R: Create a graph list with GLM-specific attributes

Creating_Graphs_GLM {brainGraph}

R Documentation

Create a graph list with GLM-specific attributes

Description

These methods create a brainGraphList with attributes specific to the results of brainGraph_GLM, mtpc, or NBS. The graphs element of the returned object will contain one graph for each contrast.

Usage

## S3 method for class 'bg_GLM'
make_brainGraphList(x, atlas = x$atlas,
  type = "observed", level = "contrast", set.attrs = FALSE,
  modality = NULL, weighting = NULL, threshold = NULL,
  gnames = x$con.name, ...)

## S3 method for class 'mtpc'
make_brainGraphList(x, atlas = x$atlas,
  type = "observed", level = "contrast", set.attrs = FALSE,
  modality = NULL, weighting = NULL, threshold = NULL,
  gnames = x$con.name, ...)

## S3 method for class 'NBS'
make_brainGraphList(x, atlas, type = "observed",
  level = "contrast", set.attrs = TRUE, modality = NULL,
  weighting = NULL, threshold = NULL, gnames = x$con.name,
  mode = "undirected", weighted = TRUE, diag = FALSE, ...)

Arguments

`x`	A `bg_GLM`, `mtpc`, or `NBS` object
`atlas`	Character string specifying the brain atlas to use
`type`	Character string indicating the type of graphs. Default: `observed`
`level`	Character string indicating whether the graphs are subject-, group-, or contrast-specific. Default: `'subject'`
`set.attrs`	Logical indicating whether to assign all graph-, vertex-, and edge-level attributes (via `set_brainGraph_attr`). Default: `TRUE`
`modality`	Character string indicating imaging modality (e.g. 'dti'). Default: `NULL`
`weighting`	Character string indicating how the edges are weighted (e.g., 'fa', 'pearson', etc.). Default: `NULL`
`threshold`	Integer or number indicating the threshold used when “sparsifying” the connectivity matrix (if any). Default: `NULL`
`gnames`	Character vector of graph names (e.g., study IDs if `level='subject'`). Default: `NULL`
`...`	Other arguments passed to `set_brainGraph_attr`
`mode`	Character string defining how the matrix should be interpreted. Default: `'undirected'`
`weighted`	Logical specifying whether to create a weighted network
`diag`	Logical indicating whether to include the diagonal of the connectivity matrix. Default: `FALSE`

Value

A brainGraphList object, with a graph object for each contrast with additional attributes:

`Graph`	name (contrast name), outcome (the outcome variable), alpha (the significance level); for MTPC: tau.mtpc, S.mtpc, S.crit, A.crit
`Vertex`	size2 (t-statistic); size (the t-stat transformed for visualization purposes); p (equal to `1-p`); p.fdr (equal to `1-p_{FDR}`, the FDR-adjusted p-value); effect.size (the contrast of parameter estimates for t-contrasts; the extra sum of squares for F-contrasts); se (the standard error of gamma); A.mtpc, sig (binary indicating whether `A.mtpc > A.crit`) (for MTPC)

make_brainGraphList.NBS returns graphs with additional attributes:

`Vertex`	comp (integer vector indicating connected component membership), p.nbs (P-value for each component)
`Edge`	stat (the test statistic for each connection), p (the P-value)

Note