Identify areas of engagement in each network from the result of (spatial) Bayesian brain mapping.
engagements(
bMap,
u = NULL,
z = NULL,
alpha = 0.01,
type = c(">", "abs >", "
A list containing engagement maps for each network, the joint and marginal PPMs for each network, 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.
Fitted (spatial) Bayesian brain map from BrainMap
.
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: NULL
(do not use a threshold). Either argument 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).
Significance level for hypothesis testing. Default: 0.01
.
Type of region: ">"
(default), "abs >"
, "<"
,
or "!="
. "abs >"
tests for magnitude by taking the absolute
value and then testing if they are greater than... .
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. See help(p.adjust)
. Default: "BH"
(Benjamini & Hochberg, i.e. the false discovery rate). Note that multiple
comparisons will account for data locations, but not networks.
If TRUE
, display progress of algorithm. Default:
FALSE
.
Indices of networks for which to identify engagements. If
NULL
(default), use all networks.
If TRUE
identify significant deviations from the
prior mean, rather than significant areas of engagement. Default:
FALSE
.
if (FALSE) {
engagements(bMap_result, alpha=.05, deviation=TRUE)
}
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