Estimates the parameters of a model created using the slca function.
Usage
estimate(x, ...)
# S3 method for class 'slca'
estimate(x,
data,
method = c("em", "hybrid", "nlm"),
fix2zero = NULL,
control = slcaControl(), ...)Arguments
- x
an
slcaobject defining theslcamodel to be estimated.- ...
additional arguments passed to the estimation process.
- data
a
data.framecontaining the observed categorical variables included in the model.- method
a character string specifying the estimation method for SLCM parameters. The default is
"em", which uses the expectation-maximization (EM) algorithm. The alternative"nlm"employs the Newton-Raphson algorithm via thenlmfunction, while"hybrid"combines both approaches, starting with EM and finishing withnlmfor refined estimates.- fix2zero
a
vectorspecifying parameters to be constrained to zero. See the 'Details' section for further information.- control
a
listof control parameters for the estimation procedure. Modify default values usingslcaControl().
Value
An object of class slcafit containing the following components:
- model
a
listdescribing of the model structure.- method
the estimation method used.
- arg
a brief description of the model used during estimation.
- mf
the
data.frameused for estimation.- par
the log of the estimated paramters.
- logit
the log-odds of the estimated parameters.
- score
the score function for the estimated parameters.
- posterior
a
listof posterior probablities for each latent class variable.- convergence
a logical indicator of whether convergence was achieved.
- loglikelihood
the loglikelihood value of the estimated model.
- control
the control settings used during the estimation process.
The returned object can be further processed using the param function to extract the estimated parameters or their standard errors. The regress function allows for logistic regression analysis using a three-step approach to evaluate the effects of external variables on latent class variables. Additionally, several other methods are available, including predict.slcafit, reorder.slcafit, gof, and others.
Details
The fix2zero argument allows you to constrain specific parameters to zero. Each parameter is associated with a unique index, which can be identified using the param function with the argument index = TRUE. To apply constraints, provide the relevant parameter indices in the fix2zero arguments with vector.
Examples
m <- slca(lc[3] ~ y1 + y2 + y3 + y4)
pi <- rep(1 / 3, 3)
rho <- c(.9, .1, .9, .1, .9, .1, .9, .1, # class 1
.9, .1, .9, .1, .1, .9, .1, .9, # class 2
.1, .9, .1, .9, .1, .9, .1, .9) # class 3
dt <- simulate(m, 200, parm = c(pi, rho))
estimate(m, dt$response)
#> Structural Latent Variable Model
#>
#> Latent variables (Root*):
#> Label: lc*
#> nclass: 3
#>
#> Measurement model:
#> lc -> { y1, y2, y3, y4 } a
#>
#> LogLik: -418.6869
# Several estimation methods
estimate(m, dt$response, method = "em",
control = slcaControl(verbose = TRUE)) # default
#> EM iteration begin.
#> 1 iterations logLik: -554.04 diff: inf
2 iterations logLik: -525.87 diff: 28.16667
3 iterations logLik: -525.85 diff: 0.02429
4 iterations logLik: -525.75 diff: 0.10077
5 iterations logLik: -525.25 diff: 0.49661
6 iterations logLik: -522.86 diff: 2.39066
7 iterations logLik: -512.76 diff: 10.10172
8 iterations logLik: -486.04 diff: 26.71932
9 iterations logLik: -460.94 diff: 25.09520
10 iterations logLik: -452.69 diff: 8.25678
11 iterations logLik: -447.72 diff: 4.96113
12 iterations logLik: -440.28 diff: 7.44853
13 iterations logLik: -430.01 diff: 10.26439
14 iterations logLik: -422.50 diff: 7.50993
15 iterations logLik: -420.23 diff: 2.27636
16 iterations logLik: -419.87 diff: 0.35361
17 iterations logLik: -419.79 diff: 0.07844
18 iterations logLik: -419.75 diff: 0.04678
19 iterations logLik: -419.71 diff: 0.04160
20 iterations logLik: -419.67 diff: 0.03996
21 iterations logLik: -419.63 diff: 0.03889
22 iterations logLik: -419.59 diff: 0.03795
23 iterations logLik: -419.55 diff: 0.03704
24 iterations logLik: -419.52 diff: 0.03617
25 iterations logLik: -419.48 diff: 0.03531
26 iterations logLik: -419.45 diff: 0.03447
27 iterations logLik: -419.41 diff: 0.03363
28 iterations logLik: -419.38 diff: 0.03281
29 iterations logLik: -419.35 diff: 0.03197
30 iterations logLik: -419.32 diff: 0.03114
31 iterations logLik: -419.29 diff: 0.03030
32 iterations logLik: -419.26 diff: 0.02944
33 iterations logLik: -419.23 diff: 0.02857
34 iterations logLik: -419.20 diff: 0.02769
35 iterations logLik: -419.17 diff: 0.02679
36 iterations logLik: -419.15 diff: 0.02587
37 iterations logLik: -419.12 diff: 0.02495
38 iterations logLik: -419.10 diff: 0.02401
39 iterations logLik: -419.08 diff: 0.02306
40 iterations logLik: -419.05 diff: 0.02210
41 iterations logLik: -419.03 diff: 0.02115
42 iterations logLik: -419.01 diff: 0.02019
43 iterations logLik: -418.99 diff: 0.01924
44 iterations logLik: -418.97 diff: 0.01829
45 iterations logLik: -418.96 diff: 0.01736
46 iterations logLik: -418.94 diff: 0.01645
47 iterations logLik: -418.92 diff: 0.01555
48 iterations logLik: -418.91 diff: 0.01468
49 iterations logLik: -418.90 diff: 0.01384
50 iterations logLik: -418.88 diff: 0.01302
51 iterations logLik: -418.87 diff: 0.01223
52 iterations logLik: -418.86 diff: 0.01148
53 iterations logLik: -418.85 diff: 0.01076
54 iterations logLik: -418.84 diff: 0.01007
55 iterations logLik: -418.83 diff: 0.00942
56 iterations logLik: -418.82 diff: 0.00880
57 iterations logLik: -418.81 diff: 0.00822
58 iterations logLik: -418.80 diff: 0.00767
59 iterations logLik: -418.80 diff: 0.00715
60 iterations logLik: -418.79 diff: 0.00667
61 iterations logLik: -418.78 diff: 0.00622
62 iterations logLik: -418.78 diff: 0.00579
63 iterations logLik: -418.77 diff: 0.00540
64 iterations logLik: -418.77 diff: 0.00503
65 iterations logLik: -418.76 diff: 0.00469
66 iterations logLik: -418.76 diff: 0.00437
67 iterations logLik: -418.76 diff: 0.00408
68 iterations logLik: -418.75 diff: 0.00380
69 iterations logLik: -418.75 diff: 0.00355
70 iterations logLik: -418.74 diff: 0.00331
71 iterations logLik: -418.74 diff: 0.00309
72 iterations logLik: -418.74 diff: 0.00289
73 iterations logLik: -418.74 diff: 0.00270
74 iterations logLik: -418.73 diff: 0.00253
75 iterations logLik: -418.73 diff: 0.00237
76 iterations logLik: -418.73 diff: 0.00222
77 iterations logLik: -418.73 diff: 0.00208
78 iterations logLik: -418.72 diff: 0.00196
79 iterations logLik: -418.72 diff: 0.00184
80 iterations logLik: -418.72 diff: 0.00173
81 iterations logLik: -418.72 diff: 0.00162
82 iterations logLik: -418.72 diff: 0.00153
83 iterations logLik: -418.72 diff: 0.00144
84 iterations logLik: -418.72 diff: 0.00136
85 iterations logLik: -418.71 diff: 0.00128
86 iterations logLik: -418.71 diff: 0.00121
87 iterations logLik: -418.71 diff: 0.00115
88 iterations logLik: -418.71 diff: 0.00108
89 iterations logLik: -418.71 diff: 0.00103
90 iterations logLik: -418.71 diff: 0.00097
91 iterations logLik: -418.71 diff: 0.00092
92 iterations logLik: -418.71 diff: 0.00088
93 iterations logLik: -418.71 diff: 0.00083
94 iterations logLik: -418.70 diff: 0.00079
95 iterations logLik: -418.70 diff: 0.00075
96 iterations logLik: -418.70 diff: 0.00072
97 iterations logLik: -418.70 diff: 0.00068
98 iterations logLik: -418.70 diff: 0.00065
99 iterations logLik: -418.70 diff: 0.00062
100 iterations logLik: -418.70 diff: 0.00059
101 iterations logLik: -418.70 diff: 0.00056
102 iterations logLik: -418.70 diff: 0.00054
103 iterations logLik: -418.70 diff: 0.00051
104 iterations logLik: -418.70 diff: 0.00049
105 iterations logLik: -418.70 diff: 0.00047
106 iterations logLik: -418.70 diff: 0.00045
107 iterations logLik: -418.70 diff: 0.00043
108 iterations logLik: -418.70 diff: 0.00041
109 iterations logLik: -418.70 diff: 0.00039
110 iterations logLik: -418.70 diff: 0.00038
111 iterations logLik: -418.70 diff: 0.00036
112 iterations logLik: -418.70 diff: 0.00035
113 iterations logLik: -418.70 diff: 0.00033
114 iterations logLik: -418.69 diff: 0.00032
115 iterations logLik: -418.69 diff: 0.00031
116 iterations logLik: -418.69 diff: 0.00029
117 iterations logLik: -418.69 diff: 0.00028
118 iterations logLik: -418.69 diff: 0.00027
119 iterations logLik: -418.69 diff: 0.00026
120 iterations logLik: -418.69 diff: 0.00025
121 iterations logLik: -418.69 diff: 0.00024
122 iterations logLik: -418.69 diff: 0.00023
123 iterations logLik: -418.69 diff: 0.00022
124 iterations logLik: -418.69 diff: 0.00021
125 iterations logLik: -418.69 diff: 0.00020
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127 iterations logLik: -418.69 diff: 0.00019
128 iterations logLik: -418.69 diff: 0.00018
129 iterations logLik: -418.69 diff: 0.00017
130 iterations logLik: -418.69 diff: 0.00017
131 iterations logLik: -418.69 diff: 0.00016
132 iterations logLik: -418.69 diff: 0.00016
133 iterations logLik: -418.69 diff: 0.00015
134 iterations logLik: -418.69 diff: 0.00014
135 iterations logLik: -418.69 diff: 0.00014
136 iterations logLik: -418.69 diff: 0.00013
137 iterations logLik: -418.69 diff: 0.00013
138 iterations logLik: -418.69 diff: 0.00012
139 iterations logLik: -418.69 diff: 0.00012
140 iterations logLik: -418.69 diff: 0.00011
141 iterations logLik: -418.69 diff: 0.00011
142 iterations logLik: -418.69 diff: 0.00011
143 iterations logLik: -418.69 diff: 0.00010
144 iterations logLik: -418.69 diff: 0.00010
145 iterations logLik: -418.69 diff: 0.00009
146 iterations logLik: -418.69 diff: 0.00009
147 iterations logLik: -418.69 diff: 0.00009
148 iterations logLik: -418.69 diff: 0.00008
149 iterations logLik: -418.69 diff: 0.00008
150 iterations logLik: -418.69 diff: 0.00008
151 iterations logLik: -418.69 diff: 0.00008
152 iterations logLik: -418.69 diff: 0.00007
153 iterations logLik: -418.69 diff: 0.00007
154 iterations logLik: -418.69 diff: 0.00007
155 iterations logLik: -418.69 diff: 0.00007
156 iterations logLik: -418.69 diff: 0.00006
157 iterations logLik: -418.69 diff: 0.00006
158 iterations logLik: -418.69 diff: 0.00006
159 iterations logLik: -418.69 diff: 0.00006
160 iterations logLik: -418.69 diff: 0.00005
161 iterations logLik: -418.69 diff: 0.00005
162 iterations logLik: -418.69 diff: 0.00005
163 iterations logLik: -418.69 diff: 0.00005
164 iterations logLik: -418.69 diff: 0.00005
165 iterations logLik: -418.69 diff: 0.00005
166 iterations logLik: -418.69 diff: 0.00004
167 iterations logLik: -418.69 diff: 0.00004
168 iterations logLik: -418.69 diff: 0.00004
169 iterations logLik: -418.69 diff: 0.00004
170 iterations logLik: -418.69 diff: 0.00004
171 iterations logLik: -418.69 diff: 0.00004
172 iterations logLik: -418.69 diff: 0.00004
173 iterations logLik: -418.69 diff: 0.00003
174 iterations logLik: -418.69 diff: 0.00003
175 iterations logLik: -418.69 diff: 0.00003
176 iterations logLik: -418.69 diff: 0.00003
177 iterations logLik: -418.69 diff: 0.00003
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180 iterations logLik: -418.69 diff: 0.00003
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182 iterations logLik: -418.69 diff: 0.00002
183 iterations logLik: -418.69 diff: 0.00002
184 iterations logLik: -418.69 diff: 0.00002
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207 iterations logLik: -418.69 diff: 9.8e-06
208 iterations logLik: -418.69 diff: 9.5e-06
209 iterations logLik: -418.69 diff: 9.1e-06
210 iterations logLik: -418.69 diff: 8.8e-06
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212 iterations logLik: -418.69 diff: 8.2e-06
213 iterations logLik: -418.69 diff: 7.9e-06
214 iterations logLik: -418.69 diff: 7.6e-06
215 iterations logLik: -418.69 diff: 7.3e-06
216 iterations logLik: -418.69 diff: 7.1e-06
217 iterations logLik: -418.69 diff: 6.8e-06
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220 iterations logLik: -418.69 diff: 6.1e-06
221 iterations logLik: -418.69 diff: 5.9e-06
222 iterations logLik: -418.69 diff: 5.7e-06
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230 iterations logLik: -418.69 diff: 4.3e-06
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236 iterations logLik: -418.69 diff: 3.4e-06
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238 iterations logLik: -418.69 diff: 3.2e-06
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240 iterations logLik: -418.69 diff: 3.0e-06
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271 iterations logLik: -418.69 diff: 9.7e-07
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385 iterations logLik: -418.69 diff: 1.6e-08
386 iterations logLik: -418.69 diff: 1.5e-08
387 iterations logLik: -418.69 diff: 1.5e-08
388 iterations logLik: -418.69 diff: 1.4e-08
389 iterations logLik: -418.69 diff: 1.4e-08
390 iterations logLik: -418.69 diff: 1.3e-08
391 iterations logLik: -418.69 diff: 1.3e-08
392 iterations logLik: -418.69 diff: 1.2e-08
393 iterations logLik: -418.69 diff: 1.2e-08
394 iterations logLik: -418.69 diff: 1.1e-08
395 iterations logLik: -418.69 diff: 1.1e-08
396 iterations logLik: -418.69 diff: 1.1e-08
397 iterations logLik: -418.69 diff: 1.0e-08
398 iterations logLik: -418.69 diff: 9.9e-09
#> .. done.
#> Structural Latent Variable Model
#>
#> Latent variables (Root*):
#> Label: lc*
#> nclass: 3
#>
#> Measurement model:
#> lc -> { y1, y2, y3, y4 } a
#>
#> LogLik: -418.6869
estimate(m, dt$response, method = "nlm",
control = slcaControl(verbose = TRUE))
#> nlm iteration begin.
#> .. done.
#> Structural Latent Variable Model
#>
#> Latent variables (Root*):
#> Label: lc*
#> nclass: 3
#>
#> Measurement model:
#> lc -> { y1, y2, y3, y4 } a
#>
#> LogLik: -419.449
estimate(m, dt$response, method = "hybrid",
control = slcaControl(verbose = TRUE))
#> EM iteration begin.
#> 1 iterations logLik: -553.95 diff: inf
2 iterations logLik: -525.81 diff: 28.13425
3 iterations logLik: -525.57 diff: 0.24010
4 iterations logLik: -524.40 diff: 1.17046
5 iterations logLik: -519.01 diff: 5.39735
6 iterations logLik: -500.04 diff: 18.96421
7 iterations logLik: -469.39 diff: 30.64874
8 iterations logLik: -453.52 diff: 15.87814
9 iterations logLik: -442.87 diff: 10.64149
10 iterations logLik: -429.74 diff: 13.13561
11 iterations logLik: -421.75 diff: 7.98906
12 iterations logLik: -419.99 diff: 1.76209
13 iterations logLik: -419.80 diff: 0.18196
14 iterations logLik: -419.78 diff: 0.02572
15 iterations logLik: -419.76 diff: 0.01456
16 iterations logLik: -419.75 diff: 0.01309
17 iterations logLik: -419.74 diff: 0.01227
18 iterations logLik: -419.73 diff: 0.01154
19 iterations logLik: -419.72 diff: 0.01088
20 iterations logLik: -419.71 diff: 0.01028
21 iterations logLik: -419.70 diff: 0.00973
22 iterations logLik: -419.69 diff: 0.00923
23 iterations logLik: -419.68 diff: 0.00877
24 iterations logLik: -419.67 diff: 0.00836
25 iterations logLik: -419.66 diff: 0.00800
26 iterations logLik: -419.65 diff: 0.00767
27 iterations logLik: -419.65 diff: 0.00737
28 iterations logLik: -419.64 diff: 0.00711
29 iterations logLik: -419.63 diff: 0.00689
30 iterations logLik: -419.63 diff: 0.00669
31 iterations logLik: -419.62 diff: 0.00652
32 iterations logLik: -419.61 diff: 0.00638
33 iterations logLik: -419.61 diff: 0.00626
34 iterations logLik: -419.60 diff: 0.00616
35 iterations logLik: -419.60 diff: 0.00609
36 iterations logLik: -419.59 diff: 0.00605
37 iterations logLik: -419.58 diff: 0.00602
38 iterations logLik: -419.58 diff: 0.00601
39 iterations logLik: -419.57 diff: 0.00602
40 iterations logLik: -419.57 diff: 0.00605
41 iterations logLik: -419.56 diff: 0.00609
42 iterations logLik: -419.55 diff: 0.00615
43 iterations logLik: -419.55 diff: 0.00623
44 iterations logLik: -419.54 diff: 0.00631
45 iterations logLik: -419.53 diff: 0.00642
46 iterations logLik: -419.53 diff: 0.00653
47 iterations logLik: -419.52 diff: 0.00666
48 iterations logLik: -419.51 diff: 0.00680
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50 iterations logLik: -419.50 diff: 0.00710
51 iterations logLik: -419.49 diff: 0.00726
52 iterations logLik: -419.49 diff: 0.00743
53 iterations logLik: -419.48 diff: 0.00761
54 iterations logLik: -419.47 diff: 0.00779
55 iterations logLik: -419.46 diff: 0.00797
56 iterations logLik: -419.45 diff: 0.00816
57 iterations logLik: -419.45 diff: 0.00834
58 iterations logLik: -419.44 diff: 0.00853
59 iterations logLik: -419.43 diff: 0.00871
60 iterations logLik: -419.42 diff: 0.00889
61 iterations logLik: -419.41 diff: 0.00906
62 iterations logLik: -419.40 diff: 0.00923
63 iterations logLik: -419.39 diff: 0.00939
64 iterations logLik: -419.38 diff: 0.00954
65 iterations logLik: -419.37 diff: 0.00968
66 iterations logLik: -419.36 diff: 0.00981
67 iterations logLik: -419.35 diff: 0.00993
68 iterations logLik: -419.34 diff: 0.01003
69 iterations logLik: -419.33 diff: 0.01012
70 iterations logLik: -419.32 diff: 0.01019
71 iterations logLik: -419.31 diff: 0.01025
72 iterations logLik: -419.30 diff: 0.01029
73 iterations logLik: -419.29 diff: 0.01031
74 iterations logLik: -419.28 diff: 0.01032
75 iterations logLik: -419.27 diff: 0.01030
76 iterations logLik: -419.26 diff: 0.01027
77 iterations logLik: -419.25 diff: 0.01022
78 iterations logLik: -419.24 diff: 0.01016
79 iterations logLik: -419.23 diff: 0.01008
80 iterations logLik: -419.22 diff: 0.00998
81 iterations logLik: -419.21 diff: 0.00987
82 iterations logLik: -419.20 diff: 0.00975
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85 iterations logLik: -419.17 diff: 0.00930
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87 iterations logLik: -419.15 diff: 0.00895
88 iterations logLik: -419.15 diff: 0.00877
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90 iterations logLik: -419.13 diff: 0.00838
91 iterations logLik: -419.12 diff: 0.00817
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93 iterations logLik: -419.10 diff: 0.00776
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95 iterations logLik: -419.09 diff: 0.00734
96 iterations logLik: -419.08 diff: 0.00713
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100 iterations logLik: -419.06 diff: 0.00631
101 iterations logLik: -419.05 diff: 0.00611
102 iterations logLik: -419.04 diff: 0.00591
103 iterations logLik: -419.04 diff: 0.00572
104 iterations logLik: -419.03 diff: 0.00553
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107 iterations logLik: -419.02 diff: 0.00498
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110 iterations logLik: -419.00 diff: 0.00448
111 iterations logLik: -419.00 diff: 0.00433
112 iterations logLik: -418.99 diff: 0.00417
113 iterations logLik: -418.99 diff: 0.00403
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116 iterations logLik: -418.98 diff: 0.00361
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120 iterations logLik: -418.97 diff: 0.00312
121 iterations logLik: -418.96 diff: 0.00301
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123 iterations logLik: -418.96 diff: 0.00280
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129 iterations logLik: -418.94 diff: 0.00225
130 iterations logLik: -418.94 diff: 0.00217
131 iterations logLik: -418.94 diff: 0.00210
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137 iterations logLik: -418.93 diff: 0.00169
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149 iterations logLik: -418.91 diff: 0.00112
150 iterations logLik: -418.91 diff: 0.00108
151 iterations logLik: -418.91 diff: 0.00105
152 iterations logLik: -418.91 diff: 0.00101
153 iterations logLik: -418.91 diff: 0.00098
154 iterations logLik: -418.91 diff: 0.00095
155 iterations logLik: -418.91 diff: 0.00091
156 iterations logLik: -418.90 diff: 0.00088
157 iterations logLik: -418.90 diff: 0.00086
158 iterations logLik: -418.90 diff: 0.00083
159 iterations logLik: -418.90 diff: 0.00080
160 iterations logLik: -418.90 diff: 0.00078
161 iterations logLik: -418.90 diff: 0.00075
162 iterations logLik: -418.90 diff: 0.00073
163 iterations logLik: -418.90 diff: 0.00070
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165 iterations logLik: -418.90 diff: 0.00066
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170 iterations logLik: -418.89 diff: 0.00056
171 iterations logLik: -418.89 diff: 0.00054
172 iterations logLik: -418.89 diff: 0.00053
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205 iterations logLik: -418.88 diff: 0.00019
206 iterations logLik: -418.88 diff: 0.00019
207 iterations logLik: -418.88 diff: 0.00018
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209 iterations logLik: -418.88 diff: 0.00017
210 iterations logLik: -418.88 diff: 0.00017
211 iterations logLik: -418.88 diff: 0.00016
212 iterations logLik: -418.88 diff: 0.00016
213 iterations logLik: -418.88 diff: 0.00015
214 iterations logLik: -418.88 diff: 0.00015
215 iterations logLik: -418.88 diff: 0.00014
216 iterations logLik: -418.88 diff: 0.00014
217 iterations logLik: -418.88 diff: 0.00013
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220 iterations logLik: -418.88 diff: 0.00012
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223 iterations logLik: -418.88 diff: 0.00011
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226 iterations logLik: -418.88 diff: 0.00010
227 iterations logLik: -418.88 diff: 0.00010
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229 iterations logLik: -418.88 diff: 0.00009
230 iterations logLik: -418.88 diff: 0.00009
231 iterations logLik: -418.88 diff: 0.00009
232 iterations logLik: -418.88 diff: 0.00009
233 iterations logLik: -418.88 diff: 0.00008
234 iterations logLik: -418.88 diff: 0.00008
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240 iterations logLik: -418.88 diff: 0.00007
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#> .. done.
#> nlm iteration begin.
#> .. done.
#> Structural Latent Variable Model
#>
#> Latent variables (Root*):
#> Label: lc*
#> nclass: 3
#>
#> Measurement model:
#> lc -> { y1, y2, y3, y4 } a
#>
#> LogLik: -418.8761
# Parameter restriction
mf <- estimate(m, dt$response)
param(mf, index = TRUE)
#> PI :
#> (lc)
#> class
#> 1 2 3
#> 0.3165 (1) 0.2838 (2) 0.3998 (3)
#>
#> RHO :
#> (a)
#> class
#> response 1 2 3
#> 1(V1) 0.0000 (4) 1.0000 (12) 0.9286 (20)
#> 2 1.0000 (5) 0.0000 (13) 0.0714 (21)
#> 1(V2) 0.1501 (6) 0.8090 (14) 0.8703 (22)
#> 2 0.8499 (7) 0.1910 (15) 0.1297 (23)
#> 1(V3) 0.1025 (8) 0.0000 (16) 0.8069 (24)
#> 2 0.8975 (9) 1.0000 (17) 0.1931 (25)
#> 1(V4) 0.1107 (10) 0.0000 (18) 0.9129 (26)
#> 2 0.8893 (11) 1.0000 (19) 0.0871 (27)
#>
#> V1 V2 V3 V4
#> lc y1 y2 y3 y4
mf0 <- estimate(mf, fix2zero = c(4, 6, 8, 10))
param(mf0)
#> PI :
#> (lc)
#> class
#> 1 2 3
#> 0.2067 0.3119 0.4814
#>
#> RHO :
#> (a)
#> class
#> response 1 2 3
#> 1(V1) 0.0000 0.8754 0.7935
#> 2 1.0000 0.1246 0.2065
#> 1(V2) 0.0000 0.8127 0.7717
#> 2 1.0000 0.1873 0.2283
#> 1(V3) 0.0000 0.0000 0.7375
#> 2 1.0000 1.0000 0.2625
#> 1(V4) 0.0000 0.0000 0.8310
#> 2 1.0000 1.0000 0.1690
#>
#> V1 V2 V3 V4
#> lc y1 y2 y3 y4
# Estimation control
estimate(m, dt$response, control = slcaControl(nrep = 3, verbose = TRUE))
#> Inital parameter test:
#> 1 / 3 logLik: -418.6869
#> 2 / 3 logLik: -418.6869
#> 3 / 3 logLik: -418.8761
#>
#> 1th parameter set has been selected.
#>
#> EM iteration begin.
#> 1 iterations logLik: -418.69 diff: inf
2 iterations logLik: -418.69 diff: 2.4e-10
#> .. done.
#> Structural Latent Variable Model
#>
#> Latent variables (Root*):
#> Label: lc*
#> nclass: 3
#>
#> Measurement model:
#> lc -> { y1, y2, y3, y4 } a
#>
#> LogLik: -418.6869
estimate(m, dt$response, control = slcaControl(init.param = c(pi, rho)))
#> Structural Latent Variable Model
#>
#> Latent variables (Root*):
#> Label: lc*
#> nclass: 3
#>
#> Measurement model:
#> lc -> { y1, y2, y3, y4 } a
#>
#> LogLik: -418.6869