使用脱字符号包交叉验证rpart模型错误：.subsetx，j中的错误：下标类型“列表”无效

如何解决使用脱字符号包交叉验证rpart模型错误：.subsetx，j中的错误：下标类型“列表”无效

问题

我有一个名为 FID 的数据框（如下所示），我正在尝试按照此tutorial构建和交叉验证rpart（）模型 。我已经成功地正确生成了代码，直到到达训练模型的部分。当我运行模型时，出现以下错误消息（如下）：

错误消息

.subset（x，j）中的错误：下标类型'list'无效

我已经阅读了几本questions，文章和有关脱字符号训练应用程序以及此错误消息的“堆栈溢出”页面，但是我不理解该问题。我不是高级R用户，因此我尝试使用不同的语法编写此代码

如果有人能够解决这个问题，我将非常感激。

提前谢谢

R代码：

  library(caret)

# Step 1: Get row numbers for the training data
trainRowNumbers <- createDataPartition(Rpart.Cross.Validate.Caret$FID,p=0.8,list=FALSE)

# Step 2: Create the training  dataset
trainData <- Rpart.Cross.Validate.Caret[trainRowNumbers,]

# Step 3: Create the test dataset
testData <- Rpart.Cross.Validate.Caret[-trainRowNumbers,]

# Subset the features we want to use
features <- dplyr::select(trainData,Year,Month)
head(features)

##Structure of features
  'data.frame': 736 obs. of  2 variables:
  $ Year : num  2015 2015 2015 2015 2015 ...
  $ Month: Factor w/ 12 levels "January","February",..: 1 1 1 1 1 1 1 1 1 1 ...

# Set up caret to perform 10-fold cross validation repeated 3 times
caret.control <- trainControl(method = "repeatedcv",number = 10,repeats = 3)

# Use caret to train the rpart decision tree using 10-fold cross 
# validation repeated 3 times and use 15 values for tuning the
# cp parameter for rpart. This code returns the best model trained
# on all the data! Mighty!
rpart.cv <- train(FID ~ .,data = trainData[,features],method = "rpart",trControl = caret.control,tuneLength = 15)

##Model predictions
Fitted_model<-predict(rpart.cv)

数据框：FID

structure(list(FID = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918),Year = c(2015,2015,2016,2017,2017),Month = structure(c(1L,1L,2L,3L,4L,5L,8L,9L,10L,11L,12L,7L,12L
    ),.Label = c("January","march","April","May","June","July","August","September","October","November","December"),class = "factor")),row.names = c(NA,917L),class = "data.frame")

解决方法

如果您删除

# Subset the features we want to use
features <- dplyr::select(trainData,Year,Month)
head(features)

并将data = trainData[,features]替换为data = trainData，您的代码可以正常运行。 只需使用

rpart.cv <- train(FID ~ .,data = trainData,method = "rpart",trControl = caret.control,tuneLength = 15)