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test for alternative-specific conditional logit
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id | male | income | size | choicecat | dealer1 | dealer2 | dealer3 | |
---|---|---|---|---|---|---|---|---|
1 | 1 | 46.7 | 2 | 3 | 18 | 8 | 5 | |
2 | 1 | 26.1 | 3 | 1 | 17 | 6 | 2 | |
3 | 1 | 32.7 | 4 | 1 | 12 | 6 | 2 | |
4 | 0 | 49.2 | 2 | 2 | 18 | 7 | 4 | |
5 | 1 | 24.3 | 4 | 1 | 10 | 7 | 3 | |
6 | 0 | 39 | 4 | 1 | 13 | 4 | 1 | |
7 | 1 | 33 | 2 | 1 | 24 | 7 | 4 | |
8 | 1 | 20.3 | 4 | 1 | 9 | 3 | 3 | |
9 | 1 | 38 | 3 | 2 | 14 | 10 | 2 | |
10 | 0 | 60.4 | 3 | 1 | 18 | 5 | 4 | |
11 | 1 | 69 | 3 | 1 | 23 | 8 | 2 | |
12 | 1 | 27.7 | 1 | 2 | 21 | 9 | 5 | |
13 | 1 | 41 | 2 | 2 | 20 | 10 | 5 | |
14 | 1 | 65.6 | 4 | 2 | 13 | 4 | 5 | |
15 | 1 | 24.8 | 3 | 1 | 21 | 5 | 3 | |
16 | 1 | 21.6 | 2 | 1 | 19 | 8 | 3 | |
17 | 0 | 43.3 | 1 | 1 | 23 | 9 | 5 | |
18 | 0 | 44.4 | 1 | 2 | 22 | 10 | 4 | |
19 | 1 | 45.3 | 4 | 1 | 17 | 8 | 1 | |
20 | 1 | 48.8 | 3 | 1 | 20 | 10 | 5 | |
21 | 1 | 44.7 | 4 | 3 | 23 | 7 | 2 | |
22 | 1 | 57.5 | 1 | 1 | 24 | 9 | 4 | |
23 | 1 | 42 | 2 | 1 | 20 | 8 | 3 | |
24 | 1 | 40.5 | 1 | 1 | 23 | 10 | 4 | |
25 | 1 | 44.4 | 1 | 1 | 24 | 10 | 5 | |
26 | 0 | 50.3 | 2 | 1 | 21 | 7 | 4 | |
27 | 0 | 41.1 | 4 | 1 | 13 | 3 | 2 | |
28 | 1 | 49 | 3 | 2 | 22 | 10 | 3 | |
29 | 0 | 43 | 3 | 1 | 21 | 7 | 2 | |
30 | 1 | 43.8 | 4 | 3 | 16 | 8 | 3 | |
31 | 1 | 46.6 | 2 | 3 | 24 | 8 | 5 | |
32 | 1 | 45.7 | 3 | 1 | 14 | 10 | 2 | |
33 | 1 | 69.8 | 3 | 1 | 23 | 10 | 5 | |
34 | 0 | 45.6 | 2 | 2 | 21 | 7 | 5 | |
35 | 1 | 20.9 | 1 | 1 | 23 | 10 | 4 | |
36 | 1 | 30.6 | 4 | 1 | 20 | 4 | 4 | |
37 | 0 | 40.7 | 2 | 3 | 18 | 7 | 3 | |
38 | 1 | 46.7 | 2 | 3 | 18 | 8 | 5 | |
39 | 1 | 26.1 | 3 | 1 | 17 | 6 | 2 | |
40 | 1 | 32.7 | 4 | 1 | 12 | 6 | 2 | |
41 | 0 | 49.2 | 2 | 2 | 18 | 7 | 4 | |
42 | 1 | 24.3 | 4 | 1 | 10 | 7 | 3 | |
43 | 0 | 39 | 4 | 1 | 13 | 4 | 1 | |
44 | 1 | 33 | 2 | 1 | 24 | 7 | 4 | |
45 | 1 | 20.3 | 4 | 1 | 9 | 3 | 3 | |
46 | 1 | 38 | 3 | 2 | 14 | 10 | 2 | |
47 | 0 | 60.4 | 3 | 1 | 18 | 5 | 4 | |
48 | 1 | 69 | 3 | 1 | 23 | 8 | 2 | |
49 | 1 | 27.7 | 1 | 2 | 21 | 9 | 5 | |
50 | 1 | 41 | 2 | 2 | 20 | 10 | 5 | |
51 | 1 | 65.6 | 4 | 2 | 13 | 4 | 5 | |
52 | 1 | 24.8 | 3 | 1 | 21 | 5 | 3 | |
53 | 1 | 21.6 | 2 | 1 | 19 | 8 | 3 | |
54 | 0 | 43.3 | 1 | 1 | 23 | 9 | 5 | |
55 | 0 | 44.4 | 1 | 2 | 22 | 10 | 4 | |
56 | 1 | 45.3 | 4 | 1 | 17 | 8 | 1 | |
57 | 1 | 48.8 | 3 | 1 | 20 | 10 | 5 | |
58 | 1 | 44.7 | 4 | 3 | 23 | 7 | 2 | |
59 | 1 | 57.5 | 1 | 1 | 24 | 9 | 4 | |
60 | 1 | 42 | 2 | 1 | 20 | 8 | 3 | |
61 | 1 | 40.5 | 1 | 1 | 23 | 10 | 4 | |
62 | 1 | 44.4 | 1 | 1 | 24 | 10 | 5 | |
63 | 0 | 50.3 | 2 | 1 | 21 | 7 | 4 | |
64 | 0 | 41.1 | 4 | 1 | 13 | 3 | 2 | |
65 | 1 | 49 | 3 | 2 | 22 | 10 | 3 | |
66 | 0 | 43 | 3 | 1 | 21 | 7 | 2 | |
67 | 1 | 43.8 | 4 | 3 | 16 | 8 | 3 | |
68 | 1 | 46.6 | 2 | 3 | 24 | 8 | 5 | |
69 | 1 | 45.7 | 3 | 1 | 14 | 10 | 2 | |
70 | 1 | 69.8 | 3 | 1 | 23 | 10 | 5 | |
71 | 0 | 45.6 | 2 | 2 | 21 | 7 | 5 | |
72 | 1 | 20.9 | 1 | 1 | 23 | 10 | 4 | |
73 | 1 | 30.6 | 4 | 1 | 20 | 4 | 4 | |
74 | 0 | 40.7 | 2 | 3 | 18 | 7 | 3 | |
75 | 1 | 46.7 | 2 | 3 | 18 | 8 | 5 | |
76 | 1 | 26.1 | 3 | 1 | 17 | 6 | 2 | |
77 | 1 | 32.7 | 4 | 1 | 12 | 6 | 2 | |
78 | 0 | 49.2 | 2 | 2 | 18 | 7 | 4 | |
79 | 1 | 24.3 | 4 | 1 | 10 | 7 | 3 | |
80 | 0 | 39 | 4 | 1 | 13 | 4 | 1 | |
81 | 1 | 33 | 2 | 1 | 24 | 7 | 4 | |
82 | 1 | 20.3 | 4 | 1 | 9 | 3 | 3 | |
83 | 1 | 38 | 3 | 2 | 14 | 10 | 2 | |
84 | 0 | 60.4 | 3 | 1 | 18 | 5 | 4 | |
85 | 1 | 69 | 3 | 1 | 23 | 8 | 2 | |
86 | 1 | 27.7 | 1 | 2 | 21 | 9 | 5 | |
87 | 1 | 41 | 2 | 2 | 20 | 10 | 5 | |
88 | 1 | 65.6 | 4 | 2 | 13 | 4 | 5 | |
89 | 1 | 24.8 | 3 | 1 | 21 | 5 | 3 | |
90 | 1 | 21.6 | 2 | 1 | 19 | 8 | 3 | |
91 | 0 | 43.3 | 1 | 1 | 23 | 9 | 5 | |
92 | 0 | 44.4 | 1 | 2 | 22 | 10 | 4 | |
93 | 1 | 45.3 | 4 | 1 | 17 | 8 | 1 | |
94 | 1 | 48.8 | 3 | 1 | 20 | 10 | 5 | |
95 | 1 | 44.7 | 4 | 3 | 23 | 7 | 2 | |
96 | 1 | 57.5 | 1 | 1 | 24 | 9 | 4 | |
97 | 1 | 42 | 2 | 1 | 20 | 8 | 3 | |
98 | 1 | 40.5 | 1 | 1 | 23 | 10 | 4 | |
99 | 1 | 44.4 | 1 | 1 | 24 | 10 | 5 | |
100 | 0 | 50.3 | 2 | 1 | 21 | 7 | 4 | |
101 | 0 | 41.1 | 4 | 1 | 13 | 3 | 2 | |
102 | 1 | 49 | 3 | 2 | 22 | 10 | 3 | |
103 | 0 | 43 | 3 | 1 | 21 | 7 | 2 | |
104 | 1 | 43.8 | 4 | 3 | 16 | 8 | 3 | |
105 | 1 | 46.6 | 2 | 3 | 24 | 8 | 5 | |
106 | 1 | 45.7 | 3 | 1 | 14 | 10 | 2 | |
107 | 1 | 69.8 | 3 | 1 | 23 | 10 | 5 | |
108 | 0 | 45.6 | 2 | 2 | 21 | 7 | 5 | |
109 | 1 | 20.9 | 1 | 1 | 23 | 10 | 4 | |
110 | 1 | 30.6 | 4 | 1 | 20 | 4 | 4 | |
111 | 0 | 40.7 | 2 | 3 | 18 | 7 | 3 | |
112 | 1 | 46.7 | 2 | 3 | 18 | 8 | 5 | |
113 | 1 | 26.1 | 3 | 1 | 17 | 6 | 2 | |
114 | 1 | 32.7 | 4 | 1 | 12 | 6 | 2 | |
115 | 0 | 49.2 | 2 | 2 | 18 | 7 | 4 | |
116 | 1 | 24.3 | 4 | 1 | 10 | 7 | 3 | |
117 | 0 | 39 | 4 | 1 | 13 | 4 | 1 | |
118 | 1 | 33 | 2 | 1 | 24 | 7 | 4 | |
119 | 1 | 20.3 | 4 | 1 | 9 | 3 | 3 | |
120 | 1 | 38 | 3 | 2 | 14 | 10 | 2 | |
121 | 0 | 60.4 | 3 | 1 | 18 | 5 | 4 | |
122 | 1 | 69 | 3 | 1 | 23 | 8 | 2 | |
123 | 1 | 27.7 | 1 | 2 | 21 | 9 | 5 | |
124 | 1 | 41 | 2 | 2 | 20 | 10 | 5 | |
125 | 1 | 65.6 | 4 | 2 | 13 | 4 | 5 | |
126 | 1 | 24.8 | 3 | 1 | 21 | 5 | 3 | |
127 | 1 | 21.6 | 2 | 1 | 19 | 8 | 3 | |
128 | 0 | 43.3 | 1 | 1 | 23 | 9 | 5 | |
129 | 0 | 44.4 | 1 | 2 | 22 | 10 | 4 | |
130 | 1 | 45.3 | 4 | 1 | 17 | 8 | 1 | |
131 | 1 | 48.8 | 3 | 1 | 20 | 10 | 5 | |
132 | 1 | 44.7 | 4 | 3 | 23 | 7 | 2 | |
133 | 1 | 57.5 | 1 | 1 | 24 | 9 | 4 | |
134 | 1 | 42 | 2 | 1 | 20 | 8 | 3 | |
135 | 1 | 40.5 | 1 | 1 | 23 | 10 | 4 | |
136 | 1 | 44.4 | 1 | 1 | 24 | 10 | 5 | |
137 | 0 | 50.3 | 2 | 1 | 21 | 7 | 4 | |
138 | 0 | 41.1 | 4 | 1 | 13 | 3 | 2 | |
139 | 1 | 49 | 3 | 2 | 22 | 10 | 3 | |
140 | 0 | 43 | 3 | 1 | 21 | 7 | 2 | |
141 | 1 | 43.8 | 4 | 3 | 16 | 8 | 3 | |
142 | 1 | 46.6 | 2 | 3 | 24 | 8 | 5 | |
143 | 1 | 45.7 | 3 | 1 | 14 | 10 | 2 | |
144 | 1 | 69.8 | 3 | 1 | 23 | 10 | 5 | |
145 | 0 | 45.6 | 2 | 2 | 21 | 7 | 5 | |
146 | 1 | 20.9 | 1 | 1 | 23 | 10 | 4 | |
147 | 1 | 30.6 | 4 | 1 | 20 | 4 | 4 | |
148 | 0 | 40.7 | 2 | 3 | 18 | 7 | 3 | |
149 | 1 | 46.7 | 2 | 3 | 18 | 8 | 5 | |
150 | 1 | 26.1 | 3 | 1 | 17 | 6 | 2 | |
151 | 1 | 32.7 | 4 | 1 | 12 | 6 | 2 | |
152 | 0 | 49.2 | 2 | 2 | 18 | 7 | 4 | |
153 | 1 | 24.3 | 4 | 1 | 10 | 7 | 3 | |
154 | 0 | 39 | 4 | 1 | 13 | 4 | 1 | |
155 | 1 | 33 | 2 | 1 | 24 | 7 | 4 | |
156 | 1 | 20.3 | 4 | 1 | 9 | 3 | 3 | |
157 | 1 | 38 | 3 | 2 | 14 | 10 | 2 | |
158 | 0 | 60.4 | 3 | 1 | 18 | 5 | 4 | |
159 | 1 | 69 | 3 | 1 | 23 | 8 | 2 | |
160 | 1 | 27.7 | 1 | 2 | 21 | 9 | 5 | |
161 | 1 | 41 | 2 | 2 | 20 | 10 | 5 | |
162 | 1 | 65.6 | 4 | 2 | 13 | 4 | 5 | |
163 | 1 | 24.8 | 3 | 1 | 21 | 5 | 3 | |
164 | 1 | 21.6 | 2 | 1 | 19 | 8 | 3 | |
165 | 0 | 43.3 | 1 | 1 | 23 | 9 | 5 | |
166 | 0 | 44.4 | 1 | 2 | 22 | 10 | 4 | |
167 | 1 | 45.3 | 4 | 1 | 17 | 8 | 1 | |
168 | 1 | 48.8 | 3 | 1 | 20 | 10 | 5 | |
169 | 1 | 44.7 | 4 | 3 | 23 | 7 | 2 | |
170 | 1 | 57.5 | 1 | 1 | 24 | 9 | 4 | |
171 | 1 | 42 | 2 | 1 | 20 | 8 | 3 | |
172 | 1 | 40.5 | 1 | 1 | 23 | 10 | 4 | |
173 | 1 | 44.4 | 1 | 1 | 24 | 10 | 5 | |
174 | 0 | 50.3 | 2 | 1 | 21 | 7 | 4 | |
175 | 0 | 41.1 | 4 | 1 | 13 | 3 | 2 | |
176 | 1 | 49 | 3 | 2 | 22 | 10 | 3 | |
177 | 0 | 43 | 3 | 1 | 21 | 7 | 2 | |
178 | 1 | 43.8 | 4 | 3 | 16 | 8 | 3 | |
179 | 1 | 46.6 | 2 | 3 | 24 | 8 | 5 | |
180 | 1 | 45.7 | 3 | 1 | 14 | 10 | 2 | |
181 | 1 | 69.8 | 3 | 1 | 23 | 10 | 5 | |
182 | 0 | 45.6 | 2 | 2 | 21 | 7 | 5 | |
183 | 1 | 20.9 | 1 | 1 | 23 | 10 | 4 | |
184 | 1 | 30.6 | 4 | 1 | 20 | 4 | 4 | |
185 | 0 | 40.7 | 2 | 3 | 18 | 7 | 3 | |
186 | 1 | 46.7 | 2 | 3 | 18 | 8 | 5 | |
187 | 1 | 26.1 | 3 | 1 | 17 | 6 | 2 | |
188 | 1 | 32.7 | 4 | 1 | 12 | 6 | 2 | |
189 | 0 | 49.2 | 2 | 2 | 18 | 7 | 4 | |
190 | 1 | 24.3 | 4 | 1 | 10 | 7 | 3 | |
191 | 0 | 39 | 4 | 1 | 13 | 4 | 1 | |
192 | 1 | 33 | 2 | 1 | 24 | 7 | 4 | |
193 | 1 | 20.3 | 4 | 1 | 9 | 3 | 3 | |
194 | 1 | 38 | 3 | 2 | 14 | 10 | 2 | |
195 | 0 | 60.4 | 3 | 1 | 18 | 5 | 4 | |
196 | 1 | 69 | 3 | 1 | 23 | 8 | 2 | |
197 | 1 | 27.7 | 1 | 2 | 21 | 9 | 5 | |
198 | 1 | 41 | 2 | 2 | 20 | 10 | 5 | |
199 | 1 | 65.6 | 4 | 2 | 13 | 4 | 5 | |
200 | 1 | 24.8 | 3 | 1 | 21 | 5 | 3 | |
201 | 1 | 21.6 | 2 | 1 | 19 | 8 | 3 | |
202 | 0 | 43.3 | 1 | 1 | 23 | 9 | 5 | |
203 | 0 | 44.4 | 1 | 2 | 22 | 10 | 4 | |
204 | 1 | 45.3 | 4 | 1 | 17 | 8 | 1 | |
205 | 1 | 48.8 | 3 | 1 | 20 | 10 | 5 | |
206 | 1 | 44.7 | 4 | 3 | 23 | 7 | 2 | |
207 | 1 | 57.5 | 1 | 1 | 24 | 9 | 4 | |
208 | 1 | 42 | 2 | 1 | 20 | 8 | 3 | |
209 | 1 | 40.5 | 1 | 1 | 23 | 10 | 4 | |
210 | 1 | 44.4 | 1 | 1 | 24 | 10 | 5 | |
211 | 0 | 50.3 | 2 | 1 | 21 | 7 | 4 | |
212 | 0 | 41.1 | 4 | 1 | 13 | 3 | 2 | |
213 | 1 | 49 | 3 | 2 | 22 | 10 | 3 | |
214 | 0 | 43 | 3 | 1 | 21 | 7 | 2 | |
215 | 1 | 43.8 | 4 | 3 | 16 | 8 | 3 | |
216 | 1 | 46.6 | 2 | 3 | 24 | 8 | 5 | |
217 | 1 | 45.7 | 3 | 1 | 14 | 10 | 2 | |
218 | 1 | 69.8 | 3 | 1 | 23 | 10 | 5 | |
219 | 0 | 45.6 | 2 | 2 | 21 | 7 | 5 | |
220 | 1 | 20.9 | 1 | 1 | 23 | 10 | 4 | |
221 | 1 | 30.6 | 4 | 1 | 20 | 4 | 4 | |
222 | 0 | 40.7 | 2 | 3 | 18 | 7 | 3 | |
223 | 1 | 46.7 | 2 | 3 | 18 | 8 | 5 | |
224 | 1 | 26.1 | 3 | 1 | 17 | 6 | 2 | |
225 | 1 | 32.7 | 4 | 1 | 12 | 6 | 2 | |
226 | 0 | 49.2 | 2 | 2 | 18 | 7 | 4 | |
227 | 1 | 24.3 | 4 | 1 | 10 | 7 | 3 | |
228 | 0 | 39 | 4 | 1 | 13 | 4 | 1 | |
229 | 1 | 33 | 2 | 1 | 24 | 7 | 4 | |
230 | 1 | 20.3 | 4 | 1 | 9 | 3 | 3 | |
231 | 1 | 38 | 3 | 2 | 14 | 10 | 2 | |
232 | 0 | 60.4 | 3 | 1 | 18 | 5 | 4 | |
233 | 1 | 69 | 3 | 1 | 23 | 8 | 2 | |
234 | 1 | 27.7 | 1 | 2 | 21 | 9 | 5 | |
235 | 1 | 41 | 2 | 2 | 20 | 10 | 5 | |
236 | 1 | 65.6 | 4 | 2 | 13 | 4 | 5 | |
237 | 1 | 24.8 | 3 | 1 | 21 | 5 | 3 | |
238 | 1 | 21.6 | 2 | 1 | 19 | 8 | 3 | |
239 | 0 | 43.3 | 1 | 1 | 23 | 9 | 5 | |
240 | 0 | 44.4 | 1 | 2 | 22 | 10 | 4 | |
241 | 1 | 45.3 | 4 | 1 | 17 | 8 | 1 | |
242 | 1 | 48.8 | 3 | 1 | 20 | 10 | 5 | |
243 | 1 | 44.7 | 4 | 3 | 23 | 7 | 2 | |
244 | 1 | 57.5 | 1 | 1 | 24 | 9 | 4 | |
245 | 1 | 42 | 2 | 1 | 20 | 8 | 3 | |
246 | 1 | 40.5 | 1 | 1 | 23 | 10 | 4 | |
247 | 1 | 44.4 | 1 | 1 | 24 | 10 | 5 | |
248 | 0 | 50.3 | 2 | 1 | 21 | 7 | 4 | |
249 | 0 | 41.1 | 4 | 1 | 13 | 3 | 2 | |
250 | 1 | 49 | 3 | 2 | 22 | 10 | 3 | |
251 | 0 | 43 | 3 | 1 | 21 | 7 | 2 | |
252 | 1 | 43.8 | 4 | 3 | 16 | 8 | 3 | |
253 | 1 | 46.6 | 2 | 3 | 24 | 8 | 5 | |
254 | 1 | 45.7 | 3 | 1 | 14 | 10 | 2 | |
255 | 1 | 69.8 | 3 | 1 | 23 | 10 | 5 | |
256 | 0 | 45.6 | 2 | 2 | 21 | 7 | 5 | |
257 | 1 | 20.9 | 1 | 1 | 23 | 10 | 4 | |
258 | 1 | 30.6 | 4 | 1 | 20 | 4 | 4 | |
259 | 0 | 40.7 | 2 | 3 | 18 | 7 | 3 | |
260 | 1 | 46.7 | 2 | 3 | 18 | 8 | 5 | |
261 | 1 | 26.1 | 3 | 1 | 17 | 6 | 2 | |
262 | 1 | 32.7 | 4 | 1 | 12 | 6 | 2 | |
263 | 0 | 49.2 | 2 | 2 | 18 | 7 | 4 | |
264 | 1 | 24.3 | 4 | 1 | 10 | 7 | 3 | |
265 | 0 | 39 | 4 | 1 | 13 | 4 | 1 | |
266 | 1 | 33 | 2 | 1 | 24 | 7 | 4 | |
267 | 1 | 20.3 | 4 | 1 | 9 | 3 | 3 | |
268 | 1 | 38 | 3 | 2 | 14 | 10 | 2 | |
269 | 0 | 60.4 | 3 | 1 | 18 | 5 | 4 | |
270 | 1 | 69 | 3 | 1 | 23 | 8 | 2 | |
271 | 1 | 27.7 | 1 | 2 | 21 | 9 | 5 | |
272 | 1 | 41 | 2 | 2 | 20 | 10 | 5 | |
273 | 1 | 65.6 | 4 | 2 | 13 | 4 | 5 | |
274 | 1 | 24.8 | 3 | 1 | 21 | 5 | 3 | |
275 | 1 | 21.6 | 2 | 1 | 19 | 8 | 3 | |
276 | 0 | 43.3 | 1 | 1 | 23 | 9 | 5 | |
277 | 0 | 44.4 | 1 | 2 | 22 | 10 | 4 | |
278 | 1 | 45.3 | 4 | 1 | 17 | 8 | 1 | |
279 | 1 | 48.8 | 3 | 1 | 20 | 10 | 5 | |
280 | 1 | 44.7 | 4 | 3 | 23 | 7 | 2 | |
281 | 1 | 57.5 | 1 | 1 | 24 | 9 | 4 | |
282 | 1 | 42 | 2 | 1 | 20 | 8 | 3 | |
283 | 1 | 40.5 | 1 | 1 | 23 | 10 | 4 | |
284 | 1 | 44.4 | 1 | 1 | 24 | 10 | 5 | |
285 | 0 | 50.3 | 2 | 1 | 21 | 7 | 4 | |
286 | 0 | 41.1 | 4 | 1 | 13 | 3 | 2 | |
287 | 1 | 49 | 3 | 2 | 22 | 10 | 3 | |
288 | 0 | 43 | 3 | 1 | 21 | 7 | 2 | |
289 | 1 | 43.8 | 4 | 3 | 16 | 8 | 3 | |
290 | 1 | 46.6 | 2 | 3 | 24 | 8 | 5 | |
291 | 1 | 45.7 | 3 | 1 | 14 | 10 | 2 | |
292 | 1 | 69.8 | 3 | 1 | 23 | 10 | 5 | |
293 | 0 | 45.6 | 2 | 2 | 21 | 7 | 5 | |
294 | 1 | 20.9 | 1 | 1 | 23 | 10 | 4 | |
295 | 1 | 30.6 | 4 | 1 | 20 | 4 | 4 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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using DelimitedFiles | |
using Base.Iterators: flatten | |
using LinearAlgebra: diag | |
using Optim | |
using DataFrames | |
using HTTP | |
using uCSV | |
using LineSearches | |
# Read in auto data from Stata example | |
fname = "https://gist.githubusercontent.com/tyleransom/696f3ef806fa4c84e1902f34b8955ca4/raw/49d3f51abe9f126e48907522a69e14b2fd7b5498/autochoice.csv" | |
data = DataFrame(uCSV.read(IOBuffer(HTTP.get(fname).body), quotes='"', header=1)) | |
data = Matrix(data) | |
id = data[:,1] | |
male = data[:,2] | |
income = data[:,3] | |
hhsize = data[:,4] | |
X = hcat(ones(length(male)),male) | |
Y = data[:,5] | |
Z = data[:,6:end] | |
Zmatlab = cat(Z[:,1],Z[:,2],Z[:,3];dims=3) | |
θ = [1.255797, -.4242443, 1.095317, -1.063336, .0436522]; | |
@views @inline function asclogit(bstart::Vector,Y::Array,X::Array,Z::Array,J::Int64,W::Array=ones(length(Y))) | |
## error checking | |
@assert ((!isempty(X) || !isempty(Z)) && !isempty(Y)) "You must supply data to the model" | |
@assert (ndims(Y)==1 && size(Y,2)==1) "Y must be a 1-D Array" | |
@assert (minimum(Y)==1 && maximum(Y)==J) "Y should contain integers numbered consecutively from 1 through J" | |
if !isempty(X) | |
@assert ndims(X)==2 "X must be a 2-dimensional matrix" | |
@assert size(X,1)==size(Y,1) "The 1st dimension of X should equal the number of observations in Y" | |
end | |
if !isempty(Z) | |
@assert ndims(Z)==3 "Z must be a 3-dimensional tensor" | |
@assert size(Z,1)==size(Y,1) "The 1st dimension of Z should equal the number of observations in Y" | |
end | |
K1 = size(X,2) | |
K2 = size(Z,2) | |
function f(b) | |
T = promote_type(promote_type(promote_type(eltype(X),eltype(b)),eltype(Z)),eltype(W)) | |
num = zeros(T,size(Y)) | |
dem = zeros(T,size(Y)) | |
temp = zeros(T,size(Y)) | |
numer = ones(T,size(Y,1),J) | |
P = zeros(T,size(Y,1),J) | |
ℓ = zero(T) | |
b2 = b[K1*(J-1)+1:K1*(J-1)+K2] | |
for j=1:(J-1) | |
temp .= X*b[(j-1)*K1+1:j*K1] .+ (Z[:,:,j].-Z[:,:,J])*b2 | |
num .= (Y.==j).*temp.+num | |
dem .+= exp.(temp) | |
numer[:,j] .= exp.(temp) | |
end | |
dem.+=1 | |
P .=numer./(1 .+ sum(numer;dims=2)) | |
ℓ = -W'*(num.-log.(dem)) | |
end | |
function g!(G,b) | |
T = promote_type(promote_type(promote_type(eltype(X),eltype(b)),eltype(Z)),eltype(W)) | |
numer = zeros(T,size(Y,1),J) | |
P = zeros(T,size(Y,1),J) | |
numg = zeros(T,K2) | |
demg = zeros(T,K2) | |
b2 = b[K1*(J-1)+1:K1*(J-1)+K2] | |
G = Array{T}(undef, length(b)) | |
for j=1:(J-1) | |
numer[:,j] .= exp.( X*b[(j-1)*K1+1:j*K1] .+ (Z[:,:,j].-Z[:,:,J])*b2 ) | |
end | |
P .=numer./(1 .+ sum(numer;dims=2)) | |
for j=1:(J-1) | |
G[(j-1)*K1+1:j*K1] .= -X'*(W.*((Y.==j).-P[:,j])) | |
end | |
for j=1:(J-1) | |
numg .+= -(Z[:,:,j]-Z[:,:,J])'*(W.*(Y.==j)) | |
demg .+= -(Z[:,:,j]-Z[:,:,J])'*(W.*P[:,j]) | |
end | |
G[K1*(J-1)+1:K1*(J-1)+K2] .= numg.-demg | |
G | |
end | |
td = TwiceDifferentiable(f, bstart, autodiff = :forwarddiff) | |
rs = optimize(td, bstart, LBFGS(; linesearch = LineSearches.BackTracking()), Optim.Options(iterations=100_000,g_tol=1e-8,f_tol=1e-8)) | |
β = Optim.minimizer(rs) | |
ℓ = Optim.minimum(rs)*(-1) | |
H = Optim.hessian!(td, β) | |
se = sqrt.(diag(inv(H))) | |
q = Array{Float64}(undef, length(β)) | |
grad = g!(q,β) | |
gradzero = g!(q,zeros(size(β))) | |
return β,se,ℓ,grad,gradzero | |
end | |
β,se_β,like,grad = asclogit(zeros(size(θ)),Y,X,Zmatlab,3) | |
β,se_β,like,g1,g0 = @time asclogit(β,Y,X,Zmatlab,3) | |
println([g1 g0]) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
using DelimitedFiles | |
using Base.Iterators: flatten | |
using LinearAlgebra: diag | |
using Optim | |
using DataFrames | |
using HTTP | |
using uCSV | |
using LineSearches | |
# Read in auto data from Stata example | |
fname = "https://gist.githubusercontent.com/tyleransom/696f3ef806fa4c84e1902f34b8955ca4/raw/49d3f51abe9f126e48907522a69e14b2fd7b5498/autochoice.csv" | |
data = DataFrame(uCSV.read(IOBuffer(HTTP.get(fname).body), quotes='"', header=1)) | |
data = Matrix(data) | |
id = data[:,1] | |
male = data[:,2] | |
income = data[:,3] | |
hhsize = data[:,4] | |
X = hcat(ones(length(male)),male) | |
Y = data[:,5] | |
Z = data[:,6:end] | |
Zmatlab = cat(Z[:,1],Z[:,2],Z[:,3];dims=3) | |
θ = [1.255797, -.4242443, 1.095317, -1.063336, .0436522]; | |
@views @inline function asclogit(bstart::Vector,Y::Array,X::Array,Z::Array,J::Int64,W::Array=ones(length(Y))) | |
## error checking | |
@assert ((!isempty(X) || !isempty(Z)) && !isempty(Y)) "You must supply data to the model" | |
@assert (ndims(Y)==1 && size(Y,2)==1) "Y must be a 1-D Array" | |
@assert (minimum(Y)==1 && maximum(Y)==J) "Y should contain integers numbered consecutively from 1 through J" | |
if !isempty(X) | |
@assert ndims(X)==2 "X must be a 2-dimensional matrix" | |
@assert size(X,1)==size(Y,1) "The 1st dimension of X should equal the number of observations in Y" | |
end | |
if !isempty(Z) | |
@assert ndims(Z)==3 "Z must be a 3-dimensional tensor" | |
@assert size(Z,1)==size(Y,1) "The 1st dimension of Z should equal the number of observations in Y" | |
end | |
K1 = size(X,2) | |
K2 = size(Z,2) | |
function f(b) | |
T = promote_type(promote_type(promote_type(eltype(X),eltype(b)),eltype(Z)),eltype(W)) | |
num = zeros(T,size(Y)) | |
dem = zeros(T,size(Y)) | |
temp = zeros(T,size(Y)) | |
numer = ones(T,size(Y,1),J) | |
P = zeros(T,size(Y,1),J) | |
ℓ = zero(T) | |
b2 = b[K1*(J-1)+1:K1*(J-1)+K2] | |
for j=1:(J-1) | |
temp .= X*b[(j-1)*K1+1:j*K1] .+ (Z[:,:,j].-Z[:,:,J])*b2 | |
num .= (Y.==j).*temp.+num | |
dem .+= exp.(temp) | |
numer[:,j] .= exp.(temp) | |
end | |
dem.+=1 | |
P .=numer./(1 .+ sum(numer;dims=2)) | |
ℓ = -W'*(num.-log.(dem)) | |
end | |
function g!(G,b) | |
T = promote_type(promote_type(promote_type(eltype(X),eltype(b)),eltype(Z)),eltype(W)) | |
numer = zeros(T,size(Y,1),J) | |
P = zeros(T,size(Y,1),J) | |
numg = zeros(T,K2) | |
demg = zeros(T,K2) | |
b2 = b[K1*(J-1)+1:K1*(J-1)+K2] | |
#G = Array{T}(undef, length(b)) | |
G .= T(0) | |
for j=1:(J-1) | |
numer[:,j] .= exp.( X*b[(j-1)*K1+1:j*K1] .+ (Z[:,:,j].-Z[:,:,J])*b2 ) | |
end | |
P .=numer./(1 .+ sum(numer;dims=2)) | |
for j=1:(J-1) | |
G[(j-1)*K1+1:j*K1] .= -X'*(W.*((Y.==j).-P[:,j])) | |
end | |
for j=1:(J-1) | |
numg .+= -(Z[:,:,j]-Z[:,:,J])'*(W.*(Y.==j)) | |
demg .+= -(Z[:,:,j]-Z[:,:,J])'*(W.*P[:,j]) | |
end | |
G[K1*(J-1)+1:K1*(J-1)+K2] .= numg.-demg | |
G | |
end | |
td = TwiceDifferentiable(f, g!, bstart, autodiff = :forwarddiff) | |
rs = optimize(td, bstart, LBFGS(; linesearch = LineSearches.BackTracking()), Optim.Options(iterations=100_000,g_tol=1e-8,f_tol=1e-8)) | |
β = Optim.minimizer(rs) | |
ℓ = Optim.minimum(rs)*(-1) | |
H = Optim.hessian!(td, β) | |
se = sqrt.(diag(inv(H))) | |
q = Array{Float64}(undef, length(β)) | |
grad = g!(q,β) | |
gradzero = g!(q,zeros(size(β))) | |
return β,se,ℓ,grad,gradzero | |
end | |
β,se_β,like,grad = asclogit(zeros(size(θ)),Y,X,Zmatlab,3) | |
β,se_β,like,g1,g0 = @time asclogit(β,Y,X,Zmatlab,3) | |
println([g1 g0]) |
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using DelimitedFiles | |
using Base.Iterators: flatten | |
using LinearAlgebra: diag | |
using Optim | |
using DataFrames | |
using HTTP | |
using uCSV | |
using LineSearches | |
using ForwardDiff | |
# Read in auto data from Stata example | |
fname = "https://gist.githubusercontent.com/tyleransom/696f3ef806fa4c84e1902f34b8955ca4/raw/49d3f51abe9f126e48907522a69e14b2fd7b5498/autochoice.csv" | |
data = DataFrame(uCSV.read(IOBuffer(HTTP.get(fname).body), quotes='"', header=1)) | |
data = Matrix(data) | |
id = data[:,1] | |
male = data[:,2] | |
income = data[:,3] | |
hhsize = data[:,4] | |
X = hcat(ones(length(male)),male) | |
Y = data[:,5] | |
Z = data[:,6:end] | |
Zmatlab = cat(Z[:,1],Z[:,2],Z[:,3];dims=3) | |
θ = [1.255797, -.4242443, 1.095317, -1.063336, .0436522]; | |
@views @inline function asclogit2(bstart::Vector,Y::Array,X::Array,Z::Array,J::Int64,W::Array=ones(length(Y))) | |
## error checking | |
@assert ((!isempty(X) || !isempty(Z)) && !isempty(Y)) "You must supply data to the model" | |
@assert (ndims(Y)==1 && size(Y,2)==1) "Y must be a 1-D Array" | |
@assert (minimum(Y)==1 && maximum(Y)==J) "Y should contain integers numbered consecutively from 1 through J" | |
if !isempty(X) | |
@assert ndims(X)==2 "X must be a 2-dimensional matrix" | |
@assert size(X,1)==size(Y,1) "The 1st dimension of X should equal the number of observations in Y" | |
end | |
if !isempty(Z) | |
@assert ndims(Z)==3 "Z must be a 3-dimensional tensor" | |
@assert size(Z,1)==size(Y,1) "The 1st dimension of Z should equal the number of observations in Y" | |
end | |
K1 = size(X,2) | |
K2 = size(Z,2) | |
function fg!(F,G,b) | |
T = promote_type(promote_type(promote_type(eltype(X),eltype(b)),eltype(Z)),eltype(W)) | |
num = zeros(T,size(Y)) | |
dem = zeros(T,size(Y)) | |
temp = zeros(T,size(Y)) | |
numer = ones(T,size(Y,1),J) | |
P = zeros(T,size(Y,1),J) | |
b2 = b[K1*(J-1)+1:K1*(J-1)+K2] | |
for j=1:(J-1) | |
temp .= X*b[(j-1)*K1+1:j*K1] .+ (Z[:,:,j].-Z[:,:,J])*b2 | |
num .= (Y.==j).*temp.+num | |
dem .+= exp.(temp) | |
numer[:,j] .= exp.(temp) | |
end | |
dem.+=1 | |
P .=numer./(1 .+ sum(numer;dims=2)) | |
if G != nothing | |
G .= T(0) | |
numg = zeros(T,K2) | |
demg = zeros(T,K2) | |
for j=1:(J-1) | |
G[(j-1)*K1+1:j*K1] .= -X'*(W.*((Y.==j).-P[:,j])) | |
end | |
for j=1:(J-1) | |
numg .-= (Z[:,:,j]-Z[:,:,J])'*(W.*(Y.==j)) | |
demg .-= (Z[:,:,j]-Z[:,:,J])'*(W.*P[:,j]) | |
end | |
G[K1*(J-1)+1:K1*(J-1)+K2] .= numg.-demg | |
return nothing | |
end | |
if F != nothing | |
ℓ = T(0) | |
ℓ = -W'*(num.-log.(dem)) | |
end | |
end | |
od = OnceDifferentiable(Optim.only_fg!(fg!), bstart) | |
td = TwiceDifferentiable(od, autodiff = :forwarddiff) | |
rs = optimize(td, bstart, LBFGS(; linesearch = LineSearches.BackTracking()), Optim.Options(iterations=100_000,g_tol=1e-6,f_tol=1e-6)) | |
β = Optim.minimizer(rs) | |
ℓ = Optim.minimum(rs)*(-1) | |
H = Optim.hessian!(td, β) | |
se = sqrt.(diag(inv(H))) | |
gf = Array{Float64}(undef, length(β)) | |
fg!(nothing,gf,β) | |
return β,se,ℓ,gf | |
end | |
β,se_β,like,grad = @time asclogit2(2*ones(size(θ)),Y,X,Zmatlab,3) |
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