sample.split {caTools}R Documentation

Split Data into Test and Train Set


Split data from vector Y into two sets in predefined ratio while preserving relative ratios of different labels in Y. Used to split the data used during classification into train and test subsets.


 sample.split( Y, SplitRatio = 2/3, group = NULL )



Vector of data labels. If there are only a few labels (as is expected) than relative ratio of data in both subsets will be the same.


Splitting ratio:

  • if (0<=SplitRatio<1) then SplitRatio fraction of points from Y will be set toTRUE

  • if (SplitRatio==1) then one random point from Y will be set to TRUE

  • if (SplitRatio>1) then SplitRatio number of points from Y will be set to TRUE


Optional vector/list used when multiple copies of each sample are present. In such a case group contains unique sample labels, marking all copies of the same sample with the same label, and the function tries to place all copies in either train or test subset. If provided than has to have the same length as Y.


Function msc.sample.split is the old name of the sample.split function. To be retired soon. Note that the function differs from base::sample by first restricting the input data set to its unique values before generating the subset(s).


Returns logical vector of the same length as Y with random SplitRatio*length(Y) elements set to TRUE.


Jarek Tuszynski (SAIC)

See Also


  data(cats)   # load cats data
  Y = cats[,1] # extract labels from the data
  msk = sample.split(Y, SplitRatio=3/4)
  t=sum( msk)  # number of elements in one class
  f=sum(!msk)  # number of elements in the other class
  stopifnot( round((t+f)*3/4) == t ) # test ratios
  # example of using group variable
  g = rep(seq(length(Y)/4), each=4); g[48]=12;
  msk = sample.split(Y, SplitRatio=1/2, group=g)
  table(Y,msk) # try to get correct split ratios ...
  split(msk,g) # ... while keeping samples with the same group label together

  # test results
  print(paste( "All Labels numbers: total=",t+f,", train=",t,", test=",f,
        ", ratio=", t/(t+f) ) )
  U = unique(Y)       # extract all unique labels
  for( i in 1:length(U)) {  # check for all labels
    lab = (Y==U[i])   # mask elements that have label U[i]
    t=sum( msk[lab])  # number of elements with label U[i] in one class
    f=sum(!msk[lab])  # number of elements with label U[i] in the other class 
    print(paste( "Label",U[i],"numbers: total=",t+f,", train=",t,", test=",f, 
                 ", ratio=", t/(t+f) ) )
  # use results
  train = cats[ msk,2:3]  # use output of sample.split to ...
  test  = cats[!msk,2:3]  # create train and test subsets
  z = lda(train, Y[msk])  # perform classification
  table(predict(z, test)$class, Y[!msk]) # predicted & true labels
  # see also LogitBoost example

[Package caTools version 1.18.2 Index]