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