mtd.apply: Apply a function to each set in a multiData structure.
In nosarcasm/WGCNA: Weighted Correlation Network Analysis

Description Usage Arguments Details Value Author(s) See Also

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)

`multiData`	A multiData structure to apply the function over
`FUN`	Function to be applied.
`...`	Other arguments to the function `FUN`.
`mdaRowIndex`	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.
`mdaColIndex`	A logical or numeric vector that specifies columns in each `data` component to select before applying the function.
`mdaExistingResults`	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.
`mdaUpdateIndex`	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`.
`mdaCopyNonData`	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.
`mdaSimplify`	Logical: should the result be simplified to an array, if possible? Note that this may lead to errors; if so, disable simplification.
`returnList`	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.
`mdaVerbose`	Integer specifying whether progress diagnistics should be printed out. Zero means silent, increasing values will lead to more diagnostic messages.
`mdaIndent`	Integer specifying the indentation of the printed progress messages. Each unit equals two spaces.

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.

A multiData structure containing the results of the supplied function on each data component in the input multiData structure. Other components are simply copied.

Peter Langfelder

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.

nosarcasm/WGCNA documentation built on May 28, 2019, 1:01 p.m.