使用跳跃进行模型选择时的警告

Question

我最近尝试根据第一页here上的代码运行leaps，对大型数据集使用leaps=regsubsets(TOTAL~.,data=TASM,nbest=10)。我最初收到错误Exhaustive search will be S L O W, must specify really.big=T，所以我在最后添加了leaps=regsubsets(TOTAL~.,data=TASM,nbest=10, really.big=T)。

我现在收到In leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax, force.in = force.in, : 83 linear dependencies found的警告。由于这是一个警告，我认为它不会阻止我运行模型选择算法，但警告后没有任何反应。 leaps警告我关于线性依赖项数量的目的是什么？在运行算法之前通常应该解决它？如果没有，我怎么能告诉leaps运行算法？

更新：我意识到这是一个不像我最初想的那么有用的问题。我缺乏R经验让我忘记打电话给summary(leaps)。作为一个后续，这个结果告诉我从1到10的最佳模型？也就是说，根据leaps，选择V1 ... V7的下面发布的顶级模型是最好的？从使用plot(leaps,scale="adjr2")并收到一个空白的情节我被引导相信某处有错误。警告消息为：In min(x) : no non-missing arguments to min; returning Inf。

         V1           V2         V3             V4        V5   V6      V7
1  ( 1 ) " "         " "        " "            " "       " "  " "     " "             
1  ( 2 ) " "         " "        " "            " "       " "  " "     " "             
1  ( 3 ) " "         " "        " "            " "       " "  " "     " "             
1  ( 4 ) " "         " "        " "            " "       " "  " "     " "             
1  ( 5 ) " "         " "        " "            " "       " "  " "     " "             
1  ( 6 ) " "         " "        " "            " "       " "  " "     " "             
1  ( 7 ) " "         " "        " "            " "       " "  " "     " "             
1  ( 8 ) " "         " "        "*"            " "       " "  " "     " "