title: R and S-PLUS produced different classification trees for predicting patient mortality.
abstract: OBJECTIVE
  There is a growing interest in using classification and regression trees in biomedical research. R and S-PLUS are two statistical programming languages that share a similar syntax and functionality. Both R and S-PLUS allow users to fit classification and regression trees. The objective was to compare classification trees grown using R with those grown using S-PLUS.
  
  
  METHODS
  Using data on 9,484 patients hospitalized with an acute myocardial infarction, we compared the classification trees for predicting mortality that were grown using R and S-PLUS. We also used repeated split-sample derivation to determine the predictive accuracy of classification trees grown using R and S-PLUS.
  
  
  RESULTS
  The classification tree grown using R was substantially more parsimonious than the one grown using S-PLUS. The pruned classification tree grown using R was equal to a classification tree that was obtained by removing six subtrees from the pruned classification tree grown using S-PLUS. Repeated split-sample validation was then used to demonstrate that classification trees constructed using S-PLUS had greater discrimination and accuracy compared to classification trees grown using R.
  
  
  CONCLUSIONS
  R can produce different classification trees than S-PLUS using the same data.
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Department of Public Health Sciences, University of Toronto, Canada; Department of Health Management, Policy and Evaluation, University of Toronto, Canada. Electronic address: peter.austin@ices.on.ca.
authors.forename: Peter C
authors.initials: PC
authors.lastname: Austin
mesh_terms: D003627:Data Interpretation, Statistical; D003663:Decision Trees; D017052:Hospital Mortality; D006801:Humans; D009203:Myocardial Infarction / Q000401:mortality*; D011379:Prognosis; D012984:Software