|
|
|
||||
|
|
|||||
Table 1 on page 310
man 5
dim 2 2 2 2 2
lab A B C D E
mod A
B|A
C|B eq1 B|A
D|C eq1 C|B
E|D eq1 D|C
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** STATISTICS ***
Number of iterations = 2
Converge criterion = 0.0000000000
X-squared = 1287.1450 (0.0000)
L-squared = 1266.0235 (0.0000)
Cressie-Read = 1264.6861 (0.0000)
Dissimilarity index = 0.2188
Degrees of freedom = 28
Log-likelihood = -15893.52403
Number of parameters = 3 (+1)
Sample size = 5147.0
BIC(L-squared) = 1026.7307
AIC(L-squared) = 1210.0235
BIC(log-likelihood) = 31812.6866
AIC(log-likelihood) = 31793.0481
Eigenvalues information matrix
17374.2297 16434.5436 5059.7212
*** FREQUENCIES ***
A B C D E observed estimated std. res.
1 1 1 1 1 891.000 553.838 14.327
1 1 1 1 2 176.000 231.401 -3.642
1 1 1 2 1 119.000 92.849 2.714
1 1 1 2 2 106.000 235.235 -8.426
1 1 2 1 1 111.000 92.849 1.884
1 1 2 1 2 60.000 38.794 3.405
1 1 2 2 1 52.000 94.388 -4.363
1 1 2 2 2 92.000 239.132 -9.515
1 2 1 1 1 120.000 92.849 2.818
1 2 1 1 2 64.000 38.794 4.047
1 2 1 2 1 51.000 15.566 8.981
1 2 1 2 2 67.000 39.436 4.389
1 2 2 1 1 54.000 94.388 -4.157
1 2 2 1 2 50.000 39.436 1.682
1 2 2 2 1 49.000 95.951 -4.793
1 2 2 2 2 176.000 243.093 -4.303
2 1 1 1 1 237.000 288.855 -3.051
2 1 1 1 2 107.000 120.688 -1.246
2 1 1 2 1 68.000 48.426 2.813
2 1 1 2 2 107.000 122.687 -1.416
2 1 2 1 1 80.000 48.426 4.537
2 1 2 1 2 75.000 20.233 12.176
2 1 2 2 1 51.000 49.228 0.253
2 1 2 2 2 200.000 124.719 6.741
2 2 1 1 1 136.000 293.640 -9.199
2 2 1 1 2 95.000 122.687 -2.500
2 2 1 2 1 64.000 49.228 2.105
2 2 1 2 2 187.000 124.719 5.577
2 2 2 1 1 99.000 298.504 -11.547
2 2 2 1 2 165.000 124.719 3.607
2 2 2 2 1 172.000 303.449 -7.546
2 2 2 2 2 1066.000 768.792 10.719
Table 2 on page 311
man 5
dim 2 2 2 2 2
lab A B C D E
mod A
B|A
C|B
D|C
E|D
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
* P(A) * 1 0.4348 (0.0069) 2 0.5652 (0.0069) * P(B|A) * 1 | 1 0.7181 (0.0095) 2 | 1 0.2819 (0.0095) 1 | 2 0.3180 (0.0086) 2 | 2 0.6820 (0.0086) * P(C|B) * 1 | 1 0.7152 (0.0090) 2 | 1 0.2848 (0.0090) 1 | 2 0.2998 (0.0090) 2 | 2 0.7002 (0.0090) * P(D|C) * 1 | 1 0.7037 (0.0090) 2 | 1 0.2963 (0.0090) 1 | 2 0.2719 (0.0088) 2 | 2 0.7281 (0.0088) * P(E|D) * 1 | 1 0.6857 (0.0092) 2 | 1 0.3143 (0.0092) 1 | 2 0.2383 (0.0083) 2 | 2 0.7617 (0.0083)
Table 3 on page 315
Model 1(M) - A: simple Markov with time homogeneous transitions
man 5
dim 2 2 2 2 2
lab A B C D E
mod A
B|A
C|B eq1 B|A
D|C eq1 C|B
E|D eq1 D|C
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** STATISTICS ***
Number of iterations = 2
Converge criterion = 0.0000000000
X-squared = 1287.1450 (0.0000)
L-squared = 1266.0235 (0.0000)
Cressie-Read = 1264.6861 (0.0000)
Dissimilarity index = 0.2188
Degrees of freedom = 28
Log-likelihood = -15893.52403
Number of parameters = 3 (+1)
Sample size = 5147.0
BIC(L-squared) = 1026.7307
AIC(L-squared) = 1210.0235
BIC(log-likelihood) = 31812.6866
AIC(log-likelihood) = 31793.0481
Model 1 (M) - Part B: simple Markov with time heterogeneous transitions
man 5
dim 2 2 2 2 2
lab A B C D E
mod A
B|A
C|B
D|C
E|D
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** STATISTICS ***
Number of iterations = 2
Converge criterion = 0.0000000000
X-squared = 1239.4728 (0.0000)
L-squared = 1209.3446 (0.0000)
Cressie-Read = 1215.0281 (0.0000)
Dissimilarity index = 0.2188
Degrees of freedom = 22
Log-likelihood = -15865.18459
Number of parameters = 9 (+1)
Sample size = 5147.0
BIC(L-squared) = 1021.3288
AIC(L-squared) = 1165.3446
BIC(log-likelihood) = 31807.2847
AIC(log-likelihood) = 31748.3692
Model 2 (MS) - Part A: Mover-Stayer with time homogeneous transitions
lat 1
man 5
dim 2 2 2 2 2 2
mod X
A|X
B|XA eq2
C|XB eq1 B|XA
D|XC eq1 B|XA
E|XD eq1 B|XA
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 -1 0 -1 0]
sta B|XA [.3 .7 .7 .3 1 0 0 1]
see 12345
*** STATISTICS ***
Number of iterations = 20
Converge criterion = 0.0000007555
Seed random values = 12345
X-squared = 322.8656 (0.0000)
L-squared = 323.5790 (0.0000)
Cressie-Read = 322.0601 (0.0000)
Dissimilarity index = 0.0874
Degrees of freedom = 26
Log-likelihood = -15422.30179
Number of parameters = 5 (+1)
Sample size = 5147.0
BIC(L-squared) = 101.3786
AIC(L-squared) = 271.5790
BIC(log-likelihood) = 30887.3344
AIC(log-likelihood) = 30854.6036
Model 2 (MS) - Part B: Mover-Stayer with time heterogeneous transitions
lat 1
man 5
dim 2 2 2 2 2 2
mod X
A|X
B|XA eq2
C|XB eq2
D|XC eq2
E|XD eq2
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 -1 0 -1 0
3 0 4 0 -1 0 -1 0
5 0 6 0 -1 0 -1 0
7 0 8 0 -1 0 -1 0]
sta B|XA [.3 .7 .7 .3 1 0 0 1]
sta C|XB [.3 .7 .7 .3 1 0 0 1]
sta D|XC [.3 .7 .7 .3 1 0 0 1]
sta E|XD [.3 .7 .7 .3 1 0 0 1]
see 12345
*** STATISTICS ***
Number of iterations = 20
Converge criterion = 0.0000008469
Seed random values = 12345
X-squared = 272.3254 (0.0000)
L-squared = 270.8196 (0.0000)
Cressie-Read = 271.2376 (0.0000)
Dissimilarity index = 0.0844
Degrees of freedom = 20
Log-likelihood = -15395.92208
Number of parameters = 11 (+1)
Sample size = 5147.0
BIC(L-squared) = 99.8962
AIC(L-squared) = 230.8196
BIC(log-likelihood) = 30885.8520
AIC(log-likelihood) = 30813.8442
Model 3 (MM) - Part A: two-chain mixed Markov with time homogeneous transitions
lat 1
man 5
dim 2 2 2 2 2 2
mod X
A|X
B|AX
C|BX eq1 B|AX
D|CX eq1 B|AX
E|DX eq1 B|AX
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
see 12345
*** STATISTICS ***
Number of iterations = 145
Converge criterion = 0.0000009866
Seed random values = 12345
X-squared = 148.9007 (0.0000)
L-squared = 146.1760 (0.0000)
Cressie-Read = 147.7271 (0.0000)
Dissimilarity index = 0.0668
Degrees of freedom = 24
Log-likelihood = -15333.60030
Number of parameters = 7 (+1)
Sample size = 5147.0
BIC(L-squared) = -58.9321
AIC(L-squared) = 98.1760
BIC(log-likelihood) = 30727.0238
AIC(log-likelihood) = 30681.2006
Model 3 (MM) - Part B: two-chain mixed Markov with time heterogeneous transitions
lat 1
man 5
dim 2 2 2 2 2 2
mod X
A|X
B|AX
C|BX
D|CX
E|DX
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** STATISTICS ***
Number of iterations = 113
Converge criterion = 0.0000009701
Seed random values = 5759
X-squared = 97.4952 (0.0000)
L-squared = 94.8786 (0.0000)
Cressie-Read = 96.4983 (0.0000)
Dissimilarity index = 0.0556
Degrees of freedom = 12
Log-likelihood = -15307.95159
Number of parameters = 19 (+1)
Sample size = 5147.0
BIC(L-squared) = -7.6755
AIC(L-squared) = 70.8786
BIC(log-likelihood) = 30778.2804
AIC(log-likelihood) = 30653.9032
Model 4 (MMS) -Part A: 2 Markov and 1 Stayer chains with time homogeneous transitions
lat 1
man 5
dim 3 2 2 2 2 2
mod X
A|X
B|XA eq2
C|XB eq1 B|XA
D|XC eq1 B|XA
E|XD eq1 B|XA
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 3 0 4 0 -1 0 -1 0]
sta B|AX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
*** STATISTICS ***
Number of iterations = 668
Converge criterion = 0.0000009869
Seed random values = 4192
X-squared = 61.8818 (0.0000)
L-squared = 62.3440 (0.0000)
Cressie-Read = 61.9801 (0.0000)
Dissimilarity index = 0.0354
Degrees of freedom = 22
Log-likelihood = -15291.68430
Number of parameters = 9 (+1)
Sample size = 5147.0
BIC(L-squared) = -125.6717
AIC(L-squared) = 18.3440
BIC(log-likelihood) = 30660.2841
AIC(log-likelihood) = 30601.3686
Model 4 (MMS) - Part B: 2 Markov and 1 Stayer chains with time heterogeneous transitions. Notice that the results below do not match with the results in the book. In particular, the BIC and the dissimilarity index don't match.
lat 1
man 5
dim 3 2 2 2 2 2
mod X
A|X
B|XA eq2
C|XB eq2
D|XC eq2
E|XD eq2
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 3 0 4 0 -1 0 -1 0
5 0 6 0 7 0 8 0 -1 0 -1 0
9 0 10 0 11 0 12 0 -1 0 -1 0
13 0 14 0 15 0 16 0 -1 0 -1 0]
sta B|XA [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
sta C|XB [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
sta D|XC [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
sta E|XD [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
see 123457
*** STATISTICS ***
Number of iterations = 652
Converge criterion = 0.0000009941
Seed random values = 123457
X-squared = 6.5738 (0.7650)
L-squared = 6.5553 (0.7667)
Cressie-Read = 6.5660 (0.7657)
Dissimilarity index = 0.0092
Degrees of freedom = 10
Log-likelihood = -15263.78996
Number of parameters = 21 (+1)
Sample size = 5147.0
BIC(L-squared) = -78.9064
AIC(L-squared) = -13.4447
BIC(log-likelihood) = 30707.0495
AIC(log-likelihood) = 30569.5799
Model 5 (IPIPS) - part A: 2 independence and 1 Stayer segments with time homogeneous transitions
lat 1
man 5
dim 3 2 2 2 2 2
mod X
A|X
B|AX eq2
C|BX eq2
D|CX eq2
E|DX eq2
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 1 0 2 0 2 0 -1 0 0 -1
1 0 1 0 2 0 2 0 -1 0 0 -1
1 0 1 0 2 0 2 0 -1 0 0 -1
1 0 1 0 2 0 2 0 -1 0 0 -1]
sta B|AX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
sta C|BX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
sta D|CX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
sta E|DX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
see 123457
*** STATISTICS ***
Number of iterations = 328
Converge criterion = 0.0000009864
Seed random values = 123457
X-squared = 135.7883 (0.0000)
L-squared = 130.8476 (0.0000)
Cressie-Read = 133.8057 (0.0000)
Dissimilarity index = 0.0482
Degrees of freedom = 24
Log-likelihood = -15325.93610
Number of parameters = 7 (+1)
Sample size = 5147.0
BIC(L-squared) = -74.2605
AIC(L-squared) = 82.8476
BIC(log-likelihood) = 30711.6954
AIC(log-likelihood) = 30665.8722
Model 5 (IPIPS) - part B: 2 independence and 1 Stayer segments with time heterogeneous transitions
lat 1
man 5
dim 3 2 2 2 2 2
mod X
A|X
B|AX eq2
C|BX eq2
D|CX eq2
E|DX eq2
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 1 0 2 0 2 0 -1 0 0 -1
3 0 3 0 4 0 4 0 -1 0 0 -1
5 0 5 0 6 0 6 0 -1 0 0 -1
7 0 7 0 8 0 8 0 -1 0 0 -1]
sta B|AX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
sta C|BX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
sta D|CX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
sta E|DX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
see 123457
*** STATISTICS ***
Number of iterations = 314
Converge criterion = 0.0000009748
Seed random values = 123457
X-squared = 80.3806 (0.0000)
L-squared = 77.2374 (0.0000)
Cressie-Read = 79.1875 (0.0000)
Dissimilarity index = 0.0331
Degrees of freedom = 18
Log-likelihood = -15299.13098
Number of parameters = 13 (+1)
Sample size = 5147.0
BIC(L-squared) = -76.5937
AIC(L-squared) = 41.2374
BIC(log-likelihood) = 30709.3622
AIC(log-likelihood) = 30624.2620
Model 6 (MMM) - Part A: three-chain mixed Markov with time homogeneous transitions
lat 1
man 5
dim 3 2 2 2 2 2
mod X
A|X
B|AX
C|BX eq1 B|AX
D|CX eq1 B|AX
E|DX eq1 B|AX
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** STATISTICS ***
Number of iterations = 1666
Converge criterion = 0.0000009985
Seed random values = 4173
X-squared = 46.5833 (0.0007)
L-squared = 47.0824 (0.0006)
Cressie-Read = 46.7078 (0.0006)
Dissimilarity index = 0.0300
Degrees of freedom = 20
Log-likelihood = -15284.05348
Number of parameters = 11 (+1)
Sample size = 5147.0
BIC(L-squared) = -123.8410
AIC(L-squared) = 7.0824
BIC(log-likelihood) = 30662.1148
AIC(log-likelihood) = 30590.1070
Model 6 (MMM) - Part B: three-chain mixed Markov with time heterogeneous transitions
lat 1
man 5
dim 3 2 2 2 2 2
mod X
A|X
B|AX
C|BX
D|CX
E|DX
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** STATISTICS ***
Number of iterations = 2877
Converge criterion = 0.0000009998
Seed random values = 3113
X-squared = 1.7243 (0.4223)
L-squared = 1.7080 (0.4257)
Cressie-Read = 1.7187 (0.4234)
Dissimilarity index = 0.0042
Degrees of freedom = 2
Log-likelihood = -15261.36632
Number of parameters = 29 (+1)
Sample size = 5147.0
BIC(L-squared) = -15.3843
AIC(L-squared) = -2.2920
BIC(log-likelihood) = 30770.5716
AIC(log-likelihood) = 30580.7326
Model 7 (LM) - part A: latent Markov with time homogeneous transitions
lat 5
man 5
dim 2 2 2 2 2 2 2 2 2 2
lab V W X Y Z A B C D E
mod V
W|V
X|W eq1 W|V
Y|X eq1 W|V
Z|Y eq1 W|V
A|V
B|W eq1 A|V
C|X eq1 A|V
D|Y eq1 A|V
E|Z eq1 A|V
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** STATISTICS ***
Number of iterations = 225
Converge criterion = 0.0000009939
Seed random values = 1634
X-squared = 243.8422 (0.0000)
L-squared = 235.9275 (0.0000)
Cressie-Read = 240.4108 (0.0000)
Dissimilarity index = 0.0824
Degrees of freedom = 26
Log-likelihood = -15378.47608
Number of parameters = 5 (+1)
Sample size = 5147.0
BIC(L-squared) = 13.7271
AIC(L-squared) = 183.9275
BIC(log-likelihood) = 30799.6830
AIC(log-likelihood) = 30766.9522
Model 7 (LM) - part B: latent Markov with time heterogeneous transitions
lat 5
man 5
dim 2 2 2 2 2 2 2 2 2 2
lab V W X Y Z A B C D E
mod V
W|V
X|W
Y|X
Z|Y
A|V
B|W eq1 A|V
C|X eq1 A|V
D|Y eq1 A|V
E|Z eq1 A|V
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** STATISTICS ***
Number of iterations = 738
Converge criterion = 0.0000009934
Seed random values = 2970
X-squared = 131.0249 (0.0000)
L-squared = 130.2462 (0.0000)
Cressie-Read = 130.6406 (0.0000)
Dissimilarity index = 0.0700
Degrees of freedom = 20
Log-likelihood = -15325.63541
Number of parameters = 11 (+1)
Sample size = 5147.0
BIC(L-squared) = -40.6772
AIC(L-squared) = 90.2462
BIC(log-likelihood) = 30745.2787
AIC(log-likelihood) = 30673.2708
Model 8 (LC) - Latent class model:
lat 1
man 5
dim 2 2 2 2 2 2
mod X
A|X
B|X eq1 A|X
C|X eq1 A|X
D|X eq1 A|X
E|X eq1 A|X
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** STATISTICS ***
Number of iterations = 38
Converge criterion = 0.0000008156
Seed random values = 4627
X-squared = 426.6019 (0.0000
L-squared = 414.0631 (0.0000)
Cressie-Read = 420.4050 (0.0000)
Dissimilarity index = 0.1160
Degrees of freedom = 28
Log-likelihood = -15467.54385
Number of parameters = 3 (+1)
Sample size = 5147.0
BIC(L-squared) = 174.7704
AIC(L-squared) = 358.0631
BIC(log-likelihood) = 30960.7262
AIC(log-likelihood) = 30941.0877
Model 9 (LMRr) - latent Markov plus random response with time homogeneous transitions
lat 5
man 5
dim 3 3 3 3 3 2 2 2 2 2
lab V W X Y Z A B C D E
mod V
W|V
X|W eq1 W|V
Y|X eq1 W|V
Z|Y eq1 W|V
A|V eq2
B|W eq1 A|V
C|X eq1 A|V
D|Y eq1 A|V
E|Z eq1 A|V
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 -1 -1]
sta A|V [.3 .7 .3 .7 .5 .5]
see 123457
ite 10000
mit 10
*** STATISTICS ***
Number of iterations = 7941
Converge criterion = 0.0000009991
Seed random values = 123457
X-squared = 116.9087 (0.0000)
L-squared = 112.7703 (0.0000)
Cressie-Read = 115.2835 (0.0000)
Dissimilarity index = 0.0486
Degrees of freedom = 21
Log-likelihood = -15316.89745
Number of parameters = 10 (+1)
Sample size = 5147.0
BIC(L-squared) = -66.6993
AIC(L-squared) = 70.7703
BIC(log-likelihood) = 30719.2566
AIC(log-likelihood) = 30653.7949
Model 10 (LMM) - latent mixed Markov model:
lat 6
man 5
dim 2 2 2 2 2 2 2 2 2 2 2
lab P V W X Y Z A B C D E
mod P
V|P
W|VP
X|WP eq1 W|VP
Y|XP eq1 W|VP
Z|YP eq1 W|VP
A|VP
B|WP eq1 A|VP
C|XP eq1 A|VP
D|YP eq1 A|VP
E|ZP eq1 A|VP
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
see 123457
ite 10000
*** STATISTICS ***
Number of iterations = 8041
Converge criterion = 0.0000009997
Seed random values = 123457
X-squared = 45.3736 (0.0010)
L-squared = 46.0087 (0.0008)
Cressie-Read = 45.5416 (0.0009)
Dissimilarity index = 0.0311
Degrees of freedom = 20
Log-likelihood = -15283.51666
Number of parameters = 11 (+1)
Sample size = 5147.0
BIC(L-squared) = -124.9147
AIC(L-squared) = 6.0087
BIC(log-likelihood) = 30661.0412
AIC(log-likelihood) = 30589.0333
Model 11(LMS) - latent Mover-Stayer with time homogeneous transitions
lat 6
man 5
dim 2 2 2 2 2 2 2 2 2 2 2
lab P V W X Y Z A B C D E
mod P
V|P
W|VP eq2
X|WP eq1 W|VP
Y|XP eq1 W|VP
Z|YP eq1 W|VP
A|VP
B|WP eq1 A|VP
C|XP eq1 A|VP
D|YP eq1 A|VP
E|ZP eq1 A|VP
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 -1 0 -1 0]
sta W|VP [.3 .7 .3 .7 1 0 0 1]
see 123457
ite 10000
*** STATISTICS ***
Number of iterations = 9495
Converge criterion = 0.0000010000
Seed random values = 123457
X-squared = 67.6133 (0.0000)
L-squared = 68.6580 (0.0000)
Cressie-Read = 67.8937 (0.0000)
Dissimilarity index = 0.0382
Degrees of freedom = 22
Log-likelihood = -15294.84133
Number of parameters = 9 (+1)
Sample size = 5147.0
BIC(L-squared) = -119.3577
AIC(L-squared) = 24.6580
BIC(log-likelihood) = 30666.5982
AIC(log-likelihood) = 30607.6827
Model 12 (pLMS) - Part A: partially latent Mover-Stayer with time homogeneous transitions
lat 6
man 5
dim 2 2 2 2 2 2 2 2 2 2 2
lab P V W X Y Z A B C D E
mod P
V|P
W|VP eq2
X|WP eq1 W|VP
Y|XP eq1 W|VP
Z|YP eq1 W|VP
A|VP eq2
B|WP eq1 A|VP
C|XP eq1 A|VP
D|YP eq1 A|VP
E|ZP eq1 A|VP
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 -1 0 -1 0
3 0 4 0 -1 0 -1 0]
sta W|VP [.3 .7 .3 .7 1 0 0 1]
sta A|VP [.3 .7 .3 .7 1 0 0 1]
see 123457
ite 10000
*** STATISTICS ***
Number of iterations = 797
Converge criterion = 0.0000009762
Seed random values = 123457
X-squared = 122.3660 (0.0000)
L-squared = 119.2194 (0.0000)
Cressie-Read = 121.1190 (0.0000)
Dissimilarity index = 0.0507
Degrees of freedom = 24
Log-likelihood = -15320.12203
Number of parameters = 7 (+1)
Sample size = 5147.0
BIC(L-squared) = -85.8886
AIC(L-squared) = 71.2194
BIC(log-likelihood) = 30700.0672
AIC(log-likelihood) = 30654.2441
Model 12 (pLMS) - Part B: partially latent Mover-Stayer with time heterogeneous transitions
lat 6
man 5
dim 2 2 2 2 2 2 2 2 2 2 2
lab P V W X Y Z A B C D E
mod P
V|P
W|VP eq2
X|WP eq2
Y|XP eq2
Z|YP eq2
A|VP eq2
B|WP eq1 A|VP
C|XP eq1 A|VP
D|YP eq1 A|VP
E|ZP eq1 A|VP
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 -1 0 -1 0
3 0 4 0 -1 0 -1 0
5 0 6 0 -1 0 -1 0
7 0 8 0 -1 0 -1 0
9 0 10 0 -1 0 -1 0]
sta W|VP [.3 .7 .3 .7 1 0 0 1]
sta X|WP [.3 .7 .3 .7 1 0 0 1]
sta Y|XP [.3 .7 .3 .7 1 0 0 1]
sta Z|YP [.3 .7 .3 .7 1 0 0 1]
sta A|VP [.3 .7 .3 .7 1 0 0 1]
see 123457
ite 10000
*** STATISTICS ***
Number of iterations = 1027
Converge criterion = 0.0000009968
Seed random values = 123457
X-squared = 10.9133 (0.8980)
L-squared = 10.9762 (0.8954)
Cressie-Read = 10.9320 (0.8972)
Dissimilarity index = 0.0132
Degrees of freedom = 18
Log-likelihood = -15266.00040
Number of parameters = 13 (+1)
Sample size = 5147.0
BIC(L-squared) = -142.8549
AIC(L-squared) = -25.0238
BIC(log-likelihood) = 30643.1010
AIC(log-likelihood) = 30558.0008
Table 4 on page 319
Model 1: Mover-Stayer model with stationary transition probabilities
lat 1
man 5
dim 2 2 2 2 2 2
mod X
A|X
B|XA eq2
C|XB eq1 B|XA
D|XC eq1 B|XA
E|XD eq1 B|XA
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 -1 0 -1 0]
sta B|XA [.3 .7 .7 .3 1 0 0 1]
see 12345
*** (CONDITIONAL) PROBABILITIES *** * P(X) * 1 0.7068 2 0.2932 * P(A|X) * 1 | 1 0.4199 2 | 1 0.5801 1 | 2 0.4707 2 | 2 0.5293 * P(B|XA) * 1 | 1 1 0.5864 2 | 1 1 0.4136 1 | 1 2 0.4034 2 | 1 2 0.5966 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(C|XB) * 1 | 1 1 0.5864 2 | 1 1 0.4136 1 | 1 2 0.4034 2 | 1 2 0.5966 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(D|XC) * 1 | 1 1 0.5864 2 | 1 1 0.4136 1 | 1 2 0.4034 2 | 1 2 0.5966 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(E|XD) * 1 | 1 1 0.5864 2 | 1 1 0.4136 1 | 1 2 0.4034 2 | 1 2 0.5966 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 *
Model 2: two-chain mixed Markov model with stationary transition probabilities
lat 1
man 5
dim 2 2 2 2 2 2
mod X
A|X
B|AX
C|BX eq1 B|AX
D|CX eq1 B|AX
E|DX eq1 B|AX
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** (CONDITIONAL) PROBABILITIES *** * P(X) * 1 0.5364 (0.0176) 2 0.4636 (0.0176) * P(A|X) * 1 | 1 0.1840 (0.0116) 2 | 1 0.8160 (0.0116) 1 | 2 0.7250 (0.0146) 2 | 2 0.2750 (0.0146) * P(B|XA) * 1 | 1 1 0.3123 (0.0234) 2 | 1 1 0.6877 (0.0234) 1 | 1 2 0.1838 (0.0070) 2 | 1 2 0.8162 (0.0070) 1 | 2 1 0.8274 (0.0075) 2 | 2 1 0.1726 (0.0075) 1 | 2 2 0.7139 (0.0245) 2 | 2 2 0.2861 (0.0245) * P(C|XB) * 1 | 1 1 0.3123 (0.0234) 2 | 1 1 0.6877 (0.0234) 1 | 1 2 0.1838 (0.0070) 2 | 1 2 0.8162 (0.0070) 1 | 2 1 0.8274 (0.0075) 2 | 2 1 0.1726 (0.0075) 1 | 2 2 0.7139 (0.0245) 2 | 2 2 0.2861 (0.0245) * P(D|XC) * 1 | 1 1 0.3123 (0.0234) 2 | 1 1 0.6877 (0.0234) 1 | 1 2 0.1838 (0.0070) 2 | 1 2 0.8162 (0.0070) 1 | 2 1 0.8274 (0.0075) 2 | 2 1 0.1726 (0.0075) 1 | 2 2 0.7139 (0.0245) 2 | 2 2 0.2861 (0.0245) * P(E|XD) * 1 | 1 1 0.3123 (0.0234) 2 | 1 1 0.6877 (0.0234) 1 | 1 2 0.1838 (0.0070) 2 | 1 2 0.8162 (0.0070) 1 | 2 1 0.8274 (0.0075) 2 | 2 1 0.1726 (0.0075) 1 | 2 2 0.7139 (0.0245) 2 | 2 2 0.2861 (0.0245)
Table 5 on page 321 based on the Mover-Mover-Stayer model with stationary transition probabilities
lat 1
man 5
dim 3 2 2 2 2 2
mod X
A|X
B|XA eq2
C|XB eq1 B|XA
D|XC eq1 B|XA
E|XD eq1 B|XA
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 3 0 4 0 -1 0 -1 0]
sta B|AX [.3 .7 .3 .7 .3 .7 .3 .7 1 0 0 1]
*** (CONDITIONAL) PROBABILITIES *** * P(X) * 1 0.4239 2 0.4215 3 0.1545 * P(A|X) * 1 | 1 0.1965 2 | 1 0.8035 1 | 2 0.6074 2 | 2 0.3926 1 | 3 0.6177 2 | 3 0.3823 * P(B|XA) * 1 | 1 1 0.2809 2 | 1 1 0.7191 1 | 1 2 0.1914 2 | 1 2 0.8086 1 | 2 1 0.7409 2 | 2 1 0.2591 1 | 2 2 0.6528 2 | 2 2 0.3472 1 | 3 1 1.0000 * 2 | 3 1 0.0000 * 1 | 3 2 0.0000 * 2 | 3 2 1.0000 * * P(C|XB) * 1 | 1 1 0.2809 2 | 1 1 0.7191 1 | 1 2 0.1914 2 | 1 2 0.8086 1 | 2 1 0.7409 2 | 2 1 0.2591 1 | 2 2 0.6528 2 | 2 2 0.3472 1 | 3 1 1.0000 * 2 | 3 1 0.0000 * 1 | 3 2 0.0000 * 2 | 3 2 1.0000 * * P(D|XC) * 1 | 1 1 0.2809 2 | 1 1 0.7191 1 | 1 2 0.1914 2 | 1 2 0.8086 1 | 2 1 0.7409 2 | 2 1 0.2591 1 | 2 2 0.6528 2 | 2 2 0.3472 1 | 3 1 1.0000 * 2 | 3 1 0.0000 * 1 | 3 2 0.0000 * 2 | 3 2 1.0000 * * P(E|XD) * 1 | 1 1 0.2809 2 | 1 1 0.7191 1 | 1 2 0.1914 2 | 1 2 0.8086 1 | 2 1 0.7409 2 | 2 1 0.2591 1 | 2 2 0.6528 2 | 2 2 0.3472 1 | 3 1 1.0000 * 2 | 3 1 0.0000 * 1 | 3 2 0.0000 * 2 | 3 2 1.0000 *
Table 6 on page 326
Model 1: simple Markov model
man 5
dim 2 2 2 2 2
lab A B C D E
mod A
B|A
C|B eq1 B|A
D|C eq1 C|B
E|D eq1 D|C
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
*** (CONDITIONAL) PROBABILITIES ***
* P(A) *
1 0.4348 (0.0069)
2 0.5652 (0.0069)
* P(B|A) *
1 | 1 0.7053 (0.0046)
2 | 1 0.2947 (0.0046)
1 | 2 0.2830 (0.0044)
2 | 2 0.7170 (0.0044)
* P(C|B) *
1 | 1 0.7053 (0.0046)
2 | 1 0.2947 (0.0046)
1 | 2 0.2830 (0.0044)
2 | 2 0.7170 (0.0044)
* P(D|C) *
1 | 1 0.7053 (0.0046)
2 | 1 0.2947 (0.0046)
1 | 2 0.2830 (0.0044)
2 | 2 0.7170 (0.0044)
* P(E|D) *
1 | 1 0.7053 (0.0046)
2 | 1 0.2947 (0.0046)
1 | 2 0.2830 (0.0044)
2 | 2 0.7170 (0.0044)
Model 2: latent Markov model
lat 5
man 5
dim 2 2 2 2 2 2 2 2 2 2
lab V W X Y Z A B C D E
mod V
W|V
X|W eq1 W|V
Y|X eq1 W|V
Z|Y eq1 W|V
A|V
B|W eq1 A|V
C|X eq1 A|V
D|Y eq1 A|V
E|Z eq1 A|V
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
* P(V) * 1 0.4453 (0.0116) 2 0.5547 (0.0116) * P(W|V) * 1 | 1 0.9441 (0.0061) 2 | 1 0.0559 (0.0061) 1 | 2 0.0645 (0.0056) 2 | 2 0.9355 (0.0056) * P(X|W) * 1 | 1 0.9441 (0.0061) 2 | 1 0.0559 (0.0061) 1 | 2 0.0645 (0.0056) 2 | 2 0.9355 (0.0056) * P(Y|X) * 1 | 1 0.9441 (0.0061) 2 | 1 0.0559 (0.0061) 1 | 2 0.0645 (0.0056) 2 | 2 0.9355 (0.0056) * P(Z|Y) * 1 | 1 0.9441 (0.0061) 2 | 1 0.0559 (0.0061) 1 | 2 0.0645 (0.0056) 2 | 2 0.9355 (0.0056) * P(A|V) * 1 | 1 0.8411 (0.0073) 2 | 1 0.1589 (0.0073) 1 | 2 0.1526 (0.0063) 2 | 2 0.8474 (0.0063) * P(B|W) * 1 | 1 0.8411 (0.0073) 2 | 1 0.1589 (0.0073) 1 | 2 0.1526 (0.0063) 2 | 2 0.8474 (0.0063) * P(C|X) * 1 | 1 0.8411 (0.0073) 2 | 1 0.1589 (0.0073) 1 | 2 0.1526 (0.0063) 2 | 2 0.8474 (0.0063) * P(D|Y) * 1 | 1 0.8411 (0.0073) 2 | 1 0.1589 (0.0073) 1 | 2 0.1526 (0.0063) 2 | 2 0.8474 (0.0063) * P(E|Z) * 1 | 1 0.8411 (0.0073) 2 | 1 0.1589 (0.0073) 1 | 2 0.1526 (0.0063) 2 | 2 0.8474 (0.0063)
Table 7 on page 328 based on the latent Markov model with random response
lat 5
man 5
dim 3 3 3 3 3 2 2 2 2 2
lab V W X Y Z A B C D E
mod V
W|V
X|W eq1 W|V
Y|X eq1 W|V
Z|Y eq1 W|V
A|V eq2
B|W eq1 A|V
C|X eq1 A|V
D|Y eq1 A|V
E|Z eq1 A|V
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 -1 -1]
sta A|V [.3 .7 .3 .7 .5 .5]
see 123457
ite 10000
mit 10
*** (CONDITIONAL) PROBABILITIES *** * P(V) * 1 0.3536 2 0.2412 3 0.4051 * P(W|V) * 1 | 1 0.9583 2 | 1 0.0125 3 | 1 0.0292 1 | 2 0.0207 2 | 2 0.9267 3 | 2 0.0526 1 | 3 0.0441 2 | 3 0.0881 3 | 3 0.8678 * P(X|W) * 1 | 1 0.9583 2 | 1 0.0125 3 | 1 0.0292 1 | 2 0.0207 2 | 2 0.9267 3 | 2 0.0526 1 | 3 0.0441 2 | 3 0.0881 3 | 3 0.8678 * P(Y|X) * 1 | 1 0.9583 2 | 1 0.0125 3 | 1 0.0292 1 | 2 0.0207 2 | 2 0.9267 3 | 2 0.0526 1 | 3 0.0441 2 | 3 0.0881 3 | 3 0.8678 * P(Z|Y) * 1 | 1 0.9583 2 | 1 0.0125 3 | 1 0.0292 1 | 2 0.0207 2 | 2 0.9267 3 | 2 0.0526 1 | 3 0.0441 2 | 3 0.0881 3 | 3 0.8678 * P(A|V) * 1 | 1 0.0926 2 | 1 0.9074 1 | 2 0.9422 2 | 2 0.0578 1 | 3 0.5000 2 | 3 0.5000 * P(B|W) * 1 | 1 0.0926 2 | 1 0.9074 1 | 2 0.9422 2 | 2 0.0578 1 | 3 0.5000 2 | 3 0.5000 * P(C|X) * 1 | 1 0.0926 2 | 1 0.9074 1 | 2 0.9422 2 | 2 0.0578 1 | 3 0.5000 2 | 3 0.5000 * P(D|Y) * 1 | 1 0.0926 2 | 1 0.9074 1 | 2 0.9422 2 | 2 0.0578 1 | 3 0.5000 2 | 3 0.5000 * P(E|Z) * 1 | 1 0.0926 2 | 1 0.9074 1 | 2 0.9422 2 | 2 0.0578 1 | 3 0.5000 2 | 3 0.5000
Table 8 on page 331 based on the partially latent Mover-Stayer model with nonstationary transition probabilities
lat 6
man 5
dim 2 2 2 2 2 2 2 2 2 2 2
lab P V W X Y Z A B C D E
mod P
V|P
W|VP eq2
X|WP eq2
Y|XP eq2
Z|YP eq2
A|VP eq2
B|WP eq1 A|VP
C|XP eq1 A|VP
D|YP eq1 A|VP
E|ZP eq1 A|VP
dat [891 176 119 106 111 60 52 92 120 64 51 67 54 50 49 176
237 107 68 107 80 75 51 200 136 95 64 187 99 165 172 1066]
des [1 0 2 0 -1 0 -1 0
3 0 4 0 -1 0 -1 0
5 0 6 0 -1 0 -1 0
7 0 8 0 -1 0 -1 0
9 0 10 0 -1 0 -1 0]
sta W|VP [.3 .7 .3 .7 1 0 0 1]
sta X|WP [.3 .7 .3 .7 1 0 0 1]
sta Y|XP [.3 .7 .3 .7 1 0 0 1]
sta Z|YP [.3 .7 .3 .7 1 0 0 1]
sta A|VP [.3 .7 .3 .7 1 0 0 1]
see 123457
ite 10000
*** (CONDITIONAL) PROBABILITIES *** * P(P) * 1 0.8336 2 0.1664 * P(V|P) * 1 | 1 0.6248 2 | 1 0.3752 1 | 2 0.6038 2 | 2 0.3962 * P(W|PV) * 1 | 1 1 0.8111 2 | 1 1 0.1889 1 | 1 2 0.0167 2 | 1 2 0.9833 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(X|PW) * 1 | 1 1 0.8922 2 | 1 1 0.1078 1 | 1 2 0.0618 2 | 1 2 0.9382 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(Y|PX) * 1 | 1 1 0.9478 2 | 1 1 0.0522 1 | 1 2 0.1037 2 | 1 2 0.8963 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(Z|PY) * 1 | 1 1 1.0000 2 | 1 1 0.0000 1 | 1 2 0.1299 2 | 1 2 0.8701 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(A|PV) * 1 | 1 1 0.1743 2 | 1 1 0.8257 1 | 1 2 0.7787 2 | 1 2 0.2213 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(B|PW) * 1 | 1 1 0.1743 2 | 1 1 0.8257 1 | 1 2 0.7787 2 | 1 2 0.2213 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(C|PX) * 1 | 1 1 0.1743 2 | 1 1 0.8257 1 | 1 2 0.7787 2 | 1 2 0.2213 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(D|PY) * 1 | 1 1 0.1743 2 | 1 1 0.8257 1 | 1 2 0.7787 2 | 1 2 0.2213 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 * * P(E|PZ) * 1 | 1 1 0.1743 2 | 1 1 0.8257 1 | 1 2 0.7787 2 | 1 2 0.2213 1 | 2 1 1.0000 * 2 | 2 1 0.0000 * 1 | 2 2 0.0000 * 2 | 2 2 1.0000 *
Table 9 on page 334 for multiple group analysis
UCLA Researchers are invited to our Statistical Consulting Services
We recommend others to our list of Other Resources for Statistical Computing Help
These pages are Copyrighted (c) by UCLA Academic Technology Services