R

rscopus_plus: An extension of the rscopus package

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.

FAQs on mixed-effects models

I am dealing with nested data, and I remember from an article by Clark (1973) that nested should be analysed using special models. I’ve looked into mixed-effects models, and I’ve reached a structure with random intercepts by subjects and by items. Is this fine? In early days, researchers would aggregate the data across these repeated measures to prevent the violation of the assumption of independence of observations, which is one of the most important assumptions in statistics.

FAIR standards for the creation of research materials, with examples

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.

Preprocessing the Norwegian Web as Corpus (NoWaC) in R

The present script can be used to preprocess data from a frequency list of the Norwegian as Web Corpus (NoWaC; Guevara, 2010). Before using the script, the frequency list should be downloaded from this URL. The list is described as ‘frequency list sorted primary alphabetic and secondary by frequency within each character’, and this is the direct URL. The download requires signing in to an institutional network. Last, the downloaded file should be unzipped.

A new function to plot convergence diagnostics from lme4::allFit()

When a model has struggled to find enough information in the data to account for every predictor---especially for every random effect---, convergence warnings appear (Brauer & Curtin, 2018; Singmann & Kellen, 2019). In this article, I review the issue of convergence before presenting a new plotting function in R that facilitates the visualisation of the fixed effects fitted by different optimization algorithms (also dubbed optimizers).

Cannot open plots created with brms::mcmc_plot due to lack of discrete_range function

I would like to ask for advice regarding some plots that were created using brms::mcmc_plot(), and cannot be opened in R now. The plots were created last year using brms 2.17.0, and were saved in RDS objects. The problem I have is that I cannot open the plots in R now because I get an error related to a missing function. I would be very grateful if someone could please advise me if they can think of a possible reason or solution.

A table of results for Bayesian mixed-effects models: Grouping variables and specifying random slopes

Here I share the format applied to tables presenting the results of Bayesian models in Bernabeu (2022). The sample table presents a mixed-effects model that was fitted using the R package 'brms' (Bürkner et al., 2022).

A table of results for frequentist mixed-effects models: Grouping variables and specifying random slopes

Here I share the format applied to tables presenting the results of frequentist models in Bernabeu (2022). The sample table presents a mixed-effects model that was fitted using the R package 'lmerTest' (Kuznetsova et al., 2022).

Plotting two-way interactions from mixed-effects models using alias variables

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 (Lüdecke, 2022). In Bernabeu (2022), the sjPlot function called plot_model served as the basis for the creation of some custom functions. One of these functions is alias_interaction_plot, which allows the plotting of interactions between a continuous variable and a categorical variable.

Plotting two-way interactions from mixed-effects models using ten or six bins

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 (Lüdecke, 2022). In Bernabeu (2022), the sjPlot function called plot_model served as the basis for the creation of some custom functions. Two of these functions are deciles_interaction_plot and sextiles_interaction_plot. These functions allow the plotting of interactions between two continuous variables.