vim.individual {logicFS}R Documentation

VIM for Individual Variables

Description

Quantifies the importance of each individual variable occuring in at least one of the logic regression models found in the application of logic.bagging.

Usage

vim.individual(object, useN = NULL, iter = NULL, prop = TRUE,
   standardize = FALSE, mu = 0, addMatImp = FALSE, prob.case = 0.5,
   rand = NA)

Arguments

object 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. If NULL (default), then the specification of useN in object is used.
iter integer specifying the number of times the values of the considered variable are permuted in the computation of its importance. If NULL (default), the values of the variable are not permuted, but the variable is removed from the model.
prop should the proportion of logic regression models containing the respective variable also be computed?
standardize should a standardized version of the individual variable importance measure be returned? For details, see mu.
mu a non-negative numeric value. Ignored if standardize = FALSE. Otherwise, a t-statistic for testing the null hypothesis that the importance of the respective variable is equal to mu is computed.
addMatImp should the matrix containing the improvements due to each of the variables in each of the logic regression models be added to the output?
prob.case a numeric value between 0 and 1. If the logistic regression approach of logic regression has been used in logic.bagging, then an observation will be classified as a case (or more exactly, as 1), if the class probability of this observation is larger than prob.case. Otherwise, prob.case is ignored.
rand an integer for setting the random number generator in a reproducible case.

Value

An object of class logicFS containing

vim the importances of the variables,
prop the proportion of logic regression models containing the respective variable (if prop = TRUE) or NULL (if prop = FALSE),
primes the names of the variables,
type the type of model (1: classification, 2:linear regression, 3: logistic regression),
param further parameters (if addInfo = TRUE in the previous call of logic.bagging),
mat.imp either a matrix containing the improvements due to the variables for each of the models (if addMatImp = TRUE), or NULL (if addMatImp = FALSE),
measure the name of the used importance measure,
useN the value of useN,
threshold NULL if standardize = FALSE, otherwise the 1-0.05/m quantile of the t-distribution with B-1 degrees of freedom, where m is the number of variables and B is the number of logic regression models composing object,
mu mu (if standardize = TRUE), or NULL (otherwise),
iter iter.

Author(s)

Holger Schwender, holger.schwender@udo.edu

References

Holger Schwender (2007). Measuring the Importances of Genotypes and Sets of Single Nucleotide Polymorphisms. Technical Report, SFB 475, Department of Statistics, University of Dortmund. Appears soon.

See Also

logic.bagging, logicFS, vim.logicFS, vim.set, vim.ebam, vim.chisq


[Package logicFS version 1.12.0 Index]