This post presents a run-through of a Bayesian workflow in R. The content is *closely* based on Bernabeu (2022), which was in turn based on lots of other references, also cited here.
Research has suggested that conceptual processing depends on both language-based and vision-based information. We tested this interplay at three levels of the experimental structure: individuals, words and tasks. To this end, we drew on three …
Research has suggested that conceptual processing depends on both language-based and sensorimotor information. In this thesis, I investigate the nature of these systems and their interplay at three levels of the experimental structure---namely, …
As technology and research methods advance, the data sets tend to be larger and the methods more exhaustive. Consequently, the analyses take longer to run. This poses a challenge when the results are to be presented using R Markdown. One has to balance reproducibility and efficiency. On the one hand, it is desirable to keep the R Markdown document as self-contained as possible, so that those who may later examine the document can easily test and edit the code.
When knitting an R Markdown document after the first time, errors may sometimes appear. Three tips are recommended below.
1. Close PDF reader window
When the document is knitted through the ‘Knit’ button, a PDF reader window opens to present the result. Closing this window can help resolve errors.
2. Delete service files
Every time the Rmd is knitted, some service files are created. Some of these files have the ‘.
This preregistration outlines a study that will investigate the dynamic nature of conceptual processing by examining the interplay between linguistic distributional systems—comprising word co-occurrence and word association—and embodied systems—comprising sensorimotor and emotional information. A set of confirmatory research questions are addressed using data from the Calgary Semantic Decision project, along with additional measures for the stimuli corresponding to distributional language statistics, embodied information, and individual differences in vocabulary size.
This open-source, R-based web application allows the conversion of video captions (subtitles) from the Web Video Text Tracks (WebVTT) Format into plain texts. For this purpose, users upload a WebVTT file with the extension of 'vtt' or 'txt'. …
In this talk, I will look over the rationale for LMEMs, and demonstrate how to fit them in R (Brauer & Curtin, 2018; Luke, 2017). Challenges will also be covered. For instance, when using the widely-accepted 'maximal' approach, based on fitting all possible random effects for each fixed effect, models sometimes fail to find a solution, or 'convergence'. Advice for the problem of nonconvergence will be demonstrated, based on the progressive lightening of the random effects structure (Singman & Kellen, 2017; for an alternative approach, especially with small samples, see Matuschek et al., 2017). At the end, on a different note, I will present a web application that facilitates data simulation for research and teaching (Bernabeu & Lynott, 2020).
Las aplicaciones web nos ayudan a facilitar el uso de nuestro trabajo, ya que no requieren programación para utilizarlas. Crear estas aplicaciones en R, mediante paquetes como "shiny" o "flexdashboard", ofrece múltiples ventajas. Entre ellas destaca la reproducibilidad, tal como veremos en torno a una aplicación para la simulación de datos (https://github.com/pablobernabeu/Experimental-data-simulation).
In a highly recommendable presentation available on Youtube, Michael Frank walks us through R Markdown. Below, I loosely summarise and partly elaborate on Frank's advice regarding collaboration among colleagues, some of whom may not be used to R Markdown (see relevant time point in Frank's presentation).
The first way is using GitHub, which has a great version control system, and even allows the rendering of Markdown text, if the file is given the extension ‘.