gpav: Generalized Pool-Adjacent Violators (GPAV)

In marconotaro/hemdag: Hierarchical Ensemble Methods for Directed Acyclic Graphs

Generalized Pool-Adjacent Violators (GPAV)

Description

Implementation of GPAV (Generalized Pool-Adjacent Violators) algorithm. (Burdakov et al., In: Di Pillo G, Roma M, editors. An O(n2) Algorithm for Isotonic Regression. Boston, MA: Springer US; 2006. p. 25–33. Available from: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/0-387-30065-1_3")}

Usage

gpav(Y, W = NULL, adj)

Arguments

Y	vector of scores relative to a single example. Y must be a numeric named vector, where names correspond to classes' names, i.e. nodes of the graph g (root node included).
W	vector of weight relative to a single example. If W=NULL (def.) it is assumed that W is a unitary vector of the same length of the columns' number of the matrix S (root node included).
adj	adjacency matrix of the graph which must be sparse, logical and upper triangular. Number of columns of adj must be equal to the length of Y and W.

Details

Given the constraints adjacency matrix of the graph, a vector of scores \hat{y} \in R^n and a vector of strictly positive weights w \in R^n, the GPAV algorithm returns a vector \bar{y} which is as close as possible, in the least-squares sense, to the response vector \hat{y} and whose components are partially ordered in accordance with the constraints matrix adj. In other words, GPAV solves the following problem:

\bar{y} = \left\{ \begin{array}{l} \min \sum_{i \in V} (\hat{y}_i - \bar{y}_i )^2\\\\ \forall i, \quad j \in par(i) \Rightarrow \bar{y}_j \geq \bar{y}_i \end{array} \right.

where V are the number of vertexes of the graph.

Value

A list of 3 elements:

• YFit: a named vector with the scores of the classes corrected according to the GPAV algorithm.

• blocks: list of vectors, containing the partitioning of nodes (represented with an integer number) into blocks;

• W: vector of weights.

Examples

data(graph);
data(scores);
Y <- S[3,];
adj <- adj.upper.tri(g);
Y.gpav <- gpav(Y,W=NULL,adj);

marconotaro/hemdag documentation built on Nov. 21, 2024, 10:05 p.m.