vim.permInput(object, n.perm = NULL, standardize = TRUE, rebuild = FALSE, prob.case = 0.5, useAll = FALSE, version = 1, adjust = "bonferroni", addMatPerm = FALSE, rand=NA)
vim.permSNP(object, n.perm = NULL, standardize = TRUE, rebuild = FALSE, prob.case = 0.5, useAll = FALSE, version = 1, adjust = "bonferroni", addMatPerm = FALSE, rand = NA)
vim.permSet(object, set = NULL, n.perm = NULL, standardize = TRUE, rebuild = FALSE, prob.case = 0.5, useAll = FALSE, version = 1, adjust = "bonferroni", addMatPerm = FALSE, rand = NA)
logicBagg
, i.e.\ the output of logic.bagging
.
NULL
(default), then it
will be assumed that data
, i.e.\ the data set used in the application of logic.bagging
,
has been generated using make.snp.dummy
or similar functions for coding variables
by binary variables, i.e.\ with a function that splits a variable, say SNPx, into the dummy variables
SNPx.1, SNPx.2, ... (where the ``." can also be any other sign, e.g., an underscore).
If a character or a numeric vector,
then the length of set
must be equal to the number of variables used in object
,
i.e.\ the number of columns of data
in the logicBagg
object, and must specify
the set to which a variable belongs either by an integer between 1 and the number of sets, or
by a set name. If a variable should not be included in any of the sets, set the corresponding
entry of set
to NA
. Using this specification of set
it is not possible to
assign a variable to more than one sets. For such a case, set set
to a list (as follows).
If set
is a list, then each object in this list represents a set of variables. Therefore,
each object must be either a character or a numeric vector specifying either the names of the variables
that belongs to the respective set or the columns of data
that contains these variables.
If names(set)
is NULL
, generic names will be employed as names for the sets. Otherwise,
names(set)
are used.
n.perm = NULL
), 100 permutations are used if rebuild = TRUE
and the regression
approach of logic regression has been used in logic.bagging
(by setting
ntrees
to an integer larger than 1, or glm.if.1tree = TRUE
). Otherwise,
1000 permutation are employed. Note that actually much more permutations should be used.
rebuild = TRUE
increases the computation time substantially.
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.
n.perm
permuted values should be used
in the computation of the permutation based p-values, where $m$ is the number of
variables or sets of variables, respectively. If FALSE
, the n.perm
permuted
values corresponding to the respective variable (or set of variables) are employed in
the determination of the p-value of this variable (or set of variables).
1
or 2
. If 1
, then the importance measure is computed
by 1 - padj, where padj is the adjusted p-value. If 2
, the importance measure is determined
by -log10(padj), where a raw p-value equal to 0 is set to 1 / (10 * n.perm
) to avoid
infinitive importances.
"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.
n.perm
+ 1) x $m$ matrix containing the original values (first column)
and the permuted values (the remaining columns) of the importance measure for the $m$
variables or $m$ sets of variables be added to the output?
logicFS
containing
NULL
,NULL
,NULL
,thres
of plot.logicFS
),NULL
,TRUE
,"Variable"
, "SNP"
, or "Set"
,addMatPerm = FALSE
, NULL
; otherwise, a matrix containing the original and the permuted
values of the respective importance measure.logic.bagging
, vim.input
, vim.set
, vim.signperm