Electroencephalography (EEG) has become a cornerstone for understanding the intricate workings of the human brain in the field of neuroscience. However, EEG software and hardware come with their own set of constraints, particularly in the management of markers, also known as triggers. This article aims to shed light on these limitations and future prospects of marker management in EEG studies, while also introducing R functions that can help deal with vmrk files from BrainVision.
Sometimes it’s useful to do a bibliometric analysis. To this end, the rscopus_plus functions (Bernabeu, 2024) extend the R package rscopus (Muschelli, 2022) to administer the search quota and enable specific searches and comparisons.
scopus_search_plus runs rscopus::scopus_search as many times as necessary based on the number of results and the search quota.
scopus_search_DOIs gets DOIs from scopus_search_plus, which can then be imported into a reference manager, such as Zotero, to create a list of references.
Frequently asked questions about mixed-effects models, covering the necessity of random slopes, appropriate p-value calculation methods, parallelization limitations, convergence issues, and optimizer selection.
In the fast-paced world of scientific research, establishing minimum standards for the creation of research materials is essential. Whether it's stimuli, custom software for data collection, or scripts for statistical analysis, the quality and transparency of these materials significantly impact the reproducibility and credibility of research. This blog post explores the importance of adhering to FAIR (Findable, Accessible, Interoperable, Reusable) principles, and offers practical examples for researchers, with a focus on the cognitive sciences.
An R script for preprocessing frequency list data from the Norwegian Web as Corpus (NoWaC), including instructions for downloading and preparing the corpus data.
Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot. For instance, using the plot_model function, I plotted the interaction between two continuous variables.
library(lme4)
#> Loading required package: Matrix
library(sjPlot)
#> Learn more about sjPlot with 'browseVignettes("sjPlot")'.
library(ggplot2)
theme_set(theme_sjplot())
# Create data partially based on code by Ben Bolker # from https://stackoverflow.
Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot. For instance, using the plot_model function, I plotted the interaction between a continuous variable and a categorical variable. The categorical variable was passed to the fill argument of plot_model.
library(lme4)
#> Loading required package: Matrix
library(sjPlot)
#> Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!
A custom R function to create ggplot2 visualizations of fixed effects from models refitted with multiple optimizers using lme4's allFit function, enabling visual assessment of convergence validity in mixed-effects models.
Here’s an example of fuzzy-matching strings in R that I shared on StackOverflow. In stringdist_join, the max_dist argument is used to constrain the degree of fuzziness.
library(fuzzyjoin)
library(dplyr)
#> #> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> #> filter, lag
#> The following objects are masked from 'package:base':
#> #> intersect, setdiff, setequal, union
library(knitr)
small_tab = data.frame(Food.Name = c('Corn', 'Squash', 'Peppers'), Food.Code = c(NA, NA, NA))
large_tab = data.