BayesBrainMap (version 0.2.0)

id_engagements: Engagements of (spatial) Bayesian brain mapping

Description

Identify areas of engagement in each network from the result of (spatial) Bayesian brain mapping.

Usage

id_engagements(
  bMap,
  u = NULL,
  z = 2,
  alpha = 0.01,
  type = c(">", "abs >", "

Value

A list containing engagement maps for each network, the joint and marginal PPMs for each network, masks of regions meeting the threshold(s) if provided, and the parameters used for computing engagement. If the input represented CIFTI- or NIFTI-format data, then the engagements maps will be formatted accordingly.

Use summary to obtain information about the engagements results. For CIFTI-format engagements, use plot to visualize the engagement maps.

Arguments

bMap

Fitted (spatial) Bayesian brain map from fit_BBM.

u, z

Set a threshold value for engagement? A threshold value can be specified directly with u, or a z-score-like threshold in terms of standard deviations (the SD of values in the mean prior) can be specified with z. Only one type of threshold can be used. Default: z=2. Set both u and z to NULL to not use a threshold. Either u or z can also be a vector to test multiple thresholds at once, as long as type is not "!=" (to ensure the engagement regions are successive subsets).

alpha

Significance level for hypothesis testing. Default: 0.01.

type

Type of region: ">" (default), "abs >", "<", or "!=". "abs >" tests for magnitude by taking the absolute value and then testing if they are greater than... .

method_p

If the input is a "bMap.[format]" model object, the type of multiple comparisons correction to use for p-values, or NULL for no correction. Default: "BH" (Benjamini & Hochberg, i.e. the false discovery rate). Another option is "bonferroni" correction. See help(p.adjust) for the full list of options. Note that multiple comparisons will account for data locations, but not networks.

verbose

If TRUE, display progress of algorithm. Default: FALSE.

which.nets

Indices of networks for which to identify engagements. If NULL (default), use all networks.

deviation

If TRUE identify significant deviations from the prior mean, rather than significant areas of engagement. Default: FALSE.

Examples

if (FALSE) {
 id_engagements(fit_BBM_result, alpha=.05, deviation=TRUE)
}