我正在使用R（test.bic.surv）中的BMA包从大量变量（100个基本变量和每个变量约60个滞后）估算Cox比例模型。当我使用以下代码尝试第一组测试时，它可以工作。
x1<- x[,c( "comprisk", "compriskL1", "compriskL2", "compriskL3", "compriskL4", "econrisk", "econrisk_1", "econrisk_2", "econrisk_3", "econrisk_4", "econrisk_5", "finrisk", "finrisk_1", "finrisk_2", "finrisk_3", "finrisk_4", "finrisk_5", "polrisk", "polrisk_1","polrisk_2","polrisk_3","polrisk_4","polrisk_5","polrisk_6","polrisk_7","polrisk_8","polrisk_9","polrisk_10","polrisk_11","polrisk_12")]
surv.t<- x$crisis1
cens<- x$cen1
test.bic.surv<- bic.surv(x1, surv.t, cens, factor.type=FALSE, strict=FALSE, nbest=2000)
但是，每当我尝试将更多自变量添加到x1中时，例如“incluk5L”或“econriskL1”，
test.bic.surv<- bic.surv(x1, surv.t, cens, factor.type=FALSE, strict=FALSE, nbest=2000)
向我展示了这样的错误：
"Error in terms.formula(formula, special, data = data) : '.' in formula and no 'data' argument".
我已经在网上搜索了几天，但无法弄清楚问题出在哪里。任何人都可以告诉我该怎么办？非常感谢你!!! :)
以下是示例数据的样子：
crisis1 cen1 comprisk econrisk econrisk_1 econrisk_2 econrisk_3 econrisk_4
1 0 1 57.0 25.5 3.3 6.7 4.0 6.7
2 0 1 57.0 25.5 3.3 6.7 4.0 6.7
3 0 1 57.0 25.5 3.3 6.7 4.0 6.7
4 0 1 58.5 26.5 3.8 7.5 4.0 7.5
5 0 1 58.5 27.0 3.8 7.5 4.0 7.5
6 0 1 58.5 26.0 3.8 7.5 4.0 7.5
7 0 1 59.0 26.5 3.8 7.5 4.0 7.5
8 0 1 59.0 26.5 3.8 7.5 4.0 7.5
9 0 1 59.0 27.0 3.8 7.5 4.0 7.5
10 0 1 59.0 26.5 3.8 7.5 4.0 7.5
11 0 1 59.0 26.5 3.8 7.5 4.0 7.5
12 0 1 59.0 27.0 3.8 7.5 4.0 7.5
13 0 1 59.0 27.0 3.8 7.5 4.0 7.5
14 0 1 57.5 27.0 3.8 7.5 4.0 7.5
15 0 1 57.5 27.5 3.8 7.5 4.0 7.5
16 0 1 57.0 27.5 3.3 6.7 4.0 6.7
17 0 1 57.0 27.5 3.3 6.7 4.0 6.7
18 0 1 57.0 27.5 3.3 6.7 4.0 6.7
19 0 1 56.0 27.0 3.3 6.7 4.0 6.7
20 0 1 56.5 28.5 2.9 5.8 4.0 5.8
21 0 1 55.5 26.5 2.9 5.8 4.0 5.8
22 0 1 55.0 26.0 2.9 5.8 4.0 5.8
23 0 1 55.0 26.0 2.9 5.8 4.0 5.8
24 0 1 55.0 26.0 2.9 5.8 4.0 5.8
25 0 1 55.0 26.0 2.9 5.8 4.0 5.8
26 0 1 54.5 25.5 2.9 5.8 6.5 5.8
27 0 1 54.0 25.5 2.9 5.8 6.5 5.8
28 0 1 53.5 25.5 2.5 5.0 6.5 5.0
29 0 1 53.5 25.5 2.5 5.0 6.5 5.0
30 0 1 54.0 26.5 2.5 5.0 6.5 5.0
31 0 1 54.0 26.5 2.5 5.0 6.5 5.0
32 0 1 54.0 26.5 2.5 5.0 6.5 5.0
33 0 1 56.0 26.5 2.5 5.0 6.5 5.0
34 0 1 56.0 27.0 2.5 5.0 6.5 5.0
35 0 1 57.0 27.0 2.5 5.0 6.5 5.0
36 0 1 58.0 27.0 2.9 5.8 6.5 5.8
37 1 1 59.0 28.5 2.9 5.8 6.5 5.8
38 1 1 60.0 29.5 2.9 5.8 6.5 5.8
39 1 1 59.5 29.5 2.9 5.8 6.5 5.8
40 1 1 60.0 29.5 2.9 5.8 6.5 5.8
41 1 1 59.5 29.5 2.9 5.8 6.5 5.8
42 1 1 59.0 28.0 2.9 5.8 6.5 5.8
43 1 1 59.5 28.0 2.9 5.8 6.5 5.8
44 1 1 59.5 28.0 2.9 5.8 6.5 5.8
45 1 1 59.5 28.5 2.9 5.8 6.5 5.8
46 1 1 56.0 28.0 2.9 5.8 6.5 5.8
47 1 1 54.0 28.0 2.5 5.0 6.5 5.0
48 1 1 53.0 24.5 2.1 4.2 6.5 4.2
49 1 1 53.0 25.0 2.1 4.2 6.5 4.2
50 1 1 54.0 26.0 2.1 4.2 6.5 4.2
51 1 1 54.5 26.0 2.1 4.2 6.5 4.2
52 1 1 54.5 25.5 2.1 4.2 6.5 4.2
53 1 1 54.0 24.0 2.1 4.2 6.0 4.2
54 1 1 54.0 24.0 2.1 4.2 6.0 4.2
55 1 1 55.0 24.0 2.1 4.2 6.0 4.2
56 1 1 55.0 24.0 2.1 4.2 6.0 4.2
57 1 1 55.0 24.0 2.1 4.2 6.0 4.2
58 1 1 55.0 24.5 2.1 4.2 6.0 4.2
59 1 1 55.0 24.5 2.1 4.2 6.0 4.2
60 1 1 55.0 25.0 2.1 4.2 6.0 4.2
61 1 1 55.0 23.5 2.1 4.2 6.0 4.2
62 1 1 55.0 24.0 2.1 4.2 6.0 4.2
63 1 1 55.0 23.5 2.1 4.2 6.5 4.2
64 1 1 55.0 23.5 1.7 3.3 6.5 3.3
65 1 1 55.0 22.5 1.7 3.3 6.5 3.3
66 1 1 56.0 25.5 1.3 2.5 6.5 2.5
67 1 1 56.0 25.5 1.3 2.5 6.5 2.5
68 1 1 56.5 25.0 1.3 2.5 6.5 2.5
69 1 1 58.5 29.5 1.3 2.5 6.5 2.5
70 1 1 58.5 28.5 1.3 2.5 6.5 2.5
71 1 1 58.5 28.5 1.3 2.5 6.5 2.5
72 1 1 59.5 29.5 1.3 2.5 6.5 2.5
73 1 1 61.5 33.0 1.3 2.5 6.0 2.5
74 1 1 61.0 33.0 1.3 2.5 6.0 2.5
75 1 1 61.5 32.0 1.7 3.3 6.0 3.3
76 1 1 59.5 32.0 1.7 3.3 6.0 3.3
77 1 1 60.0 32.5 1.7 3.3 6.0 3.3
78 1 1 57.5 32.5 2.1 4.2 6.0 4.2
79 1 1 58.0 33.0 2.1 4.2 6.0 4.2
80 1 1 58.5 32.5 2.1 4.2 6.0 4.2
81 1 1 57.5 31.5 2.1 4.2 5.0 4.2
82 1 1 57.5 31.5 2.1 4.2 5.0 4.2
83 1 1 59.0 31.5 2.5 5.0 5.0 5.0
84 1 1 58.5 30.5 2.5 5.0 4.0 5.0
85 0 1 55.5 27.5 2.5 5.0 3.5 5.0
86 0 1 54.0 27.5 2.5 5.0 3.5 5.0
87 0 1 53.5 27.0 2.5 5.0 3.5 5.0
88 0 1 53.0 27.0 2.5 5.0 3.5 5.0
89 0 1 53.0 27.5 2.1 4.2 3.5 4.2
90 0 1 52.5 27.0 2.1 4.2 3.5 4.2
91 0 1 50.5 27.5 2.1 4.2 3.5 4.2
92 0 1 51.5 27.5 2.1 4.2 3.5 4.2
93 0 1 51.5 27.0 2.5 5.0 3.5 5.0
94 0 1 52.0 27.0 2.5 5.0 3.5 5.0
95 0 1 52.0 27.0 2.5 5.0 3.5 5.0
96 0 1 52.0 28.0 2.5 5.0 3.5 5.0
97 0 1 52.5 28.5 2.5 5.0 3.5 5.0
98 0 1 54.0 28.5 2.5 5.0 3.5 5.0
99 0 1 54.0 29.0 2.5 5.0 4.0 5.0
100 0 1 53.0 28.0 2.5 5.0 4.0 5.0
101 0 1 52.5 28.0 2.1 4.2 3.5 4.2
102 0 1 52.5 28.0 2.1 4.2 3.5 4.2
103 0 1 53.0 28.0 2.1 4.2 3.5 4.2
104 0 1 53.0 28.0 2.1 4.2 3.5 4.2
105 0 1 52.5 26.0 2.1 4.2 4.0 4.2
106 0 1 54.0 26.5 2.1 4.2 4.0 4.2
107 0 1 53.5 26.5 2.1 4.2 4.0 4.2
108 0 1 53.5 26.5 2.1 4.2 4.0 4.2
109 1 1 56.0 29.5 2.1 4.2 5.0 4.2
110 1 1 53.5 27.0 2.1 4.2 4.0 4.2
111 1 1 53.5 27.0 2.1 4.2 4.0 4.2
112 1 1 53.5 26.5 2.1 4.2 5.0 4.2
113 1 1 54.0 26.5 2.1 4.2 5.0 4.2
114 1 1 52.5 24.0 2.1 4.2 4.0 4.2
115 1 1 53.0 24.5 2.1 4.2 5.0 4.2
116 1 1 54.0 26.0 2.1 4.2 4.0 4.2
117 1 1 54.0 26.0 2.1 4.2 4.0 4.2
118 1 1 54.5 26.0 2.1 4.2 4.0 4.2
119 1 1 52.5 24.5 2.1 4.2 3.5 4.2
120 1 1 52.5 24.5 2.1 4.2 3.5 4.2
121 1 1 54.0 27.5 2.1 4.2 4.0 4.2
122 1 1 54.0 27.5 2.1 4.2 4.0 4.2
123 1 1 53.0 28.5 2.1 4.2 4.0 4.2
124 1 1 53.0 28.5 2.1 4.2 4.0 4.2
125 1 1 52.5 28.0 2.1 4.2 4.0 4.2
126 1 1 52.5 27.5 2.1 4.2 4.0 4.2
127 1 1 53.0 28.0 2.1 4.2 4.5 4.2
128 1 1 53.5 28.0 2.5 5.0 4.5 5.0
129 1 1 54.5 28.0 2.5 5.0 4.5 5.0
130 1 1 54.0 26.5 2.5 5.0 3.5 5.0
131 1 1 53.5 26.0 2.5 5.0 3.5 5.0
132 1 1 54.5 26.5 2.5 5.0 3.5 5.0
133 0 1 55.5 28.0 2.5 5.0 3.5 5.0
134 0 1 56.0 28.0 2.5 5.0 3.5 5.0
135 0 1 56.0 28.0 2.5 5.0 3.5 5.0
136 0 1 54.5 27.5 2.5 5.8 3.5 5.8
137 0 1 56.0 24.5 2.9 5.8 5.0 5.8
138 0 1 58.5 29.0 2.9 5.8 5.0 5.8
139 0 1 57.5 28.5 2.9 5.8 5.0 5.8
140 0 1 57.0 28.5 2.9 5.8 5.0 5.8
141 0 1 57.0 28.5 2.9 5.8 5.0 5.8
142 0 1 58.0 28.5 2.9 5.8 5.0 5.8
143 0 1 58.0 29.5 2.9 5.8 5.0 5.8
144 0 1 59.0 29.5 2.9 5.8 5.0 5.8
145 0 1 59.0 31.0 2.9 5.8 5.5 5.8
146 0 1 59.0 31.0 2.9 5.8 5.5 5.8
147 0 1 58.5 31.0 2.9 5.8 5.5 5.8
148 0 1 58.5 31.0 2.9 5.8 5.5 5.8
149 0 1 58.5 32.0 2.5 5.0 5.5 5.0
150 0 1 58.0 32.0 2.5 5.0 5.5 5.0
151 0 1 56.8 32.5 2.5 5.0 5.5 5.0
152 0 1 58.3 31.5 3.8 7.5 5.5 7.5
153 0 1 59.0 37.0 0.5 8.5 5.5 9.5
154 0 1 59.2 37.5 1.0 8.5 5.5 9.5
155 0 1 61.0 39.5 0.5 9.0 8.0 9.0
156 0 1 60.5 39.5 0.5 9.0 8.0 9.0
157 0 1 60.0 39.5 0.5 9.0 8.0 9.0
158 0 1 59.2 39.0 0.5 8.5 8.0 9.0
159 0 1 59.5 39.5 0.5 8.5 8.5 9.0
160 0 1 59.5 39.5 0.5 8.5 8.5 9.0
161 0 1 59.5 39.5 0.5 8.5 8.5 9.0
162 0 1 59.2 39.0 0.5 8.0 8.5 9.0
163 0 1 58.7 39.0 0.5 8.0 8.5 9.0
164 0 1 58.5 38.5 0.5 7.5 8.5 9.0
165 0 1 58.0 35.0 1.0 4.0 8.5 8.0
166 0 1 57.0 35.0 1.0 4.0 8.5 8.0
167 0 1 56.2 33.5 0.5 4.0 7.5 8.0
168 0 1 56.5 34.0 1.0 4.0 7.5 8.0
169 0 1 54.7 33.5 1.0 8.5 7.5 6.0
170 0 1 52.7 30.5 1.0 6.0 7.5 6.0
171 0 1 52.7 30.5 1.0 6.0 7.5 6.0
172 0 1 54.0 33.0 1.0 8.5 7.5 6.0
173 0 1 52.1 32.7 0.2 8.5 8.0 6.0
174 0 1 50.8 32.2 0.2 8.0 8.0 6.0
175 0 1 52.1 32.2 0.2 8.0 8.0 6.0
176 0 1 51.9 32.2 0.2 8.0 8.0 6.0
177 0 1 51.7 31.5 1.0 7.0 7.5 6.0
178 0 1 51.5 31.5 1.0 7.0 7.5 6.0
179 0 1 52.7 31.5 1.0 7.0 7.5 6.0
180 0 1 52.5 31.5 1.0 7.0 7.5 6.0
181 0 1 54.5 33.5 1.0 8.5 8.5 3.5
182 0 1 55.5 33.5 1.0 8.5 8.5 3.5
183 0 1 56.7 35.0 1.0 9.0 8.5 3.5
184 0 1 56.2 35.0 1.0 9.0 8.5 3.5
185 0 1 55.5 35.0 1.0 9.0 8.5 3.5
186 0 1 56.2 35.0 1.0 9.0 8.5 3.5
187 0 1 56.7 35.0 1.0 9.0 8.5 3.5
188 0 1 56.0 34.0 1.0 9.0 7.5 3.5
189 0 1 55.0 34.0 1.0 9.0 7.5 3.5
190 0 1 55.5 34.0 1.0 9.0 7.5 3.5
191 0 1 55.2 34.0 1.0 9.0 7.5 3.5
192 0 1 59.0 37.0 1.0 9.0 8.5 3.5
193 0 1 62.2 42.0 1.0 9.5 8.0 8.5
194 0 1 61.8 42.0 1.0 9.5 8.0 8.5
195 0 1 60.2 41.0 1.0 9.5 8.0 8.5
196 0 1 63.7 41.0 1.0 9.5 8.0 8.5
197 0 1 60.2 37.0 1.0 8.5 8.0 8.5
198 0 1 64.2 42.0 1.0 9.5 9.0 8.5
199 0 1 63.0 40.0 1.0 8.5 8.0 8.5
200 0 1 61.5 38.5 1.0 8.5 8.0 8.5
201 0 1 61.7 38.5 1.0 8.5 8.0 8.5
202 0 1 62.0 38.5 1.0 8.5 8.0 8.5
203 0 1 62.0 38.5 1.0 8.5 8.0 8.5
204 0 1 62.2 38.5 1.0 8.5 8.0 8.5
205 0 1 61.5 38.5 1.0 8.5 8.0 8.5
206 0 1 61.2 38.0 1.0 8.5 8.0 8.5
207 0 1 60.5 38.0 1.0 8.5 8.0 8.5
208 0 1 61.0 38.0 1.0 8.5 8.0 8.5
209 0 1 61.5 38.0 1.0 8.5 8.0 8.5
210 0 1 61.7 38.0 1.0 8.5 8.0 8.5
211 0 1 62.0 38.0 1.0 8.5 8.0 8.5
212 0 1 61.7 38.0 1.0 8.5 8.0 8.5
213 0 1 61.5 38.0 1.0 8.5 8.0 8.5
214 0 1 61.2 38.0 1.0 8.5 8.0 8.5
215 0 1 63.7 40.5 1.0 8.0 9.0 8.5
216 0 1 63.7 40.5 1.0 8.0 9.0 8.5
217 0 1 63.7 40.5 1.0 8.0 9.0 8.5
218 0 1 65.7 43.5 1.0 9.5 8.5 9.5
219 0 1 65.5 43.5 1.0 9.5 8.5 9.5
220 0 1 65.5 43.5 1.0 9.5 8.5 9.5
221 0 1 65.0 43.5 1.0 9.5 8.5 9.5
222 0 1 65.0 43.5 1.0 9.5 8.5 9.5
223 0 1 65.0 43.5 1.0 9.5 8.5 9.5
224 0 1 66.2 43.5 1.0 10.0 9.5 8.0
225 0 1 66.2 43.5 1.0 10.0 9.5 8.0
226 0 1 66.2 43.5 1.0 10.0 9.5 8.0
227 0 1 66.0 44.0 1.0 10.0 9.5 8.5
228 0 1 65.7 44.0 1.0 10.0 9.5 8.5
229 0 1 65.5 43.5 1.0 9.5 9.5 8.5
230 0 1 65.5 43.0 1.0 10.0 9.0 8.5
231 0 1 65.5 43.0 1.0 10.0 9.0 8.5
232 0 1 68.2 43.0 1.0 10.0 9.0 8.5
233 0 1 71.5 44.5 1.0 10.0 9.0 9.5
234 0 1 71.7 44.5 1.0 10.0 9.0 9.5
235 0 1 73.2 44.5 1.0 10.0 9.0 9.5
236 0 1 74.7 44.5 1.0 10.0 9.0 9.5
237 0 1 74.7 44.5 1.0 10.0 9.0 9.5
238 0 1 74.7 44.5 1.0 10.0 9.0 9.5
239 0 1 75.5 45.0 1.0 10.0 9.0 10.0
240 0 1 75.5 45.0 1.0 10.0 9.0 10.0
241 0 1 76.0 45.0 1.0 10.0 9.0 10.0
242 0 1 76.7 44.5 1.0 10.0 8.5 10.0
243 0 1 76.7 44.5 1.0 10.0 8.5 10.0
244 0 1 76.7 44.5 1.0 10.0 8.5 10.0
245 0 1 78.0 44.5 1.0 10.0 8.5 10.0
246 0 1 78.0 44.5 1.0 10.0 8.5 10.0
247 0 1 77.0 44.5 1.0 10.0 8.5 10.0
248 0 1 77.2 44.5 1.0 10.0 8.5 10.0
249 0 1 77.2 44.5 1.0 10.0 8.5 10.0
250 0 1 77.7 44.5 1.0 10.0 8.5 10.0
答案 0 :(得分：1)
这是你的答案：
test.bic.surv <- bic.surv(
x[, 3:ncol(x)],
x$crisis1, x$cen1, factor.type=FALSE, strict=FALSE, nbest=2000, maxCol=50
)
您必须提供maxCol参数。默认值为30，因此可能不足以满足您的需求。