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Threshold Free Cluster Enhancement

Source: R/minc_vertex_statistics.R
vertexTFCE.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

vertexTFCE(x, ...)

# S3 method for class 'numeric'
vertexTFCE(
  x,
  surface,
  E = 0.5,
  H = 2,
  nsteps = 100,
  side = c("both", "positive", "negative"),
  weights = NULL,
  ...
)

# S3 method for class 'matrix'
vertexTFCE(
  x,
  surface,
  E = 0.5,
  H = 2,
  nsteps = 100,
  side = c("both", "positive", "negative"),
  weights = NULL,
  ...
)

# S3 method for class 'vertexLm'
vertexTFCE(
  x,
  surface,
  R = 500,
  alternative = c("two.sided", "greater"),
  E = 0.5,
  H = 2,
  nsteps = 100,
  weights = NULL,
  side = c("both", "positive", "negative"),
  replace = FALSE,
  parallel = NULL,
  ...
)

# S3 method for class 'character'
vertexTFCE(
  x,
  surface,
  E = 0.5,
  H = 2,
  nsteps = 100,
  side = c("both", "positive", "negative"),
  weights = NULL,
  column = 1,
  ...
)

Arguments

x

A numeric vector, a filepath to a set of values, or a matrix object, or vertexLm object.

...

additional arguments for methods

surface

Either a mesh object corresponding to the surface, an igraph graph object of surface created by obj_to_graph, or an adjacency list (see details). For the matrix and vertexLm cases, either a single surface object may be passed and used for each individual, or a vector of file names

E

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

H

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

nsteps

The number of steps to discretize the TFCE computation over

side

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

weights

A weighting vector assigning area to vertices. The default varies by surface object type. If surface is an bic_obj the area of each vertex is equal to 1/3 the sum of the areas of the triangles it is a member of. If surface is an igraph or adjacency list the default is a vector of ones.

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.

column

Which column to treat as the input from vertex files.

Value

The behaviour of vertexTFCE is to perform cluster free enhancement on a object, in the single dimensional case, a string denoting a vertex file or a numeric vector it returns a numeric vector with the result. In the matrix case each column is cluster enhanced and recomposed into a matrix. In the vertexLm case a randomization test is performed on each t-tstatistic column. The results is a list with 3 elements

  • 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-statistics, elements are the largest value obtained by the randomized TFCE

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

Details

Passing an adjacency list will save some compute time but is not recommended for general use. If an adjacency list is passed should index starting from 0 for compatibility with c++ code. Adjacency lists of this kind can be generated from graphs with lapply(as_adj_list(graph), `-`, 1) using the as_adj_list from the igraph library.

Methods (by class)

  • vertexTFCE(numeric): numeric

  • vertexTFCE(matrix): matrix

  • vertexTFCE(vertexLm): vertexLm

  • vertexTFCE(character): character