| Title: | Predict Brain Asymmetry Based on Handedness and Dichotic Listening |
|---|---|
| Description: | Functional differences between the cerebral hemispheres are a fundamental characteristic of the human brain. Researchers interested in studying these differences often infer underlying hemispheric dominance for a certain function (e.g., language) from laterality indices calculated from observed performance or brain activation measures . However, any inference from observed measures to latent (unobserved) classes has to consider the prior probability of class membership in the population. The provided functions implement a Bayesian model for predicting hemispheric dominance from observed laterality indices (Sorensen and Westerhausen, Laterality: Asymmetries of Body, Brain and Cognition, 2020, <doi:10.1080/1357650X.2020.1769124>). |
| Authors: | Oystein Sorensen [aut, cre] |
| Maintainer: | Oystein Sorensen <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.2 |
| Built: | 2024-11-11 02:58:36 UTC |
| Source: | https://github.com/LCBC-UiO/BayesianLaterality |
Example dataset with a single measurement of three individuals.
example_data1example_data1
An object of class tbl_df (inherits from tbl, data.frame) with 3 rows and 2 columns.
Example dataset with three measurements each on 100 individuals.
example_data2example_data2
An object of class tbl_df (inherits from tbl, data.frame) with 300 rows and 4 columns.
Predict hemispheric dominance based on observed laterality measures, using the methods described in Sørensen and Westerhausen (2020).
predict_dominance( data, parameters = dplyr::tibble(dominance = rep(c("left", "right", "none"), each = 2), handedness = rep(c("left", "right"), 3), mean_li = c(10, 12, -24, -24, 0, 0), sd_li = c(24.9, 17, 24.9, 17, 22, 22), prob_dominance = c(0.65, 0.87, 0.35, 0.13, 0, 0)), truncation = c(-100, 100), icc = 0 )predict_dominance( data, parameters = dplyr::tibble(dominance = rep(c("left", "right", "none"), each = 2), handedness = rep(c("left", "right"), 3), mean_li = c(10, 12, -24, -24, 0, 0), sd_li = c(24.9, 17, 24.9, 17, 22, 22), prob_dominance = c(0.65, 0.87, 0.35, 0.13, 0, 0)), truncation = c(-100, 100), icc = 0 )
data |
Data frame with the following columns:
In addition, an optional column named |
parameters |
Data frame in which the first two columns specify combinations of hemispheric dominance and handedness and the last three columns specify the corresponding parameter values. In particular, the columns are defined as follows:
|
truncation |
Numeric vector with two elements specifying the lower and upper bounds for truncation of the normal distribution for dichotic listening scores. |
icc |
Intraclass correlation for repeated measurements on the same individual. Defaults to 0. |
The probability of left or right hemispheric dominance in additional
columns of data.
Sørensen Ø, Westerhausen R (2020). “From observed laterality to latent hemispheric differences: Revisiting the inference problem.” Laterality, 25(5), 560–582. doi:10.1080/1357650x.2020.1769124.
# The package comes with two example datasets. # The first contains single measurements on three subjects. # We can first take a look at the data example_data1 # Next, compute predictions. # Since there is no ID column, predict_dominance() will print a message telling # the user that the rows are assumed to contain observations from different subjects. predict_dominance(example_data1) # The next example dataset contains repeated measurements example_data2 # We compute the predictions as before: predict_dominance(example_data2)# The package comes with two example datasets. # The first contains single measurements on three subjects. # We can first take a look at the data example_data1 # Next, compute predictions. # Since there is no ID column, predict_dominance() will print a message telling # the user that the rows are assumed to contain observations from different subjects. predict_dominance(example_data1) # The next example dataset contains repeated measurements example_data2 # We compute the predictions as before: predict_dominance(example_data2)