Description Usage Arguments Details Value Author(s) References See Also Examples

Recursive partitioning method for the prediction of preference rankings based upon Kemeny distances.

1 | ```
ranktree(Y, X, prunplot = FALSE, control = ranktreecontrol(...), ...)
``` |

`Y` |
A n by m data matrix, in which there are n judges and m objects to be judged. Each row is a ranking of the objects which are represented by the columns. |

`X` |
A dataframe containing the predictor, that must have n rows. |

`prunplot` |
prunplot=TRUE returns the plot of the pruning sequence. Default value: FALSE |

`control` |
a list of options that control details of the |

`...` |
arguments passed bypassing |

The user can use any algorithm implemented in the `consrank`

function from the ConsRank package. All algorithms allow the user to set the option 'full=TRUE'
if the median ranking(s) must be searched in the restricted space of permutations instead of in the unconstrained universe of rankings of n items including all possible ties.
The output consists in a object of the class "ranktree". It contains:

X | the predictors: it must be a dataframe | ||

Y | the response variable: the matrix of the rankings | ||

node | a list containing teh tree-based structure: | ||

number | node number | ||

terminal | logical: TRUE is terminal node | ||

father | father node number of the current node | ||

idfather | id of the father node of the current node | ||

size | sample size within node | ||

impur | impurity at node | ||

wimpur | weighted impurity at node | ||

idatnode | id of the observations within node | ||

class | median ranking within node in terms of orderings | ||

nclass | median ranking within node in terms of rankings | ||

mclass | eventual multiple median rankings | ||

tau | Tau_x rank correlation coefficient at node | ||

wtau | weighted Tau_x rank correlation coefficient at node | ||

error | error at node | ||

werror | weighted error at node | ||

varsplit | variables generating split | ||

varsplitid | id of variables generating split | ||

cutspli | splitting point | ||

children | children nodes generated by current node | ||

idchildren | id of children nodes generated by current node | ||

... | other info about node | ||

control | parameters used to build the tree | ||

numnodes | number of nodes of the tree | ||

tsynt | list containing the synthesis of the tree: | ||

children | list containing all information about leaves | ||

parents | list containing all information about parent nodes | ||

geneaoly | data frame containing information about all nodes | ||

idgenealogy | data frame containing information about all nodes in terms of nodes id | ||

idparents | id of the parents of all the nodes | ||

goodness | goodness -and badness- of fit measures of the tree: Tau_X, error, impurity | ||

nomin | information about nature of the predictors | ||

alpha | alpha parameter for pruning sequence | ||

pruneinfo | list containing information about the pruning sequence: | ||

prunelist | information about the pruning | ||

tau | tau_X rank correlation coefficient of each subtree | ||

error | error of each subtree | ||

termnodes | number of terminal nodes of each subtree | ||

subtrees | list of each subtree created with the cost-complexity pruning procedure |

An object of the class ranktree. See details for detailed information.

Antonio D'Ambrosio antdambr@unina.it

D'Ambrosio, A., and Heiser W.J. (2016). A recursive partitioning method for the prediction of preference rankings based upon Kemeny distances. Psychometrika, vol. 81 (3), pp.774-94.

`ranktreecontrol`

, `plot.ranktree`

, `summary.ranktree`

, `getsubtree`

, `validatetree`

, `treepaths`

, `nodepath`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | ```
data("Univranks")
tree <- ranktree(Univranks$rankings,Univranks$predictors,num=50)
data(Irish)
#build the tree with default options
tree <- ranktree(Irish$rankings,Irish$predictors)
#plot the tree
plot(tree,dispclass=TRUE)
#visualize information
summary(tree)
#get information about the paths leading to terminal nodes (all the paths)
infopaths <- treepaths(tree)
#the terminal nodes
infopaths$leaves
#sample size within each terminal node
infopaths$size
#visualize the path of the second leave (terminal node number 8)
infopaths$paths[[2]]
#alternatively
nodepath(termnode=8,tree)
set.seed(132) #for reproducibility
#validation of the tree via v-fold cross-validation (default value of V=5)
vtree <- validatetree(tree,method="cv")
#extract the "best" tree
dtree <- getsubtree(tree,vtree$best_tau)
summary(dtree)
#plot the validated tree
plot(dtree,dispclass=TRUE)
#predicted rankings
rankfit <- predict(dtree,newx=Irish$predictors)
#fit of rankings
rankfit$rankings
#fit in terms of orderings
rankfit$orderings
#all info about the fit (id og the leaf, predictor values, and fit)
rankfit$orderings
``` |

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