BMA包中的bic.glm數據錯誤，但不包含glm

Question

我從一組相互作用係數估計泊松模型，BMA包的bic.glm有助於導航模型空間。我一直在使用它，但是當我昨晚從2.10.x更新到2.14.2時，它停止工作。這裏的錯誤：第一，一個可行的呼叫：

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

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

Coefficients: 
<results, etc> 

現在bic.glm失敗：

> bic.glm(formula(Y~.), data=XY5, glm.family=poisson, model=TRUE) 
Error in terms.formula(formula, data = data) : 
    '.' in formula and no 'data' argument 

同樣，這個確切的代碼中R的早期版本曾與五大系統。當我使用4個交互系統運行bic.glm而不是5（即，放下x5並摺疊交互）時，bic.glm運行良好。我將在下面列出五個系統數據。提前致謝。

> 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 

Answer 1

我代表BMA團隊回答問題，我非常感謝他。下面是他們說的話：

I think there may be some unanticipated degeneracy due to the discrete nature of the data that is causing this problem to surface. I will update the package accordingly, but you should be able to fix it by adding the line indicated below to bic.glm.data.frame (and bic.glm.matrix)

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]