BMA软件包的bic.glm中出现数据错误，但没有glm [英] data error in BMA package's bic.glm but not glm
问题描述
我正在从一组相互作用系数中估计一个泊松模型，而BMA软件包的bic.glm有助于导航模型空间.我已经使用了多年，但是昨晚我将R从2.10.x更新到2.14.2时，它停止工作了.这是错误:首先，一个可以正常工作的呼叫:
I am estimating a poisson model from a set of interaction coefficients, and the BMA package's bic.glm helps navigate the model space. I've been using it for years, but when I updated R from 2.10.x to 2.14.2 last night, it stopped working. Here's the error: first, a call that works:
> glm(formula(Y~.), data=XY5, family=poisson)
Call: glm(formula = formula(Y ~ .), family = poisson, data = XY5)
Coefficients:
<results, etc>
现在bic.glm失败了:
Now bic.glm failing:
> bic.glm(formula(Y~.), data=XY5, glm.family=poisson, model=TRUE)
Error in terms.formula(formula, data = data) :
'.' in formula and no 'data' argument
同样，此精确代码在具有五个系统的R的早期版本中有效.当我使用4个交互系统而不是5个交互系统运行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.提前致谢.
Again, this exact code worked in the earlier version of R with five systems. When I run bic.glm with 4 interacting systems instead of 5 (i.e., drop x5 and collapse the interactions), bic.glm runs fine. I'll include the five system data below. Thanks in advance.
> 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
推荐答案
我代表BMA软件包团队对此表示感谢，非常感谢.他们是这样说的:
I'm answering on behalf of the BMA package team, to whom I'm very grateful. Here's what they said:
我认为，由于离散 导致此问题浮出水面的数据的性质.我会 相应地更新软件包，但是您应该能够通过以下方式对其进行修复 将下面指示的行添加到bic.glm.data.frame(和 bic.glm.matrix)
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]
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