vim.logicFS {logicFS}R Documentation

Importance Measures

Description

Computes the value of the single or the multiple tree measure, respectively, for each prime implicant contained in a logic bagging model to specify the importance of the prime implicant for classification, if the response is binary. If the response is quantitative, the importance is specified by a measure based on the log2-transformed mean square prediction error.

Usage

vim.logicFS(log.out, useN = TRUE, onlyRemove = FALSE, prob.case = 0.5,
   addInfo = FALSE, addMatImp = TRUE)

Arguments

log.out

an object of class logicBagg, i.e.\ the output of logic.bagging.

useN

logical specifying if the number of correctly classified out-of-bag observations should be used in the computation of the importance measure. If FALSE, the proportion of correctly classified oob observations is used instead.

onlyRemove

should in the single tree case the multiple tree measure be used? If TRUE, the prime implicants are only removed from the trees when determining the importance in the single tree case. If FALSE, the original single tree measure is computed for each prime implicant, i.e.\ a prime implicant is not only removed from the trees in which it is contained, but also added to the trees that do not contain this interaction. Ignored in all other than the classification case.

prob.case

a numeric value between 0 and 1. If the logistic regression approach of logic regression is used (i.e.\ if the response is binary, and in logic.bagging ntrees is set to a value larger than 1, or glm.if.1tree is set to TRUE), then an observation will be classified as a case (or more exactly as 1), if the class probability of this observation estimated by the logic bagging model is larger than prob.case.

addInfo

should further information on the logic regression models be added?

addMatImp

should the matrix containing the improvements due to the prime implicants in each of the iterations be added to the output? (For each of the prime implicants, the importance is computed by the average over the B improvements.) Must be set to TRUE, if standardized importances should be computed using vim.norm, or if permutation based importances should be computed using vim.signperm.

Value

An object of class logicFS containing

primes

the prime implicants,

vim

the importance of the prime implicants,

prop

the proportion of logic regression models containing the prime implicants,

type

the type of model (1: classification, 2: linear regression, 3: logistic regression),

param

further parameters (if addInfo = TRUE),

mat.imp

the matrix containing the improvements if addMatImp = TRUE, otherwise, NULL,

measure

the name of the used importance measure,

useN

the value of useN,

threshold

NULL,

mu

NULL.

Author(s)

Holger Schwender, holger.schwender@udo.edu

References

Schwender, H., Ickstadt, K. (2007). Identification of SNP Interactions Using Logic Regression. Biostatistics, 9(1), 187-198.

See Also

logic.bagging, logicFS, vim.norm, vim.signperm


[Package logicFS version 1.50.0 Index]