statistics

Custom functions that extend sjPlot to discretise continuous variables in interaction plots into equally-sized intervals (deciles or sextiles) that include minimum and maximum values, with legend counts showing sample sizes per level.

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.

Covariates serve two essential purposes: statistically accounting for satellite variables that may affect variables of interest, and academically preventing the development of redundant theories by enabling direct comparisons between related theoretical constructs.

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.

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).

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).

Frequentist and Bayesian statistics are sometimes regarded as fundamentally different philosophies. Indeed, can both qualify as philosophies or is one of them just a pointless ritual? Is frequentist statistics only about $p$ values? Are frequentist estimates diametrically opposed to Bayesian posterior distributions? Are confidence intervals and credible intervals irreconcilable? Will R crash if lmerTest and brms are simultaneously loaded?

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 …

Multilevel analyses investigating the interplay between language-based and vision-based information in conceptual processing across semantic priming, semantic decision and lexical decision paradigms, with power analyses revealing sample size requirements for examining perceptual simulation and individual differences.

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).

Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. It comes in very useful whenever doubts arise about the true origin of three or more variables. There are two main methods for performing a PCA: naive or less naive. In the naive method, you first check some conditions in your data which will determine the essentials of the analysis. In the less-naive method, you set those yourself based on whatever prior information or purposes you had. The 'naive' approach is characterized by a first stage that checks whether the PCA should actually be performed with your current variables, or if some should be removed. The variables that are accepted are taken to a second stage which identifies the number of principal components that seem to underlie your set of variables.

The single dependent variable, RT, was accompanied by other variables which could be analyzed as independent variables. These included Group, Trial Number, and a within-subjects Condition. What had to be done first off, in order to take the usual table? The trials!