vim.norm {logicFS} | R Documentation |
Computes a standarized or a permutation based version of either the Single Tree Measure, the Quantitative Response Measure, or the Multiple Tree Measure.
vim.norm(object, mu = 0) vim.perm(object, mu = 0, n.perm = 10000, n.subset = 1000, adjust = "bonferroni", rand = NA)
object |
either the output of logicFS or vim.logicFS
with addMatImp = TRUE , or the output of logic.bagging
with importance = TRUE and addMatImp = TRUE . |
mu |
a non-negative numeric value. Default is zero. However, mu should actually
be set to a value larger than zero. See Details . |
n.perm |
the number of (sign) permutations used in vim.perm . |
n.subset |
an integer specifying how many permutations should be considered at once. |
adjust |
character vector naming the method with which the raw permutation based
p-values are adjusted for multiplicity. If "qvalue" , the function qvalue.cal
from the package siggenes is used to compute q-values. Otherwise,
p.adjust is used to adjust for multiple comparisons. See p.adjust for all
other possible specifications of adjust . If "none" , the raw p-values will
be used. For more details, see Details . |
rand |
an integer for setting the random number generator in a reproducible case. |
In both vim.norm
and vim.perm
, an one-sample t-statistic is computed for each
prime implicant, where the numerator is given by VIM - mu
with VIM being the
single or the multiple tree importance, and the denominator is the corresponding standard
error computed by employing the B
improvements of the considered prime implicant
in the B
logic regression models. (Note that VIM is the mean over these
B
improvements.)
As using mu = 0
might lead to calling a prime implicant important, even though it actually
shows only improvements of 1 or 0, mu
should be set to a value larger than zero.
In vim.norm
, the value of this t-statistic is returned as the standardized importance
of a prime implicant. The larger this value, the more important is the prime implicant. (This applies
to all importance measures – at least for those contained in this package.) Assuming normality,
a possible threshold for a prime implicant to be considered as important is the 1-0.05/m quantile
of the t-distribution with B-1 degrees of freedom, where m is the number of prime implicants.
In vim.perm
, the sign permutation is used to determine n.perm
permuted values of the
one-sample t-statistic, and to compute the raw p-values for each of the prime implicants. Afterwards,
these p-values are adjusted for multiple comparisons using the method specified by adjust
.
The permutation based importance of a prime implicant is then given by 1- these adjusted p-values.
Here, a possible threshold for calling a prime implicant important is 0.95.
An object of class logicFS
containing
primes |
the prime implicants, |
vim |
the respective importance of the prime implicants, |
prop |
NULL, |
type |
the type of model (1: classification, 2: linear regression, 3: logistic regression), |
param |
further parameters (if addInfo = TRUE ), |
mat.imp |
NULL, |
measure |
the name of the used importance measure, |
useN |
the value of useN from the original analysis with, e.g., logicFS , |
threshold |
the threshold suggested in Details , |
mu |
mu . |
Holger Schwender, holger.schwender@udo.edu
Schwender, H. (2007). Statistical Analysis of Genotype and Gene Expression Data. Dissertation, Department of Statistics, University of Dortmund, Dortmund, Germany.
logic.bagging
, logicFS
,
vim.logicFS
, vim.chisq
, vim.ebam