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Threshold Free Cluster Enhancement

Source: R/minc_voxel_statistics.R
mincTFCE.Rd

Perform threshold free cluster enhancement as described in Smith and Nichols (2008). Cluster-like structures are enhanced to allow a hybrid cluster/voxel analysis to be performed.

Usage

mincTFCE(x, ...)

# S3 method for class 'mincSingleDim'
mincTFCE(
  x,
  d = 0.1,
  E = 0.5,
  H = 2,
  side = c("both", "positive", "negative"),
  output_file = NULL,
  keep = is.null(output_file),
  conf_file = getOption("RMINC_BATCH_CONF"),
  ...
)

# S3 method for class 'matrix'
mincTFCE(
  x,
  d = 0.1,
  E = 0.5,
  H = 2,
  side = c("both", "positive", "negative"),
  like_volume,
  ...
)

# S3 method for class 'mincMultiDim'
mincTFCE(
  x,
  d = 0.1,
  E = 0.5,
  H = 2,
  side = c("both", "positive", "negative"),
  like_volume = likeVolume(x),
  ...
)

# S3 method for class 'mincLm'
mincTFCE(
  x,
  R = 500,
  alternative = c("two.sided", "greater"),
  d = 0.1,
  E = 0.5,
  H = 2,
  side = c("both", "positive", "negative"),
  replace = FALSE,
  parallel = NULL,
  resources = list(),
  conf_file = getOption("RMINC_BATCH_CONF"),
  ...
)

Arguments

x

Either a character vector with a single filename, a mincSingleDim object, or a matrix object, or mincLm object.

...

additional arguments for methods

d

The discretization step-size for approximating the threshold integral (default .1)

E

The exponent by which to raise the extent statistic (default .5)

H

The exponent by which to raise the height (default 2)

side

Whether to consider positive and negative statistics or both (default both)

output_file

A filename for the enhanced volume.

keep

Whether or not to keep the enhanced volume, defaults to whether or not a output_file was specified.

conf_file

A batchtools configuration file defaulting to getOption("RMINC_BATCH_CONF")

like_volume

A path to a like volume specifying the dimensions of the output volumes

R

number of randomizations to perform

alternative

Whether to consider a one-sided or two-sided alternative hypothesis. Default "two-sided", use "greater" for a one sided test.

replace

Sample with or without replacement for the randomization, defaults to FALSE (no replacement)

parallel

A two component vector indicating how to parallelize the computation. If the first element is "local" the computation will be run via the parallel package, otherwise it will be computed using batchtools, see pMincApply for details. The element should be numeric indicating the number of jobs to split the computation into.

resources

A list of resources to use for the jobs, for example list(nodes = 1, memory = "8G", walltime = "01:00:00") . See system.file("parallel/pbs_script.tmpl", package = "RMINC") and system.file("parallel/sge_script.tmpl", package = "RMINC") for more examples

Value

The behaviour of mincTFCE is to perform cluster free enhancement on a object, in the single dimensional case, a string denoting a minc file or a mincSingleDim object it returns a mincSingleDim object with the result, optionally saving the file if keep is set to true. In the matrix case each column is converted to a mincSingleDim in accordance with the likeVolume, this is then cluster enhanced and recomposed into a matrix. In the mincLm case a randomization test is performed with the t-stats enhanced. The return is

  • TFCE: A matrix of the tvalue columns after randomization

  • randomization_dist: An RxT matrix where R is the number of randomizations and T is the number of t-statistic, elements are the largest value obtained by the randomized TFCE

  • args: Arguments passed to the internal randomzations and TFCE code

Methods (by class)

  • mincTFCE(mincSingleDim): mincSingleDim

  • mincTFCE(matrix): matrix

  • mincTFCE(mincMultiDim): mincMultiDim

  • mincTFCE(mincLm): mincLm