split_reduce-methods: Generic function to summarize subsets of an object by first...

Description Usage Arguments Details Value Examples

Description

Generic function to summarize subsets of an object by first splitting by row and then "reducing" by a summary function

Usage

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split_reduce(x, fac, FUN)

## S4 method for signature 'matrix,integer,'function''
split_reduce(x, fac, FUN)

## S4 method for signature 'matrix,integer,missing'
split_reduce(x, fac)

## S4 method for signature 'matrix,factor,missing'
split_reduce(x, fac)

## S4 method for signature 'matrix,factor,'function''
split_reduce(x, fac, FUN)

## S4 method for signature 'NeuroVec,factor,'function''
split_reduce(x, fac, FUN)

## S4 method for signature 'NeuroVec,factor,missing'
split_reduce(x, fac, FUN)

Arguments

x

a numeric matrix(like) object

fac

the factor to define subsets of the object

FUN

the function to apply to each subset. if FUN is missing, than the mean of each sub-matrix column is computed.

Details

if FUN is supplied it must take a vector and return a single scalar value. If it returns more than one value, an error will occur.

if x is a NeuroVec instance then voxels (dims 1:3) are treated as columns and time-series (dim 4) as rows. The summary function then is applied to groups of voxels. However, if the goal is to apply a function to groups of time-points, then this can be achieved as follows:

split_reduce(t(as.matrix(bvec)), fac)

Value

a new matrix where the original values have been "reduced" by the supplied function.

Examples

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mat = matrix(rnorm(100*100), 100, 100)
fac = factor(sample(1:3, nrow(mat), replace=TRUE))
## compute column means of each sub-matrix
ms <- split_reduce(mat, fac)
all.equal(row.names(ms), levels(fac))

## compute column medians of each sub-matrix
ms <- split_reduce(mat, fac, median)

## compute time-series means grouped over voxels.
## Here, \code{length(fac)} must equal the number of voxels: \code{prod(dim(bvec)[1:3]}
bvec <- NeuroVec(array(rnorm(24*24*24*24), c(24,24,24,24)), NeuroSpace(c(24,24,24,24), c(1,1,1)))
fac <- factor(sample(1:3, prod(dim(bvec)[1:3]), replace=TRUE))
ms <- split_reduce(bvec, fac)
ms2 <- split_reduce(bvec, fac, mean)
all.equal(row.names(ms), levels(fac))
all.equal(ms,ms2)

bbuchsbaum/neuroim2 documentation built on Oct. 15, 2019, 2:55 p.m.