11086.54
Iter = 110 Max. abs. change = 0.00014 Deviance = 11086.54
Iter = 111 Max. abs. change = 0.00014 Deviance = 11086.54
Iter = 112 Max. abs. change = 0.00013 Deviance = 11086.54
Iter = 113 Max. abs. change = 0.00012 Deviance = 11086.54
Iter = 114 Max. abs. change = 0.00012 Deviance = 11086.54
Iter = 115 Max. abs. change = 0.00011 Deviance = 11086.54
Iter = 116 Max. abs. change = 0.00011 Deviance = 11086.54
Iter = 117 Max. abs. change = 0.00010 Deviance = 11086.54
Iter = 118 Max. abs. change = 0.00010 Deviance = 11086.54
beta <-get.beta(CDM.obj = CDM.obj)
print(beta)
#> 0000 1000 0100 0010 0001 1100 1010
#> item 1 0 12.527727 6.358972 6.670214 6.552147 12.649119 12.548779
#> item 2 0 4.707686 4.805865 7.887206 4.827853 4.877648 7.790552
#> item 3 0 6.007082 5.962630 11.083919 5.817212 6.023786 11.054038
#> item 4 0 6.616149 6.429311 12.031596 6.331844 6.612520 11.961236
#> item 5 0 4.409362 4.455982 4.430382 8.285693 4.479183 4.411032
#> item 6 0 7.272588 7.201482 13.368557 7.049923 7.339736 13.470615
#> item 7 0 6.071023 5.986996 10.713982 6.043961 6.074383 10.747575
#> item 8 0 6.356890 4.405264 6.587027 4.600520 6.170474 8.374300
#> item 9 0 5.970399 4.737318 4.840204 3.881811 6.228024 5.874771
#> item 10 0 5.965935 5.106570 6.591726 5.945526 5.936155 7.836351
#> item 11 0 6.455260 6.542014 6.289457 11.504399 6.579281 6.348872
#> item 12 0 11.565140 6.553855 6.234117 6.559488 11.550940 11.557522
#> item 13 0 6.202124 3.555491 3.858179 3.722392 6.182562 5.857571
#> item 14 0 5.141324 3.080738 3.658573 2.836921 5.107840 4.794495
#> item 15 0 4.597051 4.280770 3.213487 4.655373 5.447809 4.344889
#> item 16 0 6.478585 3.868452 3.791174 4.155395 6.426057 6.508694
#> item 17 0 5.314695 7.443550 5.009076 5.533609 7.257750 5.355726
#> item 18 0 5.174471 9.204049 5.042170 5.315518 9.166368 5.075680
#> item 19 0 6.400072 6.279604 9.616281 6.660542 6.489507 9.543705
#> item 20 0 4.739066 8.683251 4.965112 4.879851 8.715230 4.773643
#> 1001 0110 0101 0011 1110 1101 1011
#> item 1 12.529287 6.569089 6.481237 6.687773 12.673875 12.662218 12.574348
#> item 2 4.954292 8.818432 4.990899 7.471134 8.686694 5.303359 7.492397
#> item 3 6.007267 11.148447 5.883571 11.115449 11.243742 6.024432 11.071739
#> item 4 6.616414 12.199613 6.246872 12.027932 12.179587 6.769262 11.966216
#> item 5 8.276556 4.444049 8.161379 8.269638 4.390555 8.176133 8.131217
#> item 6 7.272939 13.424433 7.094848 13.394183 13.535782 7.390438 13.484083
#> item 7 6.170483 10.750257 6.039117 10.614278 10.828102 6.151650 10.687193
#> item 8 6.368025 6.464822 4.432688 6.592052 8.209456 6.220541 8.427957
#> item 9 5.858782 5.713194 4.654576 4.792043 7.386009 6.344011 5.740506
#> item 10 6.359140 6.519868 6.226253 7.093199 7.933930 6.713593 8.054612
#> item 11 11.512609 6.376044 11.502417 11.634465 6.749914 11.461080 11.671445
#> item 12 11.538174 6.354960 6.741973 6.418011 11.530548 11.622188 11.603220
#> item 13 6.330339 3.839737 3.822262 3.990550 5.748591 6.402797 6.004828
#> item 14 5.132207 3.633435 3.086874 3.658142 4.818689 5.225482 4.818454
#> item 15 5.380013 4.426031 4.766321 4.387786 5.127774 5.353639 5.074417
#> item 16 6.573114 3.850395 4.208478 4.167015 6.503793 6.595944 6.663296
#> item 17 6.204606 7.424443 7.650946 5.579625 7.542292 8.544501 6.438509
#> item 18 5.488626 9.223433 9.295840 5.186586 9.238719 9.324354 5.997775
#> item 19 7.041727 9.575667 6.637693 9.504269 10.626007 7.675357 9.623505
#> item 20 4.883998 8.532811 8.614262 4.909266 8.450907 8.663136 4.883410
#> 0111 1111
#> item 1 6.859112 12.771521
#> item 2 8.749377 8.630208
#> item 3 11.071170 11.310109
#> item 4 12.163319 12.172053
#> item 5 8.016842 8.136382
#> item 6 13.453244 13.612043
#> item 7 10.663114 10.715242
#> item 8 6.491115 8.285429
#> item 9 6.074754 7.418073
#> item 10 7.313084 8.708345
#> item 11 11.670328 11.660831
#> item 12 7.407881 11.786779
#> item 13 4.676216 6.058610
#> item 14 3.718746 5.131611
#> item 15 4.823958 5.308987
#> item 16 4.081800 6.855066
#> item 17 8.398387 8.398041
#> item 18 9.264553 9.629526
#> item 19 9.630452 10.106430
#> item 20 8.343755 8.322236
# }