R BMA包中的数据错误'；s bic.glm，但不是glm

我根据一组交互系数估计泊松模型，BMA包的bic.glm帮助导航模型空间。我已经使用它很多年了，但是当我昨晚将R从2.10.x更新到2.14.2时，它停止了工作。错误如下：首先，一个有效的调用：

> glm(formula(Y~.), data=XY5, family=poisson)

Call:  glm(formula = formula(Y ~ .), family = poisson, data = XY5)

Coefficients:
<results, etc>

同样，这段代码在早期版本的R中使用了五个系统。当我使用4个交互系统而不是5个系统运行bic.glm时（即，放下x5并折叠交互），bic.glm运行良好。我将在下面包括五个系统数据。提前谢谢

> XY5
   x1 x2 x3 x4 x5 x12 x13 x14 x15 x23 x24 x25 x34 x35 x45 x123 x124 x125 x134 x135 x145
2   0  0  0  0  1   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
3   0  0  0  1  0   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
4   0  0  0  1  1   0   0   0   0   0   0   0   0   0   1    0    0    0    0    0    0
5   0  0  1  0  0   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
6   0  0  1  0  1   0   0   0   0   0   0   0   0   1   0    0    0    0    0    0    0
7   0  0  1  1  0   0   0   0   0   0   0   0   1   0   0    0    0    0    0    0    0
8   0  0  1  1  1   0   0   0   0   0   0   0   1   1   1    0    0    0    0    0    0
9   0  1  0  0  0   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
10  0  1  0  0  1   0   0   0   0   0   0   1   0   0   0    0    0    0    0    0    0
11  0  1  0  1  0   0   0   0   0   0   1   0   0   0   0    0    0    0    0    0    0
12  0  1  0  1  1   0   0   0   0   0   1   1   0   0   1    0    0    0    0    0    0
13  0  1  1  0  0   0   0   0   0   1   0   0   0   0   0    0    0    0    0    0    0
14  0  1  1  0  1   0   0   0   0   1   0   1   0   1   0    0    0    0    0    0    0
15  0  1  1  1  0   0   0   0   0   1   1   0   1   0   0    0    0    0    0    0    0
16  0  1  1  1  1   0   0   0   0   1   1   1   1   1   1    0    0    0    0    0    0
17  1  0  0  0  0   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
18  1  0  0  0  1   0   0   0   1   0   0   0   0   0   0    0    0    0    0    0    0
19  1  0  0  1  0   0   0   1   0   0   0   0   0   0   0    0    0    0    0    0    0
20  1  0  0  1  1   0   0   1   1   0   0   0   0   0   1    0    0    0    0    0    1
21  1  0  1  0  0   0   1   0   0   0   0   0   0   0   0    0    0    0    0    0    0
22  1  0  1  0  1   0   1   0   1   0   0   0   0   1   0    0    0    0    0    1    0
23  1  0  1  1  0   0   1   1   0   0   0   0   1   0   0    0    0    0    1    0    0
24  1  0  1  1  1   0   1   1   1   0   0   0   1   1   1    0    0    0    1    1    1
25  1  1  0  0  0   1   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
26  1  1  0  0  1   1   0   0   1   0   0   1   0   0   0    0    0    1    0    0    0
27  1  1  0  1  0   1   0   1   0   0   1   0   0   0   0    0    1    0    0    0    0
28  1  1  0  1  1   1   0   1   1   0   1   1   0   0   1    0    1    1    0    0    1
29  1  1  1  0  0   1   1   0   0   1   0   0   0   0   0    1    0    0    0    0    0
30  1  1  1  0  1   1   1   0   1   1   0   1   0   1   0    1    0    1    0    1    0
31  1  1  1  1  0   1   1   1   0   1   1   0   1   0   0    1    1    0    1    0    0
32  1  1  1  1  1   1   1   1   1   1   1   1   1   1   1    1    1    1    1    1    1
   x234 x235 x245 x345 x1234 x1235 x1245 x1345 x2345    Y
2     0    0    0    0     0     0     0     0     0 1276
3     0    0    0    0     0     0     0     0     0  714
4     0    0    0    0     0     0     0     0     0  481
5     0    0    0    0     0     0     0     0     0  628
6     0    0    0    0     0     0     0     0     0  365
7     0    0    0    0     0     0     0     0     0  836
8     0    0    0    1     0     0     0     0     0 1343
9     0    0    0    0     0     0     0     0     0 1348
10    0    0    0    0     0     0     0     0     0  161
11    0    0    0    0     0     0     0     0     0  266
12    0    0    1    0     0     0     0     0     0  239
13    0    0    0    0     0     0     0     0     0  144
14    0    1    0    0     0     0     0     0     0  135
15    1    0    0    0     0     0     0     0     0  469
16    1    1    1    1     0     0     0     0     1 1356
17    0    0    0    0     0     0     0     0     0  594
18    0    0    0    0     0     0     0     0     0  431
19    0    0    0    0     0     0     0     0     0   18
20    0    0    0    0     0     0     0     0     0   83
21    0    0    0    0     0     0     0     0     0   22
22    0    0    0    0     0     0     0     0     0   16
23    0    0    0    0     0     0     0     0     0   12
24    0    0    0    1     0     0     0     1     0   29
25    0    0    0    0     0     0     0     0     0   16
26    0    0    0    0     0     0     0     0     0    3
27    0    0    0    0     0     0     0     0     0    2
28    0    0    1    0     0     0     1     0     0    3
29    0    0    0    0     0     0     0     0     0    6
30    0    1    0    0     0     1     0     0     0    0
31    1    0    0    0     1     0     0     0     0   11
32    1    1    1    1     1     1     1     1     1    9

---

我代表BMA软件包团队回答这个问题，我非常感谢他们。他们是这么说的：

我认为可能会有一些未预料到的简并，由于离散 导致此问题出现的数据的性质。我会的 相应地更新包，但您应该能够通过 将下面指示的行添加到bic.glm.data.frame（和 bic.glm.矩阵）

while（长度（glm.out$系数）>maxCol）{
在打开和关闭跟踪后，any.DROPED错误变为“as.environment（where）中的错误：无效的'pos'参数”。我不觉得更接近，但我想能够影响错误总是好的。
> XY5
   x1 x2 x3 x4 x5 x12 x13 x14 x15 x23 x24 x25 x34 x35 x45 x123 x124 x125 x134 x135 x145
2   0  0  0  0  1   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
3   0  0  0  1  0   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
4   0  0  0  1  1   0   0   0   0   0   0   0   0   0   1    0    0    0    0    0    0
5   0  0  1  0  0   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
6   0  0  1  0  1   0   0   0   0   0   0   0   0   1   0    0    0    0    0    0    0
7   0  0  1  1  0   0   0   0   0   0   0   0   1   0   0    0    0    0    0    0    0
8   0  0  1  1  1   0   0   0   0   0   0   0   1   1   1    0    0    0    0    0    0
9   0  1  0  0  0   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
10  0  1  0  0  1   0   0   0   0   0   0   1   0   0   0    0    0    0    0    0    0
11  0  1  0  1  0   0   0   0   0   0   1   0   0   0   0    0    0    0    0    0    0
12  0  1  0  1  1   0   0   0   0   0   1   1   0   0   1    0    0    0    0    0    0
13  0  1  1  0  0   0   0   0   0   1   0   0   0   0   0    0    0    0    0    0    0
14  0  1  1  0  1   0   0   0   0   1   0   1   0   1   0    0    0    0    0    0    0
15  0  1  1  1  0   0   0   0   0   1   1   0   1   0   0    0    0    0    0    0    0
16  0  1  1  1  1   0   0   0   0   1   1   1   1   1   1    0    0    0    0    0    0
17  1  0  0  0  0   0   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
18  1  0  0  0  1   0   0   0   1   0   0   0   0   0   0    0    0    0    0    0    0
19  1  0  0  1  0   0   0   1   0   0   0   0   0   0   0    0    0    0    0    0    0
20  1  0  0  1  1   0   0   1   1   0   0   0   0   0   1    0    0    0    0    0    1
21  1  0  1  0  0   0   1   0   0   0   0   0   0   0   0    0    0    0    0    0    0
22  1  0  1  0  1   0   1   0   1   0   0   0   0   1   0    0    0    0    0    1    0
23  1  0  1  1  0   0   1   1   0   0   0   0   1   0   0    0    0    0    1    0    0
24  1  0  1  1  1   0   1   1   1   0   0   0   1   1   1    0    0    0    1    1    1
25  1  1  0  0  0   1   0   0   0   0   0   0   0   0   0    0    0    0    0    0    0
26  1  1  0  0  1   1   0   0   1   0   0   1   0   0   0    0    0    1    0    0    0
27  1  1  0  1  0   1   0   1   0   0   1   0   0   0   0    0    1    0    0    0    0
28  1  1  0  1  1   1   0   1   1   0   1   1   0   0   1    0    1    1    0    0    1
29  1  1  1  0  0   1   1   0   0   1   0   0   0   0   0    1    0    0    0    0    0
30  1  1  1  0  1   1   1   0   1   1   0   1   0   1   0    1    0    1    0    1    0
31  1  1  1  1  0   1   1   1   0   1   1   0   1   0   0    1    1    0    1    0    0
32  1  1  1  1  1   1   1   1   1   1   1   1   1   1   1    1    1    1    1    1    1
   x234 x235 x245 x345 x1234 x1235 x1245 x1345 x2345    Y
2     0    0    0    0     0     0     0     0     0 1276
3     0    0    0    0     0     0     0     0     0  714
4     0    0    0    0     0     0     0     0     0  481
5     0    0    0    0     0     0     0     0     0  628
6     0    0    0    0     0     0     0     0     0  365
7     0    0    0    0     0     0     0     0     0  836
8     0    0    0    1     0     0     0     0     0 1343
9     0    0    0    0     0     0     0     0     0 1348
10    0    0    0    0     0     0     0     0     0  161
11    0    0    0    0     0     0     0     0     0  266
12    0    0    1    0     0     0     0     0     0  239
13    0    0    0    0     0     0     0     0     0  144
14    0    1    0    0     0     0     0     0     0  135
15    1    0    0    0     0     0     0     0     0  469
16    1    1    1    1     0     0     0     0     1 1356
17    0    0    0    0     0     0     0     0     0  594
18    0    0    0    0     0     0     0     0     0  431
19    0    0    0    0     0     0     0     0     0   18
20    0    0    0    0     0     0     0     0     0   83
21    0    0    0    0     0     0     0     0     0   22
22    0    0    0    0     0     0     0     0     0   16
23    0    0    0    0     0     0     0     0     0   12
24    0    0    0    1     0     0     0     1     0   29
25    0    0    0    0     0     0     0     0     0   16
26    0    0    0    0     0     0     0     0     0    3
27    0    0    0    0     0     0     0     0     0    2
28    0    0    1    0     0     0     1     0     0    3
29    0    0    0    0     0     0     0     0     0    6
30    0    1    0    0     0     1     0     0     0    0
31    1    0    0    0     1     0     0     0     0   11
32    1    1    1    1     1     1     1     1     1    9

while (length(glm.out$coefficients) > maxCol) {
           any.dropped <- TRUE
           dropglm <- drop1(glm.out, test = "Chisq")
           dropped <- which.max(dropglm$"Pr(Chi)"[-1]) + 1
#
           if (length(dropped) == 0) break  #### add to prevent bug
#
           x.df <- x.df[, -(dropped - 1)]
           designx.levels <- designx.levels[-dropped]
           designx <- designx[-dropped]