 Usage:  cross = cartcv (x, y, type, opt, wv)  
 
 Input:

  x                      n x p matrix: data matrix of regression variables 
                         
  y                      n x 1 vector: contains the values of the response variable 
                         
  type                   p x 1 vector: contains the types of the regression 
                         variables, 
                         1 means that the corresponding variable is continuous and 
                         0 that it is categorical 
                         
  opt                    list of scalars: determines when the growing of the 
                         tree is stopped. Consists of opt.mincut, opt.minsize, 
                         opt.mindev. See cartsplit for the description of these 
                         parameters. 
                         
  wv                     integer >=2, wv fold cross-validation is performed, that 
                         is, the data is divided in wv number of ways to an 
                         estimation set and a test set. 
                         Division is formed randomly. 
                         
 Output:

  cross                  list of vectors, consists of cross.alfa, cross.lnumber, 
                         cross.cv, cross.cvstd. 
                         The elements of the list cross are vectors with the number 
                         of elements equal to the number of trees in the 
                         sequence of pruned subtrees of the tree grown 
                         with data x and y. 
                         The vector cross.alfa contains the values of the complexity 
                         parameter alfa. 
                         The vector cross.lnumber contains the numbers of leaves 
                         in the sequence of the pruned subtrees. 
                         The vector cross.cv contains the estimates for the 
                         expected value of the mean of squared residuals. 
                         The vector cross.cvstd contains the estimates for the 
                         standard deviation of the estimator for the 
                         expected value of the mean of squared residuals. 
                         
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(C) MD*TECH Method and Data Technologies, 17.8.2000
