Premium
Binary recursive partitioning: Background, methods, and application to psychology
Author(s) -
Merkle Edgar C.,
Shaffer Victoria A.
Publication year - 2011
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711010x503129
Subject(s) - recursive partitioning , variable (mathematics) , computer science , decision tree , code (set theory) , tree (set theory) , variables , binary number , machine learning , binary decision diagram , artificial intelligence , statistics , econometrics , data mining , mathematics , algorithm , set (abstract data type) , programming language , mathematical analysis , arithmetic
Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's ‘goodness’ is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.