self_eval

A dataset containing the parameter estimates of the
left medial prefrontal cortex (x = -6, y = 50, z = 4),
with one value for the mean activation during self-evaluation
and one value for the mean activation during the control condition for all participants.

datasets

Calculate the precision in mean differences (raw or Cohen's D) and
correlation coefficients for different sample sizes. Uses permutations of the
collected functional magnetic resonance imaging (fMRI) region of interest data.
Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024)
<doi:10.31234/osf.io/cz32t>.

Eduard Klapwijk

neuroUp

Plan Sample Size for Task fMRI Research using Bayesian Updating

Herbert Hoijtink

Joran Jongerling

self_eval function

A data frame with 149 rows and 4 variables:<dl>
<dt>participant_id</dt>
<dd>unique id for every participant</dd><dt>age</dt>
<dd>age in years (11.00-20.92)</dd><dt>mpfc_self</dt>
<dd>parameter estimates for the left medial prefrontal cortex during
self-evaluation (-2.82-4.97)</dd><dt>mpfc_control</dt>
<dd>parameter estimates for the lmedial prefrontal cortex during
the control condition (-7.17-3.50)</dd>
</dl>

Format

Self-evaluations task fMRI region of interest data — self_eval

A data frame with 149 rows and 4 variables:<dl>
<dt>participant_id</dt>
<dd>unique id for every participant</dd>

<dt>age</dt>
<dd>age in years (11.00-20.92)</dd>

<dt>mpfc_self</dt>
<dd>parameter estimates for the left medial prefrontal cortex during
self-evaluation (-2.82-4.97)</dd>

<dt>mpfc_control</dt>
<dd>parameter estimates for the lmedial prefrontal cortex during
the control condition (-7.17-3.50)</dd>


</dl>

self_eval: Self-evaluations task fMRI region of interest data

Description

Usage

Arguments

Format