Help for package neuroUp

Type:

Package

Title:

Plan Sample Size for Task fMRI Research using Bayesian Updating

Version:

0.3.1

Description:

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>.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.2

NeedsCompilation:

Imports:

dplyr, rlang, ggplot2, psychometric, tibble, magrittr, bootstrap

Suggests:

knitr, readr, rmarkdown, testthat (≥ 3.0.0), vdiffr

Config/testthat/edition:

Depends:

R (≥ 2.10)

VignetteBuilder:

knitr

URL:

https://eduardklap.github.io/neuroUp/, https://github.com/eduardklap/neuroUp

BugReports:

https://github.com/eduardklap/neuroUp/issues

Packaged:

2024-08-23 13:23:08 UTC; eduardklapwijk

Author:

Eduard Klapwijk

[aut, cre, cph], Herbert Hoijtink

[aut, cph], Joran Jongerling

[aut, cph]

Maintainer:

Eduard Klapwijk <et.klapwijk@gmail.com>

Repository:

CRAN

Date/Publication:

2024-08-28 08:20:05 UTC

Pipe operator

Description

See ⁠magrittr::[\%>\%][magrittr::pipe]⁠ for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Estimate correlations

Description

estim_corr determines point estimate, SD and SE, 95% Credibility Intervals, and interval width, for Pearson correlations for multiple sample sizes

Usage

estim_corr(data, vars_of_interest, sample_size, k = 50, name = "")

Arguments

data

Dataframe with the data to be analyzed

vars_of_interest

Vector containing the names of the variables to be correlated: c("var1", "var2")

sample_size

The range of sample size to be used: min:max

k

The number of permutations to be used for each sample size. Defaults to 50

name

The title of the dataset or variables to be displayed with the figure. Defaults to ""

Value

tbl_select returns a tibble::tibble() containing estimates of the Pearson correlation between two correlated variables with associated SD, SE, 95% CI, and width of the 95% CI (lower, upper) for five different sample sizes (starting with the minimum sample size, then 1/5th parts of the total dataset).
fig_corr returns a scatterplot where for the five different sample sizes, 10 out of the total number of HDCIs computed are displayed (in green). The average estimate with credible interval summarizing the total number of HDCIs for each sample size are plotted in orange
fig_corr_nozero returns a barplot where for each of the five sample sizes the proportion of permutations not containing zero is displayed
tbl_total returns a tibble::tibble() containing estimates of the Pearson correlation between two correlated variables with associated SD, SE, 95% CI, and width of the 95% CI (lower, upper) for all sample sizes, including the permutation number.

Examples

data_gambling <- gambling
estim_corr(data_gambling,
  c("lnacc_self_winvsloss", "age"), 20:221,
  10, "Gambling NAcc correlation with age")

Estimate differences (unstandardized and Cohen's d)

Description

estim_diff determines point estimate, SD and SE, 95% Credibility Intervals, and interval width, for both differences in raw means and Cohen's d's for multiple sample sizes

Usage

estim_diff(data, vars_of_interest, sample_size, k = 50, name = "")

Arguments

data

Dataframe with the data to be analyzed

vars_of_interest

Vector containing the names of the variables to be compared on their means: c("var1", "var2")

sample_size

The range of sample size to be used min:max

k

The number of permutations to be used for each sample size. Defaults to 50

name

The title of the dataset or variables to be displayed with the figure. Defaults to ""

Value

tbl_select returns a tibble::tibble() containing estimates of the difference in raw means and of Cohen's d with associated SD, SE, 95% CI, and width of the 95% CI (lower, upper) for five different sample sizes (starting with the minimum sample size, then 1/5th parts of the total dataset).
fig_diff returns a scatterplot for the difference in raw means, where for the five different sample sizes, 10 out of the total number of HDCI's computed are displayed (in light blue). The average estimate with credible interval summarizing the total number of HDCIs for each sample size are plotted in reddish purple
fig_nozero returns a barplot where for each of the five sample sizes the proportion of permutations not containing zero is displayed for the difference in raw means
fig_cohens_d returns a scatterplot for Cohen's d, where for the five different sample sizes, 10 out of the total number of HDCI's computed are displayed (in light blue). The average estimate with credible interval summarizing the total number of HDCIs for each sample size are plotted in reddish purple
fig_d_nozero returns a barplot where for each of the five sample sizes the proportion of permutations not containing zero is displayed for Cohen's d
tbl_total returns a tibble::tibble() containing estimates of the difference in raw means and of Cohen's d with associated SD, SE, 95% CI, and width of the 95% CI (lower, upper) for all sample sizes, including the permutation number.

Examples

data_feedback <- feedback
estim_diff(data_feedback,
  c("mfg_learning", "mfg_application"), 20:71,
  10, "Feedback middle frontal gyrus")

Feedback task fMRI region of interest data

Description

A dataset containing the parameter estimates of the atlas-based middle frontal gyrus (Harvard-Oxford cortical atlas; thresholded at 50%; center-of-mass coordinates x = -4, y = 22, z = 43), ), with one value for the mean activation during learning and one value for the mean activation during application for all participants.

Usage

feedback

Format

A data frame with 271 rows and 4 variables:

participant_id: unique id for every participant
age: age in years (8.01-25.95)
mfg_learning: parameter estimates for the middle frontal gyrus during the learning phase (-2.54-4.83)
mfg_application: parameter estimates for the middle frontal gyrus during the application phase (-6.46-3.09)

Source

Peters, S., & Crone, E. A. (2017). Increased striatal activity in adolescence benefits learning. Nature Communications, 8(1), 1983. doi:10.1038/s41467-017-02174-z

Gambling task fMRI region of interest data

Description

A dataset containing the parameter estimates of the anatomical mask of the left nucleus accumbens (Harvard-Oxford subcortical atlas; thresholded at 40%; 28 voxels included), with one value for the mean activation during winning and one value for the mean activation during losing for all participants.

Usage

gambling

Format

A data frame with 221 rows and 5 variables:

participant_id: unique id for every participant
age: age in years (11.94-28.46)
lnacc_self_win: parameter estimates for the left nucleas accumbens during winning (-2.78-3.41)
lnacc_self_loss: parameter estimates for the left nucleas accumbens during losing (-3.84-3.28)
lnacc_self_winvsloss: parameter estimates for the left nucleas accumbens for the contrast winning versus losing (-2.60-4.47)

Source

Schreuders, E., Braams, B. R., Blankenstein, N. E., Peper, J. S., Guroglu, B., & Crone, E. A. (2018). Contributions of reward sensitivity to ventral striatum activity across adolescence and early adulthood. Child development, 89(3), 797-810. doi:10.1111/cdev.13056

Self-evaluations task fMRI region of interest data

Description

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.

Usage

self_eval

Format

A data frame with 149 rows and 4 variables:

participant_id: unique id for every participant
age: age in years (11.00-20.92)
mpfc_self: parameter estimates for the left medial prefrontal cortex during self-evaluation (-2.82-4.97)
mpfc_control: parameter estimates for the lmedial prefrontal cortex during the control condition (-7.17-3.50)

Source

van der Cruijsen, R., Blankenstein, N. E., Spaans, J. P., Peters, S., & Crone, E. A. (2023). Longitudinal self-concept development in adolescence. Social Cognitive and Affective Neuroscience, 18(1), nsac062. doi:10.1093/scan/nsac062

Vicarious Charity task fMRI region of interest data

Description

A dataset containing the parameter estimates from the anatomical mask of the left nucleus accumbens (Harvard-Oxford subcortical atlas; thresholded at 40%; center-of-mass coordinates x = -10, y = 12, z = -7; 28 voxels included), with one value for the mean activation during gaining for self and one value for the mean activation during no-gain for self and charity for all participants.

Usage

vicar_char

Format

A data frame with 156 rows and 4 variables:

participant_id: unique id for every participant
age: age in years (11.00-21.17)
nacc_selfgain: parameter estimates for the left nucleus accumbens during gaining for self (-5.66-3.05)
nacc_bothnogain: parameter estimates for the left nucleus accumbens during no-gain for self and charity (-6.44-2.97)

Source

Spaans, J., Peters, S., Becht, A., van der Cruijsen, R., van de Groep, S., & Crone, E. A. (2023). Longitudinal neural and behavioral trajectories of charity contributions across adolescence. Journal of Research on Adolescence, 33(2), 480-495. doi:10.1111/jora.12820