Inspired by lapply, these functions apply a given function to each data component in
the input multiData structure, and optionally simplify the result to an array if possible.
mtd.apply(
# What to do
multiData, FUN, ..., # Pre-existing results and update options
mdaExistingResults = NULL, mdaUpdateIndex = NULL,
mdaCopyNonData = FALSE,
# Output formatting options
mdaSimplify = FALSE,
returnList = FALSE,
# Internal behaviour options
mdaVerbose = 0, mdaIndent = 0)
mtd.applyToSubset(
# What to do
multiData, FUN, ...,
# Which rows and cols to keep
mdaRowIndex = NULL, mdaColIndex = NULL,
# Pre-existing results and update options
mdaExistingResults = NULL, mdaUpdateIndex = NULL,
mdaCopyNonData = FALSE,
# Output formatting options
mdaSimplify = FALSE,
returnList = FALSE,
# Internal behaviour options
mdaVerbose = 0, mdaIndent = 0)
A multiData structure to apply the function over
Function to be applied.
Other arguments to the function FUN.
If given, must be a list of the same length as multiData. Each element must be
a logical or numeric vector that specifies rows in each data component
to select before applying the function.
A logical or numeric vector that specifies columns in each data component
to select before applying the function.
Optional list that contains previously calculated results. This can be useful
if only a few sets in multiData have changed and recalculating the unchanged ones is computationally
expensive. If not given, all calculations will be performed. If given, components of this list are copied
into the output. See mdmUpdateIndex for which components are re-calculated by default.
Optional specification of which sets in multiData the calculation should
actually be carried out. This argument has an effect only if mdaExistingResults is non-NULL. If the
length of mdaExistingResults (call the length `k')
is less than the number of sets in multiData, the function
assumes that the existing results correspond to the first `k' sets in multiData and the rest of the
sets are automatically calculated, irrespective of the setting of mdaUpdateIndex. The argument
mdaUpdateIndex can be used to specify re-calculation of some (or all) of the results that already
exist in mdaExistingResults.
Logical: should non-data components of multiData be copied into the output?
Note that the copying is incompatible with simplification; enabling both will trigger an error.
Logical: should the result be simplified to an array, if possible? Note that this may lead to errors; if so, disable simplification.
Logical: should the result be turned into a list (rather than a multiData structure)?
Note that this is incompatible with simplification: if mdaSimplify is TRUE, this argument is
ignored.
Integer specifying whether progress diagnistics should be printed out. Zero means silent, increasing values will lead to more diagnostic messages.
Integer specifying the indentation of the printed progress messages. Each unit equals two spaces.
A multiData structure containing the results of the supplied function on each data component in the
input multiData structure. Other components are simply copied.
A multiData structure is intended to store (the same type of) data for multiple, possibly independent,
realizations
(for example, expression data for several independent experiments). It is a list where
each component corresponds to an (independent) data set. Each component is in turn a list that can hold
various types of information but must have a data component. In a "strict" multiData structure, the
data components are required to each be a matrix or a data frame and have the same number of
columns. In a "loose" multiData structure, the data components can be anything (but for most
purposes should be of comparable type and content).
mtd.apply works on any "loose" multiData structure; mtd.applyToSubset assumes (and checks
for) a "strict" multiData structure.
multiData to create a multiData structure;
mtd.applyToSubset for applying a function to a subset of a multiData structure;
mtd.mapply for vectorizing over several arguments.