vim.individual {logicFS} | R Documentation |
Quantifies the importance of each individual variable occuring in at least one
of the logic regression models found in the application of logic.bagging
.
vim.individual(object, useN = NULL, iter = NULL, prop = TRUE, standardize = FALSE, mu = 0, addMatImp = FALSE, prob.case = 0.5, rand = NA)
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. |
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 . |
Holger Schwender, holger.schwender@udo.edu
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.
logic.bagging
, logicFS
,
vim.logicFS
, vim.set
, vim.ebam
, vim.chisq