decTreeImpFunc: Decision Tree for Ranking Data-Impurity Function Approach
In StatMethRank: Statistical Methods for Ranking Data

Description Usage Arguments Value Author(s) References Examples

See Section 10.1 of the book for more details

decTreeImpFunc(ITEM_SIZE, RANK_SIZE, CART_SIZE, DATA_FILE, INFO_FILE,
  OUTPUT_FILE, mode = c("E", "G", "S", "C"), TMODE = c("L", "P"),
  usePairwise = FALSE, isPW_DATA = FALSE, prediction = c("M", "F", "C"),
  NODE_SIZE = 400, MIN_NODE_SIZE = 400, RANK_ITEM = 2, ST_ALP = 0.95,
  CHI_ALP = 0.2, TRAIN_PROP = 1, useCV_TEST = FALSE, TEST_STAGE = 0)

`ITEM_SIZE`	number of items
`RANK_SIZE`	top-q size
`CART_SIZE`	number of cross validation in tree pruning
`DATA_FILE`	please specify the input file as fullpath/input_file.txt
`INFO_FILE`	please specify the input file as fullpath/input_file.txt, note that the data should be seperated by tab.
`OUTPUT_FILE`	name of the output file
`mode`	Spliting Criterion: E = Entropy G = Gini S = Statistical Test C = Chi-square test
`TMODE`	When mode = "C" P = Pearson Chi-square test; L = Likelihood ratio test
`usePairwise`	use pairwise comparison model or not
`isPW_DATA`	use pairwise data or top k-ranked data
`prediction`	M : mean, F : frequency, C : center
`NODE_SIZE`	usually one tenth of the number of observations
`MIN_NODE_SIZE`	min NODE_DIZE
`RANK_ITEM`	top-q measure (1-3)
`ST_ALP`	level of significance of Statistical test
`CHI_ALP`	level of significance of Chi-square test
`TRAIN_PROP`	proportion of training data; effective only when CV_TEST = false
`useCV_TEST`	use 10-fold CV testing or not
`TEST_STAGE`	the stage of the 10-fold CV testing; effective only when CV_TEST = true; value starts from 0 to V-1