title: R caret rpart returns Error in `[.data.frame`(m, labs) : undefined columns selected
body: <p>I am running a classification for rpart.
  I needed to prepare the data into a sparse format to run multiple models to it . </p>
  
  <p>When I run the rpart method, using this call:</p>
  
  <pre><code>control &lt;- trainControl(method="repeatedcv", number=10, repeats=3)
  #Metric Measurement for Model Performance
  fitmetric &lt;- "Accuracy"
  set.seed(seed)
  
  ptm &lt;- proc.time()
  adultFit.cart &lt;- train(response~., data=adultTraining, method="rpart", metric=fitmetric, trControl=control,
                    parms = list( split = "information"),control=rpart.control(cp = 0.04))
  proc.time() - ptm
  </code></pre>
  
  <p>I am getting this message:</p>
  
  <pre><code>`[.data.frame`(m, labs) : undefined columns selected
  </code></pre>
  
  <p>Can't seemed to figure out what is causing this since it runs good for all the other models </p>
  
  <p>Here is the definition of df that I am using to test the function and the sample below:</p>
  
  <pre><code>&gt; str(adultTraining)
  'data.frame':   22793 obs. of  57 variables:
   $ age                                : num  53 37 42 37 30 23 34 25 32 43 ...
   $ fnlwgt                             : num  234721 284582 159449 280464 141297 ...
   $ educationnum                       : num  7 14 13 10 13 13 4 9 9 14 ...
   $ maritalstatus.Divorced             : num  0 0 0 0 0 0 0 0 0 1 ...
   $ maritalstatus.Married-AF-spouse    : num  0 0 0 0 0 0 0 0 0 0 ...
   $ maritalstatus.Married-civ-spouse   : num  1 1 1 1 1 0 1 0 0 0 ...
   $ maritalstatus.Married-spouse-absent: num  0 0 0 0 0 0 0 0 0 0 ...
   $ maritalstatus.Never-married        : num  0 0 0 0 0 1 0 1 1 0 ...
   $ maritalstatus.Separated            : num  0 0 0 0 0 0 0 0 0 0 ...
   $ maritalstatus.Widowed              : num  0 0 0 0 0 0 0 0 0 0 ...
   $ occupation.?                       : num  0 0 0 0 0 0 0 0 0 0 ...
   $ occupation.Adm-clerical            : num  0 0 0 0 0 1 0 0 0 0 ...
   $ occupation.Armed-Forces            : num  0 0 0 0 0 0 0 0 0 0 ...
   $ occupation.Craft-repair            : num  0 0 0 0 0 0 0 0 0 0 ...
   $ occupation.Exec-managerial         : num  0 1 1 1 0 0 0 0 0 1 ...
   $ occupation.Farming-fishing         : num  0 0 0 0 0 0 0 1 0 0 ...
   $ occupation.Handlers-cleaners       : num  1 0 0 0 0 0 0 0 0 0 ...
   $ occupation.Machine-op-inspct       : num  0 0 0 0 0 0 0 0 1 0 ...
   $ occupation.Other-service           : num  0 0 0 0 0 0 0 0 0 0 ...
   $ occupation.Priv-house-serv         : num  0 0 0 0 0 0 0 0 0 0 ...
   $ occupation.Prof-specialty          : num  0 0 0 0 1 0 0 0 0 0 ...
   $ occupation.Protective-serv         : num  0 0 0 0 0 0 0 0 0 0 ...
   $ occupation.Sales                   : num  0 0 0 0 0 0 0 0 0 0 ...
   $ occupation.Tech-support            : num  0 0 0 0 0 0 0 0 0 0 ...
   $ occupation.Transport-moving        : num  0 0 0 0 0 0 1 0 0 0 ...
   $ race.Amer-Indian-Eskimo            : num  0 0 0 0 0 0 1 0 0 0 ...
   $ race.Asian-Pac-Islander            : num  0 0 0 0 1 0 0 0 0 0 ...
   $ race.Black                         : num  1 0 0 1 0 0 0 0 0 0 ...
   $ race.Other                         : num  0 0 0 0 0 0 0 0 0 0 ...
   $ race.White                         : num  0 1 1 0 0 1 0 1 1 1 ...
   $ sex.Female                         : num  0 1 0 0 0 1 0 0 0 1 ...
   $ sex.Male                           : num  1 0 1 1 1 0 1 1 1 0 ...
   $ hoursperweek                       : num  40 40 40 80 40 30 45 35 40 45 ...
   $ cntrymap.British-Commonwealth      : num  0 0 0 0 1 0 0 0 0 0 ...
   $ cntrymap.China                     : num  0 0 0 0 0 0 0 0 0 0 ...
   $ cntrymap.Euro-1                    : num  0 0 0 0 0 0 0 0 0 0 ...
   $ cntrymap.Euro-2                    : num  0 0 0 0 0 0 0 0 0 0 ...
   $ cntrymap.Latin-America             : num  0 0 0 0 0 0 1 0 0 0 ...
   $ cntrymap.Other                     : num  0 0 0 0 0 0 0 0 0 0 ...
   $ cntrymap.SoutEast-Asia             : num  0 0 0 0 0 0 0 0 0 0 ...
   $ cntrymap.South-America             : num  0 0 0 0 0 0 0 0 0 0 ...
   $ cntrymap.United-States             : num  1 1 1 1 0 1 0 1 1 1 ...
   $ relationship_new.Not-in-family     : num  0 0 0 0 0 0 0 0 0 0 ...
   $ relationship_new.Other-relative    : num  0 0 0 0 0 0 0 0 0 0 ...
   $ relationship_new.Own-child         : num  0 0 0 0 0 1 0 1 0 0 ...
   $ relationship_new.Spouse            : num  1 1 1 1 1 0 1 0 0 0 ...
   $ relationship_new.Unmarried         : num  0 0 0 0 0 0 0 0 1 1 ...
   $ workclass_new.?                    : num  0 0 0 0 0 0 0 0 0 0 ...
   $ workclass_new.Federal-gov          : num  0 0 0 0 0 0 0 0 0 0 ...
   $ workclass_new.Local-gov            : num  0 0 0 0 0 0 0 0 0 0 ...
   $ workclass_new.Never-worked         : num  0 0 0 0 0 0 0 0 0 0 ...
   $ workclass_new.Private              : num  1 1 1 1 0 1 1 0 1 0 ...
   $ workclass_new.Self-emp-inc         : num  0 0 0 0 0 0 0 0 0 0 ...
   $ workclass_new.Self-emp-not-inc     : num  0 0 0 0 0 0 0 1 0 1 ...
   $ workclass_new.State-gov            : num  0 0 0 0 1 0 0 0 0 0 ...
   $ capitalgainloss                    : num  0 0 5178 0 0 ...
   $ response                           : Factor w/ 2 levels "GT50K","LE50K": 2 2 1 1 1 2 2 2 2 1 ...
  </code></pre>
  
  <p>SAMPLE DATA:
  As recommended by MFlick here is sample of data</p>
  
  <blockquote>
    <p>dput(head(adultTraining))</p>
  </blockquote>
  
  <pre><code>structure(list(age = c(53, 37, 42, 37, 30, 23), fnlwgt = c(234721, 
  284582, 159449, 280464, 141297, 122272), educationnum = c(7, 
  14, 13, 10, 13, 13), maritalstatus.Divorced = c(0, 0, 0, 0, 0, 
  0), `maritalstatus.Married-AF-spouse` = c(0, 0, 0, 0, 0, 0), 
      `maritalstatus.Married-civ-spouse` = c(1, 1, 1, 1, 1, 0), 
      `maritalstatus.Married-spouse-absent` = c(0, 0, 0, 0, 0, 
      0), `maritalstatus.Never-married` = c(0, 0, 0, 0, 0, 1), 
      maritalstatus.Separated = c(0, 0, 0, 0, 0, 0), maritalstatus.Widowed = c(0, 
      0, 0, 0, 0, 0), `occupation.?` = c(0, 0, 0, 0, 0, 0), `occupation.Adm-clerical` = c(0, 
      0, 0, 0, 0, 1), `occupation.Armed-Forces` = c(0, 0, 0, 0, 
      0, 0), `occupation.Craft-repair` = c(0, 0, 0, 0, 0, 0), `occupation.Exec-managerial` = c(0, 
      1, 1, 1, 0, 0), `occupation.Farming-fishing` = c(0, 0, 0, 
      0, 0, 0), `occupation.Handlers-cleaners` = c(1, 0, 0, 0, 
      0, 0), `occupation.Machine-op-inspct` = c(0, 0, 0, 0, 0, 
      0), `occupation.Other-service` = c(0, 0, 0, 0, 0, 0), `occupation.Priv-house-serv` = c(0, 
      0, 0, 0, 0, 0), `occupation.Prof-specialty` = c(0, 0, 0, 
      0, 1, 0), `occupation.Protective-serv` = c(0, 0, 0, 0, 0, 
      0), occupation.Sales = c(0, 0, 0, 0, 0, 0), `occupation.Tech-support` = c(0, 
      0, 0, 0, 0, 0), `occupation.Transport-moving` = c(0, 0, 0, 
      0, 0, 0), `race.Amer-Indian-Eskimo` = c(0, 0, 0, 0, 0, 0), 
      `race.Asian-Pac-Islander` = c(0, 0, 0, 0, 1, 0), race.Black = c(1, 
      0, 0, 1, 0, 0), race.Other = c(0, 0, 0, 0, 0, 0), race.White = c(0, 
      1, 1, 0, 0, 1), sex.Female = c(0, 1, 0, 0, 0, 1), sex.Male = c(1, 
      0, 1, 1, 1, 0), hoursperweek = c(40, 40, 40, 80, 40, 30), 
      `cntrymap.British-Commonwealth` = c(0, 0, 0, 0, 1, 0), cntrymap.China = c(0, 
      0, 0, 0, 0, 0), `cntrymap.Euro-1` = c(0, 0, 0, 0, 0, 0), 
      `cntrymap.Euro-2` = c(0, 0, 0, 0, 0, 0), `cntrymap.Latin-America` = c(0, 
      0, 0, 0, 0, 0), cntrymap.Other = c(0, 0, 0, 0, 0, 0), `cntrymap.SoutEast-Asia` = c(0, 
      0, 0, 0, 0, 0), `cntrymap.South-America` = c(0, 0, 0, 0, 
      0, 0), `cntrymap.United-States` = c(1, 1, 1, 1, 0, 1), `relationship_new.Not-in-family` = c(0, 
      0, 0, 0, 0, 0), `relationship_new.Other-relative` = c(0, 
      0, 0, 0, 0, 0), `relationship_new.Own-child` = c(0, 0, 0, 
      0, 0, 1), relationship_new.Spouse = c(1, 1, 1, 1, 1, 0), 
      relationship_new.Unmarried = c(0, 0, 0, 0, 0, 0), `workclass_new.?` = c(0, 
      0, 0, 0, 0, 0), `workclass_new.Federal-gov` = c(0, 0, 0, 
      0, 0, 0), `workclass_new.Local-gov` = c(0, 0, 0, 0, 0, 0), 
      `workclass_new.Never-worked` = c(0, 0, 0, 0, 0, 0), workclass_new.Private = c(1, 
      1, 1, 1, 0, 1), `workclass_new.Self-emp-inc` = c(0, 0, 0, 
      0, 0, 0), `workclass_new.Self-emp-not-inc` = c(0, 0, 0, 0, 
      0, 0), `workclass_new.State-gov` = c(0, 0, 0, 0, 1, 0), capitalgainloss = c(0, 
      0, 5178, 0, 0, 0), response = structure(c(2L, 2L, 1L, 1L, 
      1L, 2L), .Label = c("GT50K", "LE50K"), class = "factor")), .Names = c("age", 
  "fnlwgt", "educationnum", "maritalstatus.Divorced", "maritalstatus.Married-AF-spouse", 
  "maritalstatus.Married-civ-spouse", "maritalstatus.Married-spouse-absent", 
  "maritalstatus.Never-married", "maritalstatus.Separated", "maritalstatus.Widowed", 
  "occupation.?", "occupation.Adm-clerical", "occupation.Armed-Forces", 
  "occupation.Craft-repair", "occupation.Exec-managerial", "occupation.Farming-fishing", 
  "occupation.Handlers-cleaners", "occupation.Machine-op-inspct", 
  "occupation.Other-service", "occupation.Priv-house-serv", "occupation.Prof-specialty", 
  "occupation.Protective-serv", "occupation.Sales", "occupation.Tech-support", 
  "occupation.Transport-moving", "race.Amer-Indian-Eskimo", "race.Asian-Pac-Islander", 
  "race.Black", "race.Other", "race.White", "sex.Female", "sex.Male", 
  "hoursperweek", "cntrymap.British-Commonwealth", "cntrymap.China", 
  "cntrymap.Euro-1", "cntrymap.Euro-2", "cntrymap.Latin-America", 
  "cntrymap.Other", "cntrymap.SoutEast-Asia", "cntrymap.South-America", 
  "cntrymap.United-States", "relationship_new.Not-in-family", "relationship_new.Other-relative", 
  "relationship_new.Own-child", "relationship_new.Spouse", "relationship_new.Unmarried", 
  "workclass_new.?", "workclass_new.Federal-gov", "workclass_new.Local-gov", 
  "workclass_new.Never-worked", "workclass_new.Private", "workclass_new.Self-emp-inc", 
  "workclass_new.Self-emp-not-inc", "workclass_new.State-gov", 
  "capitalgainloss", "response"), row.names = c(4L, 6L, 10L, 11L, 
  12L, 13L), class = "data.frame")
  </code></pre>