R错误变量的类型（列表）无效

时间：2012-12-10 00:44:29

标签： r matlab machine-learning

我是导入matlab文件并构造一个数据框，matlab文件包含两列，每行保持一个具有矩阵的单元格，我构造一个数据帧来运行随机林。但我得到了以下错误。 

Error in model.frame.default(formula = expert_data_frame$t_labels ~ .,  : 
  invalid type (list) for variable 'expert_data_frame$t_labels'

以下是我如何导入matlab文件并构建数据框的代码：

all_exp_traintest <- readMat(all_exp_filepath);
len = length(all_exp_traintest$exp.traintest)/2;
    for (i in 1:len) {
      expert_train_df <- data.frame(all_exp_traintest$exp.traintest[i]);
      labels = data.frame(all_exp_traintest$exp.traintest[i+302]);
      names(labels)[1] <- "t_labels";
      expert_train_df$t_labels <- labels;
      expert_data_frame <- data.frame(expert_train_df);
      rf_model = randomForest(expert_data_frame$t_labels ~., data=expert_data_frame, importance=TRUE, do.trace=100);
    }

Matlab输入文件的结构

[56x12 double]    [56x1 double]
[62x12 double]    [62x1 double]
[62x12 double]    [62x1 double]
[62x12 double]    [62x1 double]
[62x12 double]    [62x1 double]
[74x12 double]    [74x1 double]


> str(all_exp_traintest)
List of 1
 $ exp.traintest:List of 604
  ..$ NA: num [1:56, 1:12] 0 0 0 0 8 1 1 0 0 0 ...
  ..$ NA: num [1:62, 1:12] 2 10 11 13 5 10 13 8 11 8 ...
  ..$ NA: num [1:62, 1:12] 0 0 1 0 0 0 0 0 1 1 ...
  ..$ NA: num [1:62, 1:12] 4 2 1 3 3 20 6 3 2 2 ...
  ..$ NA: num [1:62, 1:12] 2731 2362 2937 1229 1898 ...
  ..$ NA: num [1:74, 1:12] 27 33 34 38 33 35 36 35 47 46 ...
  ..$ NA: num [1:74, 1:12] 106 79 99 94 153 104 146 105 125 146 ...
  ..$ NA: num [1:74, 1:12] 3 9 3 0 1 26 0 4 0 0 ...
  ..$ NA: num [1:51, 1:12] 5 7 3 30 0 0 0 0 0 0 ...
  ..$ NA: num [1:66, 1:12] 0 0 13 0 0 3 2 2 0 2 ...
  ..$ NA: num [1:73, 1:12] 1 0 1 0 0 0 2 1 2 5 ...
  ..$ NA: num [1:73, 1:12] 23 14 20 14 24 22 32 61 84 278 ...
  ..$ NA: num [1:75, 1:12] 1 7 0 1 2 3 3 0 16 10 ...
  ..$ NA: num [1:90, 1:12] 10 7 8 15 25 12 37 31 18 48 ...
  ..$ NA: num [1:90, 1:12] 0 6 3 1 5 7 8 6 1 1 ...
  ..$ NA: num [1:90, 1:12] 0 1 1 2 0 4 9 6 3 4 ...
  ..$ NA: num [1:90, 1:12] 6 0 5 27 11 50 22 8 10 4 ...
  ..$ NA: num [1:90, 1:12] 3 9 13 12 4 0 5 0 5 0 ...
  ..$ NA: num [1:90, 1:12] 1 0 1 0 1 2 1 0 1 2 ...
  ..$ NA: num [1:90, 1:12] 3395 3400 3360 3770 3533 ...
  ..$ NA: num [1:84, 1:12] 0 0 0 0 5 0 0 5 4 2 ...
  ..$ NA: num [1:80, 1:12] 2 3 3 3 4 28 61 26 8 1 ...
  ..$ NA: num [1:81, 1:12] 4 28 22 9 16 43 80 21 19 18 ...
  ..$ NA: num [1:76, 1:12] 1 0 0 1 49 64 60 230 222 267 ...
  ..$ NA: num [1:76, 1:12] 4786 4491 2510 1144 2071 ...
  ..$ NA: num [1:76, 1:12] 80 128 254 109 114 267 152 139 368 363 ...
  ..$ NA: num [1:76, 1:12] 1 5 8 2 14 5 3 13 8 2 ...
  ..$ NA: num [1:76, 1:12] 10 3 8 79 4 4 11 30 2 0 ...
  ..$ NA: num [1:68, 1:12] 0 0 2 0 0 2 6 0 0 4 ...
  ..$ NA: num [1:68, 1:12] 1 4 5 2 2 3 3 1 3 0 ...
  ..$ NA: num [1:68, 1:12] 0 0 1 0 0 0 0 0 0 1 ...
  ..$ NA: num [1:69, 1:12] 39 45 2 0 1 4 3 0 13 0 ...
  ..$ NA: num [1:69, 1:12] 0 4 6 0 0 4 1 6 10 1 ...
  ..$ NA: num [1:69, 1:12] 0 2 5 2 2 2 0 0 3 6 ...
  ..$ NA: num [1:69, 1:12] 3 0 1 1 1 4 7 5 5 1 ...
  ..$ NA: num [1:66, 1:12] 5 0 0 0 0 0 0 1 3 5 ...
  ..$ NA: num [1:66, 1:12] 4 3 3 0 0 4 0 0 0 0 ...
  ..$ NA: num [1:65, 1:12] 0 0 1 0 0 0 5 8 4 1 ...
  ..$ NA: num [1:65, 1:12] 0 5 6 0 2 0 0 1 1 2 ...
  ..$ NA: num [1:69, 1:12] 0 16 5 1 14 0 1 0 0 16 ...
  ..$ NA: num [1:69, 1:12] 0 0 0 0 0 25 2 3 0 0 ...
  ..$ NA: num [1:64, 1:12] 2 0 0 0 0 0 0 0 0 0 ...
  ..$ NA: num [1:42, 1:12] 0 0 0 0 0 0 0 0 0 0 ...
  ..$ NA: num [1:67, 1:12] 0 2 4 10 15 4 1 43 1 7 ...
  ..$ NA: num [1:63, 1:12] 32 6 12 5 92 8 29 7 21 20 ...
  ..$ NA: num [1:63, 1:12] 2 5 12 8 10 13 6 11 10 14 ...
  ..$ NA: num [1:63, 1:12] 3 5 10 9 0 1 8 13 2 14 ...
  ..$ NA: num [1:54, 1:12] 0 0 14 0 0 0 0 0 0 1 ...
  ..$ NA: num [1:82, 1:12] 152 99 63 57 105 44 28 33 43 49 ...
  ..$ NA: num [1:81, 1:12] 0 1 0 0 0 0 0 0 0 0 ...
  ..$ NA: num [1:75, 1:12] 0 1 3 0 0 0 0 0 0 0 ...
  ..$ NA: num [1:75, 1:12] 1 0 0 2 0 1 0 0 0 0 ...
  ..$ NA: num [1:75, 1:12] 1 6 5 5 3 8 1 3 1 0 ...
  ..$ NA: num [1:72, 1:12] 0 0 0 0 1 0 1 2 0 0 ...
  ..$ NA: num [1:62, 1:12] 310 91 4 4 9 0 0 1 0 0 ...
  ..$ NA: num [1:62, 1:12] 239 374 1060 599 805 808 139 150 490 326 ...
  ..$ NA: num [1:49, 1:12] 9 18 10 12 19 5 13 10 2 3 ...
  ..$ NA: num [1:61, 1:12] 2 0 0 0 1 0 0 0 0 0 ...
  ..$ NA: num [1:61, 1:12] 4 10 16 15 8 14 10 23 11 5 ...
  ..$ NA: num [1:61, 1:12] 0 1 4 4 5 3 0 1 1 1 ...
  ..$ NA: num [1:65, 1:12] 165 100 177 65 148 58 188 55 59 62 ...
  ..$ NA: num [1:65, 1:12] 13 0 0 2 2 3 0 0 0 0 ...
  ..$ NA: num [1:66, 1:12] 157 58 101 92 15 21 73 80 78 75 ...
  ..$ NA: num [1:66, 1:12] 8 6 1 0 6 2 2 6 10 9 ...
  ..$ NA: num [1:87, 1:12] 1 2 5 6 8 3 3 3 2 3 ...
  ..$ NA: num [1:83, 1:12] 0 0 0 0 0 0 2 13 0 0 ...
  ..$ NA: num [1:81, 1:12] 0 0 1 0 3 5 3 0 2 7 ...
  ..$ NA: num [1:81, 1:12] 33 81 94 30 5 36 16 90 121 182 ...
  ..$ NA: num [1:81, 1:12] 10 11 16 6 0 0 0 1 0 0 ...
  ..$ NA: num [1:81, 1:12] 7 0 0 2 1 3 1 4 0 0 ...
  ..$ NA: num [1:81, 1:12] 1 0 5 0 2 3 1 0 1 1 ...
  ..$ NA: num [1:95, 1:12] 30 160 116 130 444 515 225 135 108 175 ...
  ..$ NA: num [1:95, 1:12] 12 1 0 10 3 3 0 4 0 0 ...
  ..$ NA: num [1:95, 1:12] 1 0 0 0 3 3 1 0 0 0 ...
  ..$ NA: num [1:95, 1:12] 11 42 61 23 41 56 81 6 83 82 ...
  ..$ NA: num [1:95, 1:12] 1 2 5 3 6 4 2 8 28 1 ...
  ..$ NA: num [1:95, 1:12] 283 192 377 216 207 261 394 262 262 554 ...
  ..$ NA: num [1:94, 1:12] 0 0 0 0 0 0 0 0 0 0 ...
  ..$ NA: num [1:72, 1:12] 0 0 0 0 0 0 0 0 0 0 ...
  ..$ NA: num [1:72, 1:12] 5 3 0 2 13 27 6 2 12 36 ...
  ..$ NA: num [1:72, 1:12] 0 2 2 0 1 0 1 4 2 2 ...
  ..$ NA: num [1:72, 1:12] 0 0 1 0 3 1 0 4 1 0 ...
  ..$ NA: num [1:67, 1:12] 27 7 18 1 2 0 0 0 0 0 ...
  ..$ NA: num [1:67, 1:12] 10 2 1 10 7 0 0 1 1 4 ...
  ..$ NA: num [1:67, 1:12] 14 17 9 20 13 20 18 13 10 7 ...
  ..$ NA: num [1:64, 1:12] 0 0 0 0 4 0 0 0 3 0 ...
  ..$ NA: num [1:64, 1:12] 3 0 1 0 2 7 13 14 4 2 ...
  ..$ NA: num [1:64, 1:12] 0 0 0 0 0 0 0 0 2 0 ...
  ..$ NA: num [1:72, 1:12] 59 61 55 120 49 202 325 244 377 551 ...
  ..$ NA: num [1:72, 1:12] 0 0 0 0 0 0 0 0 1 0 ...
  ..$ NA: num [1:72, 1:12] 0 3 1 0 1 0 0 0 4 0 ...
  ..$ NA: num [1:72, 1:12] 5 12 6 9 15 10 15 27 15 9 ...
  ..$ NA: num [1:72, 1:12] 7 0 3 0 0 1 1 1 1 0 ...
  ..$ NA: num [1:72, 1:12] 0 0 0 0 89 0 19 3 3 2 ...
  ..$ NA: num [1:61, 1:12] 5 3 5 3 3 29 46 140 49 24 ...
  ..$ NA: num [1:63, 1:12] 23 0 0 0 0 60 7 73 13 19 ...
  ..$ NA: num [1:95, 1:12] 7 96 28 2 9 5 8 190 166 1 ...
  ..$ NA: num [1:95, 1:12] 0 0 1 1 0 0 0 0 0 0 ...
  ..$ NA: num [1:95, 1:12] 4 0 2 6 6 11 6 5 6 9 ...
  .. [list output truncated]
 - attr(*, "header")=List of 3
  ..$ description: chr "MATLAB 5.0 MAT-file, Platform: MACI64, Created on: Sun Dec  9 17:35:24 2012                                         "
  ..$ version    : chr "5"
  ..$ endian     : chr "little"

将matlab文件加载到R

后 
all_exp_traintest$exp.traintest[1]
$<NA>
      [,1] [,2]  [,3]   [,4]   [,5]    [,6]     [,7]      [,8]       [,9]       [,10]        [,11]        [,12]
 [1,]    0  0.0  0.00  0.000 0.5000 0.03125 0.015625 0.0000000 0.00000000 0.000000000 0.0000000000 0.0000000000
 [2,]    0  0.0  0.00  1.000 0.0625 0.03125 0.000000 0.0000000 0.00000000 0.000000000 0.0000000000 0.0000000000
 [3,]    0  0.0  2.00  0.125 0.0625 0.00000 0.000000 0.0000000 0.00000000 0.000000000 0.0000000000 0.0000000000
 [4,]    0  4.0  0.25  0.125 0.0000 0.00000 0.000000 0.0000000 0.00000000 0.000000000 0.0000000000 0.0009765625
 [5,]    8  0.5  0.25  0.000 0.0000 0.00000 0.000000 0.0000000 0.00000000 0.000000000 0.0019531250 0.0000000000
 [6,]    1  0.5  0.00  0.000 0.0000 0.00000 0.000000 0.0000000 0.00000000 0.003906250 0.0000000000 0.0004882812
 [7,]    1  0.0  0.00  0.000 0.0000 0.00000 0.000000 0.0000000 0.00781250 0.000000000 0.0009765625 0.0009765625
 [8,]    0  0.0  0.00  0.000 0.0000 0.00000 0.000000 0.0156250 0.00000000 0.001953125 0.0019531250 0.0000000000
 [9,]    0  0.0  0.00  0.000 0.0000 0.00000 0.031250 0.0000000 0.00390625 0.003906250 0.0000000000 0.0004882812
[10,]    0  0.0  0.00  0.000 0.0000 0.06250 0.000000 0.0078125 0.00781250 0.000000000 0.0009765625 0.0000000000
[11,]    0  0.0  0.00  0.000 0.1250 0.00000 0.015625 0.0156250 0.00000000 0.001953125 0.0000000000 0.0000000000
[12,]    0  0.0  0.00  0.250 0.0000 0.03125 0.031250 0.0000000 0.00390625 0.000000000 0.0000000000 0.0004882812
[13,]    0  0.0  0.50  0.000 0.0625 0.06250 0.000000 0.0078125 0.00000000 0.000000000 0.0009765625 0.0000000000
[14,]    0  1.0  0.00  0.125 0.1250 0.00000 0.015625 0.0000000 0.00000000 0.001953125 0.0000000000 0.0024414062
[15,]    2  0.0  0.25  0.250 0.0000 0.03125 0.000000 0.0000000 0.00390625 0.000000000 0.0048828125 0.0014648438
[16,]    0  0.5  0.50  0.000 0.0625 0.00000 0.000000 0.0078125 0.00000000 0.009765625 0.0029296875 0.0039062500
[17,]    1  1.0  0.00  0.125 0.0000 0.00000 0.015625 0.0000000 0.01953125 0.005859375 0.0078125000 0.0151367188
[18,]    2  0.0  0.25  0.000 0.0000 0.03125 0.000000 0.0390625 0.01171875 0.015625000 0.0302734375 0.0019531250
[19,]    0  0.5  0.00  0.000 0.0625 0.00000 0.078125 0.0234375 0.03125000 0.060546875 0.0039062500 0.0029296875
[20,]    1  0.0  0.00  0.125 0.0000 0.15625 0.046875 0.0625000 0.12109375 0.007812500 0.0058593750 0.0253906250
[21,]    0  0.0  0.25  0.000 0.3125 0.09375 0.125000 0.2421875 0.01562500 0.011718750 0.0507812500 0.0253906250
[22,]    0  0.5  0.00  0.625 0.1875 0.25000 0.484375 0.0312500 0.02343750 0.101562500 0.0507812500 0.0063476562
[23,]    1  0.0  1.25  0.375 0.5000 0.96875 0.062500 0.0468750 0.20312500 0.101562500 0.0126953125 0.0009765625
[24,]    0  2.5  0.75  1.000 1.9375 0.12500 0.093750 0.4062500 0.20312500 0.025390625 0.0019531250 0.0000000000
[25,]    5  1.5  2.00  3.875 0.2500 0.18750 0.812500 0.4062500 0.05078125 0.003906250 0.0000000000 0.0019531250
[26,]    3  4.0  7.75  0.500 0.3750 1.62500 0.812500 0.1015625 0.00781250 0.000000000 0.0039062500 0.0029296875
[27,]    8 15.5  1.00  0.750 3.2500 1.62500 0.203125 0.0156250 0.00000000 0.007812500 0.0058593750 0.0009765625
[28,]   31  2.0  1.50  6.500 3.2500 0.40625 0.031250 0.0000000 0.01562500 0.011718750 0.0019531250 0.0000000000
[29,]    4  3.0 13.00  6.500 0.8125 0.06250 0.000000 0.0312500 0.02343750 0.003906250 0.0000000000 0.0083007812
[30,]    6 26.0 13.00  1.625 0.1250 0.00000 0.062500 0.0468750 0.00781250 0.000000000 0.0166015625 0.0000000000
[31,]   52 26.0  3.25  0.250 0.0000 0.12500 0.093750 0.0156250 0.00000000 0.033203125 0.0000000000 0.0048828125
[32,]   52  6.5  0.50  0.000 0.2500 0.18750 0.031250 0.0000000 0.06640625 0.000000000 0.0097656250 0.0034179688
[33,]   13  1.0  0.00  0.500 0.3750 0.06250 0.000000 0.1328125 0.00000000 0.019531250 0.0068359375 0.0229492188
[34,]    2  0.0  1.00  0.750 0.1250 0.00000 0.265625 0.0000000 0.03906250 0.013671875 0.0458984375 0.0297851562
[35,]    0  2.0  1.50  0.250 0.0000 0.53125 0.000000 0.0781250 0.02734375 0.091796875 0.0595703125 0.0771484375
[36,]    4  3.0  0.50  0.000 1.0625 0.00000 0.156250 0.0546875 0.18359375 0.119140625 0.1542968750 0.0004882812
[37,]    6  1.0  0.00  2.125 0.0000 0.31250 0.109375 0.3671875 0.23828125 0.308593750 0.0009765625 0.0000000000
[38,]    2  0.0  4.25  0.000 0.6250 0.21875 0.734375 0.4765625 0.61718750 0.001953125 0.0000000000 0.0048828125
[39,]    0  8.5  0.00  1.250 0.4375 1.46875 0.953125 1.2343750 0.00390625 0.000000000 0.0097656250 0.0000000000
[40,]   17  0.0  2.50  0.875 2.9375 1.90625 2.468750 0.0078125 0.00000000 0.019531250 0.0000000000 0.0000000000
[41,]    0  5.0  1.75  5.875 3.8125 4.93750 0.015625 0.0000000 0.03906250 0.000000000 0.0000000000 0.0000000000
[42,]   10  3.5 11.75  7.625 9.8750 0.03125 0.000000 0.0781250 0.00000000 0.000000000 0.0000000000 0.0004882812
[43,]    7 23.5 15.25 19.750 0.0625 0.00000 0.156250 0.0000000 0.00000000 0.000000000 0.0009765625 0.0078125000
[44,]   47 30.5 39.50  0.125 0.0000 0.31250 0.000000 0.0000000 0.00000000 0.001953125 0.0156250000 0.0000000000
[45,]   61 79.0  0.25  0.000 0.6250 0.00000 0.000000 0.0000000 0.00390625 0.031250000 0.0000000000 0.0000000000
[46,]  158  0.5  0.00  1.250 0.0000 0.00000 0.000000 0.0078125 0.06250000 0.000000000 0.0000000000 0.0004882812
[47,]    1  0.0  2.50  0.000 0.0000 0.00000 0.015625 0.1250000 0.00000000 0.000000000 0.0009765625 0.0000000000
[48,]    0  5.0  0.00  0.000 0.0000 0.03125 0.250000 0.0000000 0.00000000 0.001953125 0.0000000000 0.0000000000
[49,]   10  0.0  0.00  0.000 0.0625 0.50000 0.000000 0.0000000 0.00390625 0.000000000 0.0000000000 0.0000000000
[50,]    0  0.0  0.00  0.125 1.0000 0.00000 0.000000 0.0078125 0.00000000 0.000000000 0.0000000000 0.0000000000
[51,]    0  0.0  0.25  2.000 0.0000 0.00000 0.015625 0.0000000 0.00000000 0.000000000 0.0000000000 0.0000000000
[52,]    0  0.5  4.00  0.000 0.0000 0.03125 0.000000 0.0000000 0.00000000 0.000000000 0.0000000000 0.0000000000
[53,]    1  8.0  0.00  0.000 0.0625 0.00000 0.000000 0.0000000 0.00000000 0.000000000 0.0000000000 0.0000000000
[54,]   16  0.0  0.00  0.125 0.0000 0.00000 0.000000 0.0000000 0.00000000 0.000000000 0.0000000000 0.0000000000
[55,]    0  0.0  0.25  0.000 0.0000 0.00000 0.000000 0.0000000 0.00000000 0.000000000 0.0000000000 0.0000000000
[56,]    0  0.5  0.00  0.000 0.0000 0.00000 0.000000 0.0000000 0.00000000 0.000000000 0.0000000000 0.0000000000

1 个答案:

答案 0 :(得分：6)

好的，我会为您解释导致您出现问题的[和[[之间的区别。我将留给您使用此信息进行适当的更改。

考虑以下列表：

l <- list(a = matrix(1:25,5,5),b = 1:5,c = letters[1:5],d = NA)
> l
$a
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    6   11   16   21
[2,]    2    7   12   17   22
[3,]    3    8   13   18   23
[4,]    4    9   14   19   24
[5,]    5   10   15   20   25

$b
[1] 1 2 3 4 5

$c
[1] "a" "b" "c" "d" "e"

$d
[1] NA

假设我们要选择此列表的第一个元素，即矩阵。你正在做这样的事情：

> l[1]
$a
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    6   11   16   21
[2,]    2    7   12   17   22
[3,]    3    8   13   18   23
[4,]    4    9   14   19   24
[5,]    5   10   15   20   25

这是错误的。 [将始终返回原始列表的子列表。所以你在l[1]看到的实际上是一个长度列表。它的一个元素是我们实际追求的矩阵。

你想要的是：

> l[[1]]
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    6   11   16   21
[2,]    2    7   12   17   22
[3,]    3    8   13   18   23
[4,]    4    9   14   19   24
[5,]    5   10   15   20   25

如果您将str(l[1])的输出与str(l[[1]])进行比较，则差异应该是显而易见的，并且还要明确为什么您要求的第一条信息涉及str的输出。它是一个非常宝贵的调试工具，可以确保对象符合您的实际目的。

最后，正如我在其中一篇评论中提到的那样，请永远不要做以下事情：

expert_data_frame$t_labels ~.

相反，只需：

t_labels ~.

公式界面的重点在于您不必编写数据框的名称。该函数将在您为公式中命名的变量提供的数据框中查看。如果你使用$来明确选择变量，你会引入一个讨厌的bug来源，你可能会强迫R使用你不想要的变量。

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